From 3250c61adb6dc227e07978da0ae99acb032fa7c6 Mon Sep 17 00:00:00 2001
From: Alexander Eichhorn <alex@eichhorn.dev>
Date: Mon, 19 Oct 2026 06:33:03 +0200
Subject: [PATCH 1/4] feat(qwen-image-2-1): add edits from reference images

The prompt encoder reads up to four references through the vision tower; their latents fill the image slots.
The encoder reserves memory for the references' attention; the denoise checks slot grids before loading.
Reference images are offered only on Diffusers models, which keep the vision tower.
---
 .../03.Models/02.model-families.mdx           |   2 +-
 .../03.Models/Local Models/qwen-image-2-1.mdx |  11 +-
 .../04.Image Generation/generate-panel.mdx    |   2 +-
 .../contributing/new-model-integration.mdx    |   4 +-
 .../docs/development/Guides/recall-api.mdx    |   5 +-
 .../qwen_image_2_1/qwen_image_2_1_denoise.py  | 194 ++++++++---
 .../qwen_image_2_1_text_encoder.py            |  89 ++++-
 .../vae/qwen_image_2_1_image_to_latents.py    |  53 ++-
 .../backend/architectures/capabilities.py     |   8 +-
 .../architectures/defs/qwen_image_2_1.py      |   3 +
 .../backend/architectures/facets/features.py  |   5 +-
 .../load/model_loaders/qwen_image_2_1.py      |  20 +-
 .../backend/qwen_image_2_1/text_encoding.py   | 110 +++++-
 invokeai/backend/qwen_image_2_1/vae.py        |  41 ++-
 .../diffusion/conditioning_data.py            |  10 +
 invokeai/frontend/api/openapi.json            |  68 +++-
 invokeai/frontend/api/schema.ts               |  33 +-
 .../architectureCapabilities.json             |  33 +-
 .../core/architectureCapabilities.ts          |   1 +
 .../core/baseGenerationPolicies.test.ts       |  35 ++
 .../generation/core/baseGenerationPolicies.ts |  33 +-
 .../features/generation/core/graph.test.ts    |  60 ++++
 .../src/features/generation/core/graph.ts     |  73 ++++
 .../src/features/generation/core/settings.ts  |   4 +
 .../src/features/generation/core/types.ts     |   1 +
 .../webv2/src/workbench/canvasMigration.ts    |   1 +
 .../invocations/test_qwen_image_2_1_nodes.py  | 314 +++++++++++++++++-
 tests/backend/architectures/test_features.py  |  15 +-
 .../load/test_qwen3_vl_visual_tower.py        |  21 ++
 .../qwen_image_2_1/test_text_encoding.py      |  97 +++++-
 tests/backend/qwen_image_2_1/test_vae.py      |  36 +-
 31 files changed, 1252 insertions(+), 130 deletions(-)

diff --git a/docs/src/content/docs/Users Guide/03.Models/02.model-families.mdx b/docs/src/content/docs/Users Guide/03.Models/02.model-families.mdx
index 757cd7a3545..aad924586b4 100644
--- a/docs/src/content/docs/Users Guide/03.Models/02.model-families.mdx	
+++ b/docs/src/content/docs/Users Guide/03.Models/02.model-families.mdx	
@@ -123,7 +123,7 @@ faster generation.
 | **Z-Image** | Images | 6B model in a fast, few-step Turbo variant and a Base variant. Understands English and Chinese prompts, with ControlNets. | Apache 2.0 | [Z-Image](/users-guide/models/local-models/z-image/)  |
 | **ERNIE-Image** | Images | Baidu's 8B text-to-image model, in standard and distilled Turbo variants. | Apache 2.0 | [ERNIE-Image](/users-guide/models/local-models/ernie-image/)  |
 | **Qwen Image** | Images | Text-to-image (2512) and instruction-based image editing (Edit 2511), with fast Lightning variants. | Apache 2.0 | [Qwen Image](/users-guide/models/local-models/qwen-image/)  |
-| **Qwen-Image-2.1** | Images | 7B text-to-image model with strong text rendering and transparent (RGBA) output, in a base and an 8-step Turbo variant. | Non-commercial | [Qwen-Image-2.1](/users-guide/models/local-models/qwen-image-2-1/)  |
+| **Qwen-Image-2.1** | Images | 7B text-to-image model with strong text rendering and transparent (RGBA) output, in a base and an 8-step Turbo variant. Edits from reference images. | Non-commercial | [Qwen-Image-2.1](/users-guide/models/local-models/qwen-image-2-1/)  |
 | **Krea-2** | Images | 12B model in fast Turbo and undistilled Raw variants, with training-free style reference. | Community, free under $1M revenue | [Krea-2](/users-guide/models/local-models/krea-2/)  |
 | **Ideogram 4** | Images | Structured prompts that place described elements in regions of the image. | Non-commercial | [Ideogram 4](/users-guide/models/local-models/ideogram4/)  |
 | **Anima** | Images | Compact 2B anime-focused model, with ControlNet-LLLite adapters for inpainting and sketch guidance. | Non-commercial | [Anima](/users-guide/models/local-models/anima/)  |
diff --git a/docs/src/content/docs/Users Guide/03.Models/Local Models/qwen-image-2-1.mdx b/docs/src/content/docs/Users Guide/03.Models/Local Models/qwen-image-2-1.mdx
index f7d9298c745..ecbb1190186 100644
--- a/docs/src/content/docs/Users Guide/03.Models/Local Models/qwen-image-2-1.mdx	
+++ b/docs/src/content/docs/Users Guide/03.Models/Local Models/qwen-image-2-1.mdx	
@@ -14,7 +14,7 @@ Two variants are available, and the variant is detected on install:
 - **Qwen-Image-2.1** — the base model. 40 steps by default.
 - **Qwen-Image-2.1 Turbo** — distilled for 8 steps on a fixed schedule of its own.
 
-Text-to-image, image-to-image, inpainting and outpainting on the Canvas are supported. Reference images (image editing), LoRAs and regional guidance are not supported yet.
+Text-to-image, image-to-image, inpainting and outpainting on the Canvas are supported, and with the Diffusers models also edits from [reference images](#reference-images). LoRAs and regional guidance are not supported yet.
 
 ### License
 
@@ -57,6 +57,15 @@ The text encoder runs first and is unloaded from the GPU when the transformer ne
 
 The VAE needs about 8 GB of working memory to decode a 1024×1024 image, and is priced the same to encode one for image-to-image, inpainting or outpainting (twice that with `precision: float32`). When an image is too large for the card to handle in one piece, Invoke decodes and encodes it in tiles; see [Large decodes](/configuration/optimization/low-vram-mode/). Invoke picks 1024-pixel tiles, and 768 or 512 only on cards with less memory: measured at 2048×2048, 1024-pixel tiles came close to an untiled decode (47.7 dB PSNR), while the 256-pixel tiles other VAEs default to left visible seams (35.6 dB).
 
+### Reference images
+
+With a Qwen-Image-2.1 **Diffusers** model, add up to four images with **Add reference image** in the Guidance section (see [Adding reference images](/users-guide/image-generation/generate-panel/#adding-reference-images)) and say in the prompt what to do with them, for example *put the jacket from the first image on the person in the second* or *the same cat, as a watercolor*. The model reads each reference twice: its Qwen3-VL encoder looks at it alongside the prompt, and the transformer sees it as clean latents next to the image it generates.
+
+- **Diffusers models only.** Only the encoder that comes with the Diffusers model keeps the Qwen3-VL vision tower, which reads the images. A single-file or GGUF transformer with a standalone encoder does not offer reference images, and on a Diffusers model the references refuse to run while a standalone **Qwen3-VL Encoder** is selected under **Components**; clear it to edit.
+- **Size.** Each reference is read at about one megapixel at its own aspect ratio, whatever its size. The generated image takes the size set in the panel; **Use size** on a reference's thumbnail matches it.
+- **Transparency.** The encoder sees a transparent reference over white, as the model was trained; its latents keep the transparency.
+- **Memory.** The vision tower adds about 1.1 GB to the Diffusers model's encoder, on every generation with it. Each reference adds about 4,100 tokens to the start of the sequence, which the model keeps for the whole generation: about 2 GB per reference, twice that with **CFG** above 1. Where that would take more than half of the GPU, Invoke reprocesses the references at every step instead of keeping them, which uses less memory but takes noticeably longer. Reading the references also needs encoder memory that grows quickly with their number: about 7 GB beyond its weights for four, so on a 24 GB card part of the encoder then runs from system memory.
+
 ### Transparent images
 
 The Qwen-Image-2.1 VAE decodes an alpha channel along with the colors, so a prompt such as *a sticker of a red fox on a transparent background* produces a cut-out image. An image is saved with transparency only when it has some: when more than 0.1% of its pixels are noticeably transparent. Opaque images are saved as ordinary RGB, so the faint edges tiled decoding leaves in the alpha channel never turn an opaque image transparent.
diff --git a/docs/src/content/docs/Users Guide/04.Image Generation/generate-panel.mdx b/docs/src/content/docs/Users Guide/04.Image Generation/generate-panel.mdx
index 1ec9cbe5742..b5f3de5a4ce 100644
--- a/docs/src/content/docs/Users Guide/04.Image Generation/generate-panel.mdx	
+++ b/docs/src/content/docs/Users Guide/04.Image Generation/generate-panel.mdx	
@@ -120,7 +120,7 @@ What a reference does, and what options it has, depends on the model family:
 | **SD 1.5, SDXL** | An IP-Adapter model carries the reference's look into the image. | **Mode**: **Style**, **Composition** or **Both**. **Weight**. Under **Advanced**: the IP-Adapter model, a **Style variant** and the **Active steps** range. |
 | **FLUX.1** | An IP-Adapter, or a **FLUX Redux** model, which transfers the reference's content. | IP-Adapter options as above, or **Image influence** (Lowest to Highest) for Redux. |
 | **FLUX.1 Kontext** | Kontext edits or recombines the reference images according to your prompt. | None beyond the model. |
-| **FLUX.2, Qwen Image Edit** | The model reads the references directly and uses them as your prompt instructs, for example *put the jacket from the first image on the person in the second*. | None. |
+| **FLUX.2, Qwen Image Edit, Qwen-Image-2.1** | The model reads the references directly and uses them as your prompt instructs, for example *put the jacket from the first image on the person in the second*. | None. |
 | **Krea-2** | One reference's look — palette, texture, rendering — is transferred into the image while the prompt keeps driving the content; see [Krea-2](/users-guide/models/local-models/krea-2/#style-reference). | **Style strength** (0–2; 0 turns the reference off). |
 
 Reference images require an extra model for SD 1.5, SDXL and FLUX.1 (an IP-Adapter or FLUX Redux model). These are available as starter models in the Model Manager, and an installed IP-Adapter is selected automatically.
diff --git a/docs/src/content/docs/contributing/new-model-integration.mdx b/docs/src/content/docs/contributing/new-model-integration.mdx
index 04e4530b2d8..aea40112611 100644
--- a/docs/src/content/docs/contributing/new-model-integration.mdx
+++ b/docs/src/content/docs/contributing/new-model-integration.mdx
@@ -884,8 +884,8 @@ Paths below are relative to `invokeai/frontend/webv2/src/features/generation/cor
 
 ### Reference images
 
-- Backend: set `FeaturesFacet.max_reference_images` (and `reference_images_require_variant` if only one variant supports them). Wire the images into the text encoder and/or the denoise node, as the reference pipeline does.
-- webv2: extend the reference-image config union (`types.ts`), `getDefaultReferenceImageConfig` and `getReferenceImageConfigSupported` (`baseGenerationPolicies.ts`), and wire the images in the builder.
+- Backend: set `FeaturesFacet.max_reference_images`, plus `reference_images_require_variant` if only one variant supports them or `reference_images_require_format` if only one format does. Wire the images into the text encoder and/or the denoise node, as the reference pipeline does.
+- webv2: extend the reference-image config union (`types.ts`); the config-type set and the normalize, clone and sync switches in `settings.ts`; the persisted union in `src/workbench/canvasMigration.ts`; and `getDefaultReferenceImageConfig` and `getReferenceImageConfigSupported` (`baseGenerationPolicies.ts`). Wire the images in the builder, and report any setting that keeps them from running in `getReferenceImageValidationReasons`, not only from the builder.
 
 ### Control and regional guidance
 
diff --git a/docs/src/content/docs/development/Guides/recall-api.mdx b/docs/src/content/docs/development/Guides/recall-api.mdx
index 8478a3fe4a9..771f1d917de 100644
--- a/docs/src/content/docs/development/Guides/recall-api.mdx
+++ b/docs/src/content/docs/development/Guides/recall-api.mdx
@@ -250,12 +250,13 @@ separate adapter model:
 - **FLUX.2 Klein** — built-in reference image support.
 - **FLUX Kontext** — reference image associated with the main model.
 - **Qwen Image Edit** — reference image associated with the main model.
+- **Qwen-Image-2.1** — reference images read by its own encoder, on the Diffusers model only.
 
 Because there is no adapter model to resolve, these entries carry only
 `image_name`. When the frontend receives them, it picks the appropriate
 config flavor (`flux2_reference_image`, `flux_kontext_reference_image`,
-or `qwen_image_reference_image`) based on the currently-selected main
-model, matching the behavior of a manual drag-and-drop.
+`qwen_image_reference_image` or `qwen_image_2_1_reference_image`) based on
+the currently-selected main model, matching the behavior of a manual drag-and-drop.
 
 ## Usage Examples
 
diff --git a/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_denoise.py b/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_denoise.py
index 9c428c8a729..7f69766c476 100644
--- a/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_denoise.py
+++ b/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_denoise.py
@@ -1,3 +1,4 @@
+import itertools
 import math
 from typing import Optional
 
@@ -63,6 +64,61 @@ def clip_sigmas(
     return list(sigmas[first_step : last_step + 1]), first_step
 
 
+def slot_runs(image_pad_mask: torch.Tensor | None) -> list[int]:
+    """The lengths of the runs of image slots in a prompt's mask: one run per reference image, in order."""
+    if image_pad_mask is None:
+        return []
+    return [len(list(run)) for is_slot, run in itertools.groupby(image_pad_mask[0].tolist()) if is_slot]
+
+
+def _describe_grids(grids: list[tuple[int, int]]) -> str:
+    # A slot is 32x32 pixels of the reference as the encoder read it.
+    return ", ".join(f"{columns * 32}x{rows * 32}" for rows, columns in grids) or "none"
+
+
+def check_reference_slots(
+    info: QwenImage21ConditioningInfo, reference_shapes: list[tuple[int, int, int]], prompt: str
+) -> None:
+    """Refuse references whose latents do not match the prompt's image slots, reference by reference.
+
+    Each reference's latents fill one run of the prompt's slots, 2x2 latents per slot, at the slot grid the
+    encoder read it at. A mismatch means the prompt was encoded with other images, in another order or at another
+    size than the latents: the transformer would place one reference's latents in another's slots.
+    """
+    expected = [(h // 2, w // 2) for _, h, w in reference_shapes]
+    found = list(info.reference_grids)
+    if found != expected or slot_runs(info.image_pad_mask) != [rows * columns for rows, columns in found]:
+        raise ValueError(
+            f"The {prompt} prompt was encoded with {len(found)} reference image(s) ({_describe_grids(found)}), but "
+            f"the denoise node received {len(expected)} reference latent(s) ({_describe_grids(expected)}). Encode "
+            "each reference with Image to Latents in reference mode, from the same images and in the same order "
+            "as the prompts."
+        )
+
+
+def prefix_length(info: QwenImage21ConditioningInfo) -> int:
+    """Tokens before the target in the joint sequence: the prompt, each reference slot widened to 2x2 latents."""
+    slots = 0 if info.image_pad_mask is None else int(info.image_pad_mask.sum())
+    return info.prompt_embeds.shape[1] + (TOKENS_PER_SLOT - 1) * slots
+
+
+# K and V of one prefix token in all 32 blocks: 2 x 4096 x 32 x 2 bytes in bf16.
+KV_BYTES_PER_PREFIX_TOKEN = 2 * 4096 * 32 * 2
+
+
+def prefix_cache_fits(cache_bytes: int, device: torch.device) -> bool:
+    """Whether the prefix caches can stay on the GPU for the whole run.
+
+    They cannot be offloaded the way the transformer's weights can, so caches larger than half the card would
+    leave too little for the model however it streams: four references at CFG above 1 hold ~16 GiB. Past that the
+    prefix is recomputed every step instead -- slower, but within the memory of an ordinary run. Memory that is
+    not a dedicated card's (CPU, MPS) is not limited here.
+    """
+    if device.type != "cuda":
+        return True
+    return cache_bytes <= torch.cuda.get_device_properties(device).total_memory // 2
+
+
 def pack_latents(latents: torch.Tensor) -> torch.Tensor:
     """(B, C, h, w) -> (B, h*w, C): one token per latent pixel, in raster order (patch size 1)."""
     batch, channels, height, width = latents.shape
@@ -80,7 +136,7 @@ def unpack_latents(latents: torch.Tensor, height: int, width: int) -> torch.Tens
     title="Denoise - Qwen-Image-2.1",
     tags=["image", "qwen_image_2_1", "qwen-image-2.1"],
     category="image",
-    version="1.0.1",
+    version="1.1.0",
     classification=Classification.Prototype,
 )
 class QwenImage21DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
@@ -92,6 +148,13 @@ class QwenImage21DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
     denoise_mask: Optional[DenoiseMaskField] = InputField(
         default=None, description=FieldDescriptions.denoise_mask, input=Input.Connection
     )
+    reference_latents: LatentsField | list[LatentsField] | None = InputField(
+        default=None,
+        description="Latents of the reference images an edit reads, from Image to Latents in reference mode, in the "
+        "order the prompts were encoded with them.",
+        input=Input.Connection,
+        title="Reference Latents",
+    )
     denoising_start: float = InputField(default=0.0, ge=0, le=1, description=FieldDescriptions.denoising_start)
     denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
     transformer: TransformerField = InputField(
@@ -135,11 +198,30 @@ def invoke(self, context: InvocationContext) -> LatentsOutput:
 
     def _load_conditioning(
         self, context: InvocationContext, field: QwenImage21ConditioningField, dtype: torch.dtype, device: torch.device
-    ) -> torch.Tensor:
+    ) -> QwenImage21ConditioningInfo:
         data = context.conditioning.load(field.conditioning_name)
         if len(data.conditionings) != 1 or not isinstance(data.conditionings[0], QwenImage21ConditioningInfo):
             raise ValueError("Expected exactly one Qwen-Image-2.1 conditioning.")
-        return data.conditionings[0].to(dtype=dtype, device=device).prompt_embeds
+        return data.conditionings[0].to(dtype=dtype, device=device)
+
+    def _load_references(self, context: InvocationContext, device: torch.device) -> list[torch.Tensor]:
+        """The reference latents, `(1, C, h, w)` each, in the order the prompts were encoded with them."""
+        if self.reference_latents is None:
+            return []
+        fields = self.reference_latents if isinstance(self.reference_latents, list) else [self.reference_latents]
+        references = []
+        for field in fields:
+            reference = context.tensors.load(field.latents_name).to(device=device, dtype=torch.float32)
+            if reference.dim() == 5:
+                reference = reference.squeeze(2)
+            if reference.shape[1] != LATENT_CHANNELS or reference.shape[-2] % 2 or reference.shape[-1] % 2:
+                raise ValueError(
+                    f"Reference latents are {tuple(reference.shape[1:])}; Qwen-Image-2.1 reads {LATENT_CHANNELS} "
+                    "channels with even sides. Encode the reference with Image to Latents - Qwen-Image-2.1 in "
+                    "reference mode."
+                )
+            references.append(reference)
+        return references
 
     def _prepare_cfg_scale(self, num_steps: int) -> list[float]:
         if isinstance(self.cfg_scale, list):
@@ -174,7 +256,6 @@ def _run_diffusion(self, context: InvocationContext) -> torch.Tensor:
         device = TorchDevice.choose_torch_device()
         dtype = TorchDevice.choose_bfloat16_safe_dtype(device)
         transformer_config = context.models.get_config(self.transformer.transformer)
-        transformer_info = context.models.load(self.transformer.transformer)
 
         latent_height, latent_width = self.height // VAE_SCALE_FACTOR, self.width // VAE_SCALE_FACTOR
         image_seq_len = latent_height * latent_width
@@ -190,13 +271,26 @@ def _run_diffusion(self, context: InvocationContext) -> torch.Tensor:
         sigmas = torch.tensor(sigmas, dtype=torch.float32, device=device)
         cfg_scale = self._prepare_cfg_scale(len(full_sigmas) - 1)[first_step : first_step + len(sigmas) - 1]
 
-        pos_embeds = self._load_conditioning(context, self.positive_conditioning, dtype, device)
+        pos = self._load_conditioning(context, self.positive_conditioning, dtype, device)
         do_cfg = self.negative_conditioning is not None and any(value > 1.0 for value in cfg_scale)
-        neg_embeds = (
+        neg = (
             self._load_conditioning(context, self.negative_conditioning, dtype, device)
             if do_cfg and self.negative_conditioning is not None
             else None
         )
+        pos_embeds = pos.prompt_embeds
+        neg_embeds = neg.prompt_embeds if neg is not None else None
+
+        references = self._load_references(context, device)
+        reference_shapes = [(1, r.shape[-2], r.shape[-1]) for r in references]
+        check_reference_slots(pos, reference_shapes, "positive")
+        if neg is not None:
+            check_reference_slots(neg, reference_shapes, "negative")
+        # Clean latents, condition images first: the transformer drops them into the prompt's image slots.
+        reference_tokens = torch.cat([pack_latents(r) for r in references], dim=1).to(dtype) if references else None
+
+        # Only now, with every input checked: loading reads the whole transformer into RAM.
+        transformer_info = context.models.load(self.transformer.transformer)
 
         # Drawn as the pipeline draws it, (B, frame, C, h, w), on the CPU so a seed means the same on any device.
         noise = torch.randn(
@@ -234,29 +328,41 @@ def _run_diffusion(self, context: InvocationContext) -> torch.Tensor:
         # upcast); matching that is what makes this node reproduce its outputs.
         latents = pack_latents(latents).to(dtype)
 
-        def image_slot_mask(text_len: int) -> torch.Tensor:
-            # The joint sequence's slots: the text, then one slot per 2x2 group of target latents. Condition
-            # images would sit between the two.
-            text = torch.zeros(1, text_len, dtype=torch.bool, device=device)
+        def image_slot_mask(info: QwenImage21ConditioningInfo) -> torch.Tensor:
+            # The joint sequence's slots: the prompt with its reference slots, then one slot per 2x2 group of
+            # target latents.
+            text_len = info.prompt_embeds.shape[1]
+            prompt = (
+                info.image_pad_mask.bool()
+                if info.image_pad_mask is not None
+                else torch.zeros(1, text_len, dtype=torch.bool, device=device)
+            )
             target = torch.ones(1, image_seq_len // TOKENS_PER_SLOT, dtype=torch.bool, device=device)
-            return torch.cat([text, target], dim=1)
-
-        img_shapes = [[(1, latent_height, latent_width)]]
-        pos_img_mask = image_slot_mask(pos_embeds.shape[1])
-        neg_img_mask = image_slot_mask(neg_embeds.shape[1]) if neg_embeds is not None else None
-
+            return torch.cat([prompt, target], dim=1)
+
+        img_shapes = [[*reference_shapes, (1, latent_height, latent_width)]]
+        pos_img_mask = image_slot_mask(pos)
+        neg_img_mask = image_slot_mask(neg) if neg is not None else None
+
+        # Each reference slot widens to 2x2 latent tokens in the joint sequence; with the text they are the
+        # prefix the cache holds.
+        pos_prefix = prefix_length(pos)
+        neg_prefix = prefix_length(neg) if neg is not None else None
+        # Text and reference keys and values do not depend on the step under `causal_condition`: the first step
+        # prefills them, the rest compute the image tokens only -- when the caches fit.
+        cache_bytes = KV_BYTES_PER_PREFIX_TOKEN * (pos_prefix + (neg_prefix or 0))
+        use_cache = bool(getattr(transformer_info.model.config, "causal_condition", False))
+        if use_cache and not prefix_cache_fits(cache_bytes, device):
+            use_cache = False
+            logger.info(
+                f"Qwen-Image-2.1: the prefix caches would hold {cache_bytes / 1024**3:.1f} GiB, more than half of "
+                "this GPU; recomputing the prefix every step instead, which is slower."
+            )
         working_memory = self._estimate_working_memory(
-            image_seq_len,
-            pos_embeds.shape[1],
-            None if neg_embeds is None else neg_embeds.shape[1],
-            do_cfg,
-            dtype.itemsize,
+            image_seq_len, pos_prefix, neg_prefix, do_cfg, use_cache, dtype.itemsize
         )
         working_memory += self._attention_score_bytes(
-            transformer_info.model,
-            image_seq_len + max(pos_embeds.shape[1], 0 if neg_embeds is None else neg_embeds.shape[1]),
-            device,
-            dtype,
+            transformer_info.model, image_seq_len, max(pos_prefix, neg_prefix or 0), device, dtype
         )
         working_memory += peak_dequant_transient_bytes(transformer_info.model, dtype)
         log_attention_backends(logger, device)
@@ -274,16 +380,15 @@ def image_slot_mask(text_len: int) -> torch.Tensor:
         )
 
         with transformer_info.model_on_device(working_mem_bytes=working_memory) as (_, transformer):
-            # Text keys and values do not depend on the step under `causal_condition`: the first step prefills
-            # them, the rest compute the image tokens only. One cache per guidance branch, for this run only.
+            # One cache per guidance branch, for this run only.
             num_blocks = len(transformer.transformer_blocks)
-            use_cache = bool(transformer.config.causal_condition)
             pos_cache = QwenImage21KVCache(num_blocks) if use_cache else None
             neg_cache = QwenImage21KVCache(num_blocks) if use_cache and neg_embeds is not None else None
 
             def predict(embeds: torch.Tensor, img_mask: torch.Tensor, cache, mode, timestep) -> torch.Tensor:
+                hidden_states = latents if reference_tokens is None else torch.cat([reference_tokens, latents], dim=1)
                 out = transformer(
-                    hidden_states=latents,
+                    hidden_states=hidden_states,
                     encoder_hidden_states=embeds,
                     encoder_hidden_states_mask=None,
                     timestep=timestep,
@@ -335,26 +440,36 @@ def predict(embeds: torch.Tensor, img_mask: torch.Tensor, cache, mode, timestep)
 
     @staticmethod
     def _estimate_working_memory(
-        image_seq_len: int, pos_text_len: int, neg_text_len: int | None, do_cfg: bool, element_size: int
+        image_seq_len: int,
+        pos_prefix: int,
+        neg_prefix: int | None,
+        do_cfg: bool,
+        use_cache: bool,
+        element_size: int,
     ) -> int:
         """Peak transformer activations, in bytes, for the model cache to keep free.
 
         Measured on an RTX 4090 in bf16 with the transformer fully resident: 0.55 GiB of activations at
         1024x1024 (4096 image tokens) and 2.2 GiB at 2048x2048 (16384), about 0.14 MiB per token. 0.25 MiB per
         token plus a 1 GiB base leaves room for the allocator and the prefix cache, which holds K and V of every
-        text token in all 32 blocks (~0.5 MiB per text token, per guidance branch). Both per-token terms are bf16
-        figures and scale with `element_size`, the compute dtype's width: a float32 fallback needs twice as much.
+        prefix token -- text and reference latents -- in all 32 blocks (~0.5 MiB per token, per guidance branch).
+        Both per-token terms are bf16 figures and scale with `element_size`, the compute dtype's width: a
+        float32 fallback needs twice as much.
         """
         mib, gib = 1024**2, 1024**3
         width = element_size / 2
-        text_len = max(pos_text_len, neg_text_len or 0)
-        estimated = int((image_seq_len + text_len) * 0.25 * mib * width) + gib
-        branches = 2 if do_cfg else 1
-        estimated += int(branches * (pos_text_len if neg_text_len is None else text_len) * 0.5 * mib * width)
+        prefix = max(pos_prefix, neg_prefix or 0)
+        estimated = int((image_seq_len + prefix) * 0.25 * mib * width) + gib
+        if use_cache:
+            branches = 2 if do_cfg else 1
+            prefix_tokens = branches * (pos_prefix if neg_prefix is None else prefix)
+            estimated += int(prefix_tokens * KV_BYTES_PER_PREFIX_TOKEN * width)
         return estimated
 
     @staticmethod
-    def _attention_score_bytes(transformer: object, seq_len: int, device: torch.device, dtype: torch.dtype) -> int:
+    def _attention_score_bytes(
+        transformer: object, image_seq_len: int, prefix: int, device: torch.device, dtype: torch.dtype
+    ) -> int:
         """The score matrix attention materializes on a build without a fused kernel for it (0 where it has one)."""
         config = getattr(transformer, "config", None)
         num_heads = getattr(config, "num_attention_heads", None)
@@ -363,13 +478,14 @@ def _attention_score_bytes(transformer: object, seq_len: int, device: torch.devi
             return 0
         # The largest call, the image queries over every key, goes unmasked both in the prefill and on decode steps:
         # only the text segments carry a mask, and they are as short as the prompt. All of them go through diffusers'
-        # attention dispatch.
+        # attention dispatch. Its score matrix is image x (prefix + image), which the square helper prices at the
+        # side with the same area.
         return sdpa_score_matrix_bytes(
             device=device,
             dtype=dtype,
             num_heads=num_heads,
             head_dim=head_dim,
-            seq_len=seq_len,
+            seq_len=math.isqrt(image_seq_len * (image_seq_len + prefix)),
             via_diffusers_dispatch=True,
         )
 
diff --git a/invokeai/app/invocations/text_encoder/qwen_image_2_1_text_encoder.py b/invokeai/app/invocations/text_encoder/qwen_image_2_1_text_encoder.py
index 0bea270aa49..7b59a30b17e 100644
--- a/invokeai/app/invocations/text_encoder/qwen_image_2_1_text_encoder.py
+++ b/invokeai/app/invocations/text_encoder/qwen_image_2_1_text_encoder.py
@@ -1,56 +1,115 @@
+from contextlib import nullcontext
+
 import torch
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, UIComponent
-from invokeai.app.invocations.model import Qwen3VLEncoderField
+from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, UIComponent
+from invokeai.app.invocations.model import ModelIdentifierField, Qwen3VLEncoderField
 from invokeai.app.invocations.primitives import QwenImage21ConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager.load.model_cache.utils import get_effective_device
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelFormat, ModelType, SubModelType
 from invokeai.backend.quantization.dequantizing_linear import peak_dequant_transient_bytes
-from invokeai.backend.qwen_image_2_1.text_encoding import encode_prompt
+from invokeai.backend.qwen_image_2_1.text_encoding import (
+    encode_prompt,
+    has_vision_tower,
+    sequence_length,
+    working_memory_bytes,
+)
+from invokeai.backend.qwen_image_2_1.vae import to_reference
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     ConditioningFieldData,
     QwenImage21ConditioningInfo,
 )
 from invokeai.backend.util.devices import TorchDevice
 
+_NO_VISION_TOWER = (
+    "Reference images are read by the Qwen3-VL vision tower, which only the Qwen-Image-2.1 Diffusers model's own "
+    "encoder brings. Clear the standalone encoder, or use a Qwen-Image-2.1 Diffusers model."
+)
+
 
 @invocation(
     "qwen_image_2_1_text_encoder",
     title="Prompt - Qwen-Image-2.1",
     tags=["prompt", "conditioning", "qwen_image_2_1", "qwen-image-2.1"],
     category="conditioning",
-    version="1.0.0",
+    version="1.1.0",
     classification=Classification.Prototype,
     idle_gpu_offloadable=True,
 )
 class QwenImage21TextEncoderInvocation(BaseInvocation):
-    """Encodes a text prompt for Qwen-Image-2.1 with its Qwen3-VL-8B encoder."""
+    """Encodes a prompt for Qwen-Image-2.1 with its Qwen3-VL-8B encoder, with the reference images an edit reads.
+
+    Each reference also goes to the denoise node, encoded by Image to Latents in its reference mode, in the same
+    order.
+    """
 
     prompt: str = InputField(description="Text prompt describing the desired image.", ui_component=UIComponent.Textarea)
     qwen3_vl_encoder: Qwen3VLEncoderField = InputField(
         title="Qwen3-VL Encoder", description=FieldDescriptions.qwen3_vl_encoder, input=Input.Connection
     )
+    reference_images: list[ImageField] = InputField(
+        default=[],
+        description="Reference images to edit or draw from, in the order the denoise node receives their latents. "
+        "Needs a Qwen-Image-2.1 Diffusers model's own encoder.",
+    )
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> QwenImage21ConditioningOutput:
-        tokenizer_info = context.models.load(self.qwen3_vl_encoder.tokenizer)
+        images = [to_reference(context.images.get_pil(field.image_name)) for field in self.reference_images]
+        processor_id = self._processor_identifier(context) if images else None
+
+        # The encoder first: loading it evicts unlocked small entries from the RAM cache, which would otherwise drop
+        # the tokenizer and processor loaded before it.
         text_encoder_info = context.models.load(self.qwen3_vl_encoder.text_encoder)
+        tokenizer_info = context.models.load(self.qwen3_vl_encoder.tokenizer)
+        processor_info = context.models.load(processor_id) if processor_id is not None else None
         # A GGUF or int8 encoder dequantizes one layer at a time; that transient is the working memory.
         dequant_bytes = peak_dequant_transient_bytes(
             text_encoder_info.model, TorchDevice.choose_bfloat16_safe_dtype(text_encoder_info.compute_device)
         )
+        # Before the encoder moves to the GPU: a graph can pair this pipeline's tokenizer with another encoder.
+        if images and not has_vision_tower(text_encoder_info.model):
+            raise ValueError(_NO_VISION_TOWER)
 
         context.util.signal_progress("Running Qwen3-VL text encoder")
-        with (
-            tokenizer_info as tokenizer,
-            text_encoder_info.model_on_device(working_mem_bytes=dequant_bytes) as (
-                _,
-                text_encoder,
-            ),
-        ):
-            embeds = encode_prompt(text_encoder, tokenizer, self.prompt, get_effective_device(text_encoder))
+        with tokenizer_info as tokenizer:
+            # Reference images make the sequence long, and its attention grows with the square of it: room the
+            # model cache has to make, or the driver spills the encode to system memory and it crawls.
+            working_memory = dequant_bytes + working_memory_bytes(sequence_length(tokenizer, self.prompt, images))
+            with (
+                processor_info.model_on_device() if processor_info is not None else nullcontext((None, None)) as (
+                    _,
+                    processor,
+                ),
+                text_encoder_info.model_on_device(working_mem_bytes=working_memory) as (_, text_encoder),
+            ):
+                encoding = encode_prompt(
+                    text_encoder,
+                    tokenizer,
+                    self.prompt,
+                    get_effective_device(text_encoder),
+                    processor=processor,
+                    images=images,
+                )
 
-        conditioning = QwenImage21ConditioningInfo(prompt_embeds=embeds.detach().cpu())
+        conditioning = QwenImage21ConditioningInfo(
+            prompt_embeds=encoding.embeds.detach().cpu(),
+            image_pad_mask=None if encoding.image_pad_mask is None else encoding.image_pad_mask.cpu(),
+            reference_grids=encoding.reference_grids,
+        )
         name = context.conditioning.save(ConditioningFieldData(conditionings=[conditioning]))
         return QwenImage21ConditioningOutput.build(name)
+
+    def _processor_identifier(self, context: InvocationContext) -> ModelIdentifierField:
+        """The image processor of the model the tokenizer came from, which has to be a Qwen-Image-2.1 pipeline."""
+        tokenizer = self.qwen3_vl_encoder.tokenizer
+        config = context.models.get_config(tokenizer)
+        if (
+            config.base is not BaseModelType.QwenImage21
+            or config.type is not ModelType.Main
+            or config.format is not ModelFormat.Diffusers
+        ):
+            raise ValueError(_NO_VISION_TOWER)
+        return tokenizer.model_copy(update={"submodel_type": SubModelType.Processor})
diff --git a/invokeai/app/invocations/vae/qwen_image_2_1_image_to_latents.py b/invokeai/app/invocations/vae/qwen_image_2_1_image_to_latents.py
index 991e744184d..e8aa7d57e9b 100644
--- a/invokeai/app/invocations/vae/qwen_image_2_1_image_to_latents.py
+++ b/invokeai/app/invocations/vae/qwen_image_2_1_image_to_latents.py
@@ -14,7 +14,13 @@
 from invokeai.app.invocations.model import VAEField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.qwen_image_2_1.vae import SPATIAL_SCALE, choose_tile_size, to_vae_input, working_memory_bytes
+from invokeai.backend.qwen_image_2_1.vae import (
+    SPATIAL_SCALE,
+    choose_tile_size,
+    to_reference,
+    to_vae_input,
+    working_memory_bytes,
+)
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.qwen_image_vae import patch_qwen_image_vae_tiling
 
@@ -27,26 +33,41 @@
     title="Image to Latents - Qwen-Image-2.1",
     tags=["image", "latents", "vae", "i2l", "qwen_image_2_1"],
     category="image",
-    version="1.0.0",
+    version="1.1.0",
     classification=Classification.Prototype,
 )
 class QwenImage21ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Encodes an image into Qwen-Image-2.1 latents. The image is encoded opaque; its alpha is not read."""
+    """Encodes an image into Qwen-Image-2.1 latents.
+
+    For image-to-image and the canvas, the image is encoded opaque at its own size; its alpha is not read. As a
+    reference for an edit, it is resized to the area the text encoder reads it at and keeps its alpha.
+    """
 
     image: ImageField = InputField(description="The image to encode.")
     vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection)
     tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
     tile_size: int = InputField(default=0, ge=0, multiple_of=16, description=FieldDescriptions.vae_tile_size)
+    reference: bool = InputField(
+        default=False,
+        description="Encode as a reference image for the denoise node's reference latents: resized to ~1 megapixel, "
+        "as the text encoder reads it, with its alpha kept.",
+    )
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         image = context.images.get_pil(self.image.image_name)
-        # Crop-free resize onto the 32px grid the transformer needs, as the canvas sizes its own images.
-        width, height = image.width // _GRID * _GRID, image.height // _GRID * _GRID
-        if width == 0 or height == 0:
-            raise ValueError(f"Qwen-Image-2.1 encodes images of at least {_GRID}x{_GRID}; this one is {image.size}.")
-        if (width, height) != image.size:
-            image = image.resize((width, height), resample=PILImage.LANCZOS)
+        if self.reference:
+            image = to_reference(image)
+            width, height = image.size
+        else:
+            # Crop-free resize onto the 32px grid the transformer needs, as the canvas sizes its own images.
+            width, height = image.width // _GRID * _GRID, image.height // _GRID * _GRID
+            if width == 0 or height == 0:
+                raise ValueError(
+                    f"Qwen-Image-2.1 encodes images of at least {_GRID}x{_GRID}; this one is {image.size}."
+                )
+            if (width, height) != image.size:
+                image = image.resize((width, height), resample=PILImage.LANCZOS)
 
         vae_info = context.models.load(self.vae.vae)
         device = vae_info.compute_device
@@ -66,12 +87,16 @@ def invoke(self, context: InvocationContext) -> LatentsOutput:
         working_memory = working_memory_bytes(height, width, tile_size, element_size)
 
         with vae_info.model_on_device(working_mem_bytes=working_memory) as (_, vae):
-            pixels = to_vae_input(image).to(device=device, dtype=vae.dtype).unsqueeze(2)
+            pixels = to_vae_input(image, keep_alpha=self.reference).to(device=device, dtype=vae.dtype).unsqueeze(2)
             with torch.inference_mode(), patch_qwen_image_vae_tiling(vae, tile_size, SPATIAL_SCALE):
-                z = vae.encode(pixels).latent_dist.mode().float()[:, :, 0]
-            mean = torch.tensor(vae.config.latents_mean, device=z.device).view(1, -1, 1, 1)
-            std = torch.tensor(vae.config.latents_std, device=z.device).view(1, -1, 1, 1)
-            latents = ((z - mean) / std).cpu()
+                z = vae.encode(pixels).latent_dist.mode()[:, :, 0]
+            # A reference is normalized in the VAE's dtype, as the pipeline normalizes it, so the transformer reads
+            # the same values; the canvas's latents only seed a sampler, and keep fp32.
+            z = z if self.reference else z.float()
+            # float32 first, then the latents' dtype: straight from Python floats to bf16 can round differently.
+            mean = torch.tensor(vae.config.latents_mean).to(z.device, z.dtype).view(1, -1, 1, 1)
+            std = torch.tensor(vae.config.latents_std).to(z.device, z.dtype).view(1, -1, 1, 1)
+            latents = ((z - mean) / std).float().cpu()
 
         TorchDevice.empty_cache()
         name = context.tensors.save(tensor=latents)
diff --git a/invokeai/backend/architectures/capabilities.py b/invokeai/backend/architectures/capabilities.py
index cbfccd56192..e72665da0d4 100644
--- a/invokeai/backend/architectures/capabilities.py
+++ b/invokeai/backend/architectures/capabilities.py
@@ -72,6 +72,10 @@ class ArchitectureFeatures(BaseModel):
         default=None,
         description="If set, reference images are only accepted for models of this variant.",
     )
+    reference_images_require_format: str | None = Field(
+        default=None,
+        description="If set, reference images are only accepted for models of this format.",
+    )
     supports_regional_guidance: bool = False
     regional_negative: bool = Field(
         default=False, description="Whether a region's negative prompt is masked, rather than applied globally."
@@ -137,6 +141,7 @@ def _features_of(facet: FeaturesFacet, variant: AnyVariant | None = None) -> Arc
         control_kinds=sorted(facet.control_kinds),
         max_reference_images=facet.max_reference_images,
         reference_images_require_variant=facet.reference_images_require_variant,
+        reference_images_require_format=facet.reference_images_require_format,
         supports_regional_guidance=facet.supports_regional_guidance,
         regional_negative=facet.regional_negative,
         clip_skip_max=facet.clip_skip_max,
@@ -171,7 +176,8 @@ def architecture_capabilities() -> list[ArchitectureCapabilities]:
     is rendered in full, so a client never has to know which fields a variant row is allowed to omit.
 
     Differences too small to have earned a mapping are expressed on the base row instead, by
-    `features.reference_images_require_variant`; Qwen-Image is the only one.
+    `features.reference_images_require_variant` (Qwen-Image) and
+    `features.reference_images_require_format` (Qwen-Image-2.1).
 
     Sorted by base value, then variant, so the response is stable and diffable.
     """
diff --git a/invokeai/backend/architectures/defs/qwen_image_2_1.py b/invokeai/backend/architectures/defs/qwen_image_2_1.py
index 0a523835091..8dea696ac0b 100644
--- a/invokeai/backend/architectures/defs/qwen_image_2_1.py
+++ b/invokeai/backend/architectures/defs/qwen_image_2_1.py
@@ -34,6 +34,9 @@
         dimension_grid=32,
         guidance_label="CFG",
         scheduler_set="flow",
+        # Each reference holds ~1 megapixel of latents in the prefix the cache keeps: ~2 GiB per guidance branch.
+        max_reference_images=4,
+        reference_images_require_format="diffusers",
     ),
     # Values are qwen_image_21_* because variant strings resolve without the base and must be unique.
     VariantFacet({ModelType.Main: QwenImage21VariantType}),
diff --git a/invokeai/backend/architectures/facets/features.py b/invokeai/backend/architectures/facets/features.py
index 0cae3669ef3..95887564fad 100644
--- a/invokeai/backend/architectures/facets/features.py
+++ b/invokeai/backend/architectures/facets/features.py
@@ -111,8 +111,11 @@ class FeaturesFacet(Facet):
     control_kinds: frozenset[ControlKind] = frozenset()
     max_reference_images: int = 0
     reference_images_require_variant: str | None = None
-    """Qwen-Image accepts reference images only as the `edit` variant — the one feature in this
+    """Qwen-Image accepts reference images only as the `edit` variant — one of the two features in this
     table that a base alone cannot answer."""
+    reference_images_require_format: str | None = None
+    """The other: Qwen-Image-2.1 reads reference images only as a Diffusers pipeline, whose encoder is
+    the only one that keeps the vision tower."""
 
     supports_regional_guidance: bool = False
     regional_negative: bool = False
diff --git a/invokeai/backend/model_manager/load/model_loaders/qwen_image_2_1.py b/invokeai/backend/model_manager/load/model_loaders/qwen_image_2_1.py
index f94637da230..08ff7d29996 100644
--- a/invokeai/backend/model_manager/load/model_loaders/qwen_image_2_1.py
+++ b/invokeai/backend/model_manager/load/model_loaders/qwen_image_2_1.py
@@ -18,7 +18,6 @@
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
-from invokeai.backend.model_manager.util.qwen3_vl import drop_qwen3vl_visual_tower
 from invokeai.backend.quantization.fp8_scaled import (
     attach_fp8_scales,
     cast_state_dict,
@@ -62,10 +61,13 @@ def _build_transformer(sd: dict[str, Any]) -> torch.nn.Module:
 class QwenImage21DiffusersModel(GenericDiffusersLoader):
     """Loads the submodels of a Qwen-Image-2.1 diffusers pipeline.
 
-    The tokenizer lives in `processor/` (the pipeline declares a `Qwen3VLProcessor`, of which text prompts
-    need only the tokenizer). The text encoder loads as `Qwen3VLModel` rather than the declared
-    `Qwen3VLForConditionalGeneration`: the LM head is ~1.2 GiB the encoder never runs, and `Qwen3VLModel` is
-    what the standalone Qwen3-VL loaders return, so the text encoder node sees one class.
+    The tokenizer lives in `processor/` (the pipeline declares a `Qwen3VLProcessor`); the processor itself, which
+    also cuts reference images into patches, loads only for an edit. The text encoder loads as `Qwen3VLModel`
+    rather than the declared `Qwen3VLForConditionalGeneration`: the LM head is ~1.2 GiB the encoder never runs,
+    and `Qwen3VLModel` is what the standalone Qwen3-VL loaders return, so the text encoder node sees one class.
+
+    Unlike those loaders, this one keeps the vision tower (~1.1 GiB): reference images reach the encoder only
+    through it, and only this pipeline's encoder carries one into Qwen-Image-2.1.
     """
 
     def _load_model(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> AnyModel:
@@ -79,6 +81,10 @@ def _load_model(self, config: AnyModelConfig, submodel_type: Optional[SubModelTy
         model_path = Path(config.path)
         if submodel_type is SubModelType.Tokenizer:
             return AutoTokenizer.from_pretrained(model_path / "processor", local_files_only=True)
+        if submodel_type is SubModelType.Processor:
+            from transformers import AutoProcessor
+
+            return AutoProcessor.from_pretrained(model_path / "processor", local_files_only=True)
 
         if submodel_type is SubModelType.VAE:
             # The configured precision, except float16, which is unstable on this autoencoder: bfloat16 where the
@@ -108,10 +114,6 @@ def _load_model(self, config: AnyModelConfig, submodel_type: Optional[SubModelTy
             else:
                 raise
 
-        if submodel_type is SubModelType.TextEncoder:
-            # Text prompts never reach the vision tower; reference images (which would) are not wired up.
-            drop_qwen3vl_visual_tower(result)
-
         return self._apply_fp8_layerwise_casting(result, config, submodel_type)
 
 
diff --git a/invokeai/backend/qwen_image_2_1/text_encoding.py b/invokeai/backend/qwen_image_2_1/text_encoding.py
index 45f95084271..a9f145ef78b 100644
--- a/invokeai/backend/qwen_image_2_1/text_encoding.py
+++ b/invokeai/backend/qwen_image_2_1/text_encoding.py
@@ -1,17 +1,48 @@
-"""Qwen-Image-2.1's prompt encoding, as `QwenImage21Pipeline._get_qwen_prompt_embeds` does it for text prompts."""
+"""Qwen-Image-2.1's prompt encoding, as `QwenImage21Pipeline._get_qwen_prompt_embeds` does it."""
+
+from collections.abc import Sequence
+from typing import NamedTuple
 
 import torch
+from PIL import Image
 
 SYSTEM_PROMPT = "Comprehend and analyze the provided prompt."
 SYSTEM_BLOCK = f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
 # A raw string handed to the tokenizer, not `apply_chat_template`: the two tokenize differently, and the
 # checkpoint was trained on this one.
 T2I_TEMPLATE = SYSTEM_BLOCK + "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+IMAGE_PAD = "<|image_pad|>"
+# Pixels per side of one vision token: 16px patches, merged 2x2.
+_PIXELS_PER_TOKEN = 32
+
+# What one encode needs above the weights for a sequence of T tokens, measured on an RTX 4090 (torch 2.13,
+# transformers 5.10): ~286 B * T^2 + 224 KiB * T at peak. The quadratic term is the language model's attention,
+# which SDPA runs on its math kernel for grouped-query heads; four 1-megapixel references (~4.1k tokens) need
+# ~5.4 GiB. The allocator reserves about a fifth more than it hands out.
+_BYTES_PER_TOKEN_PAIR = 286
+_BYTES_PER_TOKEN = 224 * 1024
+_RESERVED_OVER_ALLOCATED = 1.25
 
 
-def format_prompt(prompt: str) -> str:
+def format_prompt(prompt: str, num_images: int = 0) -> str:
+    """The prompt as the checkpoint was trained on it, with one vision placeholder per reference image first."""
+    placeholders = " ".join(f"<image{i}><|vision_start|>{IMAGE_PAD}<|vision_end|>" for i in range(1, num_images + 1))
     # Qwen has no BOS token, so an empty prompt would leave the encoder nothing to read.
-    return T2I_TEMPLATE.format(prompt or " ")
+    return T2I_TEMPLATE.format(placeholders + (prompt or " "))
+
+
+def sequence_length(tokenizer, prompt: str, images: Sequence[Image.Image] = ()) -> int:
+    """Tokens the encoder reads: the formatted prompt, each reference's placeholder one token per 32x32 pixels.
+
+    `images` are the references at `vae.reference_size`, which the image processor leaves at their size.
+    """
+    text = len(tokenizer(format_prompt(prompt, len(images))).input_ids) - len(images)
+    return text + sum((w // _PIXELS_PER_TOKEN) * (h // _PIXELS_PER_TOKEN) for w, h in (i.size for i in images))
+
+
+def working_memory_bytes(num_tokens: int) -> int:
+    """Device memory an encode of `num_tokens` tokens needs above the encoder's weights."""
+    return int((_BYTES_PER_TOKEN_PAIR * num_tokens**2 + _BYTES_PER_TOKEN * num_tokens) * _RESERVED_OVER_ALLOCATED)
 
 
 def system_prefix_length(tokenizer) -> int:
@@ -29,20 +60,83 @@ def _base_model(text_encoder: torch.nn.Module) -> torch.nn.Module:
     return text_encoder if hasattr(text_encoder, "language_model") else text_encoder.model
 
 
+def has_vision_tower(text_encoder: torch.nn.Module) -> bool:
+    """Whether this encoder can read reference images: only the Qwen-Image-2.1 pipeline's keeps its vision tower.
+
+    The standalone Qwen3-VL loaders replace the tower with an `Identity` rather than deleting it.
+    """
+    visual = getattr(_base_model(text_encoder), "visual", None)
+    return isinstance(visual, torch.nn.Module) and not isinstance(visual, torch.nn.Identity)
+
+
+def on_white(image: Image.Image) -> Image.Image:
+    """A reference as the vision encoder reads it: alpha composited over white, as the checkpoint was trained."""
+    if image.mode != "RGBA":
+        return image.convert("RGB")
+    white = Image.new("RGB", image.size, (255, 255, 255))
+    white.paste(image, mask=image.getchannel("A"))
+    return white
+
+
+class PromptEncoding(NamedTuple):
+    embeds: torch.Tensor
+    """`(1, L, hidden)`, the system turn dropped."""
+    image_pad_mask: torch.Tensor | None
+    """`(1, L)`, True at each `<|image_pad|>` slot, where the transformer places 2x2 of a reference's latents."""
+    reference_grids: tuple[tuple[int, int], ...]
+    """Per reference, in order: its slots as (rows, columns), one slot per 32x32 pixels."""
+
+
 @torch.no_grad()
-def encode_prompt(text_encoder: torch.nn.Module, tokenizer, prompt: str, device: torch.device) -> torch.Tensor:
-    """Encode a prompt into `(1, L, hidden)` embeddings, as the pipeline encodes a single prompt.
+def encode_prompt(
+    text_encoder: torch.nn.Module,
+    tokenizer,
+    prompt: str,
+    device: torch.device,
+    *,
+    processor=None,
+    images: Sequence[Image.Image] = (),
+) -> PromptEncoding:
+    """Encode a prompt, and the reference images it edits.
 
     The transformer reads the last decoder layer's output BEFORE the final RMSNorm. transformers 5 ties
     the last hidden state to the normalized output, so a forward hook returning the norm's input
     neutralizes the norm for this call -- the pipeline's approach. The system turn every prompt shares is
     dropped from the result.
+
+    `images` are the references, already at `vae.reference_size`; reading them needs the vision tower and the
+    `processor` that cuts them into patches.
     """
-    inputs = tokenizer([format_prompt(prompt)], return_tensors="pt").to(device)
     base = _base_model(text_encoder)
+    if images:
+        if processor is None or not has_vision_tower(text_encoder):
+            raise ValueError("Reading reference images needs the Qwen3-VL vision tower and its image processor.")
+        inputs = processor(
+            text=[format_prompt(prompt, len(images))], images=[on_white(i) for i in images], return_tensors="pt"
+        ).to(device)
+        forward_kwargs = {
+            "input_ids": inputs.input_ids,
+            "attention_mask": inputs.attention_mask,
+            "pixel_values": inputs.pixel_values,
+            "image_grid_thw": inputs.image_grid_thw,
+        }
+        if "mm_token_type_ids" in inputs:
+            forward_kwargs["mm_token_type_ids"] = inputs.mm_token_type_ids
+    else:
+        inputs = tokenizer([format_prompt(prompt)], return_tensors="pt").to(device)
+        forward_kwargs = {"input_ids": inputs.input_ids, "attention_mask": inputs.attention_mask}
+
     handle = base.language_model.norm.register_forward_hook(lambda module, args, output: args[0])
     try:
-        hidden = base(input_ids=inputs.input_ids, attention_mask=inputs.attention_mask).last_hidden_state
+        # One forward, so the KV cache the language model would build over the whole sequence is waste.
+        hidden = base(**forward_kwargs, use_cache=False).last_hidden_state
     finally:
         handle.remove()
-    return hidden[:, system_prefix_length(tokenizer) :]
+
+    drop = system_prefix_length(tokenizer)
+    if not images:
+        return PromptEncoding(hidden[:, drop:], None, ())
+    image_pad_id = tokenizer.convert_tokens_to_ids(IMAGE_PAD)
+    merge = processor.image_processor.merge_size
+    grids = tuple((int(h) // merge, int(w) // merge) for _, h, w in inputs.image_grid_thw.tolist())
+    return PromptEncoding(hidden[:, drop:], (inputs.input_ids == image_pad_id)[:, drop:], grids)
diff --git a/invokeai/backend/qwen_image_2_1/vae.py b/invokeai/backend/qwen_image_2_1/vae.py
index f787f2bb445..4fe48a69c90 100644
--- a/invokeai/backend/qwen_image_2_1/vae.py
+++ b/invokeai/backend/qwen_image_2_1/vae.py
@@ -4,6 +4,8 @@
 and Wan VAEs (same tiling attributes, 16x instead of 8x).
 """
 
+import math
+
 import numpy as np
 import torch
 from PIL import Image
@@ -96,11 +98,38 @@ def to_image(decoded: torch.Tensor) -> Image.Image:
     return Image.fromarray(pixels, mode="RGBA")
 
 
-def to_vae_input(image: Image.Image) -> torch.Tensor:
-    """An image as the VAE's `(1, 4, H, W)` input in [-1, 1], encoded opaque.
+def to_vae_input(image: Image.Image, keep_alpha: bool = False) -> torch.Tensor:
+    """An image as the VAE's `(1, 4, H, W)` input in [-1, 1].
 
-    Alpha is set to 1 rather than read from the image: the canvas hands its masks over in alpha, and a
-    transparent region there means "repaint", not "transparent pixels".
+    For image-to-image and the canvas, alpha is set to 1 rather than read from the image: the canvas hands its masks
+    over in alpha, and a transparent region there means "repaint", not "transparent pixels". A reference image keeps
+    its alpha (`keep_alpha`), as the pipeline encodes it.
     """
-    rgb = torch.from_numpy(np.asarray(image.convert("RGB"), dtype=np.float32)).permute(2, 0, 1) / 127.5 - 1
-    return torch.cat([rgb, torch.ones_like(rgb[:1])], dim=0)[None]
+    rgba = image.convert("RGBA")
+    # In the pipeline's `VaeImageProcessor` order, so the result matches it to the bit.
+    pixels = torch.from_numpy(np.asarray(rgba).astype(np.float32) / 255.0).permute(2, 0, 1) * 2.0 - 1.0
+    if not keep_alpha:
+        pixels[3] = 1.0
+    return pixels[None]
+
+
+# The area every reference image is resized to, as the pipeline's default `output_resolution` sets it.
+REFERENCE_AREA = 1024 * 1024
+
+
+def reference_size(width: int, height: int) -> tuple[int, int]:
+    """The size a `width` x `height` reference is read at: `REFERENCE_AREA` at its aspect ratio, on the 32px grid.
+
+    The text encoder and the VAE must both read it at this size: each `<|image_pad|>` slot the encoder emits
+    stands for 2x2 of the reference's latents, and the transformer drops the latents into those slots.
+    """
+    ratio = width / height
+    scaled_width = math.sqrt(REFERENCE_AREA * ratio)
+    # Python's round (half to even) is the pipeline's `calculate_dimensions`.
+    return round(scaled_width / 32) * 32, round(scaled_width / ratio / 32) * 32
+
+
+def to_reference(image: Image.Image) -> Image.Image:
+    """A reference image as both the text encoder and the VAE read it: RGBA, resized to `reference_size`."""
+    rgba = image.convert("RGBA")
+    return rgba.resize(reference_size(*rgba.size), resample=Image.LANCZOS)
diff --git a/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py b/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py
index 04591b0f238..43c24a6d11a 100644
--- a/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py
+++ b/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py
@@ -166,8 +166,18 @@ class QwenImage21ConditioningInfo:
     prompt_embeds: torch.Tensor
     """Shape: (1, seq_len, hidden=4096). One prompt, so no padding and no mask."""
 
+    image_pad_mask: torch.Tensor | None = None
+    """Shape: (1, seq_len), True at the reference images' `<|image_pad|>` slots; None without references. Each
+    slot stands for 2x2 latent tokens of a reference, in the order the references were encoded."""
+
+    reference_grids: tuple[tuple[int, int], ...] = ()
+    """Per reference, in order: its slots as (rows, columns), so the denoise node can match each reference's
+    latents by shape, not only by count."""
+
     def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
         self.prompt_embeds = self.prompt_embeds.to(device=device, dtype=dtype)
+        if self.image_pad_mask is not None:
+            self.image_pad_mask = self.image_pad_mask.to(device=device)
         return self
 
 
diff --git a/invokeai/frontend/api/openapi.json b/invokeai/frontend/api/openapi.json
index 537d0836d51..3bd8fae5101 100644
--- a/invokeai/frontend/api/openapi.json
+++ b/invokeai/frontend/api/openapi.json
@@ -20698,6 +20698,18 @@
             "title": "Reference Images Require Variant",
             "description": "If set, reference images are only accepted for models of this variant."
           },
+          "reference_images_require_format": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Reference Images Require Format",
+            "description": "If set, reference images are only accepted for models of this format."
+          },
           "supports_regional_guidance": {
             "type": "boolean",
             "title": "Supports Regional Guidance",
@@ -95409,6 +95421,29 @@
             "orig_default": null,
             "orig_required": false
           },
+          "reference_latents": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/LatentsField"
+              },
+              {
+                "items": {
+                  "$ref": "#/components/schemas/LatentsField"
+                },
+                "type": "array"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Latents of the reference images an edit reads, from Image to Latents in reference mode, in the order the prompts were encoded with them.",
+            "field_kind": "input",
+            "input": "connection",
+            "orig_default": null,
+            "orig_required": false,
+            "title": "Reference Latents"
+          },
           "denoising_start": {
             "default": 0.0,
             "description": "When to start denoising, expressed a percentage of total steps",
@@ -95557,7 +95592,7 @@
         "tags": ["image", "qwen_image_2_1", "qwen-image-2.1"],
         "title": "Denoise - Qwen-Image-2.1",
         "type": "object",
-        "version": "1.0.1",
+        "version": "1.1.0",
         "output": {
           "$ref": "#/components/schemas/LatentsOutput"
         }
@@ -95566,7 +95601,7 @@
         "category": "image",
         "class": "invocation",
         "classification": "prototype",
-        "description": "Encodes an image into Qwen-Image-2.1 latents. The image is encoded opaque; its alpha is not read.",
+        "description": "Encodes an image into Qwen-Image-2.1 latents.\n\nFor image-to-image and the canvas, the image is encoded opaque at its own size; its alpha is not read. As a\nreference for an edit, it is resized to the area the text encoder reads it at and keeps its alpha.",
         "node_pack": "invokeai",
         "properties": {
           "board": {
@@ -95677,6 +95712,16 @@
             "title": "Tile Size",
             "type": "integer"
           },
+          "reference": {
+            "default": false,
+            "description": "Encode as a reference image for the denoise node's reference latents: resized to ~1 megapixel, as the text encoder reads it, with its alpha kept.",
+            "field_kind": "input",
+            "input": "any",
+            "orig_default": false,
+            "orig_required": false,
+            "title": "Reference",
+            "type": "boolean"
+          },
           "type": {
             "const": "qwen_image_2_1_i2l",
             "default": "qwen_image_2_1_i2l",
@@ -95689,7 +95734,7 @@
         "tags": ["image", "latents", "vae", "i2l", "qwen_image_2_1"],
         "title": "Image to Latents - Qwen-Image-2.1",
         "type": "object",
-        "version": "1.0.0",
+        "version": "1.1.0",
         "output": {
           "$ref": "#/components/schemas/LatentsOutput"
         }
@@ -95963,7 +96008,7 @@
         "category": "conditioning",
         "class": "invocation",
         "classification": "prototype",
-        "description": "Encodes a text prompt for Qwen-Image-2.1 with its Qwen3-VL-8B encoder.",
+        "description": "Encodes a prompt for Qwen-Image-2.1 with its Qwen3-VL-8B encoder, with the reference images an edit reads.\n\nEach reference also goes to the denoise node, encoded by Image to Latents in its reference mode, in the same\norder.",
         "node_pack": "invokeai",
         "properties": {
           "id": {
@@ -96023,6 +96068,19 @@
             "orig_required": true,
             "title": "Qwen3-VL Encoder"
           },
+          "reference_images": {
+            "default": [],
+            "description": "Reference images to edit or draw from, in the order the denoise node receives their latents. Needs a Qwen-Image-2.1 Diffusers model's own encoder.",
+            "field_kind": "input",
+            "input": "any",
+            "items": {
+              "$ref": "#/components/schemas/ImageField"
+            },
+            "orig_default": [],
+            "orig_required": false,
+            "title": "Reference Images",
+            "type": "array"
+          },
           "type": {
             "const": "qwen_image_2_1_text_encoder",
             "default": "qwen_image_2_1_text_encoder",
@@ -96035,7 +96093,7 @@
         "tags": ["prompt", "conditioning", "qwen_image_2_1", "qwen-image-2.1"],
         "title": "Prompt - Qwen-Image-2.1",
         "type": "object",
-        "version": "1.0.0",
+        "version": "1.1.0",
         "output": {
           "$ref": "#/components/schemas/QwenImage21ConditioningOutput"
         }
diff --git a/invokeai/frontend/api/schema.ts b/invokeai/frontend/api/schema.ts
index a845bdf0a0f..76c5e0e9de5 100644
--- a/invokeai/frontend/api/schema.ts
+++ b/invokeai/frontend/api/schema.ts
@@ -5755,6 +5755,11 @@ export type components = {
              * @description If set, reference images are only accepted for models of this variant.
              */
             reference_images_require_variant?: string | null;
+            /**
+             * Reference Images Require Format
+             * @description If set, reference images are only accepted for models of this format.
+             */
+            reference_images_require_format?: string | null;
             /**
              * Supports Regional Guidance
              * @default false
@@ -41186,6 +41191,12 @@ export type components = {
              * @default null
              */
             denoise_mask?: components["schemas"]["DenoiseMaskField"] | null;
+            /**
+             * Reference Latents
+             * @description Latents of the reference images an edit reads, from Image to Latents in reference mode, in the order the prompts were encoded with them.
+             * @default null
+             */
+            reference_latents?: components["schemas"]["LatentsField"] | components["schemas"]["LatentsField"][] | null;
             /**
              * Denoising Start
              * @description When to start denoising, expressed a percentage of total steps
@@ -41253,7 +41264,10 @@ export type components = {
         };
         /**
          * Image to Latents - Qwen-Image-2.1
-         * @description Encodes an image into Qwen-Image-2.1 latents. The image is encoded opaque; its alpha is not read.
+         * @description Encodes an image into Qwen-Image-2.1 latents.
+         *
+         *     For image-to-image and the canvas, the image is encoded opaque at its own size; its alpha is not read. As a
+         *     reference for an edit, it is resized to the area the text encoder reads it at and keeps its alpha.
          */
         QwenImage21ImageToLatentsInvocation: {
             /**
@@ -41305,6 +41319,12 @@ export type components = {
              * @default 0
              */
             tile_size?: number;
+            /**
+             * Reference
+             * @description Encode as a reference image for the denoise node's reference latents: resized to ~1 megapixel, as the text encoder reads it, with its alpha kept.
+             * @default false
+             */
+            reference?: boolean;
             /**
              * type
              * @default qwen_image_2_1_i2l
@@ -41451,7 +41471,10 @@ export type components = {
         };
         /**
          * Prompt - Qwen-Image-2.1
-         * @description Encodes a text prompt for Qwen-Image-2.1 with its Qwen3-VL-8B encoder.
+         * @description Encodes a prompt for Qwen-Image-2.1 with its Qwen3-VL-8B encoder, with the reference images an edit reads.
+         *
+         *     Each reference also goes to the denoise node, encoded by Image to Latents in its reference mode, in the same
+         *     order.
          */
         QwenImage21TextEncoderInvocation: {
             /**
@@ -41483,6 +41506,12 @@ export type components = {
              * @default null
              */
             qwen3_vl_encoder?: components["schemas"]["Qwen3VLEncoderField"] | null;
+            /**
+             * Reference Images
+             * @description Reference images to edit or draw from, in the order the denoise node receives their latents. Needs a Qwen-Image-2.1 Diffusers model's own encoder.
+             * @default []
+             */
+            reference_images?: components["schemas"]["ImageField"][];
             /**
              * type
              * @default qwen_image_2_1_text_encoder
diff --git a/invokeai/frontend/webv2/src/features/generation/core/__fixtures__/architectureCapabilities.json b/invokeai/frontend/webv2/src/features/generation/core/__fixtures__/architectureCapabilities.json
index 005e93e4968..7be2f86da66 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/__fixtures__/architectureCapabilities.json
+++ b/invokeai/frontend/webv2/src/features/generation/core/__fixtures__/architectureCapabilities.json
@@ -18,6 +18,7 @@
       ],
       "max_reference_images": 0,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": true,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -76,6 +77,7 @@
       ],
       "max_reference_images": 0,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": true,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -132,6 +134,7 @@
       "control_kinds": [],
       "max_reference_images": 0,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": false,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -173,6 +176,7 @@
       "control_kinds": [],
       "max_reference_images": 0,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": false,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -224,6 +228,7 @@
       ],
       "max_reference_images": 5,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": true,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -268,6 +273,7 @@
       ],
       "max_reference_images": 5,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": true,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -312,6 +318,7 @@
       ],
       "max_reference_images": 5,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": true,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -353,6 +360,7 @@
       "control_kinds": [],
       "max_reference_images": 5,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": true,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -394,6 +402,7 @@
       "control_kinds": [],
       "max_reference_images": 5,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": true,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -435,6 +444,7 @@
       "control_kinds": [],
       "max_reference_images": 5,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": true,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -476,6 +486,7 @@
       "control_kinds": [],
       "max_reference_images": 5,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": true,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -517,6 +528,7 @@
       "control_kinds": [],
       "max_reference_images": 0,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": false,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -565,6 +577,7 @@
       "control_kinds": [],
       "max_reference_images": 1,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": true,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -617,6 +630,7 @@
       "control_kinds": [],
       "max_reference_images": 1,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": true,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -669,6 +683,7 @@
       "control_kinds": [],
       "max_reference_images": 0,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": false,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -710,6 +725,7 @@
       "control_kinds": [],
       "max_reference_images": 0,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": false,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -751,6 +767,7 @@
       "control_kinds": [],
       "max_reference_images": 0,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": false,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -792,6 +809,7 @@
       "control_kinds": [],
       "max_reference_images": 5,
       "reference_images_require_variant": "edit",
+      "reference_images_require_format": null,
       "supports_regional_guidance": false,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -842,8 +860,9 @@
       "scheduler_set": "flow",
       "scheduler_applies_to_graph": false,
       "control_kinds": [],
-      "max_reference_images": 0,
+      "max_reference_images": 4,
       "reference_images_require_variant": null,
+      "reference_images_require_format": "diffusers",
       "supports_regional_guidance": false,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -883,8 +902,9 @@
       "scheduler_set": "flow",
       "scheduler_applies_to_graph": false,
       "control_kinds": [],
-      "max_reference_images": 0,
+      "max_reference_images": 4,
       "reference_images_require_variant": null,
+      "reference_images_require_format": "diffusers",
       "supports_regional_guidance": false,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -929,6 +949,7 @@
       ],
       "max_reference_images": 5,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": true,
       "regional_negative": true,
       "clip_skip_max": 12,
@@ -970,6 +991,7 @@
       "control_kinds": [],
       "max_reference_images": 0,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": true,
       "regional_negative": true,
       "clip_skip_max": 24,
@@ -1011,6 +1033,7 @@
       "control_kinds": [],
       "max_reference_images": 0,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": false,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -1055,6 +1078,7 @@
       ],
       "max_reference_images": 5,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": true,
       "regional_negative": true,
       "clip_skip_max": null,
@@ -1096,6 +1120,7 @@
       "control_kinds": [],
       "max_reference_images": 0,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": false,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -1137,6 +1162,7 @@
       "control_kinds": [],
       "max_reference_images": 0,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": false,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -1185,6 +1211,7 @@
       "control_kinds": [],
       "max_reference_images": 0,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": false,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -1235,6 +1262,7 @@
       ],
       "max_reference_images": 0,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": true,
       "regional_negative": false,
       "clip_skip_max": null,
@@ -1285,6 +1313,7 @@
       ],
       "max_reference_images": 0,
       "reference_images_require_variant": null,
+      "reference_images_require_format": null,
       "supports_regional_guidance": true,
       "regional_negative": false,
       "clip_skip_max": null,
diff --git a/invokeai/frontend/webv2/src/features/generation/core/architectureCapabilities.ts b/invokeai/frontend/webv2/src/features/generation/core/architectureCapabilities.ts
index fdae256b518..794f1767002 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/architectureCapabilities.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/architectureCapabilities.ts
@@ -26,6 +26,7 @@ export interface ArchitectureCapabilitiesRow {
     control_kinds: string[];
     max_reference_images: number;
     reference_images_require_variant: string | null;
+    reference_images_require_format: string | null;
     supports_regional_guidance: boolean;
     regional_negative: boolean;
     clip_skip_max: number | null;
diff --git a/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.test.ts b/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.test.ts
index d4b75ad47ac..ff4436edbf0 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.test.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.test.ts
@@ -35,6 +35,7 @@ import {
   isReferenceImageSupported,
   isGenerateModelSelectable,
   isSupportedGenerateModel,
+  QWEN_IMAGE_21_STANDALONE_ENCODER_REASON,
 } from './baseGenerationPolicies';
 import { SUPPORTED_GENERATE_BASES } from './supportedBases';
 
@@ -1106,6 +1107,15 @@ describe('component policies', () => {
     expect(isReferenceImageSupported(undefined)).toBe(false);
   });
 
+  it('offers Qwen-Image-2.1 reference images only on its Diffusers pipeline, whose encoder reads images', () => {
+    const diffusers = createModel('qwen-image-2-1', { format: 'diffusers' });
+
+    expect(isReferenceImageSupported(diffusers)).toBe(true);
+    expect(getMaxReferenceImages(diffusers)).toBe(4);
+    expect(isReferenceImageSupported(createModel('qwen-image-2-1', { format: 'gguf_quantized' }))).toBe(false);
+    expect(isReferenceImageSupported(createModel('qwen-image-2-1', { format: 'checkpoint' }))).toBe(false);
+  });
+
   it('derives the reference image limit from external provider capabilities', () => {
     expect(getMaxReferenceImages(createModel('sdxl'))).toBe(5);
     expect(getMaxReferenceImages(createModel('cogview4'))).toBe(0);
@@ -1171,6 +1181,31 @@ describe('component policies', () => {
     ).toContain('Reference Image #1 is not supported by sd-3 model.');
   });
 
+  it('blocks Qwen-Image-2.1 references while a standalone encoder replaces the one that reads them', () => {
+    const model = createModel('qwen-image-2-1', { format: 'diffusers' });
+    const encoder = {
+      base: 'any',
+      key: 'qwen3-vl-8b',
+      name: 'Qwen3-VL 8B',
+      type: 'qwen3_vl_encoder',
+      variant: 'qwen3_vl_8b',
+    } as const;
+    const referenceImages = [
+      {
+        config: { image: referenceImage, type: 'qwen_image_2_1_reference_image' as const },
+        id: 'ref-1',
+        isEnabled: true,
+      },
+    ];
+
+    expect(
+      getGenerationValidationReasons(model, createSettings(model, { qwen3VLEncoderModel: encoder, referenceImages }))
+    ).toContain(QWEN_IMAGE_21_STANDALONE_ENCODER_REASON);
+    expect(getGenerationValidationReasons(model, createSettings(model, { referenceImages }))).not.toContain(
+      QWEN_IMAGE_21_STANDALONE_ENCODER_REASON
+    );
+  });
+
   it('reports missing reference image model dependencies', () => {
     const model = createModel('sd-1');
 
diff --git a/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.ts b/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.ts
index c70f7ca260a..b3aa6651369 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.ts
@@ -1048,7 +1048,8 @@ const getBaseComponentSectionPolicy = (
         {
           ...qwen3VlEncoderSlot(
             'Qwen-Image-2.1 conditions on the Qwen3-VL 8B encoder, not the 4B one Krea-2 uses. Required for ' +
-              'non-Diffusers Qwen-Image-2.1 models.',
+              "non-Diffusers Qwen-Image-2.1 models. On a Diffusers model it replaces the model's own encoder, the " +
+              'only one that reads reference images.',
             isQwen3Vl8bEncoder
           ),
           required: (ctx) => ctx.model.format !== 'diffusers',
@@ -1308,9 +1309,11 @@ export const isReferenceImageSupported = (model: GenerateModelConfig | undefined
     return false;
   }
 
-  // The backend declares which reference-image features require a specific variant.
+  // The backend declares which reference-image features require a specific variant or format.
   return (
-    features.reference_images_require_variant === null || model.variant === features.reference_images_require_variant
+    (features.reference_images_require_variant === null ||
+      model.variant === features.reference_images_require_variant) &&
+    (features.reference_images_require_format === null || model.format === features.reference_images_require_format)
   );
 };
 
@@ -1352,6 +1355,10 @@ export const getDefaultReferenceImageConfig = (
     return { image, styleStrength: DEFAULT_KREA2_STYLE_STRENGTH, type: 'krea2_reference_image' };
   }
 
+  if (modelBase === 'qwen-image-2-1') {
+    return { image, type: 'qwen_image_2_1_reference_image' };
+  }
+
   if (isFluxKontextModel(model)) {
     return { image, model, type: 'flux_kontext_reference_image' };
   }
@@ -1383,6 +1390,10 @@ const getReferenceImageConfigSupported = (
       return model.type !== 'external_image_generator' && model.base === 'qwen-image' && model.variant === 'edit';
     case 'krea2_reference_image':
       return model.type !== 'external_image_generator' && model.base === 'krea-2';
+    case 'qwen_image_2_1_reference_image':
+      return (
+        model.type !== 'external_image_generator' && model.base === 'qwen-image-2-1' && isReferenceImageSupported(model)
+      );
     case 'flux_kontext_reference_image':
       return isFluxKontextModel(model);
     case 'flux_redux':
@@ -1677,6 +1688,11 @@ const getPidValidationReasons = (model: GenerateModelConfig, settings: GenerateS
   return reasons;
 };
 
+/** Only the encoder of a Qwen-Image-2.1 Diffusers model keeps the vision tower that reads references. */
+export const QWEN_IMAGE_21_STANDALONE_ENCODER_REASON =
+  "Reference images need the Qwen-Image-2.1 model's own encoder, the only one with a vision tower. " +
+  'Clear the Qwen3-VL Encoder component.';
+
 const getReferenceImageValidationReasons = (model: GenerateModelConfig, settings: GenerateSettings): string[] => {
   const reasons: string[] = [];
   const enabled = settings.referenceImages.filter((referenceImage) => referenceImage.isEnabled);
@@ -1721,6 +1737,17 @@ const getReferenceImageValidationReasons = (model: GenerateModelConfig, settings
     }
   });
 
+  // The encoder slot stays filled across models (Ideogram 4 and the GGUF builds share it), so a Diffusers model can
+  // inherit one that cannot read the references.
+  if (
+    model.base === 'qwen-image-2-1' &&
+    settings.qwen3VLEncoderModel &&
+    isQwen3Vl8bEncoder(settings.qwen3VLEncoderModel) &&
+    enabled.some((referenceImage) => referenceImage.config.type === 'qwen_image_2_1_reference_image')
+  ) {
+    reasons.push(QWEN_IMAGE_21_STANDALONE_ENCODER_REASON);
+  }
+
   return reasons;
 };
 
diff --git a/invokeai/frontend/webv2/src/features/generation/core/graph.test.ts b/invokeai/frontend/webv2/src/features/generation/core/graph.test.ts
index a70cebf8087..5a9454a5958 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/graph.test.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/graph.test.ts
@@ -4,6 +4,7 @@ import { afterEach, describe, expect, it, vi } from 'vitest';
 import type {
   ComponentModelConfig,
   GenerateModelConfig,
+  GenerateReferenceImage,
   GenerateSettings,
   LoraModelConfig,
   MainModelConfig,
@@ -744,6 +745,65 @@ describe('compileGenerateGraph', () => {
     expect(getEdge(graph, 'pos_cond', 'reference_images')).toBeDefined();
     expect(getEdge(graph, 'denoise_latents', 'reference_latents')).toBeDefined();
   });
+
+  describe('Qwen-Image-2.1 reference images', () => {
+    const qwenImage21Model: MainModelConfig = {
+      base: 'qwen-image-2-1',
+      format: 'diffusers',
+      key: 'qwen21',
+      name: 'Qwen-Image-2.1',
+      type: 'main',
+    };
+    const references = (...names: string[]): GenerateReferenceImage[] =>
+      names.map((name) => ({
+        config: {
+          image: { original: { image: { height: 768, image_name: name, width: 512 } } },
+          type: 'qwen_image_2_1_reference_image',
+        },
+        id: name,
+        isEnabled: true,
+      }));
+
+    it('reads each reference in both prompts and, in the same order, as latents in denoise', () => {
+      const graph = compile(qwenImage21Model, { cfgScale: 4, referenceImages: references('a.png', 'b.png') });
+
+      const nameOf = (imageNode: string) =>
+        (graph.nodes[imageNode]?.image as { image_name: string } | undefined)?.image_name;
+      // Each chain is followed back from its end: the last collect holds b, the one before it a.
+      const chain = (lastCollect: string) => [
+        getEdge(graph, lastCollect, 'collection')?.source.node_id ?? '',
+        lastCollect,
+      ];
+
+      const imagesInto = getEdge(graph, 'pos_cond', 'reference_images')?.source.node_id ?? '';
+      expect(getEdge(graph, 'neg_cond', 'reference_images')?.source.node_id).toBe(imagesInto);
+      expect(chain(imagesInto).map((collect) => nameOf(getEdge(graph, collect, 'item')?.source.node_id ?? ''))).toEqual(
+        ['a.png', 'b.png']
+      );
+
+      const latentsInto = getEdge(graph, 'denoise_latents', 'reference_latents')?.source.node_id ?? '';
+      const latentImages = chain(latentsInto).map((collect) => {
+        const i2l = getEdge(graph, collect, 'item')?.source.node_id ?? '';
+        expect(graph.nodes[i2l]).toMatchObject({ reference: true, type: 'qwen_image_2_1_i2l' });
+        return nameOf(getEdge(graph, i2l, 'image')?.source.node_id ?? '');
+      });
+      expect(latentImages).toEqual(['a.png', 'b.png']);
+    });
+
+    it('refuses references with a standalone encoder, which has no vision tower', () => {
+      const encoder: ComponentModelConfig = {
+        base: 'any',
+        key: 'qwen3-vl-8b',
+        name: 'Qwen3-VL 8B',
+        type: 'qwen3_vl_encoder',
+        variant: 'qwen3_vl_8b',
+      };
+
+      expect(() =>
+        compile(qwenImage21Model, { qwen3VLEncoderModel: encoder, referenceImages: references('a.png') })
+      ).toThrow(/vision tower/);
+    });
+  });
 });
 
 describe('generate seeds', () => {
diff --git a/invokeai/frontend/webv2/src/features/generation/core/graph.ts b/invokeai/frontend/webv2/src/features/generation/core/graph.ts
index 598c1dbbf05..55a600b3a9f 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/graph.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/graph.ts
@@ -25,6 +25,7 @@ import {
   getFlux2DiffusersComponentSource,
   getGenerationDimensions,
   getGenerationValidationReasons,
+  QWEN_IMAGE_21_STANDALONE_ENCODER_REASON,
   type SupportedGenerateBase,
 } from './baseGenerationPolicies';
 import {
@@ -1438,14 +1439,86 @@ const buildQwenImage21Graph = (
   }
   addEdge(graph, seed, 'value', denoise, 'seed');
   addEdge(graph, denoise, 'latents', output, 'latents');
+  addQwenImage21ReferenceImages(graph, settings, {
+    conditionings: negCond ? [posCond, negCond] : [posCond],
+    denoise,
+    hasStandaloneEncoder: Boolean(qwen3VlEncoderModel),
+    modelLoader,
+  });
   addMetadata(graph, output, settings, model, 'qwen_image_2_1_txt2img', projectSettings, {
     qwen3_vl_encoder: qwen3VlEncoderModel ?? undefined,
     vae: vaeModel ?? undefined,
   });
+  addReferenceImageMetadata(graph, output, settings);
 
   return graph;
 };
 
+/**
+ * Qwen-Image-2.1 reads each reference twice, at one size: the prompt encoder sees it through the vision tower, and
+ * the denoise node places its clean latents in the prompt's image slots. Both prompts are encoded with the
+ * references, and the latents follow in the same order.
+ */
+const addQwenImage21ReferenceImages = (
+  graph: BackendGraphContract,
+  settings: GenerateSettings,
+  nodes: {
+    conditionings: BackendInvocationContract[];
+    denoise: BackendInvocationContract;
+    hasStandaloneEncoder: boolean;
+    modelLoader: BackendInvocationContract;
+  }
+): void => {
+  const referenceImages = getEnabledReferenceImages(settings, 'qwen_image_2_1_reference_image');
+  if (referenceImages.length === 0) {
+    return;
+  }
+  // A backstop: validation reports this before Invoke.
+  if (nodes.hasStandaloneEncoder) {
+    throw new Error(QWEN_IMAGE_21_STANDALONE_ENCODER_REASON);
+  }
+
+  let imageCollect: BackendInvocationContract | null = null;
+  let latentsCollect: BackendInvocationContract | null = null;
+
+  for (const { config } of referenceImages) {
+    if (!config.image) {
+      continue;
+    }
+
+    const imageNode = addNode(graph, {
+      id: createId('qwen21_ref_img'),
+      image: toImageField(config.image),
+      type: 'image',
+    });
+    const i2l = addNode(graph, { id: createId('qwen21_ref_i2l'), reference: true, type: 'qwen_image_2_1_i2l' });
+    addEdge(graph, imageNode, 'image', i2l, 'image');
+    addEdge(graph, nodes.modelLoader, 'vae', i2l, 'vae');
+
+    const nextImageCollect = addNode(graph, { id: createId('qwen21_ref_img_collect'), type: 'collect' });
+    addEdge(graph, imageNode, 'image', nextImageCollect, 'item');
+    if (imageCollect) {
+      addEdge(graph, imageCollect, 'collection', nextImageCollect, 'collection');
+    }
+    imageCollect = nextImageCollect;
+
+    const nextLatentsCollect = addNode(graph, { id: createId('qwen21_ref_latents_collect'), type: 'collect' });
+    addEdge(graph, i2l, 'latents', nextLatentsCollect, 'item');
+    if (latentsCollect) {
+      addEdge(graph, latentsCollect, 'collection', nextLatentsCollect, 'collection');
+    }
+    latentsCollect = nextLatentsCollect;
+  }
+
+  if (!imageCollect || !latentsCollect) {
+    return;
+  }
+  for (const conditioning of nodes.conditionings) {
+    addEdge(graph, imageCollect, 'collection', conditioning, 'reference_images');
+  }
+  addEdge(graph, latentsCollect, 'collection', nodes.denoise, 'reference_latents');
+};
+
 const buildIdeogram4Graph = (
   settings: GenerateSettings,
   model: MainModelConfig,
diff --git a/invokeai/frontend/webv2/src/features/generation/core/settings.ts b/invokeai/frontend/webv2/src/features/generation/core/settings.ts
index dea964ea145..178d589f339 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/settings.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/settings.ts
@@ -312,6 +312,7 @@ const REFERENCE_IMAGE_CONFIG_TYPES = new Set([
   'ip_adapter',
   'krea2_reference_image',
   'qwen_image_reference_image',
+  'qwen_image_2_1_reference_image',
 ]);
 
 const isClipVisionModel = (value: unknown): value is 'ViT-H' | 'ViT-G' | 'ViT-L' =>
@@ -347,6 +348,7 @@ const normalizeReferenceImageConfig = (value: unknown): GenerateReferenceImageCo
     case 'external_reference_image':
     case 'flux2_reference_image':
     case 'qwen_image_reference_image':
+    case 'qwen_image_2_1_reference_image':
       return { image, type: value.type };
     case 'flux_kontext_reference_image':
       return { image, model: getMainModelOrNull(value.model), type: value.type };
@@ -419,6 +421,7 @@ const cloneReferenceImageConfig = (config: GenerateReferenceImageConfig): Genera
     case 'flux2_reference_image':
     case 'krea2_reference_image':
     case 'qwen_image_reference_image':
+    case 'qwen_image_2_1_reference_image':
       return { ...config, image: config.image ? cloneCroppableImage(config.image) : null };
     case 'flux_kontext_reference_image':
       return {
@@ -589,6 +592,7 @@ const syncReferenceImageConfigWithModels = (
     case 'flux2_reference_image':
     case 'krea2_reference_image':
     case 'qwen_image_reference_image':
+    case 'qwen_image_2_1_reference_image':
       return config;
   }
 };
diff --git a/invokeai/frontend/webv2/src/features/generation/core/types.ts b/invokeai/frontend/webv2/src/features/generation/core/types.ts
index a2ba70b4727..ca7597e15ff 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/types.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/types.ts
@@ -142,6 +142,7 @@ export type GenerateReferenceImageConfig =
   | { type: 'qwen_image_reference_image'; image: GenerateReferenceImageAsset | null }
   /** Krea-2 training-free style transfer: no adapter model, a single reference, `styleStrength` 0–2 (0 bypasses). */
   | { type: 'krea2_reference_image'; image: GenerateReferenceImageAsset | null; styleStrength: number }
+  | { type: 'qwen_image_2_1_reference_image'; image: GenerateReferenceImageAsset | null }
   | { type: 'external_reference_image'; image: GenerateReferenceImageAsset | null };
 
 export interface GenerateReferenceImage {
diff --git a/invokeai/frontend/webv2/src/workbench/canvasMigration.ts b/invokeai/frontend/webv2/src/workbench/canvasMigration.ts
index 9d91441f0c5..710dd33fa88 100644
--- a/invokeai/frontend/webv2/src/workbench/canvasMigration.ts
+++ b/invokeai/frontend/webv2/src/workbench/canvasMigration.ts
@@ -183,6 +183,7 @@ const zReferenceImage = z.object({
     }),
     z.object({ image: zGeneratedImage.nullable(), type: z.literal('flux2_reference_image') }),
     z.object({ image: zGeneratedImage.nullable(), type: z.literal('qwen_image_reference_image') }),
+    z.object({ image: zGeneratedImage.nullable(), type: z.literal('qwen_image_2_1_reference_image') }),
     z.object({ image: zGeneratedImage.nullable(), type: z.literal('external_reference_image') }),
   ]),
   id: z.string(),
diff --git a/tests/app/invocations/test_qwen_image_2_1_nodes.py b/tests/app/invocations/test_qwen_image_2_1_nodes.py
index 20e7fa0ad0d..cb6e29a747e 100644
--- a/tests/app/invocations/test_qwen_image_2_1_nodes.py
+++ b/tests/app/invocations/test_qwen_image_2_1_nodes.py
@@ -4,29 +4,36 @@
 an image-to-image run starts on each variant's schedule, and which components the loader refuses.
 """
 
-from contextlib import contextmanager
+from contextlib import contextmanager, nullcontext
 from types import SimpleNamespace
 
 import numpy as np
 import pytest
 import torch
+from PIL import Image
 
-from invokeai.app.invocations.fields import LatentsField, QwenImage21ConditioningField
-from invokeai.app.invocations.model import ModelIdentifierField, TransformerField
+from invokeai.app.invocations.fields import ImageField, LatentsField, QwenImage21ConditioningField
+from invokeai.app.invocations.model import ModelIdentifierField, Qwen3VLEncoderField, TransformerField, VAEField
 from invokeai.app.invocations.qwen_image_2_1.qwen_image_2_1_denoise import (
+    KV_BYTES_PER_PREFIX_TOKEN,
     LATENT_CHANNELS,
     QwenImage21DenoiseInvocation,
     clip_sigmas,
     pack_latents,
+    prefix_cache_fits,
+    prefix_length,
     unpack_latents,
 )
 from invokeai.app.invocations.qwen_image_2_1.qwen_image_2_1_model_loader import QwenImage21ModelLoaderInvocation
+from invokeai.app.invocations.text_encoder.qwen_image_2_1_text_encoder import QwenImage21TextEncoderInvocation
+from invokeai.app.invocations.vae.qwen_image_2_1_image_to_latents import QwenImage21ImageToLatentsInvocation
 from invokeai.backend.model_manager.taxonomy import (
     BaseModelType,
     ModelFormat,
     ModelType,
     Qwen3VLVariantType,
     QwenImage21VariantType,
+    SubModelType,
 )
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     ConditioningFieldData,
@@ -52,13 +59,28 @@ class _Transformer:
 
     def __init__(self) -> None:
         self.calls: list[tuple[str, int, str | None, float]] = []
+        self.inputs: list[SimpleNamespace] = []
 
     def modules(self):
         return iter(())
 
-    def __call__(self, *, hidden_states, encoder_hidden_states, timestep, kv_cache, kv_cache_mode, **_kwargs):
+    def __call__(
+        self,
+        *,
+        hidden_states,
+        encoder_hidden_states,
+        timestep,
+        kv_cache,
+        kv_cache_mode,
+        img_shapes,
+        img_mask,
+        **_kwargs,
+    ):
         prompt = "pos" if float(encoder_hidden_states.mean()) > 0 else "neg"
         self.calls.append((prompt, id(kv_cache), kv_cache_mode, round(float(timestep[0]), 3)))
+        self.inputs.append(
+            SimpleNamespace(hidden_states=hidden_states.clone(), img_shapes=img_shapes, img_mask=img_mask)
+        )
         # The transformer returns the joint sequence; the node keeps the image tokens at its end.
         return (torch.zeros(1, encoder_hidden_states.shape[1] + hidden_states.shape[1], hidden_states.shape[2]),)
 
@@ -72,18 +94,27 @@ def model_on_device(self, **_kwargs):
         yield ({}, self.model)
 
 
-def _context(transformer: _Transformer, variant: QwenImage21VariantType, init_latents: torch.Tensor | None = None):
-    conditionings = {
-        "pos": ConditioningFieldData(conditionings=[QwenImage21ConditioningInfo(prompt_embeds=torch.ones(1, 3, 8))]),
-        "neg": ConditioningFieldData(conditionings=[QwenImage21ConditioningInfo(prompt_embeds=-torch.ones(1, 2, 8))]),
+def _context(
+    transformer: _Transformer,
+    variant: QwenImage21VariantType,
+    init_latents: torch.Tensor | None = None,
+    *,
+    infos: dict[str, QwenImage21ConditioningInfo] | None = None,
+    tensors: dict[str, torch.Tensor] | None = None,
+):
+    infos = infos or {
+        "pos": QwenImage21ConditioningInfo(prompt_embeds=torch.ones(1, 3, 8)),
+        "neg": QwenImage21ConditioningInfo(prompt_embeds=-torch.ones(1, 2, 8)),
     }
+    conditionings = {name: ConditioningFieldData(conditionings=[info]) for name, info in infos.items()}
+    tensors = tensors or {}
     return SimpleNamespace(
         models=SimpleNamespace(
             load=lambda _identifier: _TransformerInfo(transformer),
             get_config=lambda _identifier: SimpleNamespace(variant=variant),
         ),
         conditioning=SimpleNamespace(load=lambda name: conditionings[name]),
-        tensors=SimpleNamespace(load=lambda _name: init_latents),
+        tensors=SimpleNamespace(load=lambda name: tensors.get(name, init_latents)),
         util=SimpleNamespace(sd_step_callback=lambda *_args: None),
     )
 
@@ -96,6 +127,7 @@ def _denoise(**fields) -> QwenImage21DenoiseInvocation:
         "negative_conditioning": QwenImage21ConditioningField(conditioning_name="neg"),
         "latents": None,
         "denoise_mask": None,
+        "reference_latents": None,
         "denoising_start": 0.0,
         "denoising_end": 1.0,
         "cfg_scale": 1.0,
@@ -201,8 +233,8 @@ def test_a_strength_on_base_names_whole_steps_and_always_runs_one(
 def test_a_float32_fallback_reserves_twice_the_bfloat16_activations() -> None:
     estimate = QwenImage21DenoiseInvocation._estimate_working_memory
     base = 1024**3
-    bf16 = estimate(4096, 300, 300, True, 2) - base
-    assert estimate(4096, 300, 300, True, 4) - base == 2 * bf16
+    bf16 = estimate(4096, 300, 300, True, True, 2) - base
+    assert estimate(4096, 300, 300, True, True, 4) - base == 2 * bf16
 
 
 @pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
@@ -349,3 +381,263 @@ def test_the_4b_encoder_is_refused() -> None:
     loader = _loader(qwen3_vl_encoder_model=("encoder", BaseModelType.Any, ModelType.Qwen3VLEncoder))
     with pytest.raises(ValueError, match="8B"):
         loader.invoke(_loader_context(ModelFormat.Diffusers, encoder_variant=Qwen3VLVariantType.Qwen3VL_4B))
+
+
+def _edit_prompt(sign: float, grids: list[tuple[int, int]]) -> QwenImage21ConditioningInfo:
+    """A prompt encoded with references: a token, then one run of image slots per reference, then a token."""
+    mask = [False]
+    for rows, columns in grids:
+        mask += [True] * (rows * columns) + [False]
+    return QwenImage21ConditioningInfo(
+        prompt_embeds=sign * torch.ones(1, len(mask), 8),
+        image_pad_mask=torch.tensor([mask]),
+        reference_grids=tuple(grids),
+    )
+
+
+def _edit_context(transformer, prompt_grids, references: dict[str, torch.Tensor], negative=None):
+    infos = {"pos": _edit_prompt(1.0, prompt_grids), "neg": negative or _edit_prompt(-1.0, prompt_grids)}
+    return _context(transformer, QwenImage21VariantType.Base, infos=infos, tensors=references)
+
+
+def test_references_lead_the_image_tokens_and_fill_the_prompts_slots() -> None:
+    transformer = _Transformer()
+    # A 4x8 reference is 32 latent tokens in 2x4 slots; the 64x64 target is 4x4.
+    reference = torch.randn(1, LATENT_CHANNELS, 4, 8)
+    _denoise(reference_latents=[LatentsField(latents_name="ref")], steps=1)._run_diffusion(
+        _edit_context(transformer, [(2, 4)], {"ref": reference})
+    )
+
+    seen = transformer.inputs[0]
+    assert seen.img_shapes == [[(1, 4, 8), (1, 4, 4)]]
+    assert seen.hidden_states.shape[1] == 32 + 16
+    assert torch.equal(seen.hidden_states[:, :32], pack_latents(reference))
+    # The prompt's own mask, then one slot per 2x2 group of the target's 4x4 latents.
+    assert seen.img_mask.tolist() == [[False] + [True] * 8 + [False] + [True] * 4]
+
+
+def test_references_that_do_not_fill_the_slots_are_refused() -> None:
+    context = _edit_context(_Transformer(), [(2, 2)], {"ref": torch.zeros(1, LATENT_CHANNELS, 4, 8)})
+    with pytest.raises(ValueError, match=r"positive prompt was encoded with 1 reference image\(s\) \(64x64\)"):
+        _denoise(reference_latents=LatentsField(latents_name="ref"))._run_diffusion(context)
+
+
+def test_two_references_of_one_size_in_swapped_order_are_refused() -> None:
+    # Landscape then portrait in the prompt; portrait then landscape as latents. The slot counts agree.
+    context = _edit_context(
+        _Transformer(),
+        [(2, 4), (4, 2)],
+        {"portrait": torch.zeros(1, LATENT_CHANNELS, 8, 4), "landscape": torch.zeros(1, LATENT_CHANNELS, 4, 8)},
+    )
+    latents = [LatentsField(latents_name="portrait"), LatentsField(latents_name="landscape")]
+    with pytest.raises(ValueError, match="in reference mode, from the same images and in the same order"):
+        _denoise(reference_latents=latents)._run_diffusion(context)
+
+
+def test_latents_from_another_vae_are_refused() -> None:
+    context = _edit_context(_Transformer(), [(2, 4)], {"ref": torch.zeros(1, 16, 4, 8)})
+    with pytest.raises(ValueError, match="64 channels"):
+        _denoise(reference_latents=LatentsField(latents_name="ref"))._run_diffusion(context)
+
+
+def test_a_negative_prompt_encoded_without_the_references_is_refused() -> None:
+    context = _edit_context(
+        _Transformer(),
+        [(2, 4)],
+        {"ref": torch.zeros(1, LATENT_CHANNELS, 4, 8)},
+        negative=QwenImage21ConditioningInfo(prompt_embeds=-torch.ones(1, 2, 8)),
+    )
+    with pytest.raises(ValueError, match="negative prompt was encoded with 0"):
+        _denoise(reference_latents=LatentsField(latents_name="ref"), cfg_scale=4.0)._run_diffusion(context)
+
+
+def test_the_prefix_counts_each_reference_slot_as_four_latent_tokens() -> None:
+    # A 1 MP reference is 32x32 slots: 1024 slots in the prompt, 4096 tokens in the joint sequence.
+    info = _edit_prompt(1.0, [(32, 32)])
+    assert prefix_length(info) == info.prompt_embeds.shape[1] + 3 * 1024
+
+
+def test_the_prefix_caches_are_reserved_only_when_they_are_kept() -> None:
+    estimate = QwenImage21DenoiseInvocation._estimate_working_memory
+    prefix = 4200
+    cached = estimate(4096, prefix, prefix, True, True, 2)
+    recomputed = estimate(4096, prefix, prefix, True, False, 2)
+    # K and V of every prefix token in all 32 blocks, per guidance branch: ~2 GiB per 1 MP reference.
+    assert cached - recomputed == 2 * prefix * KV_BYTES_PER_PREFIX_TOKEN
+
+
+def test_caches_larger_than_half_the_card_are_not_kept(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setattr(torch.cuda, "get_device_properties", lambda _device: SimpleNamespace(total_memory=24 * 2**30))
+    cuda = torch.device("cuda")
+    # Two references at CFG fit a 24 GB card; four do not.
+    assert prefix_cache_fits(2 * 2 * 4200 * KV_BYTES_PER_PREFIX_TOKEN, cuda)
+    assert not prefix_cache_fits(2 * 4 * 4200 * KV_BYTES_PER_PREFIX_TOKEN, cuda)
+
+
+def test_without_room_for_the_caches_every_step_runs_the_whole_prefix(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setattr(f"{DENOISE}.prefix_cache_fits", lambda _bytes, _device: False)
+    transformer = _Transformer()
+    _denoise(reference_latents=LatentsField(latents_name="ref"), cfg_scale=4.0)._run_diffusion(
+        _edit_context(transformer, [(2, 4)], {"ref": torch.zeros(1, LATENT_CHANNELS, 4, 8)})
+    )
+    assert {(cache, mode) for _prompt, cache, mode, _t in transformer.calls} == {(id(None), None)}
+
+
+def test_a_standalone_encoder_cannot_read_references() -> None:
+    encoder = ModelIdentifierField(
+        key="enc", hash="h", name="qwen3vl_8b", base=BaseModelType.Any, type=ModelType.Qwen3VLEncoder
+    )
+    node = QwenImage21TextEncoderInvocation.model_construct(
+        prompt="swap them",
+        qwen3_vl_encoder=Qwen3VLEncoderField(tokenizer=encoder, text_encoder=encoder, loras=[]),
+        reference_images=[ImageField(image_name="ref.png")],
+    )
+    context = SimpleNamespace(
+        images=SimpleNamespace(get_pil=lambda _name: Image.new("RGB", (64, 64))),
+        models=SimpleNamespace(
+            get_config=lambda _identifier: SimpleNamespace(
+                base=BaseModelType.Any, type=ModelType.Qwen3VLEncoder, format=ModelFormat.Checkpoint
+            )
+        ),
+    )
+    with pytest.raises(ValueError, match="vision tower"):
+        node.invoke(context)
+
+
+def test_the_processor_comes_from_the_qwen_image_2_1_pipeline_the_tokenizer_is_from() -> None:
+    main = ModelIdentifierField(
+        key="qwen21", hash="h", name="Qwen-Image-2.1", base=BaseModelType.QwenImage21, type=ModelType.Main
+    )
+    tokenizer = main.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+    node = QwenImage21TextEncoderInvocation.model_construct(
+        prompt="swap them",
+        qwen3_vl_encoder=Qwen3VLEncoderField(tokenizer=tokenizer, text_encoder=tokenizer, loras=[]),
+        reference_images=[ImageField(image_name="ref.png")],
+    )
+    context = SimpleNamespace(
+        models=SimpleNamespace(
+            get_config=lambda _identifier: SimpleNamespace(
+                base=BaseModelType.QwenImage21, type=ModelType.Main, format=ModelFormat.Diffusers
+            )
+        )
+    )
+    processor = node._processor_identifier(context)
+    assert (processor.key, processor.submodel_type) == ("qwen21", SubModelType.Processor)
+
+
+class _ReservationRecorded(Exception):
+    pass
+
+
+@pytest.mark.parametrize("num_references", [0, 4])
+def test_the_encoder_reserves_room_for_the_attention_its_references_need(num_references: int) -> None:
+    from invokeai.backend.qwen3_vl.qwen3_vl_assets import load_bundled_qwen3_vl_tokenizer
+
+    main = ModelIdentifierField(
+        key="qwen21", hash="h", name="Qwen-Image-2.1", base=BaseModelType.QwenImage21, type=ModelType.Main
+    )
+    tokenizer = main.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+    encoder = main.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+    reserved: list[int] = []
+
+    @contextmanager
+    def on_device(*, working_mem_bytes: int):
+        reserved.append(working_mem_bytes)
+        raise _ReservationRecorded
+        yield  # pragma: no cover
+
+    text_encoder = torch.nn.Module()
+    text_encoder.language_model = torch.nn.Linear(2, 2)
+    text_encoder.visual = torch.nn.Linear(2, 2)
+    infos = {
+        SubModelType.TextEncoder: SimpleNamespace(
+            model=text_encoder, compute_device=torch.device("cpu"), model_on_device=on_device
+        ),
+        SubModelType.Tokenizer: nullcontext(load_bundled_qwen3_vl_tokenizer()),
+        SubModelType.Processor: SimpleNamespace(model_on_device=lambda: nullcontext((None, None))),
+    }
+    node = QwenImage21TextEncoderInvocation.model_construct(
+        prompt="Combine the subjects of all reference images.",
+        qwen3_vl_encoder=Qwen3VLEncoderField(tokenizer=tokenizer, text_encoder=encoder, loras=[]),
+        reference_images=[ImageField(image_name=f"ref{i}.png") for i in range(num_references)],
+    )
+    context = SimpleNamespace(
+        images=SimpleNamespace(get_pil=lambda _name: Image.new("RGB", (1024, 1024))),
+        models=SimpleNamespace(
+            get_config=lambda _identifier: SimpleNamespace(
+                base=BaseModelType.QwenImage21, type=ModelType.Main, format=ModelFormat.Diffusers
+            ),
+            load=lambda identifier: infos[identifier.submodel_type],
+        ),
+        util=SimpleNamespace(signal_progress=lambda _message: None),
+    )
+
+    with pytest.raises(_ReservationRecorded):
+        node.invoke(context)
+
+    if num_references:
+        # Measured on an RTX 4090: four 1-megapixel references peak 5.44 GiB above the weights, and the allocator
+        # reserves about a fifth more. Less than that and the driver spills the encode to system memory.
+        assert reserved[0] >= 1.2 * 5.44 * 2**30
+    else:
+        assert reserved[0] < 2**28
+
+
+class _VAE:
+    """Records what it encodes and answers with bf16 latents, as the real VAE runs in bf16."""
+
+    dtype = torch.bfloat16
+    config = SimpleNamespace(latents_mean=[0.3] * LATENT_CHANNELS, latents_std=[1.7] * LATENT_CHANNELS)
+
+    def __init__(self) -> None:
+        self.pixels: torch.Tensor | None = None
+        self.z: torch.Tensor | None = None
+
+    def parameters(self):
+        return iter([torch.zeros(1, dtype=torch.bfloat16)])
+
+    def encode(self, pixels: torch.Tensor):
+        self.pixels = pixels
+        _, _, frames, height, width = pixels.shape
+        generator = torch.Generator().manual_seed(0)
+        self.z = torch.randn(1, LATENT_CHANNELS, frames, height // 16, width // 16, generator=generator).bfloat16()
+        return SimpleNamespace(latent_dist=SimpleNamespace(mode=lambda: self.z))
+
+
+def test_a_reference_encodes_at_its_reading_size_with_its_alpha_normalized_in_bf16(monkeypatch) -> None:
+    from invokeai.app.invocations.vae import qwen_image_2_1_image_to_latents as i2l_module
+
+    monkeypatch.setattr(i2l_module, "patch_qwen_image_vae_tiling", lambda *_args: nullcontext())
+    vae = _VAE()
+    saved: dict[str, torch.Tensor] = {}
+
+    @contextmanager
+    def on_device(**_kwargs):
+        yield ({}, vae)
+
+    context = SimpleNamespace(
+        images=SimpleNamespace(get_pil=lambda _name: Image.new("RGBA", (500, 300), (255, 0, 0, 64))),
+        models=SimpleNamespace(
+            load=lambda _identifier: SimpleNamespace(
+                compute_device=torch.device("cpu"), model=vae, model_on_device=on_device
+            )
+        ),
+        config=SimpleNamespace(get=lambda: SimpleNamespace(force_tiled_decode=False, auto_tiled_decode=False)),
+        tensors=SimpleNamespace(save=lambda tensor: saved.update(latents=tensor) or "latents"),
+    )
+    vae_field = VAEField(
+        vae=ModelIdentifierField(key="v", hash="h", name="v", base=BaseModelType.QwenImage21, type=ModelType.VAE)
+    )
+    output = QwenImage21ImageToLatentsInvocation.model_construct(
+        image=ImageField(image_name="ref.png"), vae=vae_field, tiled=False, tile_size=0, reference=True
+    ).invoke(context)
+
+    # 500x300 reads at 1312x800; the alpha is the image's own (64/255), not the canvas encode's 1.
+    assert (output.width, output.height) == (1312, 800)
+    assert vae.pixels is not None and vae.pixels.shape == (1, 4, 1, 800, 1312)
+    assert vae.pixels[0, 3].unique().tolist() == [torch.tensor(64 / 255 * 2 - 1).bfloat16().item()]
+    # Normalized in bf16, as the pipeline normalizes it -- which differs from normalizing in fp32.
+    mean, std = torch.tensor(0.3).bfloat16(), torch.tensor(1.7).bfloat16()
+    expected = ((vae.z - mean) / std).float()[:, :, 0]
+    assert torch.equal(saved["latents"], expected)
+    assert not torch.equal(expected, ((vae.z.float() - 0.3) / 1.7)[:, :, 0])
diff --git a/tests/backend/architectures/test_features.py b/tests/backend/architectures/test_features.py
index 5b8aa98956e..65fae7c66d5 100644
--- a/tests/backend/architectures/test_features.py
+++ b/tests/backend/architectures/test_features.py
@@ -80,13 +80,20 @@ def test_control_kinds_match_the_frontend_policy(kind: ControlKind, expected: se
     assert declared == expected
 
 
-def test_reference_images_and_the_one_variant_condition() -> None:
-    """Qwen-Image is the only base whose answer depends on the variant, so it is the only one with
-    `reference_images_require_variant` set."""
+def test_reference_images_and_the_conditions_on_them() -> None:
+    """Qwen-Image is the only base whose answer depends on the variant, and Qwen-Image-2.1 the only one whose
+    answer depends on the format."""
     supported = {
         b.value for b in generative_bases() if (f := get(b, FeaturesFacet)) is not None and f.supports_reference_images
     }
-    assert supported == {"flux", "flux2", "sd-1", "sdxl", "qwen-image", "krea-2"}
+    assert supported == {"flux", "flux2", "sd-1", "sdxl", "qwen-image", "krea-2", "qwen-image-2-1"}
+
+    by_format = {
+        b.value: f.reference_images_require_format
+        for b in generative_bases()
+        if (f := get(b, FeaturesFacet)) is not None and f.reference_images_require_format is not None
+    }
+    assert by_format == {"qwen-image-2-1": "diffusers"}
 
     conditional = {
         b.value
diff --git a/tests/backend/model_manager/load/test_qwen3_vl_visual_tower.py b/tests/backend/model_manager/load/test_qwen3_vl_visual_tower.py
index 55c6d116c70..c807f5c73ce 100644
--- a/tests/backend/model_manager/load/test_qwen3_vl_visual_tower.py
+++ b/tests/backend/model_manager/load/test_qwen3_vl_visual_tower.py
@@ -300,3 +300,24 @@ def test_the_folder_loader_drops_the_tower(tmp_path: Path) -> None:
     model = loader._load_model(config, SubModelType.TextEncoder)
 
     _assert_tower_gone(model)
+
+
+def test_the_qwen_image_2_1_pipeline_encoder_keeps_the_tower(monkeypatch, tmp_path: Path) -> None:
+    """The one encoder that keeps it: Qwen-Image-2.1 reads reference images through it, and the node refuses an
+    encoder without it."""
+    from transformers import Qwen3VLModel
+
+    from invokeai.backend.model_manager.configs.main import Main_Diffusers_QwenImage21_Config
+    from invokeai.backend.model_manager.load.model_loaders.qwen_image_2_1 import QwenImage21DiffusersModel
+    from invokeai.backend.qwen_image_2_1.text_encoding import has_vision_tower
+
+    encoder = _tiny_encoder()
+    config = Main_Diffusers_QwenImage21_Config.model_construct(path=str(tmp_path), repo_variant=None)
+    loader = object.__new__(QwenImage21DiffusersModel)
+    loader._apply_fp8_layerwise_casting = lambda model, _config, _submodel: model
+    monkeypatch.setattr(Qwen3VLModel, "from_pretrained", classmethod(lambda _cls, *_a, **_k: encoder))
+
+    model = loader._load_model(config, SubModelType.TextEncoder)
+
+    assert has_vision_tower(model)
+    assert list(model.visual.parameters())
diff --git a/tests/backend/qwen_image_2_1/test_text_encoding.py b/tests/backend/qwen_image_2_1/test_text_encoding.py
index 5797589414c..349af916949 100644
--- a/tests/backend/qwen_image_2_1/test_text_encoding.py
+++ b/tests/backend/qwen_image_2_1/test_text_encoding.py
@@ -1,13 +1,20 @@
-"""Qwen-Image-2.1's prompt encoding: the template the checkpoint was trained on, read before the final norm."""
+"""Qwen-Image-2.1's prompt encoding: the template the checkpoint was trained on, read before the final norm, and
+the reference images an edit reads through the vision tower."""
+
+import itertools
 
 import pytest
 import torch
+from PIL import Image
 
 from invokeai.backend.qwen3_vl.qwen3_vl_assets import load_bundled_qwen3_vl_tokenizer
 from invokeai.backend.qwen_image_2_1.text_encoding import (
     SYSTEM_BLOCK,
     encode_prompt,
     format_prompt,
+    has_vision_tower,
+    on_white,
+    sequence_length,
     system_prefix_length,
 )
 from tests.backend.model_manager.load.qwen3vl_gguf_fixture import tiny_qwen3vl_config
@@ -40,7 +47,7 @@ def test_the_embeddings_are_the_last_layer_before_the_final_norm(text_encoder, t
     language_model = text_encoder.language_model
     handle = language_model.norm.register_forward_hook(lambda m, args, out: captured.update(pre=args[0], post=out))
     try:
-        embeds = encode_prompt(text_encoder, tokenizer, "a fox", torch.device("cpu"))
+        embeds, image_slots, grids = encode_prompt(text_encoder, tokenizer, "a fox", torch.device("cpu"))
         ids = tokenizer([format_prompt("a fox")], return_tensors="pt").input_ids
         text_encoder(input_ids=ids)
     finally:
@@ -49,3 +56,89 @@ def test_the_embeddings_are_the_last_layer_before_the_final_norm(text_encoder, t
     drop = system_prefix_length(tokenizer)
     torch.testing.assert_close(embeds[0], captured["pre"][0, drop:])
     assert not torch.allclose(embeds[0], captured["post"][0, drop:])
+    assert image_slots is None and grids == ()
+
+
+def test_reference_placeholders_lead_the_prompt_as_the_pipeline_writes_them() -> None:
+    assert format_prompt("swap them", 2) == (
+        SYSTEM_BLOCK + "<|im_start|>user\n<image1><|vision_start|><|image_pad|><|vision_end|> "
+        "<image2><|vision_start|><|image_pad|><|vision_end|>swap them<|im_end|>\n<|im_start|>assistant\n"
+    )
+
+
+@pytest.fixture(scope="module")
+def processor(tokenizer):
+    from transformers import Qwen2VLImageProcessor, Qwen3VLProcessor
+    from transformers.models.qwen3_vl.video_processing_qwen3_vl import Qwen3VLVideoProcessor
+
+    # The released processor's geometry: 16px patches merged 2x2, so one slot per 32x32 pixels.
+    image_processor = Qwen2VLImageProcessor(patch_size=16, merge_size=2, temporal_patch_size=2)
+    return Qwen3VLProcessor(
+        image_processor=image_processor, tokenizer=tokenizer, video_processor=Qwen3VLVideoProcessor()
+    )
+
+
+def _runs(mask: torch.Tensor) -> list[int]:
+    return [len(list(run)) for is_slot, run in itertools.groupby(mask[0].tolist()) if is_slot]
+
+
+def _encode(text_encoder, tokenizer, processor, references):
+    return encode_prompt(
+        text_encoder, tokenizer, "swap them", torch.device("cpu"), processor=processor, images=references
+    )
+
+
+def test_each_reference_fills_one_run_of_slots_per_32px_block(text_encoder, tokenizer, processor) -> None:
+    references = [Image.new("RGBA", (64, 96), (255, 0, 0, 128)), Image.new("RGB", (128, 64), (0, 255, 0))]
+    embeds, image_slots, grids = _encode(text_encoder, tokenizer, processor, references)
+    assert image_slots is not None and image_slots.shape == embeds.shape[:2]
+    # 64x96 is 3 rows of 2 blocks, 128x64 is 2 rows of 4, in the order given; the system turn is gone from both.
+    assert grids == ((3, 2), (2, 4))
+    assert _runs(image_slots) == [6, 8]
+
+
+def test_the_sequence_length_is_counted_before_encoding(text_encoder, tokenizer, processor) -> None:
+    # The working memory is reserved before the encoder runs, from this count; it has to be the length it reads.
+    references = [Image.new("RGB", (64, 96)), Image.new("RGB", (128, 64))]
+    embeds, _, _ = _encode(text_encoder, tokenizer, processor, references)
+    assert sequence_length(tokenizer, "swap them", references) == embeds.shape[1] + system_prefix_length(tokenizer)
+
+
+def test_the_vision_tower_reads_the_pixels(text_encoder, tokenizer, processor) -> None:
+    red, green = Image.new("RGB", (64, 64), (255, 0, 0)), Image.new("RGB", (64, 64), (0, 255, 0))
+    red_embeds, red_slots, _ = _encode(text_encoder, tokenizer, processor, [red])
+    green_embeds, _, _ = _encode(text_encoder, tokenizer, processor, [green])
+    # Same tokens either way; only the vision tower can tell the two apart.
+    assert not torch.allclose(red_embeds[red_slots], green_embeds[red_slots])
+
+
+def test_a_transparent_reference_reads_as_its_colors_over_white(text_encoder, tokenizer, processor) -> None:
+    # Fully transparent black must read as plain white, not as the black its RGB channels hold.
+    transparent = Image.new("RGBA", (64, 64), (0, 0, 0, 0))
+    white = Image.new("RGB", (64, 64), (255, 255, 255))
+    transparent_embeds, _, _ = _encode(text_encoder, tokenizer, processor, [transparent])
+    white_embeds, _, _ = _encode(text_encoder, tokenizer, processor, [white])
+    torch.testing.assert_close(transparent_embeds, white_embeds)
+
+
+def test_references_need_the_vision_tower(tokenizer, processor) -> None:
+    from transformers import Qwen3VLModel
+
+    from invokeai.backend.model_manager.util.qwen3_vl import drop_qwen3vl_visual_tower
+
+    config = tiny_qwen3vl_config()
+    config.text_config.vocab_size = len(tokenizer)
+    encoder = Qwen3VLModel(config).float().eval()
+    # As the standalone loaders leave it: an Identity where the tower was.
+    drop_qwen3vl_visual_tower(encoder)
+    assert not has_vision_tower(encoder)
+    with pytest.raises(ValueError, match="vision tower"):
+        encode_prompt(
+            encoder, tokenizer, "a", torch.device("cpu"), processor=processor, images=[Image.new("RGB", (32, 32))]
+        )
+
+
+def test_transparency_is_flattened_over_white_for_the_vision_encoder() -> None:
+    half_red = Image.new("RGBA", (1, 1), (255, 0, 0, 128))
+    assert on_white(half_red).getpixel((0, 0)) == (255, 127, 127)
+    assert on_white(Image.new("RGBA", (1, 1), (0, 0, 0, 0))).getpixel((0, 0)) == (255, 255, 255)
diff --git a/tests/backend/qwen_image_2_1/test_vae.py b/tests/backend/qwen_image_2_1/test_vae.py
index 69270995817..987523b3591 100644
--- a/tests/backend/qwen_image_2_1/test_vae.py
+++ b/tests/backend/qwen_image_2_1/test_vae.py
@@ -1,4 +1,5 @@
-"""Qwen-Image-2.1's RGBA VAE: how a decode becomes an image, what an encode reads, and the tile geometry it runs in."""
+"""Qwen-Image-2.1's RGBA VAE: how a decode becomes an image, what an encode reads -- a canvas image or an edit's
+reference -- and the tile geometry it runs in."""
 
 import numpy as np
 import pytest
@@ -11,7 +12,9 @@
     MIN_TILE_SIZE,
     SPATIAL_SCALE,
     choose_tile_size,
+    reference_size,
     to_image,
+    to_reference,
     to_vae_input,
     working_memory_bytes,
 )
@@ -142,3 +145,34 @@ def from_pretrained(_path, *, torch_dtype, **_kwargs):
     loader._load_model(SimpleNamespace(path=str(tmp_path)), SubModelType.VAE)
 
     assert loaded["dtype"] is expected
+
+
+@pytest.mark.parametrize(
+    ("size", "expected"),
+    [
+        # sqrt(1024^2 * 5/3) = 1322.0 -> 41.3 x 32 -> 1312; 1322.0 / (5/3) = 793.2 -> 24.8 x 32 -> 800.
+        ((500, 300), (1312, 800)),
+        ((1024, 1024), (1024, 1024)),
+        # sqrt(1024^2 * 3) = 1773.6 -> 1760; 591.2 -> 576.
+        ((3000, 1000), (1760, 576)),
+    ],
+)
+def test_a_reference_is_read_at_one_megapixel_on_the_32px_grid(
+    size: tuple[int, int], expected: tuple[int, int]
+) -> None:
+    assert reference_size(*size) == expected
+
+
+def test_a_reference_encodes_exactly_as_the_pipeline_preprocesses_it() -> None:
+    from diffusers.image_processor import VaeImageProcessor
+
+    generator = torch.Generator().manual_seed(0)
+    pixels = torch.randint(0, 256, (300, 500, 4), generator=generator, dtype=torch.uint8).numpy()
+    image = Image.fromarray(pixels, mode="RGBA")
+    width, height = reference_size(*image.size)
+    expected = VaeImageProcessor(vae_scale_factor=16, vae_latent_channels=64).preprocess(
+        image, width=width, height=height
+    )
+
+    # The alpha is the reference's own, unlike a canvas encode.
+    assert torch.equal(to_vae_input(to_reference(image), keep_alpha=True), expected.float())

From 56fe8082631b75cd44d118ff4eb71139c7a545e3 Mon Sep 17 00:00:00 2001
From: Alexander Eichhorn <alex@eichhorn.dev>
Date: Mon, 19 Oct 2026 06:46:39 +0200
Subject: [PATCH 2/4] feat(qwen-image-2-1): support LoRAs

Load ComfyUI, diffusers/PEFT and Kohya LoRAs, split ComfyUI's fused gate_up, and merge norm diffs on every format.
Identify 2.1 LoRAs by their gated MLP or 4096-wide attention; refuse any that would apply only in part.
Apply them to the denoise transformer through a collection loader, as sidecars on GGUF, int8 and fp8 weights.
---
 .../03.Models/02.model-families.mdx           |   2 +-
 .../03.Models/03.model-format-support.mdx     |   3 +-
 .../03.Models/Local Models/qwen-image-2-1.mdx |  12 +-
 .../04.Image Generation/generate-panel.mdx    |   2 +-
 .../qwen_image_2_1/qwen_image_2_1_denoise.py  |  75 ++-
 .../qwen_image_2_1_lora_loader.py             | 115 +++++
 .../backend/model_manager/configs/factory.py  |   2 +
 .../backend/model_manager/configs/lora.py     | 109 ++++-
 .../model_manager/load/model_loaders/lora.py  |   6 +
 .../flux_kohya_lora_conversion_utils.py       |   5 +-
 .../qwen_image_2_1_lora_constants.py          | 139 ++++++
 .../qwen_image_2_1_lora_conversion_utils.py   |  69 +++
 invokeai/frontend/api/openapi.json            | 448 +++++++++++++++++-
 invokeai/frontend/api/schema.ts               | 230 ++++++++-
 .../generation/core/baseGenerationPolicies.ts |   1 +
 .../features/generation/core/graph.test.ts    |  10 +
 .../src/features/generation/core/graph.ts     |   8 +-
 .../invocations/test_qwen_image_2_1_nodes.py  | 182 ++++++-
 .../test_ltx2_lora_probe_independence.py      |   3 +
 .../configs/test_qwen_image_2_1_config.py     | 173 ++++++-
 ...st_qwen_image_2_1_lora_conversion_utils.py | 181 +++++++
 ...en-Image-2.1 Manga to Photo V1.safetensors |   3 +
 .../__test_metadata__.json                    |   3 +
 23 files changed, 1721 insertions(+), 60 deletions(-)
 create mode 100644 invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_lora_loader.py
 create mode 100644 invokeai/backend/patches/lora_conversions/qwen_image_2_1_lora_constants.py
 create mode 100644 invokeai/backend/patches/lora_conversions/qwen_image_2_1_lora_conversion_utils.py
 create mode 100644 tests/backend/patches/lora_conversions/test_qwen_image_2_1_lora_conversion_utils.py
 create mode 100644 tests/model_identification/stripped_models/ab927eeb-2d10-4683-ad30-7a6b90283691/Qwen-Image-2.1 Manga to Photo V1.safetensors
 create mode 100644 tests/model_identification/stripped_models/ab927eeb-2d10-4683-ad30-7a6b90283691/__test_metadata__.json

diff --git a/docs/src/content/docs/Users Guide/03.Models/02.model-families.mdx b/docs/src/content/docs/Users Guide/03.Models/02.model-families.mdx
index aad924586b4..034fce99556 100644
--- a/docs/src/content/docs/Users Guide/03.Models/02.model-families.mdx	
+++ b/docs/src/content/docs/Users Guide/03.Models/02.model-families.mdx	
@@ -123,7 +123,7 @@ faster generation.
 | **Z-Image** | Images | 6B model in a fast, few-step Turbo variant and a Base variant. Understands English and Chinese prompts, with ControlNets. | Apache 2.0 | [Z-Image](/users-guide/models/local-models/z-image/)  |
 | **ERNIE-Image** | Images | Baidu's 8B text-to-image model, in standard and distilled Turbo variants. | Apache 2.0 | [ERNIE-Image](/users-guide/models/local-models/ernie-image/)  |
 | **Qwen Image** | Images | Text-to-image (2512) and instruction-based image editing (Edit 2511), with fast Lightning variants. | Apache 2.0 | [Qwen Image](/users-guide/models/local-models/qwen-image/)  |
-| **Qwen-Image-2.1** | Images | 7B text-to-image model with strong text rendering and transparent (RGBA) output, in a base and an 8-step Turbo variant. Edits from reference images. | Non-commercial | [Qwen-Image-2.1](/users-guide/models/local-models/qwen-image-2-1/)  |
+| **Qwen-Image-2.1** | Images | 7B text-to-image model with strong text rendering and transparent (RGBA) output, in a base and an 8-step Turbo variant. Edits from reference images, and LoRAs. | Non-commercial | [Qwen-Image-2.1](/users-guide/models/local-models/qwen-image-2-1/)  |
 | **Krea-2** | Images | 12B model in fast Turbo and undistilled Raw variants, with training-free style reference. | Community, free under $1M revenue | [Krea-2](/users-guide/models/local-models/krea-2/)  |
 | **Ideogram 4** | Images | Structured prompts that place described elements in regions of the image. | Non-commercial | [Ideogram 4](/users-guide/models/local-models/ideogram4/)  |
 | **Anima** | Images | Compact 2B anime-focused model, with ControlNet-LLLite adapters for inpainting and sketch guidance. | Non-commercial | [Anima](/users-guide/models/local-models/anima/)  |
diff --git a/docs/src/content/docs/Users Guide/03.Models/03.model-format-support.mdx b/docs/src/content/docs/Users Guide/03.Models/03.model-format-support.mdx
index 7279c395b1a..a08abc931ed 100644
--- a/docs/src/content/docs/Users Guide/03.Models/03.model-format-support.mdx	
+++ b/docs/src/content/docs/Users Guide/03.Models/03.model-format-support.mdx	
@@ -103,8 +103,9 @@ not check, so a quantized file there fails with an unclear error or decodes inco
 | FLUX.1 | ✓ (LyCORIS, Diffusers, OMI) | ✓ | Control LoRA, IP-Adapter, Redux |
 | FLUX.2, Z-Image | ✓ (LyCORIS, Diffusers) | Z-Image only | |
 | Qwen-Image, Krea-2, Wan 2.2, Anima | ✓ (LyCORIS/kohya, including LoKR where noted on the family page) | Anima (ControlNet-LLLite) | |
+| Qwen-Image-2.1 | ✓ (ComfyUI, diffusers/PEFT and kohya low-rank, and DoRA except LyCORIS DoRA on ComfyUI's fused `gate_up`; LoKR and LoHA install as unknown) | ✗ | |
 | LTX-2, MiniMax H3 | ✓ (plain low-rank; LoKR, LoHA and DoRA are refused) | ✗ | |
-| SD 3, CogView4, Ideogram 4, ERNIE-Image, Qwen-Image-2.1 | ✗ | ✗ | |
+| SD 3, CogView4, Ideogram 4, ERNIE-Image | ✗ | ✗ | |
 
 [PiD decoders](/users-guide/image-generation/advanced-topics/pid-decode/) load for FLUX.1, FLUX.2, SD 3, SDXL and
 Qwen-Image (v1 and v1.5, bf16 and int8 builds).
diff --git a/docs/src/content/docs/Users Guide/03.Models/Local Models/qwen-image-2-1.mdx b/docs/src/content/docs/Users Guide/03.Models/Local Models/qwen-image-2-1.mdx
index ecbb1190186..2a5c966a9f4 100644
--- a/docs/src/content/docs/Users Guide/03.Models/Local Models/qwen-image-2-1.mdx	
+++ b/docs/src/content/docs/Users Guide/03.Models/Local Models/qwen-image-2-1.mdx	
@@ -14,7 +14,7 @@ Two variants are available, and the variant is detected on install:
 - **Qwen-Image-2.1** — the base model. 40 steps by default.
 - **Qwen-Image-2.1 Turbo** — distilled for 8 steps on a fixed schedule of its own.
 
-Text-to-image, image-to-image, inpainting and outpainting on the Canvas are supported, and with the Diffusers models also edits from [reference images](#reference-images). LoRAs and regional guidance are not supported yet.
+Text-to-image, image-to-image, inpainting and outpainting on the Canvas are supported, with the Diffusers models also edits from [reference images](#reference-images), and [LoRAs](#loras). Regional guidance is not supported yet.
 
 ### License
 
@@ -72,6 +72,16 @@ The Qwen-Image-2.1 VAE decodes an alpha channel along with the colors, so a prom
 
 Image-to-image, inpainting and outpainting read the colors of the input image and treat it as fully opaque, whatever its alpha channel holds.
 
+### LoRAs
+
+Qwen-Image-2.1 LoRAs install like any other and appear under **Concepts** in the Generate panel. Files from ComfyUI (keys starting `diffusion_model.`), diffusers/PEFT trainers (`transformer.`) and Kohya-style trainers (`lora_unet_`) all load. ComfyUI's builds fuse each block's two MLP input projections into one `gate_up` layer; Invoke splits a LoRA on it to match the diffusers model.
+
+- LoRAs for [Qwen Image](/users-guide/models/local-models/qwen-image/) (2512, Edit 2511) do not fit Qwen-Image-2.1 and are not offered for it, nor its LoRAs for them.
+- They work on every format. On GGUF, int8 and fp8 transformers the LoRA runs alongside the packed weights rather than being merged into them. Full weight differences on the attention and text norms, which ComfyUI's Turbo LoRA carries, are merged on every format.
+- A LoRA trained on the base model also applies to Turbo; how well it holds up at Turbo's 8 steps depends on the LoRA.
+- Low-rank LoRAs (`lora_A`/`lora_B` or `lora_down`/`lora_up`) are recognized, DoRA ones included — except a LyCORIS DoRA on ComfyUI's fused `gate_up`, whose magnitude spans both halves and cannot be split. LoKR and LoHA files are not: they install as an unknown model and do not appear under **Concepts**.
+- A LoRA is recognized only if every layer it targets exists in Qwen-Image-2.1, so none applies only in part. One case cannot be told apart: a FLUX.2 Klein 9B LoRA trained on nothing but the image attention of its first blocks has exactly the layers of a Qwen-Image-2.1 one, and installs as one.
+
 ### Generation settings
 
 - **Steps** — 40 for the base model and 8 for Turbo. Turbo's schedule is tuned for 8 steps; other step counts are spread over the same curve and work, but 8 is what it was distilled for.
diff --git a/docs/src/content/docs/Users Guide/04.Image Generation/generate-panel.mdx b/docs/src/content/docs/Users Guide/04.Image Generation/generate-panel.mdx
index b5f3de5a4ce..dc1bee5b612 100644
--- a/docs/src/content/docs/Users Guide/04.Image Generation/generate-panel.mdx	
+++ b/docs/src/content/docs/Users Guide/04.Image Generation/generate-panel.mdx	
@@ -101,7 +101,7 @@ Concepts, also called LoRAs, are small add-on models that teach the main model a
 
 </Steps>
 
-You can add as many concepts as you like and combine them. Use the switch on each row to turn a concept off without removing it, and the trash button to remove it. If you change to a model from a different family, its concepts are marked **Incompatible** or removed. SD3, CogView4, ERNIE Image, Ideogram 4, Qwen-Image-2.1 and external API models don't support concepts, so with one of them selected the section says so instead of offering LoRAs.
+You can add as many concepts as you like and combine them. Use the switch on each row to turn a concept off without removing it, and the trash button to remove it. If you change to a model from a different family, its concepts are marked **Incompatible** or removed. SD3, CogView4, ERNIE Image, Ideogram 4 and external API models don't support concepts, so with one of them selected the section says so instead of offering LoRAs.
 
 ## Adding reference images
 
diff --git a/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_denoise.py b/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_denoise.py
index 7f69766c476..783a6087a0a 100644
--- a/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_denoise.py
+++ b/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_denoise.py
@@ -1,5 +1,7 @@
 import itertools
 import math
+from collections.abc import Iterator
+from contextlib import ExitStack
 from typing import Optional
 
 import torch
@@ -24,8 +26,15 @@
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.startup_utils import log_attention_backends
 from invokeai.backend.flux.sampling_utils import clip_timestep_schedule_fractional
-from invokeai.backend.model_manager.taxonomy import BaseModelType, QwenImage21VariantType
-from invokeai.backend.quantization.dequantizing_linear import peak_dequant_transient_bytes
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelFormat, QwenImage21VariantType
+from invokeai.backend.patches.layer_patcher import LayerPatcher, PatchSpec
+from invokeai.backend.patches.lora_conversions.qwen_image_2_1_lora_conversion_utils import (
+    QWEN_IMAGE_21_LORA_NORM_PREFIX,
+    QWEN_IMAGE_21_LORA_TRANSFORMER_PREFIX,
+)
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
+from invokeai.backend.quantization.dequantizing_linear import peak_dequant_transient_bytes, requires_sidecar_patching
+from invokeai.backend.quantization.fp8_scaled import count_fp8_weights
 from invokeai.backend.qwen_image_2_1.sampling import build_sigmas
 from invokeai.backend.rectified_flow.rectified_flow_inpaint_extension import RectifiedFlowInpaintExtension
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
@@ -102,6 +111,9 @@ def prefix_length(info: QwenImage21ConditioningInfo) -> int:
     return info.prompt_embeds.shape[1] + (TOKENS_PER_SLOT - 1) * slots
 
 
+# The widest Linear output a sidecar LoRA adds to: gate_layer and proj.
+_MLP_WIDTH = 12288
+
 # K and V of one prefix token in all 32 blocks: 2 x 4096 x 32 x 2 bytes in bf16.
 KV_BYTES_PER_PREFIX_TOKEN = 2 * 4096 * 32 * 2
 
@@ -136,7 +148,7 @@ def unpack_latents(latents: torch.Tensor, height: int, width: int) -> torch.Tens
     title="Denoise - Qwen-Image-2.1",
     tags=["image", "qwen_image_2_1", "qwen-image-2.1"],
     category="image",
-    version="1.1.0",
+    version="1.2.0",
     classification=Classification.Prototype,
 )
 class QwenImage21DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
@@ -365,6 +377,9 @@ def image_slot_mask(info: QwenImage21ConditioningInfo) -> torch.Tensor:
             transformer_info.model, image_seq_len, max(pos_prefix, neg_prefix or 0), device, dtype
         )
         working_memory += peak_dequant_transient_bytes(transformer_info.model, dtype)
+        working_memory += self._lora_working_memory(
+            context, transformer_info.model, transformer_config.format, image_seq_len + max(pos_prefix, neg_prefix or 0)
+        )
         log_attention_backends(logger, device)
 
         total_steps = len(sigmas) - 1
@@ -379,8 +394,33 @@ def image_slot_mask(info: QwenImage21ConditioningInfo) -> torch.Tensor:
             )
         )
 
-        with transformer_info.model_on_device(working_mem_bytes=working_memory) as (_, transformer):
-            # One cache per guidance branch, for this run only.
+        with ExitStack() as exit_stack:
+            cached_weights, transformer = exit_stack.enter_context(
+                transformer_info.model_on_device(working_mem_bytes=working_memory)
+            )
+            # GGUF, int8 and fp8 weights cannot take an update in place: those LoRAs run as sidecars.
+            exit_stack.enter_context(
+                LayerPatcher.apply_smart_model_patches(
+                    model=transformer,
+                    patches=self._lora_iterator(context),
+                    prefix=QWEN_IMAGE_21_LORA_TRANSFORMER_PREFIX,
+                    dtype=dtype,
+                    cached_weights=cached_weights,
+                    force_sidecar_patching=requires_sidecar_patching(transformer, transformer_config.format),
+                )
+            )
+            # Norm diffs are merged in every format: norms are never quantized, and no sidecar wraps them.
+            exit_stack.enter_context(
+                LayerPatcher.apply_smart_model_patches(
+                    model=transformer,
+                    patches=self._lora_iterator(context),
+                    prefix=QWEN_IMAGE_21_LORA_NORM_PREFIX,
+                    dtype=dtype,
+                    cached_weights=cached_weights,
+                    force_direct_patching=True,
+                )
+            )
+            # One cache per guidance branch, for this run only. The prefix it holds is computed with the LoRAs.
             num_blocks = len(transformer.transformer_blocks)
             pos_cache = QwenImage21KVCache(num_blocks) if use_cache else None
             neg_cache = QwenImage21KVCache(num_blocks) if use_cache and neg_embeds is not None else None
@@ -489,6 +529,31 @@ def _attention_score_bytes(
             via_diffusers_dispatch=True,
         )
 
+    def _lora_working_memory(
+        self, context: InvocationContext, transformer: torch.nn.Module, model_format: ModelFormat, seq_len: int
+    ) -> int:
+        """Room the LoRAs need while sampling: only where they run beside the weights, as sidecars.
+
+        A LoRA merged into the weights computes its update once, before the first step, in room the activations take
+        only afterwards. A sidecar -- for GGUF and int8 weights, and per layer for fp8 ones -- keeps its tensors on
+        the device and adds one output-sized update per forward.
+        """
+        if not self.transformer.loras:
+            return 0
+        if not requires_sidecar_patching(transformer, model_format) and not count_fp8_weights(transformer):
+            return 0
+        patch_bytes = sum(context.models.load(lora.lora).model.calc_size() for lora in self.transformer.loras)
+        return patch_bytes + seq_len * _MLP_WIDTH * 2
+
+    def _lora_iterator(self, context: InvocationContext) -> Iterator[PatchSpec]:
+        for lora in self.transformer.loras:
+            lora_info = context.models.load(lora.lora)
+            if not isinstance(lora_info.model, ModelPatchRaw):
+                raise TypeError(
+                    f"Expected ModelPatchRaw for LoRA '{lora.lora.key}', got {type(lora_info.model).__name__}."
+                )
+            yield (lora_info.model, lora.weight, lora_info.model_in_ram())
+
     @staticmethod
     def _step_callback(context: InvocationContext):
         def callback(state: PipelineIntermediateState) -> None:
diff --git a/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_lora_loader.py b/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_lora_loader.py
new file mode 100644
index 00000000000..724f32708c4
--- /dev/null
+++ b/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_lora_loader.py
@@ -0,0 +1,115 @@
+from typing import Optional
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
+from invokeai.app.invocations.model import LoRAField, ModelIdentifierField, TransformerField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType
+
+
+@invocation_output("qwen_image_2_1_lora_loader_output")
+class QwenImage21LoRALoaderOutput(BaseInvocationOutput):
+    """Qwen-Image-2.1 LoRA Loader Output"""
+
+    transformer: Optional[TransformerField] = OutputField(
+        default=None, description=FieldDescriptions.transformer, title="Transformer"
+    )
+
+
+def _require_qwen_image_21_lora(context: InvocationContext, lora: ModelIdentifierField) -> None:
+    """Refuse a LoRA that is not installed as a Qwen-Image-2.1 LoRA: its keys would match nothing, or the wrong
+    layers, in this transformer."""
+    if not context.models.exists(lora.key):
+        raise ValueError(f"Unknown LoRA: {lora.key}!")
+    config = context.models.get_config(lora.key)
+    if config.type is not ModelType.LoRA:
+        raise ValueError(f"'{config.name}' is a {config.type.value} model, not a LoRA.")
+    if config.base is not BaseModelType.QwenImage21:
+        hint = " Qwen Image LoRAs do not fit Qwen-Image-2.1." if config.base is BaseModelType.QwenImage else ""
+        raise ValueError(f"LoRA '{config.name}' is for {config.base.value} models, not Qwen-Image-2.1.{hint}")
+
+
+@invocation(
+    "qwen_image_2_1_lora_loader",
+    title="Apply LoRA - Qwen-Image-2.1",
+    tags=["lora", "model", "qwen_image_2_1"],
+    category="model",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class QwenImage21LoRALoaderInvocation(BaseInvocation):
+    """Apply a LoRA model to a Qwen-Image-2.1 transformer."""
+
+    lora: ModelIdentifierField = InputField(
+        description=FieldDescriptions.lora_model,
+        title="LoRA",
+        ui_model_base=BaseModelType.QwenImage21,
+        ui_model_type=ModelType.LoRA,
+    )
+    weight: float = InputField(default=1.0, description=FieldDescriptions.lora_weight)
+    transformer: TransformerField | None = InputField(
+        default=None,
+        description=FieldDescriptions.transformer,
+        input=Input.Connection,
+        title="Transformer",
+    )
+
+    def invoke(self, context: InvocationContext) -> QwenImage21LoRALoaderOutput:
+        _require_qwen_image_21_lora(context, self.lora)
+        if self.transformer and any(lora.lora.key == self.lora.key for lora in self.transformer.loras):
+            raise ValueError(f'LoRA "{self.lora.key}" already applied to transformer.')
+
+        output = QwenImage21LoRALoaderOutput()
+        if self.transformer is not None:
+            output.transformer = self.transformer.model_copy(deep=True)
+            output.transformer.loras.append(LoRAField(lora=self.lora, weight=self.weight))
+        return output
+
+
+@invocation(
+    "qwen_image_2_1_lora_collection_loader",
+    title="Apply LoRA Collection - Qwen-Image-2.1",
+    tags=["lora", "model", "qwen_image_2_1"],
+    category="model",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class QwenImage21LoRACollectionLoader(BaseInvocation):
+    """Applies a collection of LoRAs to a Qwen-Image-2.1 transformer."""
+
+    loras: Optional[LoRAField | list[LoRAField]] = InputField(
+        default=None,
+        description="LoRA models and weights. May be a single LoRA or collection.",
+        title="LoRAs",
+        ui_model_base=[BaseModelType.QwenImage21],
+        ui_model_type=ModelType.LoRA,
+    )
+    transformer: Optional[TransformerField] = InputField(
+        default=None,
+        description=FieldDescriptions.transformer,
+        input=Input.Connection,
+        title="Transformer",
+    )
+
+    def invoke(self, context: InvocationContext) -> QwenImage21LoRALoaderOutput:
+        output = QwenImage21LoRALoaderOutput()
+        if self.transformer is not None:
+            output.transformer = self.transformer.model_copy(deep=True)
+
+        loras = self.loras if isinstance(self.loras, list) else [self.loras]
+        # Including those an upstream loader applied: a LoRA applied twice doubles its weight.
+        added = {lora.lora.key for lora in output.transformer.loras} if output.transformer is not None else set()
+        for lora in loras:
+            if lora is None or lora.lora.key in added:
+                continue
+            _require_qwen_image_21_lora(context, lora.lora)
+            added.add(lora.lora.key)
+            if output.transformer is not None:
+                output.transformer.loras.append(lora)
+        return output
diff --git a/invokeai/backend/model_manager/configs/factory.py b/invokeai/backend/model_manager/configs/factory.py
index 27553c2200c..dee8c39a4c0 100644
--- a/invokeai/backend/model_manager/configs/factory.py
+++ b/invokeai/backend/model_manager/configs/factory.py
@@ -68,6 +68,7 @@
     LoRA_LyCORIS_Krea2_Config,
     LoRA_LyCORIS_LTX2_Config,
     LoRA_LyCORIS_MiniMaxH3_Config,
+    LoRA_LyCORIS_QwenImage21_Config,
     LoRA_LyCORIS_QwenImage_Config,
     LoRA_LyCORIS_SD1_Config,
     LoRA_LyCORIS_SD2_Config,
@@ -509,6 +510,7 @@ def has_model_export(module: Any, name: Any, expected_bases: tuple[type, ...]) -
         Annotated[LoRA_LyCORIS_ZImage_Config, LoRA_LyCORIS_ZImage_Config.get_tag()],
         Annotated[LoRA_LyCORIS_Krea2_Config, LoRA_LyCORIS_Krea2_Config.get_tag()],
         Annotated[LoRA_LyCORIS_QwenImage_Config, LoRA_LyCORIS_QwenImage_Config.get_tag()],
+        Annotated[LoRA_LyCORIS_QwenImage21_Config, LoRA_LyCORIS_QwenImage21_Config.get_tag()],
         # MiniMax H3 keys on H3-exclusive submodules (fused ``attn.qkv_proj``,
         # ``adaln_proj.linear``) and rejects other architectures' signatures, so it
         # is mutually exclusive with Wan/Anima regardless of order (locked in by
diff --git a/invokeai/backend/model_manager/configs/lora.py b/invokeai/backend/model_manager/configs/lora.py
index 081d276ceb3..0d29636ecff 100644
--- a/invokeai/backend/model_manager/configs/lora.py
+++ b/invokeai/backend/model_manager/configs/lora.py
@@ -60,6 +60,9 @@
     has_non_minimax_h3_architecture_keys,
     has_unsupported_minimax_h3_lora_variant_keys,
 )
+from invokeai.backend.patches.lora_conversions.qwen_image_2_1_lora_constants import (
+    unsupported_keys as qwen_image_21_unsupported_lora_keys,
+)
 from invokeai.backend.patches.lora_conversions.wan_lora_constants import (
     detect_wan_lora_variant,
     has_non_wan_architecture_keys,
@@ -911,30 +914,63 @@ def _get_base_or_raise(cls, mod: ModelOnDisk) -> BaseModelType:
 
 
 _QWEN_IMAGE21_HIDDEN_SIZE = 4096
-_QWEN_IMAGE21_MLP_MARKERS = ("img_mlp.gate_up.", "img_mlp.gate_layer.", "img_mlp_gate_up", "img_mlp_gate_layer")
-_ATTENTION_PROJECTIONS = ("attn.to_q.", "attn.to_k.", "attn.to_v.", "attn_to_q.", "attn_to_k.", "attn_to_v.")
+# Qwen-Image-2.1's gated MLP, by any of its names: diffusers' `gate_layer`/`proj`/`out`, ComfyUI's fused `gate_up`,
+# dotted or Kohya-flattened. Qwen-Image's MLP is `img_mlp.net.0.proj`/`img_mlp.net.2`, which none of these match.
+_QWEN_IMAGE21_MLP_MARKERS = (
+    "img_mlp.gate_up.",
+    "img_mlp.gate_layer.",
+    "img_mlp.proj.",
+    "img_mlp.out.",
+    "img_mlp_gate_up",
+    "img_mlp_gate_layer",
+    "img_mlp_proj",
+    "img_mlp_out",
+)
+# A block's own attention projections, dotted or Kohya-flattened. Anchored to the block: LTX-2's
+# `audio_to_video_attn.to_q` is as wide and would match the bare `attn.to_q`.
+_BLOCK_ATTENTION_PROJECTION = re.compile(r"transformer_blocks[._]\d+[._]attn[._]to_(?:[qkv]|out[._]0)[._]")
+_LOKR_FACTOR = re.compile(r"\.(lokr_w[12])(?:_b)?$")
 
 
-def _has_qwen_image21_lora_keys(state_dict: dict[str | int, Any]) -> bool:
-    """True if the state dict is a Qwen-Image-2.1 LoRA, whose module names otherwise overlap Qwen-Image's.
+def _block_attention_in_features(state_dict: dict[str | int, Any]) -> tuple[set[int], set[int]]:
+    """The input widths of the block-attention projections a LoRA targets: of its low-rank layers, and of its
+    LoKR ones.
 
-    Its gated MLP (`gate_layer` in diffusers/PEFT, ComfyUI's fused `gate_up`, and their Kohya spellings) is
-    Qwen-Image-2.1's alone. An attention-only LoRA -- PEFT's usual `to_q/to_k/to_v` targets -- names nothing
-    Qwen-Image lacks, so its width decides: the projections read 4096 features here and 3072 in Qwen-Image.
+    A low-rank layer carries the width on its input-side matrix; a LoKR weight is the Kronecker product of two
+    factors, so its input width is the product of theirs (each given whole, or as the second matrix of a pair).
     """
+    low_rank: set[int] = set()
+    factored: set[int] = set()
+    lokr: dict[str, dict[str, int]] = {}
     for key, value in state_dict.items():
-        if not isinstance(key, str):
+        if not isinstance(key, str) or _BLOCK_ATTENTION_PROJECTION.search(key) is None:
             continue
-        if any(marker in key for marker in _QWEN_IMAGE21_MLP_MARKERS):
-            return True
-        if (
-            key.endswith((".lora_A.weight", ".lora_down.weight"))
-            and any(projection in key for projection in _ATTENTION_PROJECTIONS)
-            and len(getattr(value, "shape", ())) == 2
-            and value.shape[1] == _QWEN_IMAGE21_HIDDEN_SIZE
-        ):
-            return True
-    return False
+        shape = getattr(value, "shape", ())
+        if len(shape) != 2:
+            continue
+        if key.endswith((".lora_A.weight", ".lora_down.weight")):
+            low_rank.add(shape[1])
+        elif factor := _LOKR_FACTOR.search(key):
+            lokr.setdefault(key[: factor.start()], {})[factor.group(1)] = shape[1]
+    factored.update(f["lokr_w1"] * f["lokr_w2"] for f in lokr.values() if len(f) == 2)
+    return low_rank, factored
+
+
+def _has_qwen_image21_lora_keys(state_dict: dict[str | int, Any]) -> bool:
+    """True if the state dict is a Qwen-Image-2.1 LoRA, whose module names otherwise overlap Qwen-Image's.
+
+    Its gated MLP (`gate_layer`, `proj` and `out` in diffusers/PEFT, ComfyUI's fused `gate_up`, and their Kohya
+    spellings) is Qwen-Image-2.1's alone. An attention-only LoRA -- PEFT's usual `to_q/to_k/to_v` targets, or
+    `to_out.0` -- names nothing Qwen-Image lacks, so its width decides: the projections read 4096 features here
+    and 3072 in Qwen-Image. A LoKR layer's width is a product of factor sizes, weaker evidence on its own, so it
+    counts only where every key also names a Qwen-Image-2.1 module.
+    """
+    if any(isinstance(key, str) and any(m in key for m in _QWEN_IMAGE21_MLP_MARKERS) for key in state_dict):
+        return True
+    low_rank, factored = _block_attention_in_features(state_dict)
+    if _QWEN_IMAGE21_HIDDEN_SIZE in low_rank:
+        return True
+    return _QWEN_IMAGE21_HIDDEN_SIZE in factored and not qwen_image_21_unsupported_lora_keys(state_dict)
 
 
 def _has_krea2_lora_keys(state_dict: dict[str | int, Any]) -> bool:
@@ -1143,6 +1179,43 @@ def _get_base_or_raise(cls, mod: ModelOnDisk) -> BaseModelType:
         raise NotAMatchError("model does not look like a Krea-2 LoRA")
 
 
+def _is_qwen_image21_lora(state_dict: dict[str | int, Any]) -> bool:
+    """A Qwen-Image-2.1 LoRA that applies in full, and not one for a family whose module names it shares.
+
+    FLUX.2 Klein 9B is as wide (4096) and names its double blocks' attention the same. Its fingerprints (text
+    embedder widths, fused single-block projections) rule most of its LoRAs out, and every key having to name a
+    Qwen-Image-2.1 module rules out the rest that touch a module only a dual-stream model has (`add_q_proj`, `ff`).
+    A LoRA on nothing but the image attention of Klein's first eight blocks reads the same as a Qwen-Image-2.1 one.
+    """
+    return (
+        _has_qwen_image21_lora_keys(state_dict)
+        and not _is_flux2_lora_state_dict(state_dict)
+        and not qwen_image_21_unsupported_lora_keys(state_dict)
+    )
+
+
+class LoRA_LyCORIS_QwenImage21_Config(LoRA_LyCORIS_Config_Base, Config_Base):
+    """Model config for Qwen-Image-2.1 LoRAs: ComfyUI (`diffusion_model.`, fused `gate_up`), diffusers/PEFT and
+    Kohya layouts, as plain low-rank layers."""
+
+    base: Literal[BaseModelType.QwenImage21] = Field(default=BaseModelType.QwenImage21)
+
+    @classmethod
+    def _validate_looks_like_lora(cls, mod: ModelOnDisk) -> None:
+        state_dict = mod.load_state_dict()
+        if not (_is_qwen_image21_lora(state_dict) and _has_complete_lora_pair(state_dict)):
+            raise NotAMatchError("model does not match Qwen-Image-2.1 LoRA heuristics")
+        # An orphaned half would install here and fail in conversion at generation time.
+        if not _lora_weight_keys_are_all_paired(state_dict):
+            raise NotAMatchError("Qwen-Image-2.1 LoRA has an incomplete lora_A/B (or lora_down/up) weight pair")
+
+    @classmethod
+    def _get_base_or_raise(cls, mod: ModelOnDisk) -> BaseModelType:
+        if _is_qwen_image21_lora(mod.load_state_dict()):
+            return BaseModelType.QwenImage21
+        raise NotAMatchError("model does not look like a Qwen-Image-2.1 LoRA")
+
+
 class LoRA_LyCORIS_Anima_Config(LoRA_LyCORIS_Config_Base, Config_Base):
     """Model config for Anima LoRA models in LyCORIS format."""
 
diff --git a/invokeai/backend/model_manager/load/model_loaders/lora.py b/invokeai/backend/model_manager/load/model_loaders/lora.py
index 6ab1a6a2409..33c05d806d0 100644
--- a/invokeai/backend/model_manager/load/model_loaders/lora.py
+++ b/invokeai/backend/model_manager/load/model_loaders/lora.py
@@ -64,6 +64,9 @@
     lora_model_from_minimax_h3_state_dict,
 )
 from invokeai.backend.patches.lora_conversions.peft_adapter_utils import normalize_peft_adapter_names
+from invokeai.backend.patches.lora_conversions.qwen_image_2_1_lora_conversion_utils import (
+    lora_model_from_qwen_image_21_state_dict,
+)
 from invokeai.backend.patches.lora_conversions.qwen_image_lora_conversion_utils import (
     lora_model_from_qwen_image_state_dict,
 )
@@ -179,6 +182,9 @@ def _load_model(
             model = lora_model_from_z_image_state_dict(state_dict=state_dict, alpha=None)
         elif self._model_base == BaseModelType.QwenImage:
             model = lora_model_from_qwen_image_state_dict(state_dict=state_dict, alpha=None)
+        elif self._model_base == BaseModelType.QwenImage21:
+            # ComfyUI, diffusers/PEFT or Kohya keys; ComfyUI's fused gate_up is split for diffusers.
+            model = lora_model_from_qwen_image_21_state_dict(state_dict=state_dict)
         elif self._model_base == BaseModelType.Krea2:
             # Krea-2 LoRAs use diffusers PEFT format targeting the Krea2 transformer (and optionally
             # the Qwen3-VL text encoder). alpha=None → alpha=rank (common diffusers default).
diff --git a/invokeai/backend/patches/lora_conversions/flux_kohya_lora_conversion_utils.py b/invokeai/backend/patches/lora_conversions/flux_kohya_lora_conversion_utils.py
index f5a6830c4f1..302bc32a638 100644
--- a/invokeai/backend/patches/lora_conversions/flux_kohya_lora_conversion_utils.py
+++ b/invokeai/backend/patches/lora_conversions/flux_kohya_lora_conversion_utils.py
@@ -17,8 +17,11 @@
 #   lora_unet_double_blocks_0_img_attn_proj.alpha
 #   lora_unet_double_blocks_0_img_attn_proj.lora_down.weight
 #   lora_unet_double_blocks_0_img_attn_proj.lora_up.weight
+# FLUX's own block names only: Qwen-Image's Kohya keys (`lora_unet_transformer_blocks_N_img_mlp_...`) would match a
+# bare `\w+_blocks`, and a LoRA whose every key is on that MLP would read as FLUX.
 FLUX_KOHYA_TRANSFORMER_KEY_REGEX = (
-    r"lora_unet_(\w+_blocks)_(\d+)_(img_attn|img_mlp|img_mod|txt_attn|txt_mlp|txt_mod|linear1|linear2|modulation)_?(.*)"
+    r"lora_unet_(double_blocks|single_blocks)_(\d+)_"
+    r"(img_attn|img_mlp|img_mod|txt_attn|txt_mlp|txt_mod|linear1|linear2|modulation)_?(.*)"
 )
 
 # A regex pattern that matches all of the last layer keys in the Kohya FLUX LoRA format.
diff --git a/invokeai/backend/patches/lora_conversions/qwen_image_2_1_lora_constants.py b/invokeai/backend/patches/lora_conversions/qwen_image_2_1_lora_constants.py
new file mode 100644
index 00000000000..c3604a9256b
--- /dev/null
+++ b/invokeai/backend/patches/lora_conversions/qwen_image_2_1_lora_constants.py
@@ -0,0 +1,139 @@
+"""Which keys a Qwen-Image-2.1 LoRA may hold, shared by its identification and its conversion.
+
+The published LoRAs target the module names diffusers uses, under a `diffusion_model.` (ComfyUI), `transformer.`
+(diffusers/PEFT) or `lora_unet_` (Kohya, flattened) prefix -- with one exception: ComfyUI fuses each block's gated
+MLP input into `img_mlp.gate_up`, which diffusers keeps as `gate_layer` (the first half of its output rows) and
+`proj` (the second half), as the base checkpoint converter splits it.
+
+Besides the Linears, a LoRA may carry full `diff`s on the RMSNorm weights (the official Turbo LoRA does). They are
+merged into the weight whatever the transformer's format: a norm is never quantized, and a sidecar cannot patch it.
+"""
+
+import re
+from typing import Any
+
+QWEN_IMAGE_21_LORA_TRANSFORMER_PREFIX = "lora_transformer-"
+# The norm diffs, kept apart so the denoise node can merge them while the Linears run as sidecars.
+QWEN_IMAGE_21_LORA_NORM_PREFIX = "lora_transformer_norm-"
+
+# Longest first, so `base_model.model.transformer.` wins over `base_model.model.`.
+_PREFIXES = ("base_model.model.transformer.", "base_model.model.", "diffusion_model.", "transformer.")
+
+# Each recognized parameter name and the value key the layer classes read. DoRA magnitudes come in two orientations
+# (see `DoRALayer`): LyCORIS's `dora_scale` indexes the input dim, PEFT's and ai-toolkit's the output dim.
+_PARAM_VALUE_KEYS = {
+    "lora_A.weight": "lora_down.weight",
+    "lora_B.weight": "lora_up.weight",
+    "lora_down.weight": "lora_down.weight",
+    "lora_up.weight": "lora_up.weight",
+    "alpha": "alpha",
+    "dora_scale": "dora_scale",
+    "lora_magnitude_vector.weight": "dora_magnitude",
+    # PEFT's own `get_peft_model_state_dict` writes it without the `.weight`.
+    "lora_magnitude_vector": "dora_magnitude",
+    "magnitude": "dora_magnitude",
+    "diff": "diff",
+    **{name: name for name in ("lokr_w1", "lokr_w2", "lokr_w1_a", "lokr_w1_b", "lokr_w2_a", "lokr_w2_b", "lokr_t2")},
+    **{name: name for name in ("hada_w1_a", "hada_w1_b", "hada_w2_a", "hada_w2_b", "hada_t1", "hada_t2")},
+}
+
+# Every Linear of the transformer, block-relative or top-level, plus ComfyUI's fused `gate_up`.
+_BLOCK_MODULES = frozenset(
+    {
+        "attn.to_q",
+        "attn.to_k",
+        "attn.to_v",
+        "attn.to_out.0",
+        "img_mlp.gate_up",
+        "img_mlp.gate_layer",
+        "img_mlp.proj",
+        "img_mlp.out",
+    }
+)
+# The RMSNorm weights a full diff may target.
+_BLOCK_NORMS = frozenset({"attn.norm_q", "attn.norm_k"})
+_TOP_NORMS = frozenset({"txt_in.text_norm"})
+_TOP_MODULES = frozenset(
+    {
+        "img_in",
+        "proj_out",
+        "norm_out.linear",
+        "modulation.1",
+        "txt_in.in_layer",
+        "txt_in.out_layer",
+        "time_text_embed.timestep_embedder.linear_1",
+        "time_text_embed.timestep_embedder.linear_2",
+    }
+)
+_BLOCK = re.compile(r"transformer_blocks\.(\d+)\.(.+)")
+# Kohya flattens the dots, which makes a name ambiguous on its own (`to_out_0`), so it is mapped, not re-dotted.
+_KOHYA_BLOCK = re.compile(r"transformer_blocks_(\d+)_(.+)")
+_KOHYA_BLOCK_MODULES = {module.replace(".", "_"): module for module in _BLOCK_MODULES | _BLOCK_NORMS}
+_KOHYA_TOP_MODULES = {module.replace(".", "_"): module for module in _TOP_MODULES | _TOP_NORMS}
+_KOHYA_PREFIX = "lora_unet_"
+
+GATE_UP = "img_mlp.gate_up"
+# What a layer on the fused gate_up may hold to be split by its output rows: a low-rank update (its `up` rows),
+# a PEFT/ai-toolkit DoRA (also its output-dim magnitude), or a full one. LoKR and LoHA mix the rows, and LyCORIS's
+# input-dim `dora_scale` normalizes across both halves at once.
+GATE_UP_SPLITTABLE = (
+    frozenset({"lora_down.weight", "lora_up.weight"}),
+    frozenset({"lora_down.weight", "lora_up.weight", "alpha"}),
+    frozenset({"lora_down.weight", "lora_up.weight", "dora_magnitude"}),
+    frozenset({"lora_down.weight", "lora_up.weight", "alpha", "dora_magnitude"}),
+    frozenset({"diff"}),
+)
+
+
+def _module_path(name: str) -> str | None:
+    """The diffusers module path a key's module part names, or None if this transformer has no such Linear."""
+    for prefix in _PREFIXES:
+        if name.startswith(prefix):
+            name = name[len(prefix) :]
+            break
+    if name.startswith(_KOHYA_PREFIX):
+        flat = name[len(_KOHYA_PREFIX) :]
+        if block := _KOHYA_BLOCK.fullmatch(flat):
+            module = _KOHYA_BLOCK_MODULES.get(block.group(2))
+            return f"transformer_blocks.{block.group(1)}.{module}" if module else None
+        return _KOHYA_TOP_MODULES.get(flat)
+    if block := _BLOCK.fullmatch(name):
+        return name if block.group(2) in _BLOCK_MODULES | _BLOCK_NORMS else None
+    return name if name in _TOP_MODULES | _TOP_NORMS else None
+
+
+def is_norm(module: str) -> bool:
+    """Whether a module path from `split_key` names an RMSNorm rather than a Linear."""
+    if block := _BLOCK.fullmatch(module):
+        return block.group(2) in _BLOCK_NORMS
+    return module in _TOP_NORMS
+
+
+def split_key(key: str) -> tuple[str, str] | None:
+    """`(module path, value key)` for a key of a Qwen-Image-2.1 LoRA, or None for one that is not."""
+    for param, value_key in _PARAM_VALUE_KEYS.items():
+        if key.endswith("." + param):
+            module = _module_path(key[: -len(param) - 1])
+            return (module, value_key) if module is not None else None
+    return None
+
+
+def unsupported_keys(state_dict: dict[str | int, Any]) -> list[str]:
+    """The keys that keep this state dict from being applied to Qwen-Image-2.1 as a whole.
+
+    A key that names no module of this transformer (another model's, or a text encoder's), anything but a full
+    `diff` on a norm, and a fused gate_up layer that cannot be split. Empty for a LoRA that applies in full.
+    """
+    unsupported: list[str] = []
+    gate_up_layers: dict[str, set[str]] = {}
+    for key in state_dict:
+        if not isinstance(key, str) or (split := split_key(key)) is None:
+            unsupported.append(str(key))
+            continue
+        module, value_key = split
+        if is_norm(module) and value_key != "diff":
+            unsupported.append(key)
+        elif module.endswith(GATE_UP):
+            gate_up_layers.setdefault(module, set()).add(value_key)
+    unsupported += [module for module, values in gate_up_layers.items() if frozenset(values) not in GATE_UP_SPLITTABLE]
+    return sorted(unsupported)
diff --git a/invokeai/backend/patches/lora_conversions/qwen_image_2_1_lora_conversion_utils.py b/invokeai/backend/patches/lora_conversions/qwen_image_2_1_lora_conversion_utils.py
new file mode 100644
index 00000000000..24ec1103656
--- /dev/null
+++ b/invokeai/backend/patches/lora_conversions/qwen_image_2_1_lora_conversion_utils.py
@@ -0,0 +1,69 @@
+"""Qwen-Image-2.1 LoRAs onto the diffusers `QwenImage21Transformer2DModel`; see `qwen_image_2_1_lora_constants`."""
+
+import torch
+
+from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
+from invokeai.backend.patches.layers.utils import any_lora_layer_from_state_dict
+from invokeai.backend.patches.lora_conversions.qwen_image_2_1_lora_constants import (
+    GATE_UP,
+    QWEN_IMAGE_21_LORA_NORM_PREFIX,
+    QWEN_IMAGE_21_LORA_TRANSFORMER_PREFIX,
+    is_norm,
+    split_key,
+    unsupported_keys,
+)
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
+
+# What splits along with the output rows of a fused gate_up layer.
+_ROW_KEYS = ("lora_up.weight", "dora_magnitude", "diff")
+
+
+def _split_gate_up(module: str, values: dict[str, torch.Tensor]) -> dict[str, dict[str, torch.Tensor]]:
+    """The fused gated-MLP input as diffusers' two Linears: `gate_layer` takes the first half of the output rows.
+
+    Every row-indexed tensor splits in two; the `down` matrix reads the shared input and goes to both halves.
+    """
+    stem = module[: -len("gate_up")]
+    gate, proj = dict(values), dict(values)
+    for key in _ROW_KEYS:
+        if key in values:
+            rows = values[key]
+            if rows.shape[0] % 2:
+                raise ValueError(f"LoRA layer {module!r} has {rows.shape[0]} output rows; gate_up has two halves.")
+            gate[key], proj[key] = rows[: rows.shape[0] // 2], rows[rows.shape[0] // 2 :]
+    if "lora_down.weight" in values:
+        # A copy of its own, so the model cache, which sizes a patch by its tensors' elements, counts what it holds.
+        proj["lora_down.weight"] = values["lora_down.weight"].clone()
+    return {f"{stem}gate_layer": gate, f"{stem}proj": proj}
+
+
+def lora_model_from_qwen_image_21_state_dict(state_dict: dict[str, torch.Tensor]) -> ModelPatchRaw:
+    """Convert a Qwen-Image-2.1 LoRA to a patch for the diffusers transformer.
+
+    A layer without an `alpha` scales by 1 (its alpha is its rank), as ComfyUI applies it. Norm diffs go under
+    `QWEN_IMAGE_21_LORA_NORM_PREFIX`, the Linears under `QWEN_IMAGE_21_LORA_TRANSFORMER_PREFIX`.
+    """
+    unsupported = unsupported_keys(state_dict)
+    if unsupported:
+        # Identification refuses these, so a file reaches here only by another path; applying the rest would make a
+        # partial LoRA that passes for a whole one.
+        raise ValueError(
+            f"{len(unsupported)} part(s) of this LoRA do not apply to Qwen-Image-2.1, e.g. {unsupported[0]!r}: "
+            "it targets layers this model does not have, a norm with anything but a full diff, or a LoKR, LoHA "
+            "or LyCORIS DoRA layer on the fused gate_up projection."
+        )
+
+    grouped: dict[str, dict[str, torch.Tensor]] = {}
+    for key, value in state_dict.items():
+        split = split_key(key)
+        assert split is not None  # unsupported_keys checked every key
+        module, value_key = split
+        grouped.setdefault(module, {})[value_key] = value
+
+    layers: dict[str, BaseLayerPatch] = {}
+    for module, values in grouped.items():
+        targets = _split_gate_up(module, values) if module.endswith(GATE_UP) else {module: values}
+        prefix = QWEN_IMAGE_21_LORA_NORM_PREFIX if is_norm(module) else QWEN_IMAGE_21_LORA_TRANSFORMER_PREFIX
+        for target, target_values in targets.items():
+            layers[f"{prefix}{target}"] = any_lora_layer_from_state_dict(target_values)
+    return ModelPatchRaw(layers=layers)
diff --git a/invokeai/frontend/api/openapi.json b/invokeai/frontend/api/openapi.json
index 3bd8fae5101..886964bcac9 100644
--- a/invokeai/frontend/api/openapi.json
+++ b/invokeai/frontend/api/openapi.json
@@ -1637,6 +1637,9 @@
                     {
                       "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage_Config"
                     },
+                    {
+                      "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage21_Config"
+                    },
                     {
                       "$ref": "#/components/schemas/LoRA_LyCORIS_LTX2_Config"
                     },
@@ -2134,6 +2137,9 @@
                     {
                       "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage_Config"
                     },
+                    {
+                      "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage21_Config"
+                    },
                     {
                       "$ref": "#/components/schemas/LoRA_LyCORIS_LTX2_Config"
                     },
@@ -2631,6 +2637,9 @@
                     {
                       "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage_Config"
                     },
+                    {
+                      "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage21_Config"
+                    },
                     {
                       "$ref": "#/components/schemas/LoRA_LyCORIS_LTX2_Config"
                     },
@@ -3173,6 +3182,9 @@
                     {
                       "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage_Config"
                     },
+                    {
+                      "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage21_Config"
+                    },
                     {
                       "$ref": "#/components/schemas/LoRA_LyCORIS_LTX2_Config"
                     },
@@ -3742,6 +3754,9 @@
                     {
                       "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage_Config"
                     },
+                    {
+                      "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage21_Config"
+                    },
                     {
                       "$ref": "#/components/schemas/LoRA_LyCORIS_LTX2_Config"
                     },
@@ -5183,6 +5198,9 @@
                     {
                       "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage_Config"
                     },
+                    {
+                      "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage21_Config"
+                    },
                     {
                       "$ref": "#/components/schemas/LoRA_LyCORIS_LTX2_Config"
                     },
@@ -20130,6 +20148,9 @@
           {
             "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage_Config"
           },
+          {
+            "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage21_Config"
+          },
           {
             "$ref": "#/components/schemas/LoRA_LyCORIS_LTX2_Config"
           },
@@ -44024,6 +44045,12 @@
                 {
                   "$ref": "#/components/schemas/QwenImage21LatentsToImageInvocation"
                 },
+                {
+                  "$ref": "#/components/schemas/QwenImage21LoRACollectionLoader"
+                },
+                {
+                  "$ref": "#/components/schemas/QwenImage21LoRALoaderInvocation"
+                },
                 {
                   "$ref": "#/components/schemas/QwenImage21ModelLoaderInvocation"
                 },
@@ -44681,6 +44708,9 @@
                 {
                   "$ref": "#/components/schemas/QwenImage21ConditioningOutput"
                 },
+                {
+                  "$ref": "#/components/schemas/QwenImage21LoRALoaderOutput"
+                },
                 {
                   "$ref": "#/components/schemas/QwenImage21ModelLoaderOutput"
                 },
@@ -54282,6 +54312,12 @@
               {
                 "$ref": "#/components/schemas/QwenImage21LatentsToImageInvocation"
               },
+              {
+                "$ref": "#/components/schemas/QwenImage21LoRACollectionLoader"
+              },
+              {
+                "$ref": "#/components/schemas/QwenImage21LoRALoaderInvocation"
+              },
               {
                 "$ref": "#/components/schemas/QwenImage21ModelLoaderInvocation"
               },
@@ -54935,6 +54971,9 @@
               {
                 "$ref": "#/components/schemas/QwenImage21ConditioningOutput"
               },
+              {
+                "$ref": "#/components/schemas/QwenImage21LoRALoaderOutput"
+              },
               {
                 "$ref": "#/components/schemas/QwenImage21ModelLoaderOutput"
               },
@@ -55897,6 +55936,12 @@
               {
                 "$ref": "#/components/schemas/QwenImage21LatentsToImageInvocation"
               },
+              {
+                "$ref": "#/components/schemas/QwenImage21LoRACollectionLoader"
+              },
+              {
+                "$ref": "#/components/schemas/QwenImage21LoRALoaderInvocation"
+              },
               {
                 "$ref": "#/components/schemas/QwenImage21ModelLoaderInvocation"
               },
@@ -57034,6 +57079,12 @@
           "qwen_image_2_1_l2i": {
             "$ref": "#/components/schemas/ImageOutput"
           },
+          "qwen_image_2_1_lora_collection_loader": {
+            "$ref": "#/components/schemas/QwenImage21LoRALoaderOutput"
+          },
+          "qwen_image_2_1_lora_loader": {
+            "$ref": "#/components/schemas/QwenImage21LoRALoaderOutput"
+          },
           "qwen_image_2_1_model_loader": {
             "$ref": "#/components/schemas/QwenImage21ModelLoaderOutput"
           },
@@ -57571,6 +57622,8 @@
           "qwen_image_2_1_denoise",
           "qwen_image_2_1_i2l",
           "qwen_image_2_1_l2i",
+          "qwen_image_2_1_lora_collection_loader",
+          "qwen_image_2_1_lora_loader",
           "qwen_image_2_1_model_loader",
           "qwen_image_2_1_text_encoder",
           "qwen_image_denoise",
@@ -58508,6 +58561,12 @@
               {
                 "$ref": "#/components/schemas/QwenImage21LatentsToImageInvocation"
               },
+              {
+                "$ref": "#/components/schemas/QwenImage21LoRACollectionLoader"
+              },
+              {
+                "$ref": "#/components/schemas/QwenImage21LoRALoaderInvocation"
+              },
               {
                 "$ref": "#/components/schemas/QwenImage21ModelLoaderInvocation"
               },
@@ -59753,6 +59812,12 @@
               {
                 "$ref": "#/components/schemas/QwenImage21LatentsToImageInvocation"
               },
+              {
+                "$ref": "#/components/schemas/QwenImage21LoRACollectionLoader"
+              },
+              {
+                "$ref": "#/components/schemas/QwenImage21LoRALoaderInvocation"
+              },
               {
                 "$ref": "#/components/schemas/QwenImage21ModelLoaderInvocation"
               },
@@ -69690,6 +69755,158 @@
         "title": "LoRA_LyCORIS_MiniMaxH3_Config",
         "description": "Model config for MiniMax H3 LoRA models in LyCORIS (single-file PEFT or kohya) format.\n\nH3 LoRAs (e.g. the MiniMax-H3 Turbo step-distillation LoRA) target the\n``MiniMaxH3Transformer3DModel`` in the checkpoint's native single-file layout,\nwith dotted keys or kohya-flattened ``lora_unet_*`` keys.\nDetection keys on H3-exclusive submodule names (the fused ``attn.qkv_proj``\nunder a bare ``attn.``, and ``adaln_proj.linear``) and rejects any state dict\ncarrying another architecture's signature \u2014 see\n``minimax_h3_lora_constants`` for the exact patterns and why ``mlp.fc1``\nalone would not be safe."
       },
+      "LoRA_LyCORIS_QwenImage21_Config": {
+        "properties": {
+          "key": {
+            "type": "string",
+            "title": "Key",
+            "description": "A unique key for this model."
+          },
+          "hash": {
+            "type": "string",
+            "title": "Hash",
+            "description": "The hash of the model file(s)."
+          },
+          "path": {
+            "type": "string",
+            "title": "Path",
+            "description": "Path to the model on the filesystem. Relative paths are relative to the Invoke root directory."
+          },
+          "file_size": {
+            "type": "integer",
+            "title": "File Size",
+            "description": "The size of the model in bytes."
+          },
+          "name": {
+            "type": "string",
+            "title": "Name",
+            "description": "Name of the model."
+          },
+          "description": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Description",
+            "description": "Model description"
+          },
+          "source": {
+            "type": "string",
+            "title": "Source",
+            "description": "The original source of the model (path, URL or repo_id)."
+          },
+          "source_type": {
+            "$ref": "#/components/schemas/ModelSourceType",
+            "description": "The type of source"
+          },
+          "source_api_response": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Source Api Response",
+            "description": "The original API response from the source, as stringified JSON."
+          },
+          "source_url": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Source Url",
+            "description": "Optional URL for the model (e.g. download page or model page)."
+          },
+          "cover_image": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Cover Image",
+            "description": "Url for image to preview model"
+          },
+          "type": {
+            "type": "string",
+            "const": "lora",
+            "title": "Type",
+            "default": "lora"
+          },
+          "trigger_phrases": {
+            "anyOf": [
+              {
+                "items": {
+                  "type": "string"
+                },
+                "type": "array",
+                "uniqueItems": true
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Trigger Phrases",
+            "description": "Set of trigger phrases for this model"
+          },
+          "default_settings": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/LoraModelDefaultSettings"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "description": "Default settings for this model"
+          },
+          "format": {
+            "type": "string",
+            "const": "lycoris",
+            "title": "Format",
+            "default": "lycoris"
+          },
+          "base": {
+            "type": "string",
+            "const": "qwen-image-2-1",
+            "title": "Base",
+            "default": "qwen-image-2-1"
+          }
+        },
+        "type": "object",
+        "required": [
+          "key",
+          "hash",
+          "path",
+          "file_size",
+          "name",
+          "description",
+          "source",
+          "source_type",
+          "source_api_response",
+          "source_url",
+          "cover_image",
+          "type",
+          "trigger_phrases",
+          "default_settings",
+          "format",
+          "base"
+        ],
+        "title": "LoRA_LyCORIS_QwenImage21_Config",
+        "description": "Model config for Qwen-Image-2.1 LoRAs: ComfyUI (`diffusion_model.`, fused `gate_up`), diffusers/PEFT and\nKohya layouts, as plain low-rank layers."
+      },
       "LoRA_LyCORIS_QwenImage_Config": {
         "properties": {
           "key": {
@@ -86750,6 +86967,9 @@
               {
                 "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage_Config"
               },
+              {
+                "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage21_Config"
+              },
               {
                 "$ref": "#/components/schemas/LoRA_LyCORIS_LTX2_Config"
               },
@@ -87493,6 +87713,9 @@
                   {
                     "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage_Config"
                   },
+                  {
+                    "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage21_Config"
+                  },
                   {
                     "$ref": "#/components/schemas/LoRA_LyCORIS_LTX2_Config"
                   },
@@ -88121,6 +88344,9 @@
               {
                 "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage_Config"
               },
+              {
+                "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage21_Config"
+              },
               {
                 "$ref": "#/components/schemas/LoRA_LyCORIS_LTX2_Config"
               },
@@ -88605,6 +88831,9 @@
               {
                 "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage_Config"
               },
+              {
+                "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage21_Config"
+              },
               {
                 "$ref": "#/components/schemas/LoRA_LyCORIS_LTX2_Config"
               },
@@ -89582,6 +89811,9 @@
                 {
                   "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage_Config"
                 },
+                {
+                  "$ref": "#/components/schemas/LoRA_LyCORIS_QwenImage21_Config"
+                },
                 {
                   "$ref": "#/components/schemas/LoRA_LyCORIS_LTX2_Config"
                 },
@@ -95592,7 +95824,7 @@
         "tags": ["image", "qwen_image_2_1", "qwen-image-2.1"],
         "title": "Denoise - Qwen-Image-2.1",
         "type": "object",
-        "version": "1.1.0",
+        "version": "1.2.0",
         "output": {
           "$ref": "#/components/schemas/LatentsOutput"
         }
@@ -95871,6 +96103,220 @@
           "$ref": "#/components/schemas/ImageOutput"
         }
       },
+      "QwenImage21LoRACollectionLoader": {
+        "category": "model",
+        "class": "invocation",
+        "classification": "prototype",
+        "description": "Applies a collection of LoRAs to a Qwen-Image-2.1 transformer.",
+        "node_pack": "invokeai",
+        "properties": {
+          "id": {
+            "description": "The id of this instance of an invocation. Must be unique among all instances of invocations.",
+            "field_kind": "node_attribute",
+            "title": "Id",
+            "type": "string"
+          },
+          "is_intermediate": {
+            "default": false,
+            "description": "Whether or not this is an intermediate invocation.",
+            "field_kind": "node_attribute",
+            "input": "direct",
+            "orig_required": true,
+            "title": "Is Intermediate",
+            "type": "boolean",
+            "ui_hidden": false,
+            "ui_type": "IsIntermediate"
+          },
+          "use_cache": {
+            "default": true,
+            "description": "Whether or not to use the cache",
+            "field_kind": "node_attribute",
+            "title": "Use Cache",
+            "type": "boolean"
+          },
+          "loras": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/LoRAField"
+              },
+              {
+                "items": {
+                  "$ref": "#/components/schemas/LoRAField"
+                },
+                "type": "array"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "LoRA models and weights. May be a single LoRA or collection.",
+            "field_kind": "input",
+            "input": "any",
+            "orig_default": null,
+            "orig_required": false,
+            "title": "LoRAs",
+            "ui_model_base": ["qwen-image-2-1"],
+            "ui_model_type": ["lora"]
+          },
+          "transformer": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/TransformerField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Transformer",
+            "field_kind": "input",
+            "input": "connection",
+            "orig_default": null,
+            "orig_required": false,
+            "title": "Transformer"
+          },
+          "type": {
+            "const": "qwen_image_2_1_lora_collection_loader",
+            "default": "qwen_image_2_1_lora_collection_loader",
+            "field_kind": "node_attribute",
+            "title": "type",
+            "type": "string"
+          }
+        },
+        "required": ["type", "id"],
+        "tags": ["lora", "model", "qwen_image_2_1"],
+        "title": "Apply LoRA Collection - Qwen-Image-2.1",
+        "type": "object",
+        "version": "1.0.0",
+        "output": {
+          "$ref": "#/components/schemas/QwenImage21LoRALoaderOutput"
+        }
+      },
+      "QwenImage21LoRALoaderInvocation": {
+        "category": "model",
+        "class": "invocation",
+        "classification": "prototype",
+        "description": "Apply a LoRA model to a Qwen-Image-2.1 transformer.",
+        "node_pack": "invokeai",
+        "properties": {
+          "id": {
+            "description": "The id of this instance of an invocation. Must be unique among all instances of invocations.",
+            "field_kind": "node_attribute",
+            "title": "Id",
+            "type": "string"
+          },
+          "is_intermediate": {
+            "default": false,
+            "description": "Whether or not this is an intermediate invocation.",
+            "field_kind": "node_attribute",
+            "input": "direct",
+            "orig_required": true,
+            "title": "Is Intermediate",
+            "type": "boolean",
+            "ui_hidden": false,
+            "ui_type": "IsIntermediate"
+          },
+          "use_cache": {
+            "default": true,
+            "description": "Whether or not to use the cache",
+            "field_kind": "node_attribute",
+            "title": "Use Cache",
+            "type": "boolean"
+          },
+          "lora": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/ModelIdentifierField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "LoRA model to load",
+            "field_kind": "input",
+            "input": "any",
+            "orig_required": true,
+            "title": "LoRA",
+            "ui_model_base": ["qwen-image-2-1"],
+            "ui_model_type": ["lora"]
+          },
+          "weight": {
+            "default": 1.0,
+            "description": "The weight at which the LoRA is applied to each model",
+            "field_kind": "input",
+            "input": "any",
+            "orig_default": 1.0,
+            "orig_required": false,
+            "title": "Weight",
+            "type": "number"
+          },
+          "transformer": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/TransformerField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Transformer",
+            "field_kind": "input",
+            "input": "connection",
+            "orig_default": null,
+            "orig_required": false,
+            "title": "Transformer"
+          },
+          "type": {
+            "const": "qwen_image_2_1_lora_loader",
+            "default": "qwen_image_2_1_lora_loader",
+            "field_kind": "node_attribute",
+            "title": "type",
+            "type": "string"
+          }
+        },
+        "required": ["type", "id"],
+        "tags": ["lora", "model", "qwen_image_2_1"],
+        "title": "Apply LoRA - Qwen-Image-2.1",
+        "type": "object",
+        "version": "1.0.0",
+        "output": {
+          "$ref": "#/components/schemas/QwenImage21LoRALoaderOutput"
+        }
+      },
+      "QwenImage21LoRALoaderOutput": {
+        "class": "output",
+        "description": "Qwen-Image-2.1 LoRA Loader Output",
+        "properties": {
+          "transformer": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/TransformerField"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Transformer",
+            "field_kind": "output",
+            "title": "Transformer",
+            "ui_hidden": false
+          },
+          "type": {
+            "const": "qwen_image_2_1_lora_loader_output",
+            "default": "qwen_image_2_1_lora_loader_output",
+            "field_kind": "node_attribute",
+            "title": "type",
+            "type": "string"
+          }
+        },
+        "required": ["output_meta", "transformer", "type", "type"],
+        "title": "QwenImage21LoRALoaderOutput",
+        "type": "object"
+      },
       "QwenImage21ModelLoaderInvocation": {
         "category": "model",
         "class": "invocation",
diff --git a/invokeai/frontend/api/schema.ts b/invokeai/frontend/api/schema.ts
index 76c5e0e9de5..8965fac3632 100644
--- a/invokeai/frontend/api/schema.ts
+++ b/invokeai/frontend/api/schema.ts
@@ -5562,7 +5562,7 @@ export type components = {
          * @enum {string}
          */
         AnimaVariantType: "anima_qwen3" | "anima_qwen35";
-        AnyModelConfig: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+        AnyModelConfig: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage21_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
         /**
          * AppVersion
          * @description App Version Response
@@ -17405,7 +17405,7 @@ export type components = {
              * @description The nodes in this graph
              */
             nodes?: {
-                [key: string]: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaDenoiseMetaInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["AtlasCloudImageGenerationInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BooleanLogicInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CollectionCartesianInvocation"] | components["schemas"]["CollectionConcatInvocation"] | components["schemas"]["CollectionZipInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2DevLoRACollectionLoader"] | components["schemas"]["Flux2DevLoRALoaderInvocation"] | components["schemas"]["Flux2DevModelLoaderInvocation"] | components["schemas"]["Flux2DevTextEncoderInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | 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components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["MiniMaxH3DenoiseInvocation"] | components["schemas"]["MiniMaxH3FrameConditioningInvocation"] | components["schemas"]["MiniMaxH3HybridOverlayInvocation"] | components["schemas"]["MiniMaxH3IdealDimensionsInvocation"] | components["schemas"]["MiniMaxH3ImageReferenceInvocation"] | components["schemas"]["MiniMaxH3LatentsToImageInvocation"] | components["schemas"]["MiniMaxH3LatentsToVideoInvocation"] | components["schemas"]["MiniMaxH3LoRACollectionLoader"] | components["schemas"]["MiniMaxH3LoRALoaderInvocation"] | components["schemas"]["MiniMaxH3ModelLoaderInvocation"] | components["schemas"]["MiniMaxH3ReferenceConditioningInvocation"] | components["schemas"]["MiniMaxH3TextEncoderInvocation"] | components["schemas"]["MiniMaxH3VideoReferenceInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImage21DenoiseInvocation"] | components["schemas"]["QwenImage21ImageToLatentsInvocation"] | components["schemas"]["QwenImage21LatentsToImageInvocation"] | components["schemas"]["QwenImage21LoRACollectionLoader"] | components["schemas"]["QwenImage21LoRALoaderInvocation"] | components["schemas"]["QwenImage21ModelLoaderInvocation"] | components["schemas"]["QwenImage21TextEncoderInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StateEmptyInvocation"] | components["schemas"]["StateGetInvocation"] | components["schemas"]["StateMergeInvocation"] | components["schemas"]["StateSetInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
             };
             /**
              * Edges
@@ -17442,7 +17442,7 @@ export type components = {
              * @description The results of node executions
              */
             results: {
-                [key: string]: components["schemas"]["AnimaConditioningOutput"] | components["schemas"]["AnimaLLLiteOutput"] | components["schemas"]["AnimaLoRALoaderOutput"] | components["schemas"]["AnimaModelLoaderOutput"] | components["schemas"]["BooleanCollectionOutput"] | components["schemas"]["BooleanOutput"] | components["schemas"]["BoundingBoxCollectionOutput"] | components["schemas"]["BoundingBoxOutput"] | components["schemas"]["CLIPOutput"] | components["schemas"]["CLIPSkipInvocationOutput"] | components["schemas"]["CalculateImageTilesOutput"] | components["schemas"]["CogView4ConditioningOutput"] | components["schemas"]["CogView4ModelLoaderOutput"] | components["schemas"]["CollectInvocationOutput"] | components["schemas"]["CollectionCartesianInvocationOutput"] | components["schemas"]["CollectionConcatInvocationOutput"] | components["schemas"]["CollectionZipInvocationOutput"] | components["schemas"]["ColorCollectionOutput"] | components["schemas"]["ColorOutput"] | components["schemas"]["ConditioningCollectionOutput"] | components["schemas"]["ConditioningOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["DenoiseMaskOutput"] | components["schemas"]["ErnieImageConditioningOutput"] | components["schemas"]["ErnieImageModelLoaderOutput"] | components["schemas"]["ExtractVideoRangeOutput"] | components["schemas"]["FaceMaskOutput"] | components["schemas"]["FaceOffOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["FloatGeneratorOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["Flux2DevLoRALoaderOutput"] | components["schemas"]["Flux2DevModelLoaderOutput"] | components["schemas"]["Flux2KleinLoRALoaderOutput"] | components["schemas"]["Flux2KleinModelLoaderOutput"] | components["schemas"]["FluxConditioningCollectionOutput"] | components["schemas"]["FluxConditioningOutput"] | components["schemas"]["FluxControlLoRALoaderOutput"] | components["schemas"]["FluxControlNetOutput"] | components["schemas"]["FluxFillOutput"] | components["schemas"]["FluxKontextOutput"] | components["schemas"]["FluxLoRALoaderOutput"] | components["schemas"]["FluxModelLoaderOutput"] | components["schemas"]["FluxReduxOutput"] | components["schemas"]["ForInvocationOutput"] | components["schemas"]["ForReturnInvocationOutput"] | components["schemas"]["Gemma2EncoderOutput"] | components["schemas"]["GradientMaskOutput"] | components["schemas"]["IPAdapterOutput"] | components["schemas"]["IdealSizeOutput"] | components["schemas"]["Ideogram4ConditioningOutput"] | components["schemas"]["Ideogram4ModelLoaderOutput"] | components["schemas"]["IfInvocationOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["ImageGeneratorOutput"] | components["schemas"]["ImageOutput"] | components["schemas"]["ImagePanelCoordinateOutput"] | components["schemas"]["IntegerCollectionOutput"] | components["schemas"]["IntegerGeneratorOutput"] | components["schemas"]["IntegerOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["Krea2ConditioningOutput"] | components["schemas"]["Krea2LoRALoaderOutput"] | components["schemas"]["Krea2ModelLoaderOutput"] | components["schemas"]["Krea2StyleReferenceOutput"] | components["schemas"]["LTX2AudioConditioningOutput"] | components["schemas"]["LTX2DenoiseOutput"] | components["schemas"]["LTX2DurationOutput"] | components["schemas"]["LTX2ExtendConditioningOutput"] | components["schemas"]["LTX2IdealDimensionsOutput"] | components["schemas"]["LTX2ImageConditioningOutput"] | components["schemas"]["LTX2LatentUpsampleOutput"] | components["schemas"]["LTX2LoRACollectionLoaderOutput"] | components["schemas"]["LTX2LoRALoaderOutput"] | components["schemas"]["LTX2ModelLoaderOutput"] | components["schemas"]["LTX2TextEncoderOutput"] | components["schemas"]["LTX2VideoConditioningOutput"] | components["schemas"]["LatentsCollectionOutput"] | components["schemas"]["LatentsMetaOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["LoRALoaderOutput"] | components["schemas"]["LoRASelectorOutput"] | components["schemas"]["LoopStateOutput"] | components["schemas"]["LoopStateValueOutput"] | components["schemas"]["MDControlListOutput"] | components["schemas"]["MDIPAdapterListOutput"] | components["schemas"]["MDT2IAdapterListOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["MetadataItemOutput"] | components["schemas"]["MetadataOutput"] | components["schemas"]["MetadataToLorasCollectionOutput"] | components["schemas"]["MetadataToModelOutput"] | components["schemas"]["MetadataToSDXLModelOutput"] | components["schemas"]["MiniMaxH3ConditioningOutput"] | components["schemas"]["MiniMaxH3DenoiseOutput"] | components["schemas"]["MiniMaxH3FrameConditioningOutput"] | components["schemas"]["MiniMaxH3HybridOverlayOutput"] | components["schemas"]["MiniMaxH3LoRACollectionLoaderOutput"] | components["schemas"]["MiniMaxH3LoRALoaderOutput"] | components["schemas"]["MiniMaxH3ModelLoaderOutput"] | components["schemas"]["MiniMaxH3ReferenceConditioningOutput"] | components["schemas"]["MiniMaxH3ReferenceMediaOutput"] | components["schemas"]["ModelIdentifierOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PBRMapsOutput"] | components["schemas"]["PairTileImageOutput"] | components["schemas"]["PiDDecoderOutput"] | components["schemas"]["PromptTemplateOutput"] | components["schemas"]["QwenImage21ConditioningOutput"] | components["schemas"]["QwenImage21ModelLoaderOutput"] | components["schemas"]["QwenImageConditioningOutput"] | components["schemas"]["QwenImageLoRALoaderOutput"] | components["schemas"]["QwenImageModelLoaderOutput"] | components["schemas"]["SD3ConditioningOutput"] | components["schemas"]["SDXLLoRALoaderOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["SchedulerOutput"] | components["schemas"]["Sd3ModelLoaderOutput"] | components["schemas"]["SeamlessModeOutput"] | components["schemas"]["String2Output"] | components["schemas"]["StringCollectionOutput"] | components["schemas"]["StringGeneratorOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["StringPosNegOutput"] | components["schemas"]["T2IAdapterOutput"] | components["schemas"]["TileToPropertiesOutput"] | components["schemas"]["UNetOutput"] | components["schemas"]["VAEOutput"] | components["schemas"]["VideoOutput"] | components["schemas"]["WanConditioningOutput"] | components["schemas"]["WanLoRALoaderOutput"] | components["schemas"]["WanModelLoaderOutput"] | components["schemas"]["WanRefImageOutput"] | components["schemas"]["WorkflowReturnGetOutput"] | components["schemas"]["WorkflowReturnOutput"] | components["schemas"]["WorkflowReturnValueOutput"] | components["schemas"]["ZImageConditioningOutput"] | components["schemas"]["ZImageControlOutput"] | components["schemas"]["ZImageLoRALoaderOutput"] | components["schemas"]["ZImageModelLoaderOutput"];
+                [key: string]: components["schemas"]["AnimaConditioningOutput"] | components["schemas"]["AnimaLLLiteOutput"] | components["schemas"]["AnimaLoRALoaderOutput"] | components["schemas"]["AnimaModelLoaderOutput"] | components["schemas"]["BooleanCollectionOutput"] | components["schemas"]["BooleanOutput"] | components["schemas"]["BoundingBoxCollectionOutput"] | components["schemas"]["BoundingBoxOutput"] | components["schemas"]["CLIPOutput"] | components["schemas"]["CLIPSkipInvocationOutput"] | components["schemas"]["CalculateImageTilesOutput"] | components["schemas"]["CogView4ConditioningOutput"] | components["schemas"]["CogView4ModelLoaderOutput"] | components["schemas"]["CollectInvocationOutput"] | components["schemas"]["CollectionCartesianInvocationOutput"] | components["schemas"]["CollectionConcatInvocationOutput"] | components["schemas"]["CollectionZipInvocationOutput"] | components["schemas"]["ColorCollectionOutput"] | components["schemas"]["ColorOutput"] | components["schemas"]["ConditioningCollectionOutput"] | components["schemas"]["ConditioningOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["DenoiseMaskOutput"] | components["schemas"]["ErnieImageConditioningOutput"] | components["schemas"]["ErnieImageModelLoaderOutput"] | components["schemas"]["ExtractVideoRangeOutput"] | components["schemas"]["FaceMaskOutput"] | components["schemas"]["FaceOffOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["FloatGeneratorOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["Flux2DevLoRALoaderOutput"] | components["schemas"]["Flux2DevModelLoaderOutput"] | components["schemas"]["Flux2KleinLoRALoaderOutput"] | components["schemas"]["Flux2KleinModelLoaderOutput"] | components["schemas"]["FluxConditioningCollectionOutput"] | components["schemas"]["FluxConditioningOutput"] | components["schemas"]["FluxControlLoRALoaderOutput"] | components["schemas"]["FluxControlNetOutput"] | components["schemas"]["FluxFillOutput"] | components["schemas"]["FluxKontextOutput"] | components["schemas"]["FluxLoRALoaderOutput"] | components["schemas"]["FluxModelLoaderOutput"] | components["schemas"]["FluxReduxOutput"] | components["schemas"]["ForInvocationOutput"] | components["schemas"]["ForReturnInvocationOutput"] | components["schemas"]["Gemma2EncoderOutput"] | components["schemas"]["GradientMaskOutput"] | components["schemas"]["IPAdapterOutput"] | components["schemas"]["IdealSizeOutput"] | components["schemas"]["Ideogram4ConditioningOutput"] | components["schemas"]["Ideogram4ModelLoaderOutput"] | components["schemas"]["IfInvocationOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["ImageGeneratorOutput"] | components["schemas"]["ImageOutput"] | components["schemas"]["ImagePanelCoordinateOutput"] | components["schemas"]["IntegerCollectionOutput"] | components["schemas"]["IntegerGeneratorOutput"] | components["schemas"]["IntegerOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["Krea2ConditioningOutput"] | components["schemas"]["Krea2LoRALoaderOutput"] | components["schemas"]["Krea2ModelLoaderOutput"] | components["schemas"]["Krea2StyleReferenceOutput"] | components["schemas"]["LTX2AudioConditioningOutput"] | components["schemas"]["LTX2DenoiseOutput"] | components["schemas"]["LTX2DurationOutput"] | components["schemas"]["LTX2ExtendConditioningOutput"] | components["schemas"]["LTX2IdealDimensionsOutput"] | components["schemas"]["LTX2ImageConditioningOutput"] | components["schemas"]["LTX2LatentUpsampleOutput"] | components["schemas"]["LTX2LoRACollectionLoaderOutput"] | components["schemas"]["LTX2LoRALoaderOutput"] | components["schemas"]["LTX2ModelLoaderOutput"] | components["schemas"]["LTX2TextEncoderOutput"] | components["schemas"]["LTX2VideoConditioningOutput"] | components["schemas"]["LatentsCollectionOutput"] | components["schemas"]["LatentsMetaOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["LoRALoaderOutput"] | components["schemas"]["LoRASelectorOutput"] | components["schemas"]["LoopStateOutput"] | components["schemas"]["LoopStateValueOutput"] | components["schemas"]["MDControlListOutput"] | components["schemas"]["MDIPAdapterListOutput"] | components["schemas"]["MDT2IAdapterListOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["MetadataItemOutput"] | components["schemas"]["MetadataOutput"] | components["schemas"]["MetadataToLorasCollectionOutput"] | components["schemas"]["MetadataToModelOutput"] | components["schemas"]["MetadataToSDXLModelOutput"] | components["schemas"]["MiniMaxH3ConditioningOutput"] | components["schemas"]["MiniMaxH3DenoiseOutput"] | components["schemas"]["MiniMaxH3FrameConditioningOutput"] | components["schemas"]["MiniMaxH3HybridOverlayOutput"] | components["schemas"]["MiniMaxH3LoRACollectionLoaderOutput"] | components["schemas"]["MiniMaxH3LoRALoaderOutput"] | components["schemas"]["MiniMaxH3ModelLoaderOutput"] | components["schemas"]["MiniMaxH3ReferenceConditioningOutput"] | components["schemas"]["MiniMaxH3ReferenceMediaOutput"] | components["schemas"]["ModelIdentifierOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PBRMapsOutput"] | components["schemas"]["PairTileImageOutput"] | components["schemas"]["PiDDecoderOutput"] | components["schemas"]["PromptTemplateOutput"] | components["schemas"]["QwenImage21ConditioningOutput"] | components["schemas"]["QwenImage21LoRALoaderOutput"] | components["schemas"]["QwenImage21ModelLoaderOutput"] | components["schemas"]["QwenImageConditioningOutput"] | components["schemas"]["QwenImageLoRALoaderOutput"] | components["schemas"]["QwenImageModelLoaderOutput"] | components["schemas"]["SD3ConditioningOutput"] | components["schemas"]["SDXLLoRALoaderOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["SchedulerOutput"] | components["schemas"]["Sd3ModelLoaderOutput"] | components["schemas"]["SeamlessModeOutput"] | components["schemas"]["String2Output"] | components["schemas"]["StringCollectionOutput"] | components["schemas"]["StringGeneratorOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["StringPosNegOutput"] | components["schemas"]["T2IAdapterOutput"] | components["schemas"]["TileToPropertiesOutput"] | components["schemas"]["UNetOutput"] | components["schemas"]["VAEOutput"] | components["schemas"]["VideoOutput"] | components["schemas"]["WanConditioningOutput"] | components["schemas"]["WanLoRALoaderOutput"] | components["schemas"]["WanModelLoaderOutput"] | components["schemas"]["WanRefImageOutput"] | components["schemas"]["WorkflowReturnGetOutput"] | components["schemas"]["WorkflowReturnOutput"] | components["schemas"]["WorkflowReturnValueOutput"] | components["schemas"]["ZImageConditioningOutput"] | components["schemas"]["ZImageControlOutput"] | components["schemas"]["ZImageLoRALoaderOutput"] | components["schemas"]["ZImageModelLoaderOutput"];
             };
             /**
              * Errors
@@ -22236,7 +22236,7 @@ export type components = {
              * Invocation
              * @description The ID of the invocation
              */
-            invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaDenoiseMetaInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["AtlasCloudImageGenerationInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BooleanLogicInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CollectionCartesianInvocation"] | components["schemas"]["CollectionConcatInvocation"] | components["schemas"]["CollectionZipInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2DevLoRACollectionLoader"] | components["schemas"]["Flux2DevLoRALoaderInvocation"] | components["schemas"]["Flux2DevModelLoaderInvocation"] | components["schemas"]["Flux2DevTextEncoderInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["ForInvocation"] | components["schemas"]["ForReturnInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2StyleReferenceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LTX2AudioConditioningInvocation"] | components["schemas"]["LTX2DenoiseInvocation"] | components["schemas"]["LTX2DurationInvocation"] | components["schemas"]["LTX2ExtendConditioningInvocation"] | components["schemas"]["LTX2IdealDimensionsInvocation"] | components["schemas"]["LTX2ImageConditioningInvocation"] | components["schemas"]["LTX2LatentUpsampleInvocation"] | components["schemas"]["LTX2LatentsToVideoInvocation"] | components["schemas"]["LTX2LoRACollectionLoader"] | components["schemas"]["LTX2LoRALoaderInvocation"] | components["schemas"]["LTX2ModelLoaderInvocation"] | components["schemas"]["LTX2TextEncoderInvocation"] | components["schemas"]["LTX2VideoConditioningInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["MiniMaxH3DenoiseInvocation"] | components["schemas"]["MiniMaxH3FrameConditioningInvocation"] | components["schemas"]["MiniMaxH3HybridOverlayInvocation"] | components["schemas"]["MiniMaxH3IdealDimensionsInvocation"] | components["schemas"]["MiniMaxH3ImageReferenceInvocation"] | components["schemas"]["MiniMaxH3LatentsToImageInvocation"] | components["schemas"]["MiniMaxH3LatentsToVideoInvocation"] | components["schemas"]["MiniMaxH3LoRACollectionLoader"] | components["schemas"]["MiniMaxH3LoRALoaderInvocation"] | components["schemas"]["MiniMaxH3ModelLoaderInvocation"] | components["schemas"]["MiniMaxH3ReferenceConditioningInvocation"] | components["schemas"]["MiniMaxH3TextEncoderInvocation"] | components["schemas"]["MiniMaxH3VideoReferenceInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImage21DenoiseInvocation"] | components["schemas"]["QwenImage21ImageToLatentsInvocation"] | components["schemas"]["QwenImage21LatentsToImageInvocation"] | components["schemas"]["QwenImage21ModelLoaderInvocation"] | components["schemas"]["QwenImage21TextEncoderInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StateEmptyInvocation"] | components["schemas"]["StateGetInvocation"] | components["schemas"]["StateMergeInvocation"] | components["schemas"]["StateSetInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
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components["schemas"]["BooleanInvocation"] | components["schemas"]["BooleanLogicInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CollectionCartesianInvocation"] | components["schemas"]["CollectionConcatInvocation"] | components["schemas"]["CollectionZipInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2DevLoRACollectionLoader"] | components["schemas"]["Flux2DevLoRALoaderInvocation"] | components["schemas"]["Flux2DevModelLoaderInvocation"] | components["schemas"]["Flux2DevTextEncoderInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["ForInvocation"] | components["schemas"]["ForReturnInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | 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components["schemas"]["LTX2TextEncoderInvocation"] | components["schemas"]["LTX2VideoConditioningInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["MiniMaxH3DenoiseInvocation"] | components["schemas"]["MiniMaxH3FrameConditioningInvocation"] | components["schemas"]["MiniMaxH3HybridOverlayInvocation"] | components["schemas"]["MiniMaxH3IdealDimensionsInvocation"] | components["schemas"]["MiniMaxH3ImageReferenceInvocation"] | components["schemas"]["MiniMaxH3LatentsToImageInvocation"] | components["schemas"]["MiniMaxH3LatentsToVideoInvocation"] | components["schemas"]["MiniMaxH3LoRACollectionLoader"] | components["schemas"]["MiniMaxH3LoRALoaderInvocation"] | components["schemas"]["MiniMaxH3ModelLoaderInvocation"] | components["schemas"]["MiniMaxH3ReferenceConditioningInvocation"] | components["schemas"]["MiniMaxH3TextEncoderInvocation"] | components["schemas"]["MiniMaxH3VideoReferenceInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImage21DenoiseInvocation"] | components["schemas"]["QwenImage21ImageToLatentsInvocation"] | components["schemas"]["QwenImage21LatentsToImageInvocation"] | components["schemas"]["QwenImage21LoRACollectionLoader"] | components["schemas"]["QwenImage21LoRALoaderInvocation"] | components["schemas"]["QwenImage21ModelLoaderInvocation"] | components["schemas"]["QwenImage21TextEncoderInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StateEmptyInvocation"] | components["schemas"]["StateGetInvocation"] | components["schemas"]["StateMergeInvocation"] | components["schemas"]["StateSetInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
             /**
              * Invocation Source Id
              * @description The ID of the prepared invocation's source node
@@ -22264,7 +22264,7 @@ export type components = {
              * Result
              * @description The result of the invocation
              */
-            result: components["schemas"]["AnimaConditioningOutput"] | components["schemas"]["AnimaLLLiteOutput"] | components["schemas"]["AnimaLoRALoaderOutput"] | components["schemas"]["AnimaModelLoaderOutput"] | components["schemas"]["BooleanCollectionOutput"] | components["schemas"]["BooleanOutput"] | components["schemas"]["BoundingBoxCollectionOutput"] | components["schemas"]["BoundingBoxOutput"] | components["schemas"]["CLIPOutput"] | components["schemas"]["CLIPSkipInvocationOutput"] | components["schemas"]["CalculateImageTilesOutput"] | components["schemas"]["CogView4ConditioningOutput"] | components["schemas"]["CogView4ModelLoaderOutput"] | components["schemas"]["CollectInvocationOutput"] | components["schemas"]["CollectionCartesianInvocationOutput"] | components["schemas"]["CollectionConcatInvocationOutput"] | components["schemas"]["CollectionZipInvocationOutput"] | components["schemas"]["ColorCollectionOutput"] | components["schemas"]["ColorOutput"] | components["schemas"]["ConditioningCollectionOutput"] | components["schemas"]["ConditioningOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["DenoiseMaskOutput"] | components["schemas"]["ErnieImageConditioningOutput"] | components["schemas"]["ErnieImageModelLoaderOutput"] | components["schemas"]["ExtractVideoRangeOutput"] | components["schemas"]["FaceMaskOutput"] | components["schemas"]["FaceOffOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["FloatGeneratorOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["Flux2DevLoRALoaderOutput"] | components["schemas"]["Flux2DevModelLoaderOutput"] | components["schemas"]["Flux2KleinLoRALoaderOutput"] | components["schemas"]["Flux2KleinModelLoaderOutput"] | components["schemas"]["FluxConditioningCollectionOutput"] | components["schemas"]["FluxConditioningOutput"] | components["schemas"]["FluxControlLoRALoaderOutput"] | components["schemas"]["FluxControlNetOutput"] | components["schemas"]["FluxFillOutput"] | components["schemas"]["FluxKontextOutput"] | components["schemas"]["FluxLoRALoaderOutput"] | components["schemas"]["FluxModelLoaderOutput"] | components["schemas"]["FluxReduxOutput"] | components["schemas"]["ForInvocationOutput"] | components["schemas"]["ForReturnInvocationOutput"] | components["schemas"]["Gemma2EncoderOutput"] | components["schemas"]["GradientMaskOutput"] | components["schemas"]["IPAdapterOutput"] | components["schemas"]["IdealSizeOutput"] | components["schemas"]["Ideogram4ConditioningOutput"] | components["schemas"]["Ideogram4ModelLoaderOutput"] | components["schemas"]["IfInvocationOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["ImageGeneratorOutput"] | components["schemas"]["ImageOutput"] | components["schemas"]["ImagePanelCoordinateOutput"] | components["schemas"]["IntegerCollectionOutput"] | components["schemas"]["IntegerGeneratorOutput"] | components["schemas"]["IntegerOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["Krea2ConditioningOutput"] | components["schemas"]["Krea2LoRALoaderOutput"] | components["schemas"]["Krea2ModelLoaderOutput"] | components["schemas"]["Krea2StyleReferenceOutput"] | components["schemas"]["LTX2AudioConditioningOutput"] | components["schemas"]["LTX2DenoiseOutput"] | components["schemas"]["LTX2DurationOutput"] | components["schemas"]["LTX2ExtendConditioningOutput"] | components["schemas"]["LTX2IdealDimensionsOutput"] | components["schemas"]["LTX2ImageConditioningOutput"] | components["schemas"]["LTX2LatentUpsampleOutput"] | components["schemas"]["LTX2LoRACollectionLoaderOutput"] | components["schemas"]["LTX2LoRALoaderOutput"] | components["schemas"]["LTX2ModelLoaderOutput"] | components["schemas"]["LTX2TextEncoderOutput"] | components["schemas"]["LTX2VideoConditioningOutput"] | components["schemas"]["LatentsCollectionOutput"] | components["schemas"]["LatentsMetaOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["LoRALoaderOutput"] | components["schemas"]["LoRASelectorOutput"] | components["schemas"]["LoopStateOutput"] | components["schemas"]["LoopStateValueOutput"] | components["schemas"]["MDControlListOutput"] | components["schemas"]["MDIPAdapterListOutput"] | components["schemas"]["MDT2IAdapterListOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["MetadataItemOutput"] | components["schemas"]["MetadataOutput"] | components["schemas"]["MetadataToLorasCollectionOutput"] | components["schemas"]["MetadataToModelOutput"] | components["schemas"]["MetadataToSDXLModelOutput"] | components["schemas"]["MiniMaxH3ConditioningOutput"] | components["schemas"]["MiniMaxH3DenoiseOutput"] | components["schemas"]["MiniMaxH3FrameConditioningOutput"] | components["schemas"]["MiniMaxH3HybridOverlayOutput"] | components["schemas"]["MiniMaxH3LoRACollectionLoaderOutput"] | components["schemas"]["MiniMaxH3LoRALoaderOutput"] | components["schemas"]["MiniMaxH3ModelLoaderOutput"] | components["schemas"]["MiniMaxH3ReferenceConditioningOutput"] | components["schemas"]["MiniMaxH3ReferenceMediaOutput"] | components["schemas"]["ModelIdentifierOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PBRMapsOutput"] | components["schemas"]["PairTileImageOutput"] | components["schemas"]["PiDDecoderOutput"] | components["schemas"]["PromptTemplateOutput"] | components["schemas"]["QwenImage21ConditioningOutput"] | components["schemas"]["QwenImage21ModelLoaderOutput"] | components["schemas"]["QwenImageConditioningOutput"] | components["schemas"]["QwenImageLoRALoaderOutput"] | components["schemas"]["QwenImageModelLoaderOutput"] | components["schemas"]["SD3ConditioningOutput"] | components["schemas"]["SDXLLoRALoaderOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["SchedulerOutput"] | components["schemas"]["Sd3ModelLoaderOutput"] | components["schemas"]["SeamlessModeOutput"] | components["schemas"]["String2Output"] | components["schemas"]["StringCollectionOutput"] | components["schemas"]["StringGeneratorOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["StringPosNegOutput"] | components["schemas"]["T2IAdapterOutput"] | components["schemas"]["TileToPropertiesOutput"] | components["schemas"]["UNetOutput"] | components["schemas"]["VAEOutput"] | components["schemas"]["VideoOutput"] | components["schemas"]["WanConditioningOutput"] | components["schemas"]["WanLoRALoaderOutput"] | components["schemas"]["WanModelLoaderOutput"] | components["schemas"]["WanRefImageOutput"] | components["schemas"]["WorkflowReturnGetOutput"] | components["schemas"]["WorkflowReturnOutput"] | components["schemas"]["WorkflowReturnValueOutput"] | components["schemas"]["ZImageConditioningOutput"] | components["schemas"]["ZImageControlOutput"] | components["schemas"]["ZImageLoRALoaderOutput"] | components["schemas"]["ZImageModelLoaderOutput"];
+            result: components["schemas"]["AnimaConditioningOutput"] | components["schemas"]["AnimaLLLiteOutput"] | components["schemas"]["AnimaLoRALoaderOutput"] | components["schemas"]["AnimaModelLoaderOutput"] | components["schemas"]["BooleanCollectionOutput"] | components["schemas"]["BooleanOutput"] | components["schemas"]["BoundingBoxCollectionOutput"] | components["schemas"]["BoundingBoxOutput"] | components["schemas"]["CLIPOutput"] | components["schemas"]["CLIPSkipInvocationOutput"] | components["schemas"]["CalculateImageTilesOutput"] | components["schemas"]["CogView4ConditioningOutput"] | components["schemas"]["CogView4ModelLoaderOutput"] | components["schemas"]["CollectInvocationOutput"] | components["schemas"]["CollectionCartesianInvocationOutput"] | components["schemas"]["CollectionConcatInvocationOutput"] | components["schemas"]["CollectionZipInvocationOutput"] | components["schemas"]["ColorCollectionOutput"] | components["schemas"]["ColorOutput"] | components["schemas"]["ConditioningCollectionOutput"] | components["schemas"]["ConditioningOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["DenoiseMaskOutput"] | components["schemas"]["ErnieImageConditioningOutput"] | components["schemas"]["ErnieImageModelLoaderOutput"] | components["schemas"]["ExtractVideoRangeOutput"] | components["schemas"]["FaceMaskOutput"] | components["schemas"]["FaceOffOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["FloatGeneratorOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["Flux2DevLoRALoaderOutput"] | components["schemas"]["Flux2DevModelLoaderOutput"] | components["schemas"]["Flux2KleinLoRALoaderOutput"] | components["schemas"]["Flux2KleinModelLoaderOutput"] | components["schemas"]["FluxConditioningCollectionOutput"] | components["schemas"]["FluxConditioningOutput"] | components["schemas"]["FluxControlLoRALoaderOutput"] | components["schemas"]["FluxControlNetOutput"] | components["schemas"]["FluxFillOutput"] | components["schemas"]["FluxKontextOutput"] | components["schemas"]["FluxLoRALoaderOutput"] | components["schemas"]["FluxModelLoaderOutput"] | components["schemas"]["FluxReduxOutput"] | components["schemas"]["ForInvocationOutput"] | components["schemas"]["ForReturnInvocationOutput"] | components["schemas"]["Gemma2EncoderOutput"] | components["schemas"]["GradientMaskOutput"] | components["schemas"]["IPAdapterOutput"] | components["schemas"]["IdealSizeOutput"] | components["schemas"]["Ideogram4ConditioningOutput"] | components["schemas"]["Ideogram4ModelLoaderOutput"] | components["schemas"]["IfInvocationOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["ImageGeneratorOutput"] | components["schemas"]["ImageOutput"] | components["schemas"]["ImagePanelCoordinateOutput"] | components["schemas"]["IntegerCollectionOutput"] | components["schemas"]["IntegerGeneratorOutput"] | components["schemas"]["IntegerOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["Krea2ConditioningOutput"] | components["schemas"]["Krea2LoRALoaderOutput"] | components["schemas"]["Krea2ModelLoaderOutput"] | components["schemas"]["Krea2StyleReferenceOutput"] | components["schemas"]["LTX2AudioConditioningOutput"] | components["schemas"]["LTX2DenoiseOutput"] | components["schemas"]["LTX2DurationOutput"] | components["schemas"]["LTX2ExtendConditioningOutput"] | components["schemas"]["LTX2IdealDimensionsOutput"] | components["schemas"]["LTX2ImageConditioningOutput"] | components["schemas"]["LTX2LatentUpsampleOutput"] | components["schemas"]["LTX2LoRACollectionLoaderOutput"] | components["schemas"]["LTX2LoRALoaderOutput"] | components["schemas"]["LTX2ModelLoaderOutput"] | components["schemas"]["LTX2TextEncoderOutput"] | components["schemas"]["LTX2VideoConditioningOutput"] | components["schemas"]["LatentsCollectionOutput"] | components["schemas"]["LatentsMetaOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["LoRALoaderOutput"] | components["schemas"]["LoRASelectorOutput"] | components["schemas"]["LoopStateOutput"] | components["schemas"]["LoopStateValueOutput"] | components["schemas"]["MDControlListOutput"] | components["schemas"]["MDIPAdapterListOutput"] | components["schemas"]["MDT2IAdapterListOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["MetadataItemOutput"] | components["schemas"]["MetadataOutput"] | components["schemas"]["MetadataToLorasCollectionOutput"] | components["schemas"]["MetadataToModelOutput"] | components["schemas"]["MetadataToSDXLModelOutput"] | components["schemas"]["MiniMaxH3ConditioningOutput"] | components["schemas"]["MiniMaxH3DenoiseOutput"] | components["schemas"]["MiniMaxH3FrameConditioningOutput"] | components["schemas"]["MiniMaxH3HybridOverlayOutput"] | components["schemas"]["MiniMaxH3LoRACollectionLoaderOutput"] | components["schemas"]["MiniMaxH3LoRALoaderOutput"] | components["schemas"]["MiniMaxH3ModelLoaderOutput"] | components["schemas"]["MiniMaxH3ReferenceConditioningOutput"] | components["schemas"]["MiniMaxH3ReferenceMediaOutput"] | components["schemas"]["ModelIdentifierOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PBRMapsOutput"] | components["schemas"]["PairTileImageOutput"] | components["schemas"]["PiDDecoderOutput"] | components["schemas"]["PromptTemplateOutput"] | components["schemas"]["QwenImage21ConditioningOutput"] | components["schemas"]["QwenImage21LoRALoaderOutput"] | components["schemas"]["QwenImage21ModelLoaderOutput"] | components["schemas"]["QwenImageConditioningOutput"] | components["schemas"]["QwenImageLoRALoaderOutput"] | components["schemas"]["QwenImageModelLoaderOutput"] | components["schemas"]["SD3ConditioningOutput"] | components["schemas"]["SDXLLoRALoaderOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["SchedulerOutput"] | components["schemas"]["Sd3ModelLoaderOutput"] | components["schemas"]["SeamlessModeOutput"] | components["schemas"]["String2Output"] | components["schemas"]["StringCollectionOutput"] | components["schemas"]["StringGeneratorOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["StringPosNegOutput"] | components["schemas"]["T2IAdapterOutput"] | components["schemas"]["TileToPropertiesOutput"] | components["schemas"]["UNetOutput"] | components["schemas"]["VAEOutput"] | components["schemas"]["VideoOutput"] | components["schemas"]["WanConditioningOutput"] | components["schemas"]["WanLoRALoaderOutput"] | components["schemas"]["WanModelLoaderOutput"] | components["schemas"]["WanRefImageOutput"] | components["schemas"]["WorkflowReturnGetOutput"] | components["schemas"]["WorkflowReturnOutput"] | components["schemas"]["WorkflowReturnValueOutput"] | components["schemas"]["ZImageConditioningOutput"] | components["schemas"]["ZImageControlOutput"] | components["schemas"]["ZImageLoRALoaderOutput"] | components["schemas"]["ZImageModelLoaderOutput"];
         };
         /**
          * InvocationErrorEvent
@@ -22318,7 +22318,7 @@ export type components = {
              * Invocation
              * @description The ID of the invocation
              */
-            invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaDenoiseMetaInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["AtlasCloudImageGenerationInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BooleanLogicInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CollectionCartesianInvocation"] | components["schemas"]["CollectionConcatInvocation"] | components["schemas"]["CollectionZipInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2DevLoRACollectionLoader"] | components["schemas"]["Flux2DevLoRALoaderInvocation"] | components["schemas"]["Flux2DevModelLoaderInvocation"] | components["schemas"]["Flux2DevTextEncoderInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["ForInvocation"] | components["schemas"]["ForReturnInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2StyleReferenceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LTX2AudioConditioningInvocation"] | components["schemas"]["LTX2DenoiseInvocation"] | components["schemas"]["LTX2DurationInvocation"] | components["schemas"]["LTX2ExtendConditioningInvocation"] | components["schemas"]["LTX2IdealDimensionsInvocation"] | components["schemas"]["LTX2ImageConditioningInvocation"] | components["schemas"]["LTX2LatentUpsampleInvocation"] | components["schemas"]["LTX2LatentsToVideoInvocation"] | components["schemas"]["LTX2LoRACollectionLoader"] | components["schemas"]["LTX2LoRALoaderInvocation"] | components["schemas"]["LTX2ModelLoaderInvocation"] | components["schemas"]["LTX2TextEncoderInvocation"] | components["schemas"]["LTX2VideoConditioningInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["MiniMaxH3DenoiseInvocation"] | components["schemas"]["MiniMaxH3FrameConditioningInvocation"] | components["schemas"]["MiniMaxH3HybridOverlayInvocation"] | components["schemas"]["MiniMaxH3IdealDimensionsInvocation"] | components["schemas"]["MiniMaxH3ImageReferenceInvocation"] | components["schemas"]["MiniMaxH3LatentsToImageInvocation"] | components["schemas"]["MiniMaxH3LatentsToVideoInvocation"] | components["schemas"]["MiniMaxH3LoRACollectionLoader"] | components["schemas"]["MiniMaxH3LoRALoaderInvocation"] | components["schemas"]["MiniMaxH3ModelLoaderInvocation"] | components["schemas"]["MiniMaxH3ReferenceConditioningInvocation"] | components["schemas"]["MiniMaxH3TextEncoderInvocation"] | components["schemas"]["MiniMaxH3VideoReferenceInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImage21DenoiseInvocation"] | components["schemas"]["QwenImage21ImageToLatentsInvocation"] | components["schemas"]["QwenImage21LatentsToImageInvocation"] | components["schemas"]["QwenImage21ModelLoaderInvocation"] | components["schemas"]["QwenImage21TextEncoderInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StateEmptyInvocation"] | components["schemas"]["StateGetInvocation"] | components["schemas"]["StateMergeInvocation"] | components["schemas"]["StateSetInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
+            invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaDenoiseMetaInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["AtlasCloudImageGenerationInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BooleanLogicInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CollectionCartesianInvocation"] | components["schemas"]["CollectionConcatInvocation"] | components["schemas"]["CollectionZipInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2DevLoRACollectionLoader"] | components["schemas"]["Flux2DevLoRALoaderInvocation"] | components["schemas"]["Flux2DevModelLoaderInvocation"] | components["schemas"]["Flux2DevTextEncoderInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["ForInvocation"] | components["schemas"]["ForReturnInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2StyleReferenceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LTX2AudioConditioningInvocation"] | components["schemas"]["LTX2DenoiseInvocation"] | components["schemas"]["LTX2DurationInvocation"] | components["schemas"]["LTX2ExtendConditioningInvocation"] | components["schemas"]["LTX2IdealDimensionsInvocation"] | components["schemas"]["LTX2ImageConditioningInvocation"] | components["schemas"]["LTX2LatentUpsampleInvocation"] | components["schemas"]["LTX2LatentsToVideoInvocation"] | components["schemas"]["LTX2LoRACollectionLoader"] | components["schemas"]["LTX2LoRALoaderInvocation"] | components["schemas"]["LTX2ModelLoaderInvocation"] | components["schemas"]["LTX2TextEncoderInvocation"] | components["schemas"]["LTX2VideoConditioningInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["MiniMaxH3DenoiseInvocation"] | components["schemas"]["MiniMaxH3FrameConditioningInvocation"] | components["schemas"]["MiniMaxH3HybridOverlayInvocation"] | components["schemas"]["MiniMaxH3IdealDimensionsInvocation"] | components["schemas"]["MiniMaxH3ImageReferenceInvocation"] | components["schemas"]["MiniMaxH3LatentsToImageInvocation"] | components["schemas"]["MiniMaxH3LatentsToVideoInvocation"] | components["schemas"]["MiniMaxH3LoRACollectionLoader"] | components["schemas"]["MiniMaxH3LoRALoaderInvocation"] | components["schemas"]["MiniMaxH3ModelLoaderInvocation"] | components["schemas"]["MiniMaxH3ReferenceConditioningInvocation"] | components["schemas"]["MiniMaxH3TextEncoderInvocation"] | components["schemas"]["MiniMaxH3VideoReferenceInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImage21DenoiseInvocation"] | components["schemas"]["QwenImage21ImageToLatentsInvocation"] | components["schemas"]["QwenImage21LatentsToImageInvocation"] | components["schemas"]["QwenImage21LoRACollectionLoader"] | components["schemas"]["QwenImage21LoRALoaderInvocation"] | components["schemas"]["QwenImage21ModelLoaderInvocation"] | components["schemas"]["QwenImage21TextEncoderInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StateEmptyInvocation"] | components["schemas"]["StateGetInvocation"] | components["schemas"]["StateMergeInvocation"] | components["schemas"]["StateSetInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
             /**
              * Invocation Source Id
              * @description The ID of the prepared invocation's source node
@@ -22618,6 +22618,8 @@ export type components = {
             qwen_image_2_1_denoise: components["schemas"]["LatentsOutput"];
             qwen_image_2_1_i2l: components["schemas"]["LatentsOutput"];
             qwen_image_2_1_l2i: components["schemas"]["ImageOutput"];
+            qwen_image_2_1_lora_collection_loader: components["schemas"]["QwenImage21LoRALoaderOutput"];
+            qwen_image_2_1_lora_loader: components["schemas"]["QwenImage21LoRALoaderOutput"];
             qwen_image_2_1_model_loader: components["schemas"]["QwenImage21ModelLoaderOutput"];
             qwen_image_2_1_text_encoder: components["schemas"]["QwenImage21ConditioningOutput"];
             qwen_image_denoise: components["schemas"]["LatentsOutput"];
@@ -22763,7 +22765,7 @@ export type components = {
              * Invocation
              * @description The ID of the invocation
              */
-            invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaDenoiseMetaInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["AtlasCloudImageGenerationInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BooleanLogicInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CollectionCartesianInvocation"] | components["schemas"]["CollectionConcatInvocation"] | components["schemas"]["CollectionZipInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2DevLoRACollectionLoader"] | components["schemas"]["Flux2DevLoRALoaderInvocation"] | components["schemas"]["Flux2DevModelLoaderInvocation"] | components["schemas"]["Flux2DevTextEncoderInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["ForInvocation"] | components["schemas"]["ForReturnInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2StyleReferenceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LTX2AudioConditioningInvocation"] | components["schemas"]["LTX2DenoiseInvocation"] | components["schemas"]["LTX2DurationInvocation"] | components["schemas"]["LTX2ExtendConditioningInvocation"] | components["schemas"]["LTX2IdealDimensionsInvocation"] | components["schemas"]["LTX2ImageConditioningInvocation"] | components["schemas"]["LTX2LatentUpsampleInvocation"] | components["schemas"]["LTX2LatentsToVideoInvocation"] | components["schemas"]["LTX2LoRACollectionLoader"] | components["schemas"]["LTX2LoRALoaderInvocation"] | components["schemas"]["LTX2ModelLoaderInvocation"] | components["schemas"]["LTX2TextEncoderInvocation"] | components["schemas"]["LTX2VideoConditioningInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["MiniMaxH3DenoiseInvocation"] | components["schemas"]["MiniMaxH3FrameConditioningInvocation"] | components["schemas"]["MiniMaxH3HybridOverlayInvocation"] | components["schemas"]["MiniMaxH3IdealDimensionsInvocation"] | components["schemas"]["MiniMaxH3ImageReferenceInvocation"] | components["schemas"]["MiniMaxH3LatentsToImageInvocation"] | components["schemas"]["MiniMaxH3LatentsToVideoInvocation"] | components["schemas"]["MiniMaxH3LoRACollectionLoader"] | components["schemas"]["MiniMaxH3LoRALoaderInvocation"] | components["schemas"]["MiniMaxH3ModelLoaderInvocation"] | components["schemas"]["MiniMaxH3ReferenceConditioningInvocation"] | components["schemas"]["MiniMaxH3TextEncoderInvocation"] | components["schemas"]["MiniMaxH3VideoReferenceInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImage21DenoiseInvocation"] | components["schemas"]["QwenImage21ImageToLatentsInvocation"] | components["schemas"]["QwenImage21LatentsToImageInvocation"] | components["schemas"]["QwenImage21ModelLoaderInvocation"] | components["schemas"]["QwenImage21TextEncoderInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StateEmptyInvocation"] | components["schemas"]["StateGetInvocation"] | components["schemas"]["StateMergeInvocation"] | components["schemas"]["StateSetInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
+            invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaDenoiseMetaInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["AtlasCloudImageGenerationInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BooleanLogicInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CollectionCartesianInvocation"] | components["schemas"]["CollectionConcatInvocation"] | components["schemas"]["CollectionZipInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2DevLoRACollectionLoader"] | components["schemas"]["Flux2DevLoRALoaderInvocation"] | components["schemas"]["Flux2DevModelLoaderInvocation"] | components["schemas"]["Flux2DevTextEncoderInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["ForInvocation"] | components["schemas"]["ForReturnInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2StyleReferenceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LTX2AudioConditioningInvocation"] | components["schemas"]["LTX2DenoiseInvocation"] | components["schemas"]["LTX2DurationInvocation"] | components["schemas"]["LTX2ExtendConditioningInvocation"] | components["schemas"]["LTX2IdealDimensionsInvocation"] | components["schemas"]["LTX2ImageConditioningInvocation"] | components["schemas"]["LTX2LatentUpsampleInvocation"] | components["schemas"]["LTX2LatentsToVideoInvocation"] | components["schemas"]["LTX2LoRACollectionLoader"] | components["schemas"]["LTX2LoRALoaderInvocation"] | components["schemas"]["LTX2ModelLoaderInvocation"] | components["schemas"]["LTX2TextEncoderInvocation"] | components["schemas"]["LTX2VideoConditioningInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["MiniMaxH3DenoiseInvocation"] | components["schemas"]["MiniMaxH3FrameConditioningInvocation"] | components["schemas"]["MiniMaxH3HybridOverlayInvocation"] | components["schemas"]["MiniMaxH3IdealDimensionsInvocation"] | components["schemas"]["MiniMaxH3ImageReferenceInvocation"] | components["schemas"]["MiniMaxH3LatentsToImageInvocation"] | components["schemas"]["MiniMaxH3LatentsToVideoInvocation"] | components["schemas"]["MiniMaxH3LoRACollectionLoader"] | components["schemas"]["MiniMaxH3LoRALoaderInvocation"] | components["schemas"]["MiniMaxH3ModelLoaderInvocation"] | components["schemas"]["MiniMaxH3ReferenceConditioningInvocation"] | components["schemas"]["MiniMaxH3TextEncoderInvocation"] | components["schemas"]["MiniMaxH3VideoReferenceInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImage21DenoiseInvocation"] | components["schemas"]["QwenImage21ImageToLatentsInvocation"] | components["schemas"]["QwenImage21LatentsToImageInvocation"] | components["schemas"]["QwenImage21LoRACollectionLoader"] | components["schemas"]["QwenImage21LoRALoaderInvocation"] | components["schemas"]["QwenImage21ModelLoaderInvocation"] | components["schemas"]["QwenImage21TextEncoderInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StateEmptyInvocation"] | components["schemas"]["StateGetInvocation"] | components["schemas"]["StateMergeInvocation"] | components["schemas"]["StateSetInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
             /**
              * Invocation Source Id
              * @description The ID of the prepared invocation's source node
@@ -22868,7 +22870,7 @@ export type components = {
              * Invocation
              * @description The ID of the invocation
              */
-            invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaDenoiseMetaInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["AtlasCloudImageGenerationInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BooleanLogicInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CollectionCartesianInvocation"] | components["schemas"]["CollectionConcatInvocation"] | components["schemas"]["CollectionZipInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2DevLoRACollectionLoader"] | components["schemas"]["Flux2DevLoRALoaderInvocation"] | components["schemas"]["Flux2DevModelLoaderInvocation"] | components["schemas"]["Flux2DevTextEncoderInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["ForInvocation"] | components["schemas"]["ForReturnInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2StyleReferenceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LTX2AudioConditioningInvocation"] | components["schemas"]["LTX2DenoiseInvocation"] | components["schemas"]["LTX2DurationInvocation"] | components["schemas"]["LTX2ExtendConditioningInvocation"] | components["schemas"]["LTX2IdealDimensionsInvocation"] | components["schemas"]["LTX2ImageConditioningInvocation"] | components["schemas"]["LTX2LatentUpsampleInvocation"] | components["schemas"]["LTX2LatentsToVideoInvocation"] | components["schemas"]["LTX2LoRACollectionLoader"] | components["schemas"]["LTX2LoRALoaderInvocation"] | components["schemas"]["LTX2ModelLoaderInvocation"] | components["schemas"]["LTX2TextEncoderInvocation"] | components["schemas"]["LTX2VideoConditioningInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["MiniMaxH3DenoiseInvocation"] | components["schemas"]["MiniMaxH3FrameConditioningInvocation"] | components["schemas"]["MiniMaxH3HybridOverlayInvocation"] | components["schemas"]["MiniMaxH3IdealDimensionsInvocation"] | components["schemas"]["MiniMaxH3ImageReferenceInvocation"] | components["schemas"]["MiniMaxH3LatentsToImageInvocation"] | components["schemas"]["MiniMaxH3LatentsToVideoInvocation"] | components["schemas"]["MiniMaxH3LoRACollectionLoader"] | components["schemas"]["MiniMaxH3LoRALoaderInvocation"] | components["schemas"]["MiniMaxH3ModelLoaderInvocation"] | components["schemas"]["MiniMaxH3ReferenceConditioningInvocation"] | components["schemas"]["MiniMaxH3TextEncoderInvocation"] | components["schemas"]["MiniMaxH3VideoReferenceInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImage21DenoiseInvocation"] | components["schemas"]["QwenImage21ImageToLatentsInvocation"] | components["schemas"]["QwenImage21LatentsToImageInvocation"] | components["schemas"]["QwenImage21ModelLoaderInvocation"] | components["schemas"]["QwenImage21TextEncoderInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StateEmptyInvocation"] | components["schemas"]["StateGetInvocation"] | components["schemas"]["StateMergeInvocation"] | components["schemas"]["StateSetInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
+            invocation: components["schemas"]["AddInvocation"] | components["schemas"]["AlibabaCloudImageGenerationInvocation"] | components["schemas"]["AlphaMaskToTensorInvocation"] | components["schemas"]["AnimaDenoiseInvocation"] | components["schemas"]["AnimaDenoiseMetaInvocation"] | components["schemas"]["AnimaImageToLatentsInvocation"] | components["schemas"]["AnimaLLLiteInvocation"] | components["schemas"]["AnimaLatentsToImageInvocation"] | components["schemas"]["AnimaLoRACollectionLoader"] | components["schemas"]["AnimaLoRALoaderInvocation"] | components["schemas"]["AnimaModelLoaderInvocation"] | components["schemas"]["AnimaTextEncoderInvocation"] | components["schemas"]["ApplyMaskTensorToImageInvocation"] | components["schemas"]["ApplyMaskToImageInvocation"] | components["schemas"]["AtlasCloudImageGenerationInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BooleanLogicInvocation"] | components["schemas"]["BoundingBoxInvocation"] | components["schemas"]["CLIPSkipInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["CalculateImageTilesEvenSplitInvocation"] | components["schemas"]["CalculateImageTilesInvocation"] | components["schemas"]["CalculateImageTilesMinimumOverlapInvocation"] | components["schemas"]["CallSavedWorkflowInvocation"] | components["schemas"]["CannyEdgeDetectionInvocation"] | components["schemas"]["CanvasOutputInvocation"] | components["schemas"]["CanvasPasteBackInvocation"] | components["schemas"]["CanvasV2MaskAndCropInvocation"] | components["schemas"]["CenterPadCropInvocation"] | components["schemas"]["CogView4DenoiseInvocation"] | components["schemas"]["CogView4ImageToLatentsInvocation"] | components["schemas"]["CogView4LatentsToImageInvocation"] | components["schemas"]["CogView4ModelLoaderInvocation"] | components["schemas"]["CogView4TextEncoderInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CollectionCartesianInvocation"] | components["schemas"]["CollectionConcatInvocation"] | components["schemas"]["CollectionZipInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ColorMapInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ContentShuffleInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["CreateGradientMaskInvocation"] | components["schemas"]["CropImageToBoundingBoxInvocation"] | components["schemas"]["CropLatentsCoreInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["DWOpenposeDetectionInvocation"] | components["schemas"]["DecodeInvisibleWatermarkInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["DenoiseLatentsMetaInvocation"] | components["schemas"]["DepthAnythingDepthEstimationInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["ErnieImageDenoiseInvocation"] | components["schemas"]["ErnieImageModelLoaderInvocation"] | components["schemas"]["ErnieImagePromptEnhancerInvocation"] | components["schemas"]["ErnieImageTextEncoderInvocation"] | components["schemas"]["ErnieImageVaeDecodeInvocation"] | components["schemas"]["ExpandMaskWithFadeInvocation"] | components["schemas"]["ExtractVideoRangeInvocation"] | components["schemas"]["FLUXLoRACollectionLoader"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FloatBatchInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["FloatGenerator"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["Flux2DenoiseInvocation"] | components["schemas"]["Flux2DevLoRACollectionLoader"] | components["schemas"]["Flux2DevLoRALoaderInvocation"] | components["schemas"]["Flux2DevModelLoaderInvocation"] | components["schemas"]["Flux2DevTextEncoderInvocation"] | components["schemas"]["Flux2KleinLoRACollectionLoader"] | components["schemas"]["Flux2KleinLoRALoaderInvocation"] | components["schemas"]["Flux2KleinModelLoaderInvocation"] | components["schemas"]["Flux2KleinTextEncoderInvocation"] | components["schemas"]["Flux2PiDDecodeInvocation"] | components["schemas"]["Flux2VaeDecodeInvocation"] | components["schemas"]["Flux2VaeEncodeInvocation"] | components["schemas"]["FluxControlLoRALoaderInvocation"] | components["schemas"]["FluxControlNetInvocation"] | components["schemas"]["FluxDenoiseInvocation"] | components["schemas"]["FluxDenoiseLatentsMetaInvocation"] | components["schemas"]["FluxFillInvocation"] | components["schemas"]["FluxIPAdapterInvocation"] | components["schemas"]["FluxKontextConcatenateImagesInvocation"] | components["schemas"]["FluxKontextInvocation"] | components["schemas"]["FluxLoRALoaderInvocation"] | components["schemas"]["FluxModelLoaderInvocation"] | components["schemas"]["FluxPiDDecodeInvocation"] | components["schemas"]["FluxReduxInvocation"] | components["schemas"]["FluxTextEncoderInvocation"] | components["schemas"]["FluxVaeDecodeInvocation"] | components["schemas"]["FluxVaeEncodeInvocation"] | components["schemas"]["ForInvocation"] | components["schemas"]["ForReturnInvocation"] | components["schemas"]["FreeUInvocation"] | components["schemas"]["GeminiImageGenerationInvocation"] | components["schemas"]["Gemma2EncoderLoaderInvocation"] | components["schemas"]["GetMaskBoundingBoxInvocation"] | components["schemas"]["GroundingDinoInvocation"] | components["schemas"]["HEDEdgeDetectionInvocation"] | components["schemas"]["HeuristicResizeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["IdealSizeInvocation"] | components["schemas"]["Ideogram4CaptionBuilderInvocation"] | components["schemas"]["Ideogram4DenoiseInvocation"] | components["schemas"]["Ideogram4LatentsToImageInvocation"] | components["schemas"]["Ideogram4ModelLoaderInvocation"] | components["schemas"]["Ideogram4TextEncoderInvocation"] | components["schemas"]["IfInvocation"] | components["schemas"]["ImageBatchInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImageGenerator"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageMaskToTensorInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageNoiseInvocation"] | components["schemas"]["ImagePanelLayoutInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["IntegerBatchInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["IntegerGenerator"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["InvertTensorMaskInvocation"] | components["schemas"]["InvokeAdjustImageHuePlusInvocation"] | components["schemas"]["InvokeEquivalentAchromaticLightnessInvocation"] | components["schemas"]["InvokeImageBlendInvocation"] | components["schemas"]["InvokeImageCompositorInvocation"] | components["schemas"]["InvokeImageDilateOrErodeInvocation"] | components["schemas"]["InvokeImageEnhanceInvocation"] | components["schemas"]["InvokeImageValueThresholdsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["Krea2ConditioningRebalanceInvocation"] | components["schemas"]["Krea2DenoiseInvocation"] | components["schemas"]["Krea2LoRACollectionLoader"] | components["schemas"]["Krea2LoRALoaderInvocation"] | components["schemas"]["Krea2ModelLoaderInvocation"] | components["schemas"]["Krea2SeedVarianceInvocation"] | components["schemas"]["Krea2StyleReferenceInvocation"] | components["schemas"]["Krea2TextEncoderInvocation"] | components["schemas"]["LTX2AudioConditioningInvocation"] | components["schemas"]["LTX2DenoiseInvocation"] | components["schemas"]["LTX2DurationInvocation"] | components["schemas"]["LTX2ExtendConditioningInvocation"] | components["schemas"]["LTX2IdealDimensionsInvocation"] | components["schemas"]["LTX2ImageConditioningInvocation"] | components["schemas"]["LTX2LatentUpsampleInvocation"] | components["schemas"]["LTX2LatentsToVideoInvocation"] | components["schemas"]["LTX2LoRACollectionLoader"] | components["schemas"]["LTX2LoRALoaderInvocation"] | components["schemas"]["LTX2ModelLoaderInvocation"] | components["schemas"]["LTX2TextEncoderInvocation"] | components["schemas"]["LTX2VideoConditioningInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["LineartAnimeEdgeDetectionInvocation"] | components["schemas"]["LineartEdgeDetectionInvocation"] | components["schemas"]["LlavaOnevisionVllmInvocation"] | components["schemas"]["LoRACollectionLoader"] | components["schemas"]["LoRALoaderInvocation"] | components["schemas"]["LoRASelectorInvocation"] | components["schemas"]["MLSDDetectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["MaskFromIDInvocation"] | components["schemas"]["MaskTensorToImageInvocation"] | components["schemas"]["MediaPipeFaceDetectionInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["MergeTilesToImageInvocation"] | components["schemas"]["MetadataFieldExtractorInvocation"] | components["schemas"]["MetadataFromImageInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["MetadataItemLinkedInvocation"] | components["schemas"]["MetadataToBoolCollectionInvocation"] | components["schemas"]["MetadataToBoolInvocation"] | components["schemas"]["MetadataToControlnetsInvocation"] | components["schemas"]["MetadataToFloatCollectionInvocation"] | components["schemas"]["MetadataToFloatInvocation"] | components["schemas"]["MetadataToIPAdaptersInvocation"] | components["schemas"]["MetadataToIntegerCollectionInvocation"] | components["schemas"]["MetadataToIntegerInvocation"] | components["schemas"]["MetadataToLorasCollectionInvocation"] | components["schemas"]["MetadataToLorasInvocation"] | components["schemas"]["MetadataToModelInvocation"] | components["schemas"]["MetadataToSDXLLorasInvocation"] | components["schemas"]["MetadataToSDXLModelInvocation"] | components["schemas"]["MetadataToSchedulerInvocation"] | components["schemas"]["MetadataToStringCollectionInvocation"] | components["schemas"]["MetadataToStringInvocation"] | components["schemas"]["MetadataToT2IAdaptersInvocation"] | components["schemas"]["MetadataToVAEInvocation"] | components["schemas"]["MiniMaxH3DenoiseInvocation"] | components["schemas"]["MiniMaxH3FrameConditioningInvocation"] | components["schemas"]["MiniMaxH3HybridOverlayInvocation"] | components["schemas"]["MiniMaxH3IdealDimensionsInvocation"] | components["schemas"]["MiniMaxH3ImageReferenceInvocation"] | components["schemas"]["MiniMaxH3LatentsToImageInvocation"] | components["schemas"]["MiniMaxH3LatentsToVideoInvocation"] | components["schemas"]["MiniMaxH3LoRACollectionLoader"] | components["schemas"]["MiniMaxH3LoRALoaderInvocation"] | components["schemas"]["MiniMaxH3ModelLoaderInvocation"] | components["schemas"]["MiniMaxH3ReferenceConditioningInvocation"] | components["schemas"]["MiniMaxH3TextEncoderInvocation"] | components["schemas"]["MiniMaxH3VideoReferenceInvocation"] | components["schemas"]["ModelIdentifierInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["NormalMapInvocation"] | components["schemas"]["OklabUnsharpMaskInvocation"] | components["schemas"]["OklchImageHueAdjustmentInvocation"] | components["schemas"]["OpenAIImageGenerationInvocation"] | components["schemas"]["PBRMapsInvocation"] | components["schemas"]["PairTileImageInvocation"] | components["schemas"]["PasteImageIntoBoundingBoxInvocation"] | components["schemas"]["PiDDecoderLoaderInvocation"] | components["schemas"]["PiDUpscaleInvocation"] | components["schemas"]["PiDiNetEdgeDetectionInvocation"] | components["schemas"]["PromptTemplateInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["QwenImage21DenoiseInvocation"] | components["schemas"]["QwenImage21ImageToLatentsInvocation"] | components["schemas"]["QwenImage21LatentsToImageInvocation"] | components["schemas"]["QwenImage21LoRACollectionLoader"] | components["schemas"]["QwenImage21LoRALoaderInvocation"] | components["schemas"]["QwenImage21ModelLoaderInvocation"] | components["schemas"]["QwenImage21TextEncoderInvocation"] | components["schemas"]["QwenImageDenoiseInvocation"] | components["schemas"]["QwenImageImageToLatentsInvocation"] | components["schemas"]["QwenImageLatentsToImageInvocation"] | components["schemas"]["QwenImageLoRACollectionLoader"] | components["schemas"]["QwenImageLoRALoaderInvocation"] | components["schemas"]["QwenImageModelLoaderInvocation"] | components["schemas"]["QwenImagePiDDecodeInvocation"] | components["schemas"]["QwenImageTextEncoderInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RectangleMaskInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["SD3DenoiseInvocation"] | components["schemas"]["SD3ImageToLatentsInvocation"] | components["schemas"]["SD3LatentsToImageInvocation"] | components["schemas"]["SD3PiDDecodeInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLLoRACollectionLoader"] | components["schemas"]["SDXLLoRALoaderInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLPiDDecodeInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["SaveImageToFileInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["Sd3ModelLoaderInvocation"] | components["schemas"]["Sd3TextEncoderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SeedreamImageGenerationInvocation"] | components["schemas"]["SegmentAnythingInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SpandrelImageToImageAutoscaleInvocation"] | components["schemas"]["SpandrelImageToImageInvocation"] | components["schemas"]["StateEmptyInvocation"] | components["schemas"]["StateGetInvocation"] | components["schemas"]["StateMergeInvocation"] | components["schemas"]["StateSetInvocation"] | components["schemas"]["StringBatchInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["StringGenerator"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["TextLLMInvocation"] | components["schemas"]["TextLLMWithPresetInvocation"] | components["schemas"]["TileToPropertiesInvocation"] | components["schemas"]["TiledMultiDiffusionDenoiseLatents"] | components["schemas"]["UnsharpMaskInvocation"] | components["schemas"]["VAELoaderInvocation"] | components["schemas"]["VideoConcatInvocation"] | components["schemas"]["VideoFrameExtractInvocation"] | components["schemas"]["VideoInvocation"] | components["schemas"]["WanDenoiseInvocation"] | components["schemas"]["WanI2VIdealDimensionsInvocation"] | components["schemas"]["WanImageToLatentsInvocation"] | components["schemas"]["WanLatentsToImageInvocation"] | components["schemas"]["WanLatentsToVideoInvocation"] | components["schemas"]["WanLoRACollectionLoader"] | components["schemas"]["WanLoRALoaderInvocation"] | components["schemas"]["WanModelLoaderInvocation"] | components["schemas"]["WanRefImageEncoderInvocation"] | components["schemas"]["WanTI2VIdealDimensionsInvocation"] | components["schemas"]["WanTextEncoderInvocation"] | components["schemas"]["WanVideoDenoiseInvocation"] | components["schemas"]["WorkflowReturnGetInvocation"] | components["schemas"]["WorkflowReturnInvocation"] | components["schemas"]["WorkflowReturnValueInvocation"] | components["schemas"]["ZImageControlInvocation"] | components["schemas"]["ZImageDenoiseInvocation"] | components["schemas"]["ZImageDenoiseMetaInvocation"] | components["schemas"]["ZImageImageToLatentsInvocation"] | components["schemas"]["ZImageLatentsToImageInvocation"] | components["schemas"]["ZImageLoRACollectionLoader"] | components["schemas"]["ZImageLoRALoaderInvocation"] | components["schemas"]["ZImageModelLoaderInvocation"] | components["schemas"]["ZImagePiDDecodeInvocation"] | components["schemas"]["ZImageSeedVarianceEnhancerInvocation"] | components["schemas"]["ZImageTextEncoderInvocation"];
             /**
              * Invocation Source Id
              * @description The ID of the prepared invocation's source node
@@ -28449,6 +28451,90 @@ export type components = {
              */
             base: "minimax-h3";
         };
+        /**
+         * LoRA_LyCORIS_QwenImage21_Config
+         * @description Model config for Qwen-Image-2.1 LoRAs: ComfyUI (`diffusion_model.`, fused `gate_up`), diffusers/PEFT and
+         *     Kohya layouts, as plain low-rank layers.
+         */
+        LoRA_LyCORIS_QwenImage21_Config: {
+            /**
+             * Key
+             * @description A unique key for this model.
+             */
+            key: string;
+            /**
+             * Hash
+             * @description The hash of the model file(s).
+             */
+            hash: string;
+            /**
+             * Path
+             * @description Path to the model on the filesystem. Relative paths are relative to the Invoke root directory.
+             */
+            path: string;
+            /**
+             * File Size
+             * @description The size of the model in bytes.
+             */
+            file_size: number;
+            /**
+             * Name
+             * @description Name of the model.
+             */
+            name: string;
+            /**
+             * Description
+             * @description Model description
+             */
+            description: string | null;
+            /**
+             * Source
+             * @description The original source of the model (path, URL or repo_id).
+             */
+            source: string;
+            /** @description The type of source */
+            source_type: components["schemas"]["ModelSourceType"];
+            /**
+             * Source Api Response
+             * @description The original API response from the source, as stringified JSON.
+             */
+            source_api_response: string | null;
+            /**
+             * Source Url
+             * @description Optional URL for the model (e.g. download page or model page).
+             */
+            source_url: string | null;
+            /**
+             * Cover Image
+             * @description Url for image to preview model
+             */
+            cover_image: string | null;
+            /**
+             * Type
+             * @default lora
+             * @constant
+             */
+            type: "lora";
+            /**
+             * Trigger Phrases
+             * @description Set of trigger phrases for this model
+             */
+            trigger_phrases: string[] | null;
+            /** @description Default settings for this model */
+            default_settings: components["schemas"]["LoraModelDefaultSettings"] | null;
+            /**
+             * Format
+             * @default lycoris
+             * @constant
+             */
+            format: "lycoris";
+            /**
+             * Base
+             * @default qwen-image-2-1
+             * @constant
+             */
+            base: "qwen-image-2-1";
+        };
         /**
          * LoRA_LyCORIS_QwenImage_Config
          * @description Model config for Qwen Image Edit LoRA models in LyCORIS format.
@@ -37329,7 +37415,7 @@ export type components = {
              * Config
              * @description The installed model's config
              */
-            config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+            config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage21_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
         };
         /**
          * ModelInstallDownloadProgressEvent
@@ -37495,7 +37581,7 @@ export type components = {
              * Config Out
              * @description After successful installation, this will hold the configuration object.
              */
-            config_out?: (components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]) | null;
+            config_out?: (components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage21_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]) | null;
             /**
              * Inplace
              * @description Leave model in its current location; otherwise install under models directory
@@ -37581,7 +37667,7 @@ export type components = {
              * Config
              * @description The model's config
              */
-            config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+            config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage21_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
             /**
              * @description The submodel type, if any
              * @default null
@@ -37608,7 +37694,7 @@ export type components = {
              * Config
              * @description The model's config
              */
-            config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+            config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage21_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
             /**
              * @description The submodel type, if any
              * @default null
@@ -37835,7 +37921,7 @@ export type components = {
          */
         ModelsList: {
             /** Models */
-            models: (components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"])[];
+            models: (components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage21_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"])[];
         };
         /**
          * Multiply Integers
@@ -41393,6 +41479,112 @@ export type components = {
              */
             type: "qwen_image_2_1_l2i";
         };
+        /**
+         * Apply LoRA Collection - Qwen-Image-2.1
+         * @description Applies a collection of LoRAs to a Qwen-Image-2.1 transformer.
+         */
+        QwenImage21LoRACollectionLoader: {
+            /**
+             * Id
+             * @description The id of this instance of an invocation. Must be unique among all instances of invocations.
+             */
+            id: string;
+            /**
+             * Is Intermediate
+             * @description Whether or not this is an intermediate invocation.
+             * @default false
+             */
+            is_intermediate?: boolean;
+            /**
+             * Use Cache
+             * @description Whether or not to use the cache
+             * @default true
+             */
+            use_cache?: boolean;
+            /**
+             * LoRAs
+             * @description LoRA models and weights. May be a single LoRA or collection.
+             * @default null
+             */
+            loras?: components["schemas"]["LoRAField"] | components["schemas"]["LoRAField"][] | null;
+            /**
+             * Transformer
+             * @description Transformer
+             * @default null
+             */
+            transformer?: components["schemas"]["TransformerField"] | null;
+            /**
+             * type
+             * @default qwen_image_2_1_lora_collection_loader
+             * @constant
+             */
+            type: "qwen_image_2_1_lora_collection_loader";
+        };
+        /**
+         * Apply LoRA - Qwen-Image-2.1
+         * @description Apply a LoRA model to a Qwen-Image-2.1 transformer.
+         */
+        QwenImage21LoRALoaderInvocation: {
+            /**
+             * Id
+             * @description The id of this instance of an invocation. Must be unique among all instances of invocations.
+             */
+            id: string;
+            /**
+             * Is Intermediate
+             * @description Whether or not this is an intermediate invocation.
+             * @default false
+             */
+            is_intermediate?: boolean;
+            /**
+             * Use Cache
+             * @description Whether or not to use the cache
+             * @default true
+             */
+            use_cache?: boolean;
+            /**
+             * LoRA
+             * @description LoRA model to load
+             * @default null
+             */
+            lora?: components["schemas"]["ModelIdentifierField"] | null;
+            /**
+             * Weight
+             * @description The weight at which the LoRA is applied to each model
+             * @default 1
+             */
+            weight?: number;
+            /**
+             * Transformer
+             * @description Transformer
+             * @default null
+             */
+            transformer?: components["schemas"]["TransformerField"] | null;
+            /**
+             * type
+             * @default qwen_image_2_1_lora_loader
+             * @constant
+             */
+            type: "qwen_image_2_1_lora_loader";
+        };
+        /**
+         * QwenImage21LoRALoaderOutput
+         * @description Qwen-Image-2.1 LoRA Loader Output
+         */
+        QwenImage21LoRALoaderOutput: {
+            /**
+             * Transformer
+             * @description Transformer
+             * @default null
+             */
+            transformer: components["schemas"]["TransformerField"] | null;
+            /**
+             * type
+             * @default qwen_image_2_1_lora_loader_output
+             * @constant
+             */
+            type: "qwen_image_2_1_lora_loader_output";
+        };
         /**
          * Main Model - Qwen-Image-2.1
          * @description Loads a Qwen-Image-2.1 model, outputting its submodels.
@@ -54531,7 +54723,7 @@ export interface operations {
                     [name: string]: unknown;
                 };
                 content: {
-                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage21_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
                 };
             };
             /** @description Validation Error */
@@ -54563,7 +54755,7 @@ export interface operations {
                     [name: string]: unknown;
                 };
                 content: {
-                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage21_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
                 };
             };
             /** @description Validation Error */
@@ -54615,7 +54807,7 @@ export interface operations {
                      *       "upcast_attention": false
                      *     }
                      */
-                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage21_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
                 };
             };
             /** @description Bad request */
@@ -54729,7 +54921,7 @@ export interface operations {
                      *       "upcast_attention": false
                      *     }
                      */
-                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage21_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
                 };
             };
             /** @description Bad request */
@@ -54802,7 +54994,7 @@ export interface operations {
                      *       "upcast_attention": false
                      *     }
                      */
-                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage21_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
                 };
             };
             /** @description Bad request */
@@ -55603,7 +55795,7 @@ export interface operations {
                      *       "upcast_attention": false
                      *     }
                      */
-                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_QwenImage21_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Diffusers_LTX2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_QwenImage21_Config"] | components["schemas"]["Main_Checkpoint_Wan_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_ErnieImage_Config"] | components["schemas"]["Main_Checkpoint_Ideogram4_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_LTX2_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_QwenImage21_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["Main_GGUF_Ideogram4_Config"] | components["schemas"]["Main_GGUF_ErnieImage_Config"] | components["schemas"]["Main_SDNQ_Diffusers_Flux2_Config"] | components["schemas"]["Main_SDNQ_Diffusers_FLUX_Config"] | components["schemas"]["Main_SDNQ_Diffusers_ZImage_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_SD3_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage21_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_FLUX_Config"] | components["schemas"]["VAE_Diffusers_SD3_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["VAE_Diffusers_QwenImage21_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage21_Config"] | components["schemas"]["LoRA_LyCORIS_LTX2_Config"] | components["schemas"]["LoRA_LyCORIS_MiniMaxH3_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_SDNQ_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_MiniMaxH3_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen35Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Qwen3Encoder_SDNQ_Folder_Config"] | components["schemas"]["MistralEncoder_Diffusers_Config"] | components["schemas"]["MistralEncoder_Checkpoint_Config"] | components["schemas"]["MistralEncoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma4Encoder_Gemma4Encoder_LTX2_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_G_Config"] | components["schemas"]["CLIPEmbed_Checkpoint_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LTX2DurationHead_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
                 };
             };
             /** @description Bad request */
diff --git a/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.ts b/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.ts
index b3aa6651369..94735007d39 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.ts
@@ -1283,6 +1283,7 @@ const LORA_SUPPORTED_BASES: ReadonlySet<string> = new Set<SupportedGenerateBase>
   'flux2',
   'krea-2',
   'qwen-image',
+  'qwen-image-2-1',
   'sd-1',
   'sd-2',
   'sdxl',
diff --git a/invokeai/frontend/webv2/src/features/generation/core/graph.test.ts b/invokeai/frontend/webv2/src/features/generation/core/graph.test.ts
index 5a9454a5958..c5e748e71c1 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/graph.test.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/graph.test.ts
@@ -790,6 +790,16 @@ describe('compileGenerateGraph', () => {
       expect(latentImages).toEqual(['a.png', 'b.png']);
     });
 
+    it('applies LoRAs to the transformer through its own collection loader', () => {
+      const lora: LoraModelConfig = { base: 'qwen-image-2-1', key: 'qwen21-lora', name: 'A LoRA', type: 'lora' };
+      const graph = compile(qwenImage21Model, { loras: [{ isEnabled: true, model: lora, weight: 0.7 }] });
+
+      const loader = getNodeByType(graph, 'qwen_image_2_1_lora_collection_loader');
+      expect(loader).toBeDefined();
+      expect(getEdge(graph, 'denoise_latents', 'transformer')?.source.node_id).toBe(loader?.id);
+      expect(getEdge(graph, loader?.id ?? '', 'transformer')?.source.node_id).toBe('model_loader');
+    });
+
     it('refuses references with a standalone encoder, which has no vision tower', () => {
       const encoder: ComponentModelConfig = {
         base: 'any',
diff --git a/invokeai/frontend/webv2/src/features/generation/core/graph.ts b/invokeai/frontend/webv2/src/features/generation/core/graph.ts
index 55a600b3a9f..f5bc35b37ab 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/graph.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/graph.ts
@@ -1415,6 +1415,12 @@ const buildQwenImage21Graph = (
     type: 'qwen_image_2_1_model_loader',
     vae_model: vaeModel ?? undefined,
   });
+  const activeLoras = getActiveCompatibleLoras(settings, model);
+  const loraSource = activeLoras.length
+    ? addTransformerLoraCollectionLoader(graph, activeLoras, 'qwen_image_2_1_lora_collection_loader', modelLoader, [
+        'transformer',
+      ])
+    : modelLoader;
   const posCond = addNode(graph, { id: 'pos_cond', type: 'qwen_image_2_1_text_encoder' });
   const negCond = useCfg ? addNode(graph, { id: 'neg_cond', type: 'qwen_image_2_1_text_encoder' }) : null;
   const denoise = addNode(graph, {
@@ -1427,7 +1433,7 @@ const buildQwenImage21Graph = (
   });
   const output = addImageOutputNode(graph, 'qwen_image_2_1_l2i', outputIsIntermediate);
 
-  addEdge(graph, modelLoader, 'transformer', denoise, 'transformer');
+  addEdge(graph, loraSource, 'transformer', denoise, 'transformer');
   addEdge(graph, modelLoader, 'qwen3_vl_encoder', posCond, 'qwen3_vl_encoder');
   addEdge(graph, modelLoader, 'vae', output, 'vae');
   addEdge(graph, positivePrompt, 'value', posCond, 'prompt');
diff --git a/tests/app/invocations/test_qwen_image_2_1_nodes.py b/tests/app/invocations/test_qwen_image_2_1_nodes.py
index cb6e29a747e..495185c725b 100644
--- a/tests/app/invocations/test_qwen_image_2_1_nodes.py
+++ b/tests/app/invocations/test_qwen_image_2_1_nodes.py
@@ -10,10 +10,18 @@
 import numpy as np
 import pytest
 import torch
+from diffusers.models.normalization import RMSNorm as DiffusersRMSNorm
+from diffusers.models.transformers.transformer_qwenimage21 import QwenImage21ZeroCenterRMSNorm
 from PIL import Image
 
 from invokeai.app.invocations.fields import ImageField, LatentsField, QwenImage21ConditioningField
-from invokeai.app.invocations.model import ModelIdentifierField, Qwen3VLEncoderField, TransformerField, VAEField
+from invokeai.app.invocations.model import (
+    LoRAField,
+    ModelIdentifierField,
+    Qwen3VLEncoderField,
+    TransformerField,
+    VAEField,
+)
 from invokeai.app.invocations.qwen_image_2_1.qwen_image_2_1_denoise import (
     KV_BYTES_PER_PREFIX_TOKEN,
     LATENT_CHANNELS,
@@ -24,6 +32,10 @@
     prefix_length,
     unpack_latents,
 )
+from invokeai.app.invocations.qwen_image_2_1.qwen_image_2_1_lora_loader import (
+    QwenImage21LoRACollectionLoader,
+    QwenImage21LoRALoaderInvocation,
+)
 from invokeai.app.invocations.qwen_image_2_1.qwen_image_2_1_model_loader import QwenImage21ModelLoaderInvocation
 from invokeai.app.invocations.text_encoder.qwen_image_2_1_text_encoder import QwenImage21TextEncoderInvocation
 from invokeai.app.invocations.vae.qwen_image_2_1_image_to_latents import QwenImage21ImageToLatentsInvocation
@@ -111,7 +123,7 @@ def _context(
     return SimpleNamespace(
         models=SimpleNamespace(
             load=lambda _identifier: _TransformerInfo(transformer),
-            get_config=lambda _identifier: SimpleNamespace(variant=variant),
+            get_config=lambda _identifier: SimpleNamespace(variant=variant, format=ModelFormat.Diffusers),
         ),
         conditioning=SimpleNamespace(load=lambda name: conditionings[name]),
         tensors=SimpleNamespace(load=lambda name: tensors.get(name, init_latents)),
@@ -641,3 +653,169 @@ def on_device(**_kwargs):
     expected = ((vae.z - mean) / std).float()[:, :, 0]
     assert torch.equal(saved["latents"], expected)
     assert not torch.equal(expected, ((vae.z.float() - 0.3) / 1.7)[:, :, 0])
+
+
+def _lora_context(base: BaseModelType):
+    configs = {"lora": SimpleNamespace(name="a LoRA", base=base, type=ModelType.LoRA)}
+    return SimpleNamespace(
+        models=SimpleNamespace(exists=lambda key: key in configs, get_config=lambda key: configs[key])
+    )
+
+
+def _lora_field(weight: float = 0.8) -> LoRAField:
+    return LoRAField(
+        lora=ModelIdentifierField(
+            key="lora", hash="h", name="a LoRA", base=BaseModelType.QwenImage21, type=ModelType.LoRA
+        ),
+        weight=weight,
+    )
+
+
+def _transformer_field() -> TransformerField:
+    model = ModelIdentifierField(key="m", hash="h", name="m", base=BaseModelType.QwenImage21, type=ModelType.Main)
+    return TransformerField(transformer=model, loras=[])
+
+
+def test_a_lora_collection_is_added_to_the_transformer_once() -> None:
+    node = QwenImage21LoRACollectionLoader.model_construct(
+        loras=[_lora_field(0.8), _lora_field(0.5)], transformer=_transformer_field()
+    )
+    output = node.invoke(_lora_context(BaseModelType.QwenImage21))
+    assert output.transformer is not None
+    assert [(lora.lora.key, lora.weight) for lora in output.transformer.loras] == [("lora", 0.8)]
+
+
+@pytest.mark.parametrize("node_class", ["single", "collection"])
+def test_a_qwen_image_lora_is_refused(node_class: str) -> None:
+    # Its keys would match no layer, or the wrong ones, in Qwen-Image-2.1's transformer.
+    context = _lora_context(BaseModelType.QwenImage)
+    if node_class == "single":
+        node = QwenImage21LoRALoaderInvocation.model_construct(
+            lora=_lora_field().lora, weight=1.0, transformer=_transformer_field()
+        )
+    else:
+        node = QwenImage21LoRACollectionLoader.model_construct(loras=[_lora_field()], transformer=_transformer_field())
+    with pytest.raises(ValueError, match="not Qwen-Image-2.1"):
+        node.invoke(context)
+
+
+class _LinearTransformer(torch.nn.Module):
+    """A transformer with one real Linear and one real norm a LoRA can target, reading both on every forward."""
+
+    config = SimpleNamespace(causal_condition=True, num_attention_heads=2, attention_head_dim=4)
+
+    def __init__(self) -> None:
+        super().__init__()
+        block = torch.nn.Module()
+        block.attn = torch.nn.Module()
+        block.attn.to_q = torch.nn.Linear(8, 8, bias=False)
+        torch.nn.init.zeros_(block.attn.to_q.weight)
+        block.attn.norm_q = DiffusersRMSNorm(8, eps=1e-6)
+        self.transformer_blocks = torch.nn.ModuleList([block])
+        # The text norm has no custom wrapper, so only a forced direct patch can reach it.
+        self.txt_in = torch.nn.Module()
+        self.txt_in.text_norm = QwenImage21ZeroCenterRMSNorm(8)
+        self.seen: list[tuple[str | None, torch.Tensor]] = []
+        self.norm_seen: list[torch.Tensor] = []
+        self.text_norm_seen: list[torch.Tensor] = []
+
+    def forward(self, *, hidden_states, encoder_hidden_states, kv_cache_mode, **_kwargs):
+        to_q = self.transformer_blocks[0].attn.to_q
+        self.seen.append((kv_cache_mode, to_q(torch.ones(1, 8)).detach().clone()))
+        self.weight_was_patched = bool(to_q.weight.any())
+        self.norm_seen.append(self.transformer_blocks[0].attn.norm_q.weight.detach().clone())
+        self.text_norm_seen.append(self.txt_in.text_norm.weight.detach().clone())
+        return (torch.zeros(1, encoder_hidden_states.shape[1] + hidden_states.shape[1], hidden_states.shape[2]),)
+
+
+@pytest.mark.parametrize("model_format", [ModelFormat.Diffusers, ModelFormat.GGUFQuantized], ids=["merged", "sidecar"])
+def test_loras_reach_the_transformer_from_the_first_step_and_leave_it_unchanged(
+    model_format: ModelFormat, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    from invokeai.backend.model_manager.load.model_cache.torch_module_autocast.torch_module_autocast import (
+        apply_custom_layers_to_model,
+    )
+    from invokeai.backend.patches.layer_patcher import LayerPatcher
+    from invokeai.backend.patches.lora_conversions.qwen_image_2_1_lora_conversion_utils import (
+        lora_model_from_qwen_image_21_state_dict,
+    )
+
+    # As if the layer were on the GPU: on the CPU every LoRA runs as a sidecar, whatever the format.
+    monkeypatch.setattr(LayerPatcher, "_is_any_part_of_layer_on_cpu", staticmethod(lambda _module: False))
+    transformer = _LinearTransformer()
+    # As the model cache holds it: GGUF's sidecar path wraps these layers.
+    apply_custom_layers_to_model(transformer)
+    up, down = torch.full((8, 2), 0.5), torch.full((2, 8), 0.25)
+    patch = lora_model_from_qwen_image_21_state_dict(
+        {
+            "diffusion_model.transformer_blocks.0.attn.to_q.lora_A.weight": down,
+            "diffusion_model.transformer_blocks.0.attn.to_q.lora_B.weight": up,
+            "diffusion_model.transformer_blocks.0.attn.norm_q.diff": torch.full((8,), 0.25),
+            "diffusion_model.txt_in.text_norm.diff": torch.full((8,), -0.5),
+        }
+    )
+    lora_info = SimpleNamespace(model=patch, model_in_ram=nullcontext)
+    lora = ModelIdentifierField(key="lora", hash="h", name="l", base=BaseModelType.QwenImage21, type=ModelType.LoRA)
+    context = _context(_Transformer(), QwenImage21VariantType.Base)
+    context.models = SimpleNamespace(
+        load=lambda identifier: lora_info if identifier.key == "lora" else _TransformerInfo(transformer),
+        get_config=lambda _identifier: SimpleNamespace(variant=QwenImage21VariantType.Base, format=model_format),
+    )
+    node = _denoise(steps=2)
+    node.transformer.loras.append(LoRAField(lora=lora, weight=0.5))
+
+    node._run_diffusion(context)
+
+    # The zero base weight plus the LoRA at weight 0.5 (scale 1, as no alpha is given), on both steps -- the prefill
+    # that fills the prefix cache included.
+    expected = (up @ down * 0.5) @ torch.ones(8)
+    assert [mode for mode, _ in transformer.seen] == ["extract", "cached"]
+    for _, out in transformer.seen:
+        torch.testing.assert_close(out[0], expected)
+    # Merged into the weight for a plain model; beside it, the weight untouched, for a GGUF one.
+    assert transformer.weight_was_patched is (model_format is ModelFormat.Diffusers)
+    assert not transformer.transformer_blocks[0].attn.to_q.weight.any()
+    # A norm diff is merged in both, as no sidecar patches a norm, and taken back out afterwards.
+    for weight in transformer.norm_seen:
+        torch.testing.assert_close(weight, torch.full((8,), 1.125))
+    for weight in transformer.text_norm_seen:
+        torch.testing.assert_close(weight, torch.full((8,), -0.25))
+    assert torch.equal(transformer.transformer_blocks[0].attn.norm_q.weight, torch.ones(8))
+    assert not transformer.txt_in.text_norm.weight.any()
+
+
+def test_loras_reserve_room_only_where_they_run_beside_the_weights() -> None:
+    patch = SimpleNamespace(calc_size=lambda: 100 * 2**20)
+    lora = ModelIdentifierField(key="lora", hash="h", name="l", base=BaseModelType.QwenImage21, type=ModelType.LoRA)
+    context = SimpleNamespace(models=SimpleNamespace(load=lambda _identifier: SimpleNamespace(model=patch)))
+    node = _denoise()
+    node.transformer.loras.append(LoRAField(lora=lora, weight=1.0))
+    plain = torch.nn.Linear(4, 4)
+
+    # Merged once, before the first step: nothing to hold while sampling.
+    assert node._lora_working_memory(context, plain, ModelFormat.Diffusers, 4096) == 0
+    # A sidecar holds its tensors and adds one gate_layer-wide update per forward.
+    assert node._lora_working_memory(context, plain, ModelFormat.GGUFQuantized, 4096) == 100 * 2**20 + 4096 * 12288 * 2
+
+
+def test_a_lora_already_on_the_transformer_is_not_applied_again() -> None:
+    applied = _transformer_field()
+    applied.loras.append(_lora_field(0.3))
+    output = QwenImage21LoRACollectionLoader.model_construct(loras=[_lora_field(0.8)], transformer=applied).invoke(
+        _lora_context(BaseModelType.QwenImage21)
+    )
+    assert output.transformer is not None
+    assert [(lora.lora.key, lora.weight) for lora in output.transformer.loras] == [("lora", 0.3)]
+
+    single = QwenImage21LoRALoaderInvocation.model_construct(lora=_lora_field().lora, weight=1.0, transformer=applied)
+    with pytest.raises(ValueError, match="already applied"):
+        single.invoke(_lora_context(BaseModelType.QwenImage21))
+
+
+def test_another_familys_lora_is_named_without_the_qwen_image_hint() -> None:
+    node = QwenImage21LoRALoaderInvocation.model_construct(
+        lora=_lora_field().lora, weight=1.0, transformer=_transformer_field()
+    )
+    with pytest.raises(ValueError) as refused:
+        node.invoke(_lora_context(BaseModelType.StableDiffusionXL))
+    assert str(refused.value) == "LoRA 'a LoRA' is for sdxl models, not Qwen-Image-2.1."
diff --git a/tests/backend/model_manager/configs/test_ltx2_lora_probe_independence.py b/tests/backend/model_manager/configs/test_ltx2_lora_probe_independence.py
index 30503eef019..d00edffcf5a 100644
--- a/tests/backend/model_manager/configs/test_ltx2_lora_probe_independence.py
+++ b/tests/backend/model_manager/configs/test_ltx2_lora_probe_independence.py
@@ -27,6 +27,7 @@
     LoRA_LyCORIS_Krea2_Config,
     LoRA_LyCORIS_LTX2_Config,
     LoRA_LyCORIS_MiniMaxH3_Config,
+    LoRA_LyCORIS_QwenImage21_Config,
     LoRA_LyCORIS_QwenImage_Config,
     LoRA_LyCORIS_Wan_Config,
     LoRA_LyCORIS_ZImage_Config,
@@ -172,6 +173,8 @@ def test_an_ltx2_lora_identifies_as_ltx2(prefix: str) -> None:
         LoRA_LyCORIS_Flux2_Config,
         LoRA_LyCORIS_QwenImage_Config,
         LoRA_LyCORIS_ZImage_Config,
+        # As wide (4096) and also named transformer_blocks.N.attn.*, but attn1 and audio towers are LTX-2's.
+        LoRA_LyCORIS_QwenImage21_Config,
     ],
     ids=lambda cls: cls.__name__,
 )
diff --git a/tests/backend/model_manager/configs/test_qwen_image_2_1_config.py b/tests/backend/model_manager/configs/test_qwen_image_2_1_config.py
index b3a3ee5176a..7f87bf96d0a 100644
--- a/tests/backend/model_manager/configs/test_qwen_image_2_1_config.py
+++ b/tests/backend/model_manager/configs/test_qwen_image_2_1_config.py
@@ -1,4 +1,4 @@
-"""Identification of Qwen-Image-2.1 single-file transformers, and of LoRAs against Qwen-Image's.
+"""Identification of Qwen-Image-2.1 single-file transformers, and of its LoRAs against every other family's.
 
 Qwen-Image-2.1 reuses Qwen-Image's module names almost everywhere, so both directions matter: a 2.1 file must
 not be claimed as Qwen-Image, and the quantizations the loader cannot build must be refused at install
@@ -14,8 +14,9 @@
 import torch
 from safetensors.torch import save_file
 
+from invokeai.backend.model_manager.configs.base import Config_Base
 from invokeai.backend.model_manager.configs.identification_utils import InvalidMatchError, NotAMatchError
-from invokeai.backend.model_manager.configs.lora import LoRA_LyCORIS_QwenImage_Config
+from invokeai.backend.model_manager.configs.lora import LoRA_LyCORIS_QwenImage21_Config, LoRA_LyCORIS_QwenImage_Config
 from invokeai.backend.model_manager.configs.main import Main_Checkpoint_QwenImage21_Config
 from invokeai.backend.model_manager.taxonomy import BaseModelType, QwenImage21VariantType
 
@@ -84,6 +85,16 @@ def test_an_nvfp4_file_is_refused_at_install() -> None:
         _identify(sd, Path("qwen_image_2.1_nvfp4.safetensors"))
 
 
+def test_a_lora_with_norm_diffs_is_qwen_image_2_1(tmp_path: Path) -> None:
+    # ComfyUI's Turbo LoRA: besides the Linears, full diffs on the q/k norms and the text norm.
+    sd = _comfy_gate_up_lora() | {
+        "diffusion_model.transformer_blocks.1.attn.norm_q.diff": torch.zeros(128),
+        "diffusion_model.transformer_blocks.1.attn.norm_k.diff": torch.zeros(128),
+        "diffusion_model.txt_in.text_norm.diff": torch.zeros(4096),
+    }
+    assert _probe(LoRA_LyCORIS_QwenImage21_Config, sd, tmp_path)
+
+
 def test_a_torchao_file_is_refused_at_install() -> None:
     # unsloth's FP8 build: torchao Float8Tensors, saved as the payload and a per-row scale, no `.weight`.
     layer = "transformer_blocks.0.attn.to_q"
@@ -135,7 +146,7 @@ def _attention_lora(width: int) -> dict[str, torch.Tensor]:
     return sd
 
 
-def _probe_qwen_image_lora(sd: dict[str, torch.Tensor], tmp_path: Path) -> bool:
+def _probe(config_class, sd: dict[str, torch.Tensor], tmp_path: Path) -> bool:
     path = tmp_path / "lora.safetensors"
     path.touch()
     mod = MagicMock()
@@ -143,20 +154,164 @@ def _probe_qwen_image_lora(sd: dict[str, torch.Tensor], tmp_path: Path) -> bool:
     mod.load_state_dict.return_value = sd
     fields = {**_FIELDS, "path": str(path), "source": str(path)}
     try:
-        LoRA_LyCORIS_QwenImage_Config.from_model_on_disk(mod, fields)
+        config_class.from_model_on_disk(mod, fields)
     except NotAMatchError:
         return False
     return True
 
 
+def _comfy_gate_up_lora() -> dict[str, torch.Tensor]:
+    """As the published Qwen-Image-2.1 LoRAs are written: ComfyUI's prefix and its fused gated MLP."""
+    prefix = "diffusion_model.transformer_blocks.0.img_mlp.gate_up"
+    return {f"{prefix}.lora_A.weight": torch.zeros(4, 4096), f"{prefix}.lora_B.weight": torch.zeros(24576, 4)}
+
+
 def test_an_attention_only_lora_is_told_apart_by_its_width(tmp_path: Path) -> None:
-    assert _probe_qwen_image_lora(_attention_lora(3072), tmp_path)
-    assert not _probe_qwen_image_lora(_attention_lora(4096), tmp_path)
+    assert _probe(LoRA_LyCORIS_QwenImage_Config, _attention_lora(3072), tmp_path)
+    assert not _probe(LoRA_LyCORIS_QwenImage_Config, _attention_lora(4096), tmp_path)
+    assert _probe(LoRA_LyCORIS_QwenImage21_Config, _attention_lora(4096), tmp_path)
+    assert not _probe(LoRA_LyCORIS_QwenImage21_Config, _attention_lora(3072), tmp_path)
 
 
-def test_a_lora_on_the_gated_mlp_is_not_qwen_image(tmp_path: Path) -> None:
-    sd = {
+def test_a_lora_on_the_gated_mlp_is_qwen_image_2_1(tmp_path: Path) -> None:
+    kohya = {
         "lora_unet_transformer_blocks_0_img_mlp_gate_up.lora_down.weight": torch.zeros(4, 3072),
         "lora_unet_transformer_blocks_0_img_mlp_gate_up.lora_up.weight": torch.zeros(3072, 4),
     }
-    assert not _probe_qwen_image_lora(sd, tmp_path)
+    for sd in (kohya, _comfy_gate_up_lora()):
+        assert not _probe(LoRA_LyCORIS_QwenImage_Config, sd, tmp_path)
+        assert _probe(LoRA_LyCORIS_QwenImage21_Config, sd, tmp_path)
+
+
+_SHAPES = {"attn.to_out.0": (4096, 4096), "img_mlp.proj": (12288, 4096), "img_mlp.out": (4096, 12288)}
+
+
+def _lora_on(*modules: str, kohya: bool = False) -> dict[str, torch.Tensor]:
+    """A low-rank LoRA on the given block modules only, in diffusers/PEFT or Kohya naming."""
+    sd: dict[str, torch.Tensor] = {}
+    for module in modules:
+        out_features, in_features = _SHAPES[module]
+        name = f"transformer_blocks.0.{module}"
+        if kohya:
+            prefix, down, up = "lora_unet_" + name.replace(".", "_"), "lora_down", "lora_up"
+        else:
+            prefix, down, up = f"transformer.{name}", "lora_A", "lora_B"
+        sd[f"{prefix}.{down}.weight"] = torch.zeros(4, in_features)
+        sd[f"{prefix}.{up}.weight"] = torch.zeros(out_features, 4)
+    return sd
+
+
+_OUTPUT_OR_MLP = {
+    # No to_q/to_k/to_v and no gate_up: the attention output is told apart by its width, the MLP halves by name.
+    "attention-output": _lora_on("attn.to_out.0"),
+    "mlp-halves": _lora_on("img_mlp.proj", "img_mlp.out"),
+    "mlp-halves-kohya": _lora_on("img_mlp.proj", "img_mlp.out", kohya=True),
+}
+
+
+def _attention_lokr(width: int, split_w2: bool = False) -> dict[str, torch.Tensor]:
+    """An attention-only LoKR: the weight is kron(w1, w2), so its input width is 4 x w2's. LyCORIS usually keeps
+    w1 whole and stores w2 as a low-rank pair, w2_a @ w2_b."""
+    prefix = "transformer.transformer_blocks.0.attn.to_q"
+    sd = {f"{prefix}.lokr_w1": torch.zeros(4, 4)}
+    if split_w2:
+        sd[f"{prefix}.lokr_w2_a"] = torch.zeros(width // 4, 8)
+        sd[f"{prefix}.lokr_w2_b"] = torch.zeros(8, width // 4)
+    else:
+        sd[f"{prefix}.lokr_w2"] = torch.zeros(width // 4, width // 4)
+    return sd
+
+
+@pytest.mark.parametrize("sd", _OUTPUT_OR_MLP.values(), ids=_OUTPUT_OR_MLP.keys())
+def test_a_lora_on_the_attention_output_or_the_mlp_halves_is_qwen_image_2_1(
+    sd: dict[str, torch.Tensor], tmp_path: Path
+) -> None:
+    assert not _probe(LoRA_LyCORIS_QwenImage_Config, sd, tmp_path)
+    assert _probe(LoRA_LyCORIS_QwenImage21_Config, sd, tmp_path)
+
+
+@pytest.mark.parametrize("split_w2", [False, True], ids=["whole", "split-w2"])
+def test_an_attention_only_lokr_is_told_apart_by_its_width(split_w2: bool, tmp_path: Path) -> None:
+    assert _probe(LoRA_LyCORIS_QwenImage_Config, _attention_lokr(3072, split_w2), tmp_path)
+    # Not Qwen-Image's; and as a LoKR, not installable as Qwen-Image-2.1's either: it is left unknown.
+    assert not _probe(LoRA_LyCORIS_QwenImage_Config, _attention_lokr(4096, split_w2), tmp_path)
+    assert not _probe(LoRA_LyCORIS_QwenImage21_Config, _attention_lokr(4096, split_w2), tmp_path)
+    # A factored width is weaker evidence: beside a module only Qwen-Image has, it stays Qwen-Image's.
+    with_qwen_image_mlp = _attention_lokr(4096) | {
+        "transformer.transformer_blocks.0.img_mlp.net.0.proj.lokr_w1": torch.zeros(4, 4),
+        "transformer.transformer_blocks.0.img_mlp.net.0.proj.lokr_w2": torch.zeros(3072, 768),
+    }
+    assert _probe(LoRA_LyCORIS_QwenImage_Config, with_qwen_image_mlp, tmp_path)
+
+
+@pytest.mark.parametrize(
+    "sd",
+    [_comfy_gate_up_lora(), _attention_lora(4096), *_OUTPUT_OR_MLP.values()],
+    ids=["gate_up", "attention", *_OUTPUT_OR_MLP.keys()],
+)
+def test_a_qwen_image_2_1_lora_is_claimed_by_no_other_lora_config(sd: dict[str, torch.Tensor], tmp_path: Path) -> None:
+    # Identification tries every config; two that accept one file leave the outcome to iteration order.
+    others = [
+        c
+        for c in Config_Base.CONFIG_CLASSES
+        if c.__name__.startswith("LoRA_") and c is not LoRA_LyCORIS_QwenImage21_Config
+    ]
+    assert others
+    assert [c.__name__ for c in others if _probe(c, sd, tmp_path)] == []
+
+
+def _klein_double_block(module: str) -> dict[str, torch.Tensor]:
+    prefix = f"transformer.transformer_blocks.0.attn.{module}"
+    return {f"{prefix}.lora_A.weight": torch.zeros(4, 4096), f"{prefix}.lora_B.weight": torch.zeros(4096, 4)}
+
+
+@pytest.mark.parametrize(
+    "extra",
+    [
+        # Its text embedder's width.
+        {
+            "transformer.context_embedder.lora_A.weight": torch.zeros(4, 12288),
+            "transformer.context_embedder.lora_B.weight": torch.zeros(4096, 4),
+        },
+        # Its double blocks' text stream, which a single-stream Qwen-Image-2.1 block does not have.
+        _klein_double_block("add_q_proj") | _klein_double_block("to_add_out"),
+    ],
+    ids=["text-embedder", "text-stream"],
+)
+def test_a_flux2_klein_9b_lora_is_not_qwen_image_2_1(extra: dict[str, torch.Tensor], tmp_path: Path) -> None:
+    # Klein 9B is as wide (4096) and names its double blocks' image attention the same.
+    assert not _probe(LoRA_LyCORIS_QwenImage21_Config, _attention_lora(4096) | extra, tmp_path)
+
+
+def test_image_attention_alone_reads_as_qwen_image_2_1(tmp_path: Path) -> None:
+    """A known ambiguity, pinned: a LoRA on only `to_q/to_k/to_v` of Klein 9B's first blocks has exactly the keys
+    and shapes of a Qwen-Image-2.1 one, and is filed as the latter."""
+    assert _probe(LoRA_LyCORIS_QwenImage21_Config, _attention_lora(4096), tmp_path)
+
+
+@pytest.mark.parametrize(
+    "extra",
+    [
+        # A text encoder's layer: no Qwen-Image-2.1 transformer module.
+        {
+            "text_encoder.layers.0.mlp.fc1.lora_A.weight": torch.zeros(4, 4096),
+            "text_encoder.layers.0.mlp.fc1.lora_B.weight": torch.zeros(4096, 4),
+        },
+        # LoKR on the fused gate_up, which cannot be split into gate_layer and proj.
+        {
+            "diffusion_model.transformer_blocks.1.img_mlp.gate_up.lokr_w1": torch.zeros(4, 4),
+            "diffusion_model.transformer_blocks.1.img_mlp.gate_up.lokr_w2": torch.zeros(6144, 1024),
+        },
+    ],
+    ids=["foreign-layer", "lokr-gate-up"],
+)
+def test_a_lora_that_would_apply_only_in_part_is_not_installed_as_one(
+    extra: dict[str, torch.Tensor], tmp_path: Path
+) -> None:
+    # Refused at identification, rather than installed and refused at the first generation.
+    assert not _probe(LoRA_LyCORIS_QwenImage21_Config, _comfy_gate_up_lora() | extra, tmp_path)
+
+
+def test_a_lora_with_an_orphaned_half_is_refused(tmp_path: Path) -> None:
+    sd = _comfy_gate_up_lora() | {"diffusion_model.transformer_blocks.1.attn.to_q.lora_A.weight": torch.zeros(4, 4096)}
+    assert not _probe(LoRA_LyCORIS_QwenImage21_Config, sd, tmp_path)
diff --git a/tests/backend/patches/lora_conversions/test_qwen_image_2_1_lora_conversion_utils.py b/tests/backend/patches/lora_conversions/test_qwen_image_2_1_lora_conversion_utils.py
new file mode 100644
index 00000000000..307a52f1121
--- /dev/null
+++ b/tests/backend/patches/lora_conversions/test_qwen_image_2_1_lora_conversion_utils.py
@@ -0,0 +1,181 @@
+"""Qwen-Image-2.1 LoRAs onto the diffusers transformer: every layout, and ComfyUI's fused gate_up split in two."""
+
+import accelerate
+import pytest
+import torch
+
+from invokeai.backend.patches.layers.dora_layer import DoRALayer
+from invokeai.backend.patches.layers.full_layer import FullLayer
+from invokeai.backend.patches.layers.lora_layer import LoRALayer
+from invokeai.backend.patches.lora_conversions.qwen_image_2_1_lora_conversion_utils import (
+    QWEN_IMAGE_21_LORA_NORM_PREFIX as NORM_PREFIX,
+)
+from invokeai.backend.patches.lora_conversions.qwen_image_2_1_lora_conversion_utils import (
+    QWEN_IMAGE_21_LORA_TRANSFORMER_PREFIX as PREFIX,
+)
+from invokeai.backend.patches.lora_conversions.qwen_image_2_1_lora_conversion_utils import (
+    lora_model_from_qwen_image_21_state_dict as convert,
+)
+
+RANK = 4
+BLOCK_MODULES = ("attn.to_q", "attn.to_k", "attn.to_v", "attn.to_out.0", "img_mlp.gate_up", "img_mlp.out")
+TOP_MODULES = (
+    "img_in",
+    "proj_out",
+    "norm_out.linear",
+    "modulation.1",
+    "txt_in.in_layer",
+    "txt_in.out_layer",
+    "time_text_embed.timestep_embedder.linear_1",
+    "time_text_embed.timestep_embedder.linear_2",
+)
+
+
+@pytest.fixture(scope="module")
+def transformer():
+    from diffusers import QwenImage21Transformer2DModel
+
+    with accelerate.init_empty_weights():
+        # The class defaults are the released configuration; two blocks are enough to address.
+        return QwenImage21Transformer2DModel(num_layers=2)
+
+
+def _pair(module_key: str, out_features: int, in_features: int, *, a="lora_A", b="lora_B") -> dict:
+    generator = torch.Generator().manual_seed(len(module_key))
+    return {
+        f"{module_key}.{a}.weight": torch.randn(RANK, in_features, generator=generator),
+        f"{module_key}.{b}.weight": torch.randn(out_features, RANK, generator=generator),
+    }
+
+
+def _shape(transformer, module: str) -> tuple[int, int]:
+    """The weight shape a LoRA on `module` updates; the fused gate_up is gate_layer and proj stacked."""
+    if module.endswith("gate_up"):
+        out, inp = transformer.get_submodule(module.replace("gate_up", "gate_layer")).weight.shape
+        return 2 * out, inp
+    return tuple(transformer.get_submodule(module).weight.shape)
+
+
+def _kohya(module: str) -> str:
+    return "lora_unet_" + module.replace(".", "_")
+
+
+@pytest.mark.parametrize("layout", ["comfy", "kohya"])
+def test_every_module_lands_on_a_linear_of_its_shape(transformer, layout: str) -> None:
+    modules = [f"transformer_blocks.{b}.{m}" for b in range(2) for m in BLOCK_MODULES] + list(TOP_MODULES)
+    sd: dict = {}
+    for module in modules:
+        name = f"diffusion_model.{module}" if layout == "comfy" else _kohya(module)
+        lora = ("lora_A", "lora_B") if layout == "comfy" else ("lora_down", "lora_up")
+        sd |= _pair(name, *_shape(transformer, module), a=lora[0], b=lora[1])
+
+    layers = convert(sd).layers
+    # Every module, gate_up as two.
+    assert len(layers) == len(modules) + 2
+    for key, layer in layers.items():
+        target = transformer.get_submodule(key.removeprefix(PREFIX))
+        assert isinstance(target, torch.nn.Linear), key
+        assert (layer.up.shape[0], layer.down.shape[1]) == tuple(target.weight.shape), key
+
+
+def _delta(layer: LoRALayer) -> torch.Tensor:
+    return layer.up @ layer.down * layer.scale()
+
+
+def test_gate_up_splits_into_gate_layer_then_proj_exactly() -> None:
+    # The split does not depend on the size: 8 output rows, two halves of 4.
+    sd = _pair("diffusion_model.transformer_blocks.0.img_mlp.gate_up", 8, 6)
+    sd["diffusion_model.transformer_blocks.0.img_mlp.gate_up.alpha"] = torch.tensor(2.0)
+    up = sd["diffusion_model.transformer_blocks.0.img_mlp.gate_up.lora_B.weight"]
+    down = sd["diffusion_model.transformer_blocks.0.img_mlp.gate_up.lora_A.weight"]
+
+    layers = convert(sd).layers
+    gate = _delta(layers[f"{PREFIX}transformer_blocks.0.img_mlp.gate_layer"])
+    proj = _delta(layers[f"{PREFIX}transformer_blocks.0.img_mlp.proj"])
+    torch.testing.assert_close(torch.cat([gate, proj]), up @ down * (2.0 / RANK))
+
+
+@pytest.mark.parametrize("magnitude_key", ["lora_magnitude_vector.weight", "lora_magnitude_vector", "magnitude"])
+def test_an_output_dim_dora_on_gate_up_splits_its_magnitude_with_the_rows(magnitude_key: str) -> None:
+    # PEFT writes the magnitude with or without `.weight`, ai-toolkit as `.magnitude`: all one per output row.
+    module = "transformer.transformer_blocks.0.img_mlp.gate_up"
+    magnitude = torch.arange(8.0)
+    sd = _pair(module, 8, 6) | {f"{module}.{magnitude_key}": magnitude}
+
+    layers = convert(sd).layers
+    gate, proj = (layers[f"{PREFIX}transformer_blocks.0.img_mlp.{half}"] for half in ("gate_layer", "proj"))
+    assert isinstance(gate, DoRALayer) and isinstance(proj, DoRALayer)
+    assert gate.magnitude_is_out_dim and proj.magnitude_is_out_dim
+    assert torch.equal(gate.dora_scale, magnitude[:4]) and torch.equal(proj.dora_scale, magnitude[4:])
+
+
+@pytest.mark.parametrize("layout", ["comfy", "kohya"])
+def test_norm_diffs_land_on_their_norm_weights_apart_from_the_linears(transformer, layout: str) -> None:
+    # As the official Turbo LoRA carries them: a full diff on every block's q/k norm and on the text norm.
+    norms = ["transformer_blocks.1.attn.norm_q", "transformer_blocks.1.attn.norm_k", "txt_in.text_norm"]
+    name = (lambda m: f"diffusion_model.{m}") if layout == "comfy" else _kohya
+    sd = {f"{name(m)}.diff": torch.randn(transformer.get_submodule(m).weight.shape) for m in norms}
+    sd |= _pair(name("transformer_blocks.1.attn.to_q"), 4096, 4096, a="lora_down", b="lora_up")
+
+    layers = convert(sd).layers
+    assert set(layers) == {f"{NORM_PREFIX}{m}" for m in norms} | {f"{PREFIX}transformer_blocks.1.attn.to_q"}
+    for module in norms:
+        layer = layers[f"{NORM_PREFIX}{module}"]
+        assert isinstance(layer, FullLayer)
+        assert layer.weight.shape == transformer.get_submodule(module).weight.shape
+
+
+def test_a_full_layer_on_gate_up_splits_by_rows() -> None:
+    diff = torch.randn(8, 6)
+    layers = convert({"diffusion_model.transformer_blocks.0.img_mlp.gate_up.diff": diff}).layers
+    gate, proj = (layers[f"{PREFIX}transformer_blocks.0.img_mlp.{half}"] for half in ("gate_layer", "proj"))
+    assert isinstance(gate, FullLayer) and isinstance(proj, FullLayer)
+    assert torch.equal(torch.cat([gate.weight, proj.weight]), diff)
+
+
+@pytest.mark.parametrize(
+    "prefix", ["diffusion_model.", "transformer.", "base_model.model.transformer.", "base_model.model."]
+)
+def test_every_dotted_prefix_names_the_same_layer(prefix: str) -> None:
+    sd = _pair(f"{prefix}transformer_blocks.3.attn.to_out.0", 6, 6)
+    assert list(convert(sd).layers) == [f"{PREFIX}transformer_blocks.3.attn.to_out.0"]
+
+
+def test_a_layers_own_alpha_scales_it_and_a_missing_one_means_its_rank() -> None:
+    sd = _pair("diffusion_model.transformer_blocks.0.attn.to_q", 6, 6)
+    sd |= _pair("diffusion_model.transformer_blocks.0.attn.to_k", 6, 6)
+    sd["diffusion_model.transformer_blocks.0.attn.to_q.alpha"] = torch.tensor(2.0)
+    layers = convert(sd).layers
+    assert layers[f"{PREFIX}transformer_blocks.0.attn.to_q"].scale() == 2.0 / RANK
+    assert layers[f"{PREFIX}transformer_blocks.0.attn.to_k"].scale() == 1.0
+
+
+@pytest.mark.parametrize(
+    "gate_up_layer",
+    [
+        {"lokr_w1": torch.zeros(4, 4), "lokr_w2": torch.zeros(2, 6)},
+        # LyCORIS's input-dim magnitude normalizes across both halves at once.
+        {"lora_A.weight": torch.zeros(RANK, 6), "lora_B.weight": torch.zeros(8, RANK), "dora_scale": torch.ones(1, 6)},
+    ],
+    ids=["lokr", "lycoris-dora"],
+)
+def test_a_gate_up_layer_that_cannot_be_split_is_refused(gate_up_layer: dict) -> None:
+    sd = {f"diffusion_model.transformer_blocks.0.img_mlp.gate_up.{k}": v for k, v in gate_up_layer.items()}
+    with pytest.raises(ValueError, match="gate_up"):
+        convert(sd)
+
+
+def test_a_low_rank_layer_on_a_norm_is_refused() -> None:
+    # A norm has no matrix to factor; only a full diff of its weight applies.
+    with pytest.raises(ValueError, match="norm_q"):
+        convert(_pair("diffusion_model.transformer_blocks.0.attn.norm_q", 128, 128))
+
+
+def test_a_lora_with_layers_this_model_lacks_is_refused_whole() -> None:
+    # A FLUX.2 double block's text stream, beside image attention Qwen-Image-2.1 has: applying only the part that
+    # fits would pass a partial LoRA off as a whole one.
+    sd = _pair("transformer.transformer_blocks.0.attn.to_q", 6, 6) | _pair(
+        "transformer.transformer_blocks.0.attn.add_q_proj", 6, 6
+    )
+    with pytest.raises(ValueError, match="add_q_proj"):
+        convert(sd)
diff --git a/tests/model_identification/stripped_models/ab927eeb-2d10-4683-ad30-7a6b90283691/Qwen-Image-2.1 Manga to Photo V1.safetensors b/tests/model_identification/stripped_models/ab927eeb-2d10-4683-ad30-7a6b90283691/Qwen-Image-2.1 Manga to Photo V1.safetensors
new file mode 100644
index 00000000000..be61be80a3e
--- /dev/null
+++ b/tests/model_identification/stripped_models/ab927eeb-2d10-4683-ad30-7a6b90283691/Qwen-Image-2.1 Manga to Photo V1.safetensors	
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d01a4928c12daa995629790d847bb27b1169a74d5ca2f6299945318d38b4a05d
+size 69494
diff --git a/tests/model_identification/stripped_models/ab927eeb-2d10-4683-ad30-7a6b90283691/__test_metadata__.json b/tests/model_identification/stripped_models/ab927eeb-2d10-4683-ad30-7a6b90283691/__test_metadata__.json
new file mode 100644
index 00000000000..e523835ead5
--- /dev/null
+++ b/tests/model_identification/stripped_models/ab927eeb-2d10-4683-ad30-7a6b90283691/__test_metadata__.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7ed62a251014a4112677f91f0441164b29068655f88d4fa0e4afbe23536e5a0
+size 328

From d0f200e5f9a08b4f4f19771a93321c3ad3fd9bbe Mon Sep 17 00:00:00 2001
From: Alexander Eichhorn <alex@eichhorn.dev>
Date: Thu, 22 Oct 2026 05:02:41 +0200
Subject: [PATCH 3/4] fix(qwen-image-2-1): load the vision tower only for edits
 and tighten edit pricing

The tower is its own submodel, attached to the cached encoder only while it reads references; text prompts skip it.
Memory estimates follow the dtype, the reference prefill and dedicated cards; the denoise records its cache mode.
At most four references are accepted; a GGUF or single-file model explains why references are unavailable.
---
 .../03.Models/Local Models/qwen-image-2-1.mdx |   6 +-
 invokeai/app/api/routers/recall_parameters.py |   8 +-
 .../qwen_image_2_1/qwen_image_2_1_denoise.py  | 127 +++++---
 .../qwen_image_2_1_text_encoder.py            |  72 +++--
 .../model_manager/load/load_default.py        |  11 +-
 .../load/model_loaders/qwen_image_2_1.py      |  82 ++++-
 invokeai/backend/model_manager/taxonomy.py    |   3 +
 .../backend/qwen_image_2_1/text_encoding.py   |  69 ++++-
 invokeai/frontend/api/openapi.json            |   9 +-
 invokeai/frontend/api/schema.ts               |  14 +-
 .../frontend/webv2/public/locales/en.json     |   1 +
 .../__snapshots__/generateGraphNodeTypes.json |  48 ++-
 .../core/baseGenerationPolicies.test.ts       |  15 +
 .../generation/core/baseGenerationPolicies.ts |  22 ++
 .../core/canvas/compileCanvasGraph.test.ts    |  16 +
 .../features/generation/core/graph.test.ts    |  27 +-
 .../src/features/generation/core/graph.ts     |  12 +-
 .../generation/core/graphCoverage.testing.ts  |  39 ++-
 .../features/generation/core/settings.test.ts |  40 +++
 .../generation/ui/GenerateGuidanceSection.tsx |   9 +-
 .../GenerateReferenceImagesSection.tsx        |  23 +-
 .../referenceImageFormatHint.browser.test.tsx | 113 +++++++
 .../webv2/src/workbench/canvasMigration.ts    |   1 -
 .../image-actions/imageRecall.test.ts         |  42 +++
 .../invocations/test_qwen_image_2_1_nodes.py  | 292 ++++++++++++++++--
 .../load/test_load_default_cpu_only.py        |  10 +
 .../load/test_load_default_fp8.py             |  12 +
 .../load/test_qwen3_vl_visual_tower.py        |  96 +++++-
 .../qwen_image_2_1/test_text_encoding.py      | 110 +++++++
 29 files changed, 1162 insertions(+), 167 deletions(-)
 create mode 100644 invokeai/frontend/webv2/src/features/generation/ui/reference-images/referenceImageFormatHint.browser.test.tsx

diff --git a/docs/src/content/docs/Users Guide/03.Models/Local Models/qwen-image-2-1.mdx b/docs/src/content/docs/Users Guide/03.Models/Local Models/qwen-image-2-1.mdx
index ecbb1190186..8ae96a8bc6f 100644
--- a/docs/src/content/docs/Users Guide/03.Models/Local Models/qwen-image-2-1.mdx	
+++ b/docs/src/content/docs/Users Guide/03.Models/Local Models/qwen-image-2-1.mdx	
@@ -1,7 +1,7 @@
 ---
 title: Qwen-Image-2.1
 description: Generate images with Qwen-Image-2.1 and its 8-step Turbo variant, from the Diffusers folders or a GGUF transformer, including transparent (RGBA) output.
-lastUpdated: 2026-10-17
+lastUpdated: 2026-10-22
 sidebar:
   order: 8
 ---
@@ -64,7 +64,9 @@ With a Qwen-Image-2.1 **Diffusers** model, add up to four images with **Add refe
 - **Diffusers models only.** Only the encoder that comes with the Diffusers model keeps the Qwen3-VL vision tower, which reads the images. A single-file or GGUF transformer with a standalone encoder does not offer reference images, and on a Diffusers model the references refuse to run while a standalone **Qwen3-VL Encoder** is selected under **Components**; clear it to edit.
 - **Size.** Each reference is read at about one megapixel at its own aspect ratio, whatever its size. The generated image takes the size set in the panel; **Use size** on a reference's thumbnail matches it.
 - **Transparency.** The encoder sees a transparent reference over white, as the model was trained; its latents keep the transparency.
-- **Memory.** The vision tower adds about 1.1 GB to the Diffusers model's encoder, on every generation with it. Each reference adds about 4,100 tokens to the start of the sequence, which the model keeps for the whole generation: about 2 GB per reference, twice that with **CFG** above 1. Where that would take more than half of the GPU, Invoke reprocesses the references at every step instead of keeping them, which uses less memory but takes noticeably longer. Reading the references also needs encoder memory that grows quickly with their number: about 7 GB beyond its weights for four, so on a 24 GB card part of the encoder then runs from system memory.
+- **At most four.** A graph or API request with more than four references is refused with an error.
+- **Memory.** The encoder's vision tower, about 1.1 GB, is loaded only for generations with references and joins the encoder while it reads them; plain prompts never load it, and the encoder itself is not loaded again when you switch between edits and plain prompts. Each reference adds about 4,100 tokens to the start of the sequence, which the model keeps for the whole generation: about 2 GB per reference, twice that with **CFG** above 1. Where that would take more than half of the GPU, Invoke reprocesses the references at every step instead of keeping them, which uses less memory but takes noticeably longer. Reading the references also needs encoder memory that grows quickly with their number: about 7 GB beyond its weights for four, so on a 24 GB card part of the encoder then runs from system memory.
+- **Reproducing an edit.** Keeping the references and reprocessing them give slightly different images from the same seed, and which one runs depends on the GPU's memory. The image's metadata records which one ran, as `qwen_image_2_1_prefix_kv_cache`: `true` when the references were kept, `false` when they were reprocessed. Generations without references record it too.
 
 ### Transparent images
 
diff --git a/invokeai/app/api/routers/recall_parameters.py b/invokeai/app/api/routers/recall_parameters.py
index 20caef0d1b5..f32e18bf008 100644
--- a/invokeai/app/api/routers/recall_parameters.py
+++ b/invokeai/app/api/routers/recall_parameters.py
@@ -65,10 +65,10 @@ class ReferenceImageRecallParameter(BaseModel):
 
     Used for reference images that feed directly into the main model rather
     than through a separate IP-Adapter / ControlNet model — for example
-    FLUX.2 Klein, FLUX Kontext, and Qwen Image Edit. The receiving frontend
-    picks the correct config type (``flux2_reference_image`` /
-    ``qwen_image_reference_image`` / ``flux_kontext_reference_image``) based
-    on the currently-selected main model.
+    FLUX.2 Klein, FLUX Kontext, Qwen Image Edit and Qwen-Image-2.1. The
+    receiving frontend picks the correct config type (``flux2_reference_image`` /
+    ``qwen_image_reference_image`` / ``qwen_image_2_1_reference_image`` /
+    ``flux_kontext_reference_image``) based on the currently-selected main model.
     """
 
     image_name: str = Field(description="The filename of the reference image in outputs/images")
diff --git a/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_denoise.py b/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_denoise.py
index 7f69766c476..2fe3dfa4ae3 100644
--- a/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_denoise.py
+++ b/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_denoise.py
@@ -15,12 +15,13 @@
     Input,
     InputField,
     LatentsField,
+    MetadataField,
     QwenImage21ConditioningField,
     WithBoard,
     WithMetadata,
 )
+from invokeai.app.invocations.metadata_linked import LatentsMetaOutput
 from invokeai.app.invocations.model import TransformerField
-from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.startup_utils import log_attention_backends
 from invokeai.backend.flux.sampling_utils import clip_timestep_schedule_fractional
@@ -31,13 +32,16 @@
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import QwenImage21ConditioningInfo
 from invokeai.backend.util.attention import sdpa_score_matrix_bytes
-from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.devices import TorchDevice, device_has_dedicated_vram
 from invokeai.backend.util.logging import InvokeAILogger
 
 LATENT_CHANNELS = 64
 VAE_SCALE_FACTOR = 16
 # One vision-language slot of the transformer's joint sequence covers 2x2 latent tokens.
 TOKENS_PER_SLOT = 4
+# The metadata entry that records whether a run kept the prefix KV cache (True) or recomputed the prefix on every
+# step (False).
+PREFIX_CACHE_METADATA_KEY = "qwen_image_2_1_prefix_kv_cache"
 
 logger = InvokeAILogger.get_logger(__name__)
 
@@ -82,17 +86,18 @@ def check_reference_slots(
     """Refuse references whose latents do not match the prompt's image slots, reference by reference.
 
     Each reference's latents fill one run of the prompt's slots, 2x2 latents per slot, at the slot grid the
-    encoder read it at. A mismatch means the prompt was encoded with other images, in another order or at another
-    size than the latents: the transformer would place one reference's latents in another's slots.
+    encoder read it at. This catches a different number of references, or one at a different size, than the
+    prompt was encoded with -- including references of different sizes in another order. It cannot tell apart
+    same-size references in another order, or a same-size image swapped for another: the slots carry no identity.
     """
     expected = [(h // 2, w // 2) for _, h, w in reference_shapes]
     found = list(info.reference_grids)
     if found != expected or slot_runs(info.image_pad_mask) != [rows * columns for rows, columns in found]:
         raise ValueError(
             f"The {prompt} prompt was encoded with {len(found)} reference image(s) ({_describe_grids(found)}), but "
-            f"the denoise node received {len(expected)} reference latent(s) ({_describe_grids(expected)}). Encode "
-            "each reference with Image to Latents in reference mode, from the same images and in the same order "
-            "as the prompts."
+            f"the denoise node received {len(expected)} reference latent(s) ({_describe_grids(expected)}): the "
+            "counts or sizes differ. Encode each reference with Image to Latents in reference mode, from the same "
+            "images and in the same order as the prompts."
         )
 
 
@@ -102,8 +107,14 @@ def prefix_length(info: QwenImage21ConditioningInfo) -> int:
     return info.prompt_embeds.shape[1] + (TOKENS_PER_SLOT - 1) * slots
 
 
-# K and V of one prefix token in all 32 blocks: 2 x 4096 x 32 x 2 bytes in bf16.
-KV_BYTES_PER_PREFIX_TOKEN = 2 * 4096 * 32 * 2
+def kv_bytes_per_prefix_token(transformer: torch.nn.Module, dtype: torch.dtype) -> int:
+    """K and V of one prefix token in every block of `transformer`, in the compute dtype the caches are kept in.
+
+    2 x 4096 x 32 x 2 bytes for the released model in bf16 (32 heads of 128, 32 blocks): 0.5 MiB.
+    """
+    config = transformer.config
+    hidden = config.num_attention_heads * config.attention_head_dim
+    return 2 * hidden * len(transformer.transformer_blocks) * dtype.itemsize
 
 
 def prefix_cache_fits(cache_bytes: int, device: torch.device) -> bool:
@@ -112,11 +123,41 @@ def prefix_cache_fits(cache_bytes: int, device: torch.device) -> bool:
     They cannot be offloaded the way the transformer's weights can, so caches larger than half the card would
     leave too little for the model however it streams: four references at CFG above 1 hold ~16 GiB. Past that the
     prefix is recomputed every step instead -- slower, but within the memory of an ordinary run. Memory that is
-    not a dedicated card's (CPU, MPS) is not limited here.
+    not a dedicated card's (CPU, MPS, an integrated XPU) is not limited here, nor a card that reports no size.
     """
-    if device.type != "cuda":
+    if not device_has_dedicated_vram(device):
         return True
-    return cache_bytes <= torch.cuda.get_device_properties(device).total_memory // 2
+    backend = torch.xpu if device.type == "xpu" else torch.cuda
+    total = int(backend.get_device_properties(device).total_memory)
+    return total == 0 or cache_bytes <= total // 2
+
+
+def prefix_segments(info: QwenImage21ConditioningInfo) -> list[int]:
+    """The prefix's blocks in the joint sequence, in order: each run of text, and each reference widened to 2x2
+    latents per slot. The prefill attends each block over every key up to its end."""
+    if info.image_pad_mask is None:
+        return [info.prompt_embeds.shape[1]]
+    return [
+        len(list(run)) * (TOKENS_PER_SLOT if is_slot else 1)
+        for is_slot, run in itertools.groupby(info.image_pad_mask[0].tolist())
+    ]
+
+
+def largest_attention_call(infos: list[QwenImage21ConditioningInfo], image_seq_len: int) -> int:
+    """Queries x keys of the largest attention call a run makes, over every prompt it runs.
+
+    The prefill runs each prefix block over the keys before and in it, and the target over every key; a cached
+    step repeats only the target's call. With large references and a small target, the last reference's call is
+    the largest: four 1 MP references before a 512x512 target make it ~4x the target's.
+    """
+    largest = 0
+    for info in infos:
+        end = 0
+        for length in prefix_segments(info):
+            end += length
+            largest = max(largest, length * end)
+        largest = max(largest, image_seq_len * (end + image_seq_len))
+    return largest
 
 
 def pack_latents(latents: torch.Tensor) -> torch.Tensor:
@@ -188,12 +229,20 @@ def validate_cfg_scale_is_finite(cls, value: float | list[float]) -> float | lis
         return value
 
     @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = self._run_diffusion(context).detach().to("cpu")
-        name = context.tensors.save(tensor=latents)
+    def invoke(self, context: InvocationContext) -> LatentsMetaOutput:
+        latents, used_prefix_cache = self._run_diffusion(context)
+        name = context.tensors.save(tensor=latents.detach().to("cpu"))
+        # The two modes sample visibly differently in bf16, and which one runs depends on the card's memory: the
+        # image's metadata has to say, or nothing tells why one graph and seed give another image on another card.
+        # A graph routes this output to the node that saves the image.
+        metadata = dict(self.metadata.root) if self.metadata is not None else {}
+        metadata[PREFIX_CACHE_METADATA_KEY] = used_prefix_cache
         # `LatentsOutput.build` assumes 8x latents; these are 16x, so report the requested size directly.
-        return LatentsOutput(
-            latents=LatentsField(latents_name=name, seed=self.seed), width=self.width, height=self.height
+        return LatentsMetaOutput(
+            latents=LatentsField(latents_name=name, seed=self.seed),
+            width=self.width,
+            height=self.height,
+            metadata=MetadataField.model_validate(metadata),
         )
 
     def _load_conditioning(
@@ -245,7 +294,8 @@ def _prep_inpaint_mask(self, context: InvocationContext, latents: torch.Tensor)
         )
         return mask.to(device=latents.device, dtype=latents.dtype)
 
-    def _run_diffusion(self, context: InvocationContext) -> torch.Tensor:
+    def _run_diffusion(self, context: InvocationContext) -> tuple[torch.Tensor, bool]:
+        """The denoised latents, and whether the prefix KV cache ran (rather than the prefix being recomputed)."""
         from diffusers.models.transformers.transformer_qwenimage21 import QwenImage21KVCache
 
         if self.denoising_start >= self.denoising_end:
@@ -350,7 +400,8 @@ def image_slot_mask(info: QwenImage21ConditioningInfo) -> torch.Tensor:
         neg_prefix = prefix_length(neg) if neg is not None else None
         # Text and reference keys and values do not depend on the step under `causal_condition`: the first step
         # prefills them, the rest compute the image tokens only -- when the caches fit.
-        cache_bytes = KV_BYTES_PER_PREFIX_TOKEN * (pos_prefix + (neg_prefix or 0))
+        kv_bytes_per_token = kv_bytes_per_prefix_token(transformer_info.model, dtype)
+        cache_bytes = kv_bytes_per_token * (pos_prefix + (neg_prefix or 0))
         use_cache = bool(getattr(transformer_info.model.config, "causal_condition", False))
         if use_cache and not prefix_cache_fits(cache_bytes, device):
             use_cache = False
@@ -359,10 +410,13 @@ def image_slot_mask(info: QwenImage21ConditioningInfo) -> torch.Tensor:
                 "this GPU; recomputing the prefix every step instead, which is slower."
             )
         working_memory = self._estimate_working_memory(
-            image_seq_len, pos_prefix, neg_prefix, do_cfg, use_cache, dtype.itemsize
+            image_seq_len, pos_prefix, neg_prefix, do_cfg, use_cache, dtype.itemsize, kv_bytes_per_token
         )
         working_memory += self._attention_score_bytes(
-            transformer_info.model, image_seq_len, max(pos_prefix, neg_prefix or 0), device, dtype
+            transformer_info.model,
+            largest_attention_call([pos] if neg is None else [pos, neg], image_seq_len),
+            device,
+            dtype,
         )
         working_memory += peak_dequant_transient_bytes(transformer_info.model, dtype)
         log_attention_backends(logger, device)
@@ -436,7 +490,7 @@ def predict(embeds: torch.Tensor, img_mask: torch.Tensor, cache, mode, timestep)
                     )
                 )
 
-        return unpack_latents(latents, latent_height, latent_width).float()
+        return unpack_latents(latents, latent_height, latent_width).float(), use_cache
 
     @staticmethod
     def _estimate_working_memory(
@@ -446,15 +500,16 @@ def _estimate_working_memory(
         do_cfg: bool,
         use_cache: bool,
         element_size: int,
+        kv_bytes_per_token: int,
     ) -> int:
         """Peak transformer activations, in bytes, for the model cache to keep free.
 
         Measured on an RTX 4090 in bf16 with the transformer fully resident: 0.55 GiB of activations at
         1024x1024 (4096 image tokens) and 2.2 GiB at 2048x2048 (16384), about 0.14 MiB per token. 0.25 MiB per
-        token plus a 1 GiB base leaves room for the allocator and the prefix cache, which holds K and V of every
-        prefix token -- text and reference latents -- in all 32 blocks (~0.5 MiB per token, per guidance branch).
-        Both per-token terms are bf16 figures and scale with `element_size`, the compute dtype's width: a
-        float32 fallback needs twice as much.
+        token plus a 1 GiB base leaves room for the allocator. That term is a bf16 figure and scales with
+        `element_size`, the compute dtype's width: a float32 fallback needs twice as much. The prefix cache adds
+        `kv_bytes_per_token` (K and V in every block, already in that dtype: 0.5 MiB for the released model in
+        bf16) for every prefix token -- text and reference latents -- per guidance branch.
         """
         mib, gib = 1024**2, 1024**3
         width = element_size / 2
@@ -463,29 +518,29 @@ def _estimate_working_memory(
         if use_cache:
             branches = 2 if do_cfg else 1
             prefix_tokens = branches * (pos_prefix if neg_prefix is None else prefix)
-            estimated += int(prefix_tokens * KV_BYTES_PER_PREFIX_TOKEN * width)
+            estimated += prefix_tokens * kv_bytes_per_token
         return estimated
 
     @staticmethod
-    def _attention_score_bytes(
-        transformer: object, image_seq_len: int, prefix: int, device: torch.device, dtype: torch.dtype
-    ) -> int:
-        """The score matrix attention materializes on a build without a fused kernel for it (0 where it has one)."""
+    def _attention_score_bytes(transformer: object, largest_call: int, device: torch.device, dtype: torch.dtype) -> int:
+        """The score matrix attention materializes on a build without a fused kernel for it (0 where it has one).
+
+        `largest_call` is queries x keys of the largest call, from `largest_attention_call`.
+        """
         config = getattr(transformer, "config", None)
         num_heads = getattr(config, "num_attention_heads", None)
         head_dim = getattr(config, "attention_head_dim", None)
         if not isinstance(num_heads, int) or not isinstance(head_dim, int):
             return 0
-        # The largest call, the image queries over every key, goes unmasked both in the prefill and on decode steps:
-        # only the text segments carry a mask, and they are as short as the prompt. All of them go through diffusers'
-        # attention dispatch. Its score matrix is image x (prefix + image), which the square helper prices at the
-        # side with the same area.
+        # The largest call is a reference block's or the target's, both unmasked: only the text blocks carry a mask,
+        # and they are as short as the prompt. All of them go through diffusers' attention dispatch. The square
+        # helper prices the call at the side with the same area, rounded up.
         return sdpa_score_matrix_bytes(
             device=device,
             dtype=dtype,
             num_heads=num_heads,
             head_dim=head_dim,
-            seq_len=math.isqrt(image_seq_len * (image_seq_len + prefix)),
+            seq_len=math.isqrt(largest_call - 1) + 1 if largest_call > 0 else 0,
             via_diffusers_dispatch=True,
         )
 
diff --git a/invokeai/app/invocations/text_encoder/qwen_image_2_1_text_encoder.py b/invokeai/app/invocations/text_encoder/qwen_image_2_1_text_encoder.py
index 7b59a30b17e..31b5384f4be 100644
--- a/invokeai/app/invocations/text_encoder/qwen_image_2_1_text_encoder.py
+++ b/invokeai/app/invocations/text_encoder/qwen_image_2_1_text_encoder.py
@@ -7,13 +7,15 @@
 from invokeai.app.invocations.model import ModelIdentifierField, Qwen3VLEncoderField
 from invokeai.app.invocations.primitives import QwenImage21ConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.architectures import FeaturesFacet, require
 from invokeai.backend.model_manager.load.model_cache.utils import get_effective_device
 from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelFormat, ModelType, SubModelType
 from invokeai.backend.quantization.dequantizing_linear import peak_dequant_transient_bytes
 from invokeai.backend.qwen_image_2_1.text_encoding import (
+    attached_vision_tower,
     encode_prompt,
-    has_vision_tower,
     sequence_length,
+    vision_attention_bytes,
     working_memory_bytes,
 )
 from invokeai.backend.qwen_image_2_1.vae import to_reference
@@ -51,39 +53,58 @@ class QwenImage21TextEncoderInvocation(BaseInvocation):
     )
     reference_images: list[ImageField] = InputField(
         default=[],
-        description="Reference images to edit or draw from, in the order the denoise node receives their latents. "
-        "Needs a Qwen-Image-2.1 Diffusers model's own encoder.",
+        description="Reference images to edit or draw from, in the order the denoise node receives their latents, "
+        f"at most {require(BaseModelType.QwenImage21, FeaturesFacet).max_reference_images}. Needs a Qwen-Image-2.1 "
+        "Diffusers model's own encoder.",
     )
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> QwenImage21ConditioningOutput:
+        max_references = require(BaseModelType.QwenImage21, FeaturesFacet).max_reference_images
+        if len(self.reference_images) > max_references:
+            raise ValueError(
+                f"Qwen-Image-2.1 reads at most {max_references} reference images; this prompt has "
+                f"{len(self.reference_images)}. Remove references until {max_references} remain."
+            )
         images = [to_reference(context.images.get_pil(field.image_name)) for field in self.reference_images]
-        processor_id = self._processor_identifier(context) if images else None
+        tower_id, processor_id = self._edit_identifiers(context) if images else (None, None)
 
         # The encoder first: loading it evicts unlocked small entries from the RAM cache, which would otherwise drop
-        # the tokenizer and processor loaded before it.
+        # the tokenizer, tower and processor loaded after it.
         text_encoder_info = context.models.load(self.qwen3_vl_encoder.text_encoder)
         tokenizer_info = context.models.load(self.qwen3_vl_encoder.tokenizer)
+        tower_info = context.models.load(tower_id) if tower_id is not None else None
         processor_info = context.models.load(processor_id) if processor_id is not None else None
+        if tower_info is not None and tower_info.compute_device != text_encoder_info.compute_device:
+            raise RuntimeError(
+                f"The vision tower runs on {tower_info.compute_device} and the encoder on "
+                f"{text_encoder_info.compute_device}; an edit attaches one to the other, so they need one device."
+            )
+        compute_dtype = TorchDevice.choose_bfloat16_safe_dtype(text_encoder_info.compute_device)
         # A GGUF or int8 encoder dequantizes one layer at a time; that transient is the working memory.
-        dequant_bytes = peak_dequant_transient_bytes(
-            text_encoder_info.model, TorchDevice.choose_bfloat16_safe_dtype(text_encoder_info.compute_device)
+        dequant_bytes = peak_dequant_transient_bytes(text_encoder_info.model, compute_dtype)
+        vision_bytes = (
+            vision_attention_bytes(tower_info.model, images, tower_info.compute_device, compute_dtype)
+            if tower_info is not None
+            else 0
         )
-        # Before the encoder moves to the GPU: a graph can pair this pipeline's tokenizer with another encoder.
-        if images and not has_vision_tower(text_encoder_info.model):
-            raise ValueError(_NO_VISION_TOWER)
 
         context.util.signal_progress("Running Qwen3-VL text encoder")
         with tokenizer_info as tokenizer:
             # Reference images make the sequence long, and its attention grows with the square of it: room the
             # model cache has to make, or the driver spills the encode to system memory and it crawls.
-            working_memory = dequant_bytes + working_memory_bytes(sequence_length(tokenizer, self.prompt, images))
+            num_tokens = sequence_length(tokenizer, self.prompt, images)
+            working_memory = dequant_bytes + working_memory_bytes(num_tokens, compute_dtype.itemsize, vision_bytes)
+            # The tower locks before the encoder, so the room the encoder reserves is still free once both are on
+            # the device.
             with (
                 processor_info.model_on_device() if processor_info is not None else nullcontext((None, None)) as (
                     _,
                     processor,
                 ),
+                tower_info.model_on_device() if tower_info is not None else nullcontext((None, None)) as (_, tower),
                 text_encoder_info.model_on_device(working_mem_bytes=working_memory) as (_, text_encoder),
+                attached_vision_tower(text_encoder, tower) if tower is not None else nullcontext(),
             ):
                 encoding = encode_prompt(
                     text_encoder,
@@ -102,14 +123,21 @@ def invoke(self, context: InvocationContext) -> QwenImage21ConditioningOutput:
         name = context.conditioning.save(ConditioningFieldData(conditionings=[conditioning]))
         return QwenImage21ConditioningOutput.build(name)
 
-    def _processor_identifier(self, context: InvocationContext) -> ModelIdentifierField:
-        """The image processor of the model the tokenizer came from, which has to be a Qwen-Image-2.1 pipeline."""
-        tokenizer = self.qwen3_vl_encoder.tokenizer
-        config = context.models.get_config(tokenizer)
-        if (
-            config.base is not BaseModelType.QwenImage21
-            or config.type is not ModelType.Main
-            or config.format is not ModelFormat.Diffusers
-        ):
-            raise ValueError(_NO_VISION_TOWER)
-        return tokenizer.model_copy(update={"submodel_type": SubModelType.Processor})
+    def _edit_identifiers(self, context: InvocationContext) -> tuple[ModelIdentifierField, ModelIdentifierField]:
+        """The vision tower and the image processor an edit adds, which only a Qwen-Image-2.1 pipeline brings.
+
+        Checked on the configs, before anything loads: a graph can pair a standalone encoder with this pipeline's
+        tokenizer, or the other way round.
+        """
+        for identifier in (self.qwen3_vl_encoder.tokenizer, self.qwen3_vl_encoder.text_encoder):
+            config = context.models.get_config(identifier)
+            if (
+                config.base is not BaseModelType.QwenImage21
+                or config.type is not ModelType.Main
+                or config.format is not ModelFormat.Diffusers
+            ):
+                raise ValueError(_NO_VISION_TOWER)
+        return (
+            self.qwen3_vl_encoder.text_encoder.model_copy(update={"submodel_type": SubModelType.VisionTower}),
+            self.qwen3_vl_encoder.tokenizer.model_copy(update={"submodel_type": SubModelType.Processor}),
+        )
diff --git a/invokeai/backend/model_manager/load/load_default.py b/invokeai/backend/model_manager/load/load_default.py
index 5f68261da37..1ba0800730f 100644
--- a/invokeai/backend/model_manager/load/load_default.py
+++ b/invokeai/backend/model_manager/load/load_default.py
@@ -51,6 +51,9 @@
 # quality, which fp8 rounding degrades. Its tokenizer is listed for symmetry (it is not an nn.Module,
 # so casting is a no-op today only by accident).
 #
+# The vision tower follows the text encoder it attaches to. It also could not take the cast as it stands:
+# `Qwen3VLModel` casts the pixels to `visual.dtype`, which a cast tower reports as float8.
+#
 # Unlike the model-kind rules in `fp8_capability.py`, these are about which *component* of a model is
 # being loaded, which no client can see and nothing outside this file asks about.
 _FP8_EXCLUDED_SUBMODEL_TYPES: frozenset[SubModelType] = frozenset(
@@ -58,6 +61,7 @@
         SubModelType.TextEncoder,
         SubModelType.TextEncoder2,
         SubModelType.TextEncoder3,
+        SubModelType.VisionTower,
         SubModelType.Tokenizer,
         SubModelType.Tokenizer2,
         SubModelType.Tokenizer3,
@@ -342,7 +346,12 @@ def _get_execution_device(
         if hasattr(config, "default_settings") and config.default_settings is not None:
             if hasattr(config.default_settings, "cpu_only") and config.default_settings.cpu_only is True:
                 # Only apply CPU execution to text encoder submodels
-                if submodel_type in [SubModelType.TextEncoder, SubModelType.TextEncoder2, SubModelType.TextEncoder3]:
+                if submodel_type in [
+                    SubModelType.TextEncoder,
+                    SubModelType.TextEncoder2,
+                    SubModelType.TextEncoder3,
+                    SubModelType.VisionTower,
+                ]:
                     return torch.device("cpu")
 
         # Check if this is a standalone text encoder config with cpu_only field (T5Encoder, Qwen3Encoder, etc.)
diff --git a/invokeai/backend/model_manager/load/model_loaders/qwen_image_2_1.py b/invokeai/backend/model_manager/load/model_loaders/qwen_image_2_1.py
index 08ff7d29996..549ee7f5be0 100644
--- a/invokeai/backend/model_manager/load/model_loaders/qwen_image_2_1.py
+++ b/invokeai/backend/model_manager/load/model_loaders/qwen_image_2_1.py
@@ -1,5 +1,7 @@
 """Loaders for Qwen-Image-2.1 main models: diffusers pipelines, single-file and GGUF transformers."""
 
+import json
+import math
 from pathlib import Path
 from typing import Any, Optional
 
@@ -18,6 +20,7 @@
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
+from invokeai.backend.model_manager.util.qwen3_vl import drop_qwen3vl_visual_tower
 from invokeai.backend.quantization.fp8_scaled import (
     attach_fp8_scales,
     cast_state_dict,
@@ -66,10 +69,24 @@ class QwenImage21DiffusersModel(GenericDiffusersLoader):
     rather than the declared `Qwen3VLForConditionalGeneration`: the LM head is ~1.2 GiB the encoder never runs,
     and `Qwen3VLModel` is what the standalone Qwen3-VL loaders return, so the text encoder node sees one class.
 
-    Unlike those loaders, this one keeps the vision tower (~1.1 GiB): reference images reach the encoder only
-    through it, and only this pipeline's encoder carries one into Qwen-Image-2.1.
+    `TextEncoder` drops the vision tower, as the standalone loaders do: text prompts never reach it.
+    `VisionTower` is that tower alone (~1.1 GiB), read from the same folder for edits, which attach it to the
+    cached encoder while they encode. The language model's weights are cached once for both.
     """
 
+    def get_size_fs(
+        self, config: AnyModelConfig, model_path: Path, submodel_type: Optional[SubModelType] = None
+    ) -> int:
+        if submodel_type is SubModelType.VisionTower:
+            # A few hundred tensors of the encoder's folder, not the folder: reserving its ~17 GB for the tower
+            # would evict models for nothing. Sized at the dtype they load in, whatever the file stores.
+            element_size = TorchDevice.choose_bfloat16_safe_dtype(TorchDevice.choose_torch_device()).itemsize
+            return sum(
+                math.prod(shape) * element_size
+                for _shard, _key, shape in _vision_tower_tensors(model_path / SubModelType.TextEncoder.value)
+            )
+        return super().get_size_fs(config, model_path, submodel_type)
+
     def _load_model(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> AnyModel:
         if isinstance(config, Checkpoint_Config_Base):
             raise NotImplementedError(
@@ -92,6 +109,11 @@ def _load_model(self, config: AnyModelConfig, submodel_type: Optional[SubModelTy
             dtype = self._torch_dtype_avoiding_float16()
         else:
             dtype = TorchDevice.choose_bfloat16_safe_dtype(TorchDevice.choose_torch_device())
+
+        if submodel_type is SubModelType.VisionTower:
+            tower = _load_vision_tower(model_path / SubModelType.TextEncoder.value, dtype)
+            return self._apply_fp8_layerwise_casting(tower, config, submodel_type)
+
         repo_variant = config.repo_variant if isinstance(config, Diffusers_Config_Base) else None
         variant = repo_variant.value if repo_variant else None
         if submodel_type is SubModelType.TextEncoder:
@@ -114,9 +136,65 @@ def _load_model(self, config: AnyModelConfig, submodel_type: Optional[SubModelTy
             else:
                 raise
 
+        if submodel_type is SubModelType.TextEncoder:
+            # Text prompts never reach the vision tower; edits load it as `VisionTower` and attach it while they
+            # encode.
+            drop_qwen3vl_visual_tower(result)
+
         return self._apply_fp8_layerwise_casting(result, config, submodel_type)
 
 
+# The tower's tensors in an encoder folder: `model.visual.*` as `Qwen3VLForConditionalGeneration` saves them (the
+# released pipeline), `visual.*` as `Qwen3VLModel` does.
+_VISION_TOWER_PREFIXES = ("model.visual.", "visual.")
+
+
+def _vision_tower_tensors(folder: Path) -> list[tuple[Path, str, list[int]]]:
+    """(shard, key, shape) of every vision-tower tensor in the folder, read from the headers.
+
+    The shard index names the shards that hold them; without an index, every safetensors file in the folder.
+    """
+    from safetensors import safe_open
+
+    index = folder / "model.safetensors.index.json"
+    if index.exists():
+        weight_map: dict[str, str] = json.loads(index.read_text(encoding="utf-8"))["weight_map"]
+        shards = sorted({folder / shard for key, shard in weight_map.items() if key.startswith(_VISION_TOWER_PREFIXES)})
+    else:
+        shards = sorted(folder.glob("*.safetensors"))
+    tensors = []
+    for shard in shards:
+        with safe_open(shard, framework="pt", device="cpu") as f:
+            for key in f.keys():
+                if key.startswith(_VISION_TOWER_PREFIXES):
+                    tensors.append((shard, key, f.get_slice(key).get_shape()))
+    if not tensors:
+        raise ValueError(f"{folder} holds no Qwen3-VL vision tower ({' or '.join(_VISION_TOWER_PREFIXES)} tensors).")
+    return tensors
+
+
+def _load_vision_tower(folder: Path, dtype: torch.dtype) -> torch.nn.Module:
+    """The encoder's vision tower alone, as `Qwen3VLModel.visual` would hold it, without reading the language model."""
+    from safetensors import safe_open
+    from transformers import AutoConfig
+    from transformers.models.qwen3_vl.modeling_qwen3_vl import Qwen3VLVisionModel
+
+    vision_config = AutoConfig.from_pretrained(folder, local_files_only=True).vision_config
+    with accelerate.init_empty_weights():
+        tower = Qwen3VLVisionModel._from_config(vision_config, torch_dtype=dtype)
+
+    state_dict: dict[str, torch.Tensor] = {}
+    tensors = _vision_tower_tensors(folder)
+    for shard in sorted({shard for shard, *_ in tensors}):
+        with safe_open(shard, framework="pt", device="cpu") as f:
+            for _shard, key, _shape in (t for t in tensors if t[0] == shard):
+                prefix = next(p for p in _VISION_TOWER_PREFIXES if key.startswith(p))
+                state_dict[key[len(prefix) :]] = f.get_tensor(key).to(dtype)
+    load_state_dict_ignoring_extras(tower, state_dict, source=f"Qwen3-VL vision tower in {folder}", assign=True)
+    reject_incomplete_load(tower, what="Qwen3-VL vision tower")
+    return tower.eval()
+
+
 @ModelLoaderRegistry.register(base=BaseModelType.QwenImage21, type=ModelType.Main, format=ModelFormat.Checkpoint)
 class QwenImage21CheckpointModel(ModelLoader):
     """Loads a Qwen-Image-2.1 transformer from a single safetensors file.
diff --git a/invokeai/backend/model_manager/taxonomy.py b/invokeai/backend/model_manager/taxonomy.py
index 3a328b436ed..4a45c175c52 100644
--- a/invokeai/backend/model_manager/taxonomy.py
+++ b/invokeai/backend/model_manager/taxonomy.py
@@ -123,6 +123,9 @@ class SubModelType(str, Enum):
     TextEncoder = "text_encoder"
     TextEncoder2 = "text_encoder_2"
     TextEncoder3 = "text_encoder_3"
+    # A vision-language encoder's vision tower alone, which the text encoder is loaded without; edits attach it
+    # while they encode, so the language model is cached once. Qwen-Image-2.1 only.
+    VisionTower = "vision_tower"
     Tokenizer = "tokenizer"
     Tokenizer2 = "tokenizer_2"
     Tokenizer3 = "tokenizer_3"
diff --git a/invokeai/backend/qwen_image_2_1/text_encoding.py b/invokeai/backend/qwen_image_2_1/text_encoding.py
index a9f145ef78b..bb1a80c7b31 100644
--- a/invokeai/backend/qwen_image_2_1/text_encoding.py
+++ b/invokeai/backend/qwen_image_2_1/text_encoding.py
@@ -1,11 +1,14 @@
 """Qwen-Image-2.1's prompt encoding, as `QwenImage21Pipeline._get_qwen_prompt_embeds` does it."""
 
-from collections.abc import Sequence
+from collections.abc import Iterator, Sequence
+from contextlib import contextmanager
 from typing import NamedTuple
 
 import torch
 from PIL import Image
 
+from invokeai.backend.util.attention import sdpa_score_matrix_bytes
+
 SYSTEM_PROMPT = "Comprehend and analyze the provided prompt."
 SYSTEM_BLOCK = f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
 # A raw string handed to the tokenizer, not `apply_chat_template`: the two tokenize differently, and the
@@ -14,11 +17,14 @@
 IMAGE_PAD = "<|image_pad|>"
 # Pixels per side of one vision token: 16px patches, merged 2x2.
 _PIXELS_PER_TOKEN = 32
+# The vision tower reads each reference's 16px patches before the 2x2 merge: four per vision token.
+_PATCHES_PER_TOKEN = 4
 
-# What one encode needs above the weights for a sequence of T tokens, measured on an RTX 4090 (torch 2.13,
+# What one encode needs above the weights for a sequence of T tokens, measured on an RTX 4090 in bf16 (torch 2.13,
 # transformers 5.10): ~286 B * T^2 + 224 KiB * T at peak. The quadratic term is the language model's attention,
 # which SDPA runs on its math kernel for grouped-query heads; four 1-megapixel references (~4.1k tokens) need
-# ~5.4 GiB. The allocator reserves about a fifth more than it hands out.
+# ~5.4 GiB. Both terms are activations, so they scale with the compute dtype's width. The allocator reserves about
+# a fifth more than it hands out.
 _BYTES_PER_TOKEN_PAIR = 286
 _BYTES_PER_TOKEN = 224 * 1024
 _RESERVED_OVER_ALLOCATED = 1.25
@@ -26,6 +32,10 @@
 
 def format_prompt(prompt: str, num_images: int = 0) -> str:
     """The prompt as the checkpoint was trained on it, with one vision placeholder per reference image first."""
+    if num_images and IMAGE_PAD in prompt:
+        # The processor expands every placeholder into one image's tokens, so a literal one would claim an image
+        # that is not there.
+        raise ValueError(f"A prompt that reads reference images cannot contain {IMAGE_PAD}. Remove it from the prompt.")
     placeholders = " ".join(f"<image{i}><|vision_start|>{IMAGE_PAD}<|vision_end|>" for i in range(1, num_images + 1))
     # Qwen has no BOS token, so an empty prompt would leave the encoder nothing to read.
     return T2I_TEMPLATE.format(placeholders + (prompt or " "))
@@ -40,9 +50,35 @@ def sequence_length(tokenizer, prompt: str, images: Sequence[Image.Image] = ())
     return text + sum((w // _PIXELS_PER_TOKEN) * (h // _PIXELS_PER_TOKEN) for w, h in (i.size for i in images))
 
 
-def working_memory_bytes(num_tokens: int) -> int:
-    """Device memory an encode of `num_tokens` tokens needs above the encoder's weights."""
-    return int((_BYTES_PER_TOKEN_PAIR * num_tokens**2 + _BYTES_PER_TOKEN * num_tokens) * _RESERVED_OVER_ALLOCATED)
+def vision_attention_bytes(
+    tower: torch.nn.Module, images: Sequence[Image.Image], device: torch.device, dtype: torch.dtype
+) -> int:
+    """The score matrix the vision tower's attention materializes for the largest reference, 0 where it is fused.
+
+    The tower attends within one image at a time, over its patches: four per vision token, ~4k for 1 megapixel.
+    """
+    if not images:
+        return 0
+    patches = max((w // _PIXELS_PER_TOKEN) * (h // _PIXELS_PER_TOKEN) for w, h in (i.size for i in images))
+    return sdpa_score_matrix_bytes(
+        device=device,
+        dtype=dtype,
+        num_heads=tower.config.num_heads,
+        head_dim=tower.config.hidden_size // tower.config.num_heads,
+        seq_len=_PATCHES_PER_TOKEN * patches,
+    )
+
+
+def working_memory_bytes(num_tokens: int, element_size: int, vision_attention: int = 0) -> int:
+    """Device memory an encode of `num_tokens` tokens needs above the encoder's weights.
+
+    `element_size` is the compute dtype's width. `vision_attention` is the tower's score matrix
+    (`vision_attention_bytes`): the tower runs, and frees it, before the language model starts, so only the larger
+    of the two attention peaks counts.
+    """
+    width = element_size / 2
+    attention = max(_BYTES_PER_TOKEN_PAIR * num_tokens**2 * width, vision_attention)
+    return int((attention + _BYTES_PER_TOKEN * num_tokens * width) * _RESERVED_OVER_ALLOCATED)
 
 
 def system_prefix_length(tokenizer) -> int:
@@ -61,14 +97,31 @@ def _base_model(text_encoder: torch.nn.Module) -> torch.nn.Module:
 
 
 def has_vision_tower(text_encoder: torch.nn.Module) -> bool:
-    """Whether this encoder can read reference images: only the Qwen-Image-2.1 pipeline's keeps its vision tower.
+    """Whether this encoder can read reference images: whether a vision tower is attached (`attached_vision_tower`).
 
-    The standalone Qwen3-VL loaders replace the tower with an `Identity` rather than deleting it.
+    Every encoder loads with an `Identity` where the tower was.
     """
     visual = getattr(_base_model(text_encoder), "visual", None)
     return isinstance(visual, torch.nn.Module) and not isinstance(visual, torch.nn.Identity)
 
 
+@contextmanager
+def attached_vision_tower(text_encoder: torch.nn.Module, tower: torch.nn.Module) -> Iterator[None]:
+    """Attach the pipeline's vision tower to its cached encoder for one encode.
+
+    The encoder and the tower are separate model-cache entries, so the language model is cached once for text
+    prompts and edits. Both must be locked on the same device for the whole block. Whatever the block does, the
+    encoder leaves it with the module it had, so the cache never holds an encoder with a tower it does not own.
+    """
+    base = _base_model(text_encoder)
+    previous = base.visual
+    base.visual = tower
+    try:
+        yield
+    finally:
+        base.visual = previous
+
+
 def on_white(image: Image.Image) -> Image.Image:
     """A reference as the vision encoder reads it: alpha composited over white, as the checkpoint was trained."""
     if image.mode != "RGBA":
diff --git a/invokeai/frontend/api/openapi.json b/invokeai/frontend/api/openapi.json
index af354195dc7..ef1d9ea1be6 100644
--- a/invokeai/frontend/api/openapi.json
+++ b/invokeai/frontend/api/openapi.json
@@ -57786,7 +57786,7 @@
             "$ref": "#/components/schemas/PromptTemplateOutput"
           },
           "qwen_image_2_1_denoise": {
-            "$ref": "#/components/schemas/LatentsOutput"
+            "$ref": "#/components/schemas/LatentsMetaOutput"
           },
           "qwen_image_2_1_i2l": {
             "$ref": "#/components/schemas/LatentsOutput"
@@ -96401,7 +96401,7 @@
         "type": "object",
         "version": "1.1.0",
         "output": {
-          "$ref": "#/components/schemas/LatentsOutput"
+          "$ref": "#/components/schemas/LatentsMetaOutput"
         }
       },
       "QwenImage21ImageToLatentsInvocation": {
@@ -96877,7 +96877,7 @@
           },
           "reference_images": {
             "default": [],
-            "description": "Reference images to edit or draw from, in the order the denoise node receives their latents. Needs a Qwen-Image-2.1 Diffusers model's own encoder.",
+            "description": "Reference images to edit or draw from, in the order the denoise node receives their latents, at most 4. Needs a Qwen-Image-2.1 Diffusers model's own encoder.",
             "field_kind": "input",
             "input": "any",
             "items": {
@@ -99706,7 +99706,7 @@
         "type": "object",
         "required": ["image_name"],
         "title": "ReferenceImageRecallParameter",
-        "description": "Global reference-image configuration for recall.\n\nUsed for reference images that feed directly into the main model rather\nthan through a separate IP-Adapter / ControlNet model \u2014 for example\nFLUX.2 Klein, FLUX Kontext, and Qwen Image Edit. The receiving frontend\npicks the correct config type (``flux2_reference_image`` /\n``qwen_image_reference_image`` / ``flux_kontext_reference_image``) based\non the currently-selected main model."
+        "description": "Global reference-image configuration for recall.\n\nUsed for reference images that feed directly into the main model rather\nthan through a separate IP-Adapter / ControlNet model \u2014 for example\nFLUX.2 Klein, FLUX Kontext, Qwen Image Edit and Qwen-Image-2.1. The\nreceiving frontend picks the correct config type (``flux2_reference_image`` /\n``qwen_image_reference_image`` / ``qwen_image_2_1_reference_image`` /\n``flux_kontext_reference_image``) based on the currently-selected main model."
       },
       "RemoteModelFile": {
         "properties": {
@@ -106081,6 +106081,7 @@
           "text_encoder",
           "text_encoder_2",
           "text_encoder_3",
+          "vision_tower",
           "tokenizer",
           "tokenizer_2",
           "tokenizer_3",
diff --git a/invokeai/frontend/api/schema.ts b/invokeai/frontend/api/schema.ts
index cba788848f4..a97d0d0aeba 100644
--- a/invokeai/frontend/api/schema.ts
+++ b/invokeai/frontend/api/schema.ts
@@ -22998,7 +22998,7 @@ export type components = {
             pidi_edge_detection: components["schemas"]["ImageOutput"];
             prompt_from_file: components["schemas"]["StringCollectionOutput"];
             prompt_template: components["schemas"]["PromptTemplateOutput"];
-            qwen_image_2_1_denoise: components["schemas"]["LatentsOutput"];
+            qwen_image_2_1_denoise: components["schemas"]["LatentsMetaOutput"];
             qwen_image_2_1_i2l: components["schemas"]["LatentsOutput"];
             qwen_image_2_1_l2i: components["schemas"]["ImageOutput"];
             qwen_image_2_1_model_loader: components["schemas"]["QwenImage21ModelLoaderOutput"];
@@ -41892,7 +41892,7 @@ export type components = {
             qwen3_vl_encoder?: components["schemas"]["Qwen3VLEncoderField"] | null;
             /**
              * Reference Images
-             * @description Reference images to edit or draw from, in the order the denoise node receives their latents. Needs a Qwen-Image-2.1 Diffusers model's own encoder.
+             * @description Reference images to edit or draw from, in the order the denoise node receives their latents, at most 4. Needs a Qwen-Image-2.1 Diffusers model's own encoder.
              * @default []
              */
             reference_images?: components["schemas"]["ImageField"][];
@@ -43354,10 +43354,10 @@ export type components = {
          *
          *     Used for reference images that feed directly into the main model rather
          *     than through a separate IP-Adapter / ControlNet model — for example
-         *     FLUX.2 Klein, FLUX Kontext, and Qwen Image Edit. The receiving frontend
-         *     picks the correct config type (``flux2_reference_image`` /
-         *     ``qwen_image_reference_image`` / ``flux_kontext_reference_image``) based
-         *     on the currently-selected main model.
+         *     FLUX.2 Klein, FLUX Kontext, Qwen Image Edit and Qwen-Image-2.1. The
+         *     receiving frontend picks the correct config type (``flux2_reference_image`` /
+         *     ``qwen_image_reference_image`` / ``qwen_image_2_1_reference_image`` /
+         *     ``flux_kontext_reference_image``) based on the currently-selected main model.
          */
         ReferenceImageRecallParameter: {
             /**
@@ -46586,7 +46586,7 @@ export type components = {
          * @description Submodel type.
          * @enum {string}
          */
-        SubModelType: "unet" | "transformer" | "transformer_2" | "text_encoder" | "text_encoder_2" | "text_encoder_3" | "tokenizer" | "tokenizer_2" | "tokenizer_3" | "processor" | "pe" | "pe_tokenizer" | "vae" | "vae_decoder" | "vae_encoder" | "audio_vae" | "vocoder" | "connectors" | "latent_upsampler" | "temporal_latent_upsampler" | "scheduler" | "safety_checker";
+        SubModelType: "unet" | "transformer" | "transformer_2" | "text_encoder" | "text_encoder_2" | "text_encoder_3" | "vision_tower" | "tokenizer" | "tokenizer_2" | "tokenizer_3" | "processor" | "pe" | "pe_tokenizer" | "vae" | "vae_decoder" | "vae_encoder" | "audio_vae" | "vocoder" | "connectors" | "latent_upsampler" | "temporal_latent_upsampler" | "scheduler" | "safety_checker";
         /** SubmodelDefinition */
         SubmodelDefinition: {
             /** Path Or Prefix */
diff --git a/invokeai/frontend/webv2/public/locales/en.json b/invokeai/frontend/webv2/public/locales/en.json
index 6b04ac9322e..be79fdeb608 100644
--- a/invokeai/frontend/webv2/public/locales/en.json
+++ b/invokeai/frontend/webv2/public/locales/en.json
@@ -2832,6 +2832,7 @@
       "referenceImage": "Reference Image",
       "referenceImageModel": "Reference image model",
       "referenceImages": "Reference images",
+      "referenceImagesNeedFormat": "Reference images need a {{format}} {{base}} model.",
       "referenceImagesUnsupported": "Reference images are not supported by the selected model.",
       "removeConcept": "Remove concept",
       "removeConceptNamed": "Remove {{name}}",
diff --git a/invokeai/frontend/webv2/src/features/generation/core/__snapshots__/generateGraphNodeTypes.json b/invokeai/frontend/webv2/src/features/generation/core/__snapshots__/generateGraphNodeTypes.json
index 6d79322aa91..80aac9dfcd9 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/__snapshots__/generateGraphNodeTypes.json
+++ b/invokeai/frontend/webv2/src/features/generation/core/__snapshots__/generateGraphNodeTypes.json
@@ -265,11 +265,14 @@
         ]
       },
       "nodeTypes": [
+        "collect",
         "core_metadata",
+        "image",
         "img_nsfw",
         "img_watermark",
         "integer",
         "qwen_image_2_1_denoise",
+        "qwen_image_2_1_i2l",
         "qwen_image_2_1_l2i",
         "qwen_image_2_1_model_loader",
         "qwen_image_2_1_text_encoder",
@@ -1252,6 +1255,21 @@
         "conditioning"
       ]
     },
+    "image": {
+      "inputs": [],
+      "literalInputs": {
+        "image": [],
+        "is_intermediate": [
+          true
+        ],
+        "use_cache": [
+          true
+        ]
+      },
+      "outputs": [
+        "image"
+      ]
+    },
     "img_nsfw": {
       "inputs": [
         "image"
@@ -1463,13 +1481,17 @@
     },
     "qwen_image_2_1_denoise": {
       "inputs": [
+        "metadata",
+        "negative_conditioning",
         "positive_conditioning",
+        "reference_latents",
         "seed",
         "transformer"
       ],
       "literalInputs": {
         "cfg_scale": [
-          1
+          1,
+          4
         ],
         "height": [
           1024
@@ -1487,6 +1509,27 @@
           1024
         ]
       },
+      "outputs": [
+        "latents",
+        "metadata"
+      ]
+    },
+    "qwen_image_2_1_i2l": {
+      "inputs": [
+        "image",
+        "vae"
+      ],
+      "literalInputs": {
+        "is_intermediate": [
+          true
+        ],
+        "reference": [
+          true
+        ],
+        "use_cache": [
+          true
+        ]
+      },
       "outputs": [
         "latents"
       ]
@@ -1532,7 +1575,8 @@
     "qwen_image_2_1_text_encoder": {
       "inputs": [
         "prompt",
-        "qwen3_vl_encoder"
+        "qwen3_vl_encoder",
+        "reference_images"
       ],
       "literalInputs": {
         "is_intermediate": [
diff --git a/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.test.ts b/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.test.ts
index d0d799bc87d..80b62638d1b 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.test.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.test.ts
@@ -29,6 +29,7 @@ import {
   getGenerationUiPolicy,
   getGenerationValidationReasons,
   getMaxReferenceImages,
+  getMissingReferenceImageFormat,
   getPromptPolicy,
   getSchedulerOptions,
   getSettingsWithModelDefaults,
@@ -1158,6 +1159,20 @@ describe('component policies', () => {
     expect(isReferenceImageSupported(createModel('qwen-image-2-1', { format: 'checkpoint' }))).toBe(false);
   });
 
+  // The UI names the missing format only when switching to it would enable references.
+  it('names the format a model lacks for reference images only when the format alone blocks them', () => {
+    expect(getMissingReferenceImageFormat(createModel('qwen-image-2-1', { format: 'gguf_quantized' }))).toBe(
+      'diffusers'
+    );
+    expect(getMissingReferenceImageFormat(createModel('qwen-image-2-1', { format: 'checkpoint' }))).toBe('diffusers');
+    expect(getMissingReferenceImageFormat(createModel('qwen-image-2-1', { format: 'diffusers' }))).toBeNull();
+    // Qwen-Image needs the Edit variant, not a format; CogView4 takes no references at all.
+    expect(getMissingReferenceImageFormat(createModel('qwen-image', { format: 'gguf_quantized' }))).toBeNull();
+    expect(getMissingReferenceImageFormat(createModel('cogview4', { format: 'gguf_quantized' }))).toBeNull();
+    expect(getMissingReferenceImageFormat(externalModel)).toBeNull();
+    expect(getMissingReferenceImageFormat(undefined)).toBeNull();
+  });
+
   it('derives the reference image limit from external provider capabilities', () => {
     expect(getMaxReferenceImages(createModel('sdxl'))).toBe(5);
     expect(getMaxReferenceImages(createModel('cogview4'))).toBe(0);
diff --git a/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.ts b/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.ts
index a97cc615c6d..149421d0d20 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.ts
@@ -1333,6 +1333,28 @@ export const isReferenceImageSupported = (model: GenerateModelConfig | undefined
   );
 };
 
+/**
+ * The model format the selected base needs for reference images, when the model's format is all that keeps them
+ * unavailable, so the UI can say what to switch to instead of hiding the section without a reason.
+ */
+export const getMissingReferenceImageFormat = (model: GenerateModelConfig | undefined): string | null => {
+  if (!model || model.type === 'external_image_generator' || isReferenceImageSupported(model)) {
+    return null;
+  }
+
+  const features = getArchitectureFeatures(model.base);
+
+  if (
+    !features ||
+    features.max_reference_images <= 0 ||
+    (features.reference_images_require_variant !== null && model.variant !== features.reference_images_require_variant)
+  ) {
+    return null;
+  }
+
+  return features.reference_images_require_format;
+};
+
 export const getMaxReferenceImages = (model: GenerateModelConfig | undefined): number => {
   if (!model || !isReferenceImageSupported(model)) {
     return 0;
diff --git a/invokeai/frontend/webv2/src/features/generation/core/canvas/compileCanvasGraph.test.ts b/invokeai/frontend/webv2/src/features/generation/core/canvas/compileCanvasGraph.test.ts
index ea3a660c815..5821f83fb2a 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/canvas/compileCanvasGraph.test.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/canvas/compileCanvasGraph.test.ts
@@ -712,6 +712,22 @@ describe('compileCanvasGraph — inpaint per base', () => {
     expect(metaEdge?.destination.node_id).toBe('canvas_output');
   });
 
+  // Qwen-Image-2.1's denoise adds its cache mode to the record, so the record still passes through it once the blend
+  // takes over the output.
+  it('routes Qwen-Image-2.1 inpaint metadata through the denoise onto the final blend output', () => {
+    const { backendGraph } = compile(qwenImage21Model, 'inpaint');
+    const metadata = getNodeByType(backendGraph, 'core_metadata');
+    const metadataEdges = backendGraph.edges.filter(
+      (edge) => edge.source.field === 'metadata' || edge.destination.field === 'metadata'
+    );
+
+    expect(backendGraph.nodes.canvas_output?.type).toBe('invokeai_img_blend');
+    expect(metadataEdges.map(({ destination, source }) => [source.node_id, destination.node_id])).toEqual([
+      [metadata?.id, 'denoise_latents'],
+      ['denoise_latents', 'canvas_output'],
+    ]);
+  });
+
   it('outputs only the generated mask region with transparency when requested', () => {
     const { backendGraph } = compile(sd1Model, 'inpaint', { outputOnlyMaskedRegions: true });
 
diff --git a/invokeai/frontend/webv2/src/features/generation/core/graph.test.ts b/invokeai/frontend/webv2/src/features/generation/core/graph.test.ts
index 625e857b683..679b87ffac9 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/graph.test.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/graph.test.ts
@@ -813,18 +813,18 @@ describe('compileGenerateGraph', () => {
       expect(latentImages).toEqual(['a.png', 'b.png']);
     });
 
-    it('refuses references with a standalone encoder, which has no vision tower', () => {
-      const encoder: ComponentModelConfig = {
-        base: 'any',
-        key: 'qwen3-vl-8b',
-        name: 'Qwen3-VL 8B',
-        type: 'qwen3_vl_encoder',
-        variant: 'qwen3_vl_8b',
-      };
+    // The denoise records whether it reused the prompt's attention cache, which changes the image in bf16.
+    it('passes the image metadata through the denoise to the decoder that saves the image', () => {
+      const graph = compile(qwenImage21Model);
+      const metadata = getNodeByType(graph, 'core_metadata');
+      const metadataEdges = graph.edges.filter(
+        (edge) => edge.source.field === 'metadata' || edge.destination.field === 'metadata'
+      );
 
-      expect(() =>
-        compile(qwenImage21Model, { qwen3VLEncoderModel: encoder, referenceImages: references('a.png') })
-      ).toThrow(/vision tower/);
+      expect(metadataEdges.map(({ destination, source }) => [source.node_id, destination.node_id])).toEqual([
+        [metadata?.id, 'denoise_latents'],
+        ['denoise_latents', 'canvas_output'],
+      ]);
     });
   });
 });
@@ -1818,7 +1818,10 @@ describe('output filters', () => {
       node_id: 'canvas_unfiltered_output',
     });
     expect(getEdge(graph, 'canvas_output', 'image')?.source).toEqual({ field: 'image', node_id: 'canvas_img_nsfw' });
-    expect(getEdge(graph, 'canvas_output', 'metadata')?.source.node_id).toBe(getNodeByType(graph, 'core_metadata')?.id);
+    // Qwen-Image-2.1's denoise adds its cache mode to the record on the way, so the record reaches the result from it.
+    const metadataSource =
+      base === 'qwen-image-2-1' ? 'denoise_latents' : (getNodeByType(graph, 'core_metadata')?.id ?? '');
+    expect(getEdge(graph, 'canvas_output', 'metadata')?.source).toEqual({ field: 'metadata', node_id: metadataSource });
     expect(getEdge(graph, 'canvas_unfiltered_output', 'metadata')).toBeUndefined();
   });
 
diff --git a/invokeai/frontend/webv2/src/features/generation/core/graph.ts b/invokeai/frontend/webv2/src/features/generation/core/graph.ts
index fcf2563b428..c4b0151a212 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/graph.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/graph.ts
@@ -25,7 +25,6 @@ import {
   getFlux2DiffusersComponentSource,
   getGenerationDimensions,
   getGenerationValidationReasons,
-  QWEN_IMAGE_21_STANDALONE_ENCODER_REASON,
   type SupportedGenerateBase,
 } from './baseGenerationPolicies';
 import {
@@ -1442,13 +1441,15 @@ const buildQwenImage21Graph = (
   addQwenImage21ReferenceImages(graph, settings, {
     conditionings: negCond ? [posCond, negCond] : [posCond],
     denoise,
-    hasStandaloneEncoder: Boolean(qwen3VlEncoderModel),
     modelLoader,
   });
-  addMetadata(graph, output, settings, model, 'qwen_image_2_1_txt2img', projectSettings, {
+  // The denoise adds the prefix-cache mode it ran to the record, which changes the image, so the metadata passes
+  // through it on the way to the decoder.
+  addMetadata(graph, denoise, settings, model, 'qwen_image_2_1_txt2img', projectSettings, {
     qwen3_vl_encoder: qwen3VlEncoderModel ?? undefined,
     vae: vaeModel ?? undefined,
   });
+  addEdge(graph, denoise, 'metadata', output, 'metadata');
   addReferenceImageMetadata(graph, output, settings);
 
   return graph;
@@ -1465,7 +1466,6 @@ const addQwenImage21ReferenceImages = (
   nodes: {
     conditionings: BackendInvocationContract[];
     denoise: BackendInvocationContract;
-    hasStandaloneEncoder: boolean;
     modelLoader: BackendInvocationContract;
   }
 ): void => {
@@ -1473,10 +1473,6 @@ const addQwenImage21ReferenceImages = (
   if (referenceImages.length === 0) {
     return;
   }
-  // A backstop: validation reports this before Invoke.
-  if (nodes.hasStandaloneEncoder) {
-    throw new Error(QWEN_IMAGE_21_STANDALONE_ENCODER_REASON);
-  }
 
   let imageCollect: BackendInvocationContract | null = null;
   let latentsCollect: BackendInvocationContract | null = null;
diff --git a/invokeai/frontend/webv2/src/features/generation/core/graphCoverage.testing.ts b/invokeai/frontend/webv2/src/features/generation/core/graphCoverage.testing.ts
index 81e4ec21c27..885e623419b 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/graphCoverage.testing.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/graphCoverage.testing.ts
@@ -6,11 +6,18 @@ import type {
   GenerateComponentValueKey,
   SupportedGenerateBase,
 } from './baseGenerationPolicies';
-import type { GenerateSettings, MainModelConfig, ModelIdentifierConfig } from './types';
+import type {
+  GenerateReferenceImage,
+  GenerateReferenceImageAsset,
+  GenerateSettings,
+  MainModelConfig,
+  ModelIdentifierConfig,
+} from './types';
 
 import {
   getComponentSectionPolicy,
   getDefaultGenerateSettings,
+  isReferenceImageSupported,
   SUPPORTED_GENERATE_BASES,
 } from './baseGenerationPolicies';
 import { getIsPidSupportedBase } from './pid';
@@ -210,6 +217,23 @@ export const fullyFilledSettingsFor = (base: SupportedGenerateBase, shape: Model
   return { filled: [...allFilled].sort(), model, settings };
 };
 
+/** References for the families whose builders wire them through their own nodes rather than an adapter. */
+const tunedReferenceImagesFor = (
+  model: MainModelConfig,
+  image: GenerateReferenceImageAsset
+): GenerateReferenceImage[] | undefined => {
+  if (model.base === 'krea-2') {
+    return [{ config: { image, styleStrength: 1, type: 'krea2_reference_image' }, id: 'style', isEnabled: true }];
+  }
+
+  // Only the Diffusers pipeline's own encoder reads references; other shapes compile without them.
+  if (model.base === 'qwen-image-2-1' && isReferenceImageSupported(model)) {
+    return [{ config: { image, type: 'qwen_image_2_1_reference_image' }, id: 'edit', isEnabled: true }];
+  }
+
+  return undefined;
+};
+
 /**
  * The optional per-family tuning and output filters, all switched on, so the backend contract also covers the nodes
  * and literal values a default recipe never emits. Settings a family ignores are harmless to carry.
@@ -224,21 +248,14 @@ export const tunedSettingsFor = (base: SupportedGenerateBase, shape: ModelShape)
     projectSettings: { useCpuNoise: true, useNsfwChecker: true, useWatermark: true },
     settings: {
       ...settings,
+      // CFG above 1 adds the negative prompt, which Qwen-Image-2.1 also encodes with the references.
+      cfgScale: base === 'qwen-image-2-1' ? 4 : settings.cfgScale,
       fluxDypeExponent: 1,
       fluxDypePreset: 'manual' as const,
       fluxDypeScale: 3,
       qwenImageEncoderQuantization: 'nf4' as const,
       qwenImageShift: 3,
-      referenceImages:
-        base === 'krea-2'
-          ? [
-              {
-                config: { image, styleStrength: 1, type: 'krea2_reference_image' as const },
-                id: 'style',
-                isEnabled: true,
-              },
-            ]
-          : settings.referenceImages,
+      referenceImages: tunedReferenceImagesFor(model, image) ?? settings.referenceImages,
       zImageSeedVarianceEnabled: true,
       zImageSeedVarianceStrength: 0.2,
       zImageShift: 3,
diff --git a/invokeai/frontend/webv2/src/features/generation/core/settings.test.ts b/invokeai/frontend/webv2/src/features/generation/core/settings.test.ts
index f5d373e07d4..0f81cc4707a 100644
--- a/invokeai/frontend/webv2/src/features/generation/core/settings.test.ts
+++ b/invokeai/frontend/webv2/src/features/generation/core/settings.test.ts
@@ -191,6 +191,46 @@ describe('normalizeGenerateSettings', () => {
     ).toBe(false);
   });
 
+  // Qwen-Image-2.1 references are their own flavor, so a stored one must not fold into Qwen-Image Edit's.
+  it('keeps persisted Qwen-Image-2.1 reference images as their own flavor', () => {
+    const normalized = normalizeGenerateSettings({
+      ...legacyStoredValues,
+      referenceImages: [
+        {
+          id: 'edit-1',
+          isEnabled: false,
+          config: {
+            type: 'qwen_image_2_1_reference_image',
+            image: {
+              height: 640,
+              imageName: 'edit.png',
+              imageUrl: '/api/v1/images/i/edit.png/full',
+              queuedAt: '2026-01-01T00:00:00.000Z',
+              sourceQueueItemId: 'backend-gallery',
+              thumbnailUrl: '/api/v1/images/i/edit.png/thumbnail',
+              width: 480,
+            },
+          },
+        },
+        { id: 'empty', isEnabled: true, config: { type: 'qwen_image_2_1_reference_image', image: null } },
+        { id: 'broken', isEnabled: true, config: { type: 'qwen_image_2_1_reference_image', image: { width: 1 } } },
+      ],
+    });
+
+    expect(normalized?.referenceImages).toEqual([
+      {
+        config: {
+          image: { original: { image: { height: 640, image_name: 'edit.png', width: 480 } } },
+          type: 'qwen_image_2_1_reference_image',
+        },
+        id: 'edit-1',
+        isEnabled: false,
+      },
+      { config: { image: null, type: 'qwen_image_2_1_reference_image' }, id: 'empty', isEnabled: true },
+    ]);
+    expect(normalized && isGenerateSettings(normalized)).toBe(true);
+  });
+
   it('keeps more than five valid persisted reference images for provider-specific caps', () => {
     const referenceImages = Array.from({ length: 6 }, (_, index) => ({
       id: `ref-${index}`,
diff --git a/invokeai/frontend/webv2/src/features/generation/ui/GenerateGuidanceSection.tsx b/invokeai/frontend/webv2/src/features/generation/ui/GenerateGuidanceSection.tsx
index 5bc8723152e..4043d293809 100644
--- a/invokeai/frontend/webv2/src/features/generation/ui/GenerateGuidanceSection.tsx
+++ b/invokeai/frontend/webv2/src/features/generation/ui/GenerateGuidanceSection.tsx
@@ -2,7 +2,10 @@ import type { GenerationModelCatalogItem as ModelConfig } from '@features/genera
 import type { GenerateModelConfig, GenerateSettings, LoraModelConfig } from '@features/generation/core/types';
 
 import { Badge, Separator, Stack } from '@chakra-ui/react';
-import { isReferenceImageSupported } from '@features/generation/core/baseGenerationPolicies';
+import {
+  getMissingReferenceImageFormat,
+  isReferenceImageSupported,
+} from '@features/generation/core/baseGenerationPolicies';
 import {
   type GenerateDraft,
   type GenerateSettingsUpdate,
@@ -85,7 +88,9 @@ export const GenerateGuidanceSection = ({
           onCommit={onReferenceCommit}
           onCommitImmediate={onCommitImmediate}
         />
-        {referenceImagesSupported || referenceImageCount > 0 ? <Separator borderColor="bg.subtle" /> : null}
+        {referenceImagesSupported || referenceImageCount > 0 || getMissingReferenceImageFormat(selectedModel) ? (
+          <Separator borderColor="bg.subtle" />
+        ) : null}
         <GenerateConceptsContent
           loraModels={loraModels}
           loras={settings.loras}
diff --git a/invokeai/frontend/webv2/src/features/generation/ui/reference-images/GenerateReferenceImagesSection.tsx b/invokeai/frontend/webv2/src/features/generation/ui/reference-images/GenerateReferenceImagesSection.tsx
index 5ac47f0b066..94a79231498 100644
--- a/invokeai/frontend/webv2/src/features/generation/ui/reference-images/GenerateReferenceImagesSection.tsx
+++ b/invokeai/frontend/webv2/src/features/generation/ui/reference-images/GenerateReferenceImagesSection.tsx
@@ -19,6 +19,7 @@ import {
   getDefaultReferenceImageConfig,
   getGenerationDimensions,
   getMaxReferenceImages,
+  getMissingReferenceImageFormat,
   isReferenceImageSupported,
 } from '@features/generation/core/baseGenerationPolicies';
 import { generatedImageToReferenceImage, getEffectiveReferenceImage } from '@features/generation/core/referenceImage';
@@ -32,6 +33,8 @@ import { useTranslation } from 'react-i18next';
 import { ReferenceImageCard } from './ReferenceImageCard';
 
 const UPLOAD_ZONE_HOVER_STYLES = { bg: 'bg.muted', color: 'fg' };
+/** Display names for the formats the backend can require of reference-image models. */
+const MODEL_FORMAT_LABELS: Record<string, string> = { diffusers: 'Diffusers' };
 const IMAGE_ONLY = ['image'] as const;
 
 interface GenerateReferenceImagesContentProps {
@@ -50,8 +53,9 @@ export const GenerateReferenceImagesContent = ({
   selectedModel,
 }: GenerateReferenceImagesContentProps) => {
   const { t } = useTranslation();
-  const { gallery } = useGenerationUi();
+  const { gallery, models: modelUi } = useGenerationUi();
   const isSupported = isReferenceImageSupported(selectedModel);
+  const missingFormat = getMissingReferenceImageFormat(selectedModel);
   const maxReferenceImages = getMaxReferenceImages(selectedModel);
   const canAdd = referenceImages.length < maxReferenceImages;
   const { acceptsActiveDrag, isOver, setNodeRef } = useGalleryImageDroppable({
@@ -194,8 +198,21 @@ export const GenerateReferenceImagesContent = ({
     },
   });
 
+  // A model that only lacks the right format says which one would take references.
+  const unsupportedMessage =
+    selectedModel && missingFormat
+      ? t('widgets.generate.referenceImagesNeedFormat', {
+          base: modelUi.getBaseLabel(selectedModel.base),
+          format: MODEL_FORMAT_LABELS[missingFormat] ?? missingFormat,
+        })
+      : t('widgets.generate.referenceImagesUnsupported');
+
   if (!isSupported && referenceImageCount === 0) {
-    return null;
+    return missingFormat ? (
+      <Text color="fg.muted" fontSize="xs">
+        {unsupportedMessage}
+      </Text>
+    ) : null;
   }
 
   // Unsupported-model references remain read-only with removal as recovery.
@@ -203,7 +220,7 @@ export const GenerateReferenceImagesContent = ({
     return (
       <HStack gap="2" justify="space-between">
         <Text color="fg.muted" fontSize="xs" minW="0">
-          {t('widgets.generate.referenceImagesUnsupported')}
+          {unsupportedMessage}
         </Text>
         <Button colorPalette="red" flexShrink="0" variant="outline" onClick={clearReferenceImages}>
           {t('widgets.generate.clearReferenceImages')}
diff --git a/invokeai/frontend/webv2/src/features/generation/ui/reference-images/referenceImageFormatHint.browser.test.tsx b/invokeai/frontend/webv2/src/features/generation/ui/reference-images/referenceImageFormatHint.browser.test.tsx
new file mode 100644
index 00000000000..462c56e71e0
--- /dev/null
+++ b/invokeai/frontend/webv2/src/features/generation/ui/reference-images/referenceImageFormatHint.browser.test.tsx
@@ -0,0 +1,113 @@
+import type { GenerateModelConfig, GenerateReferenceImage } from '@features/generation/core/types';
+
+import { ChakraProvider } from '@chakra-ui/react';
+import { DndContext } from '@dnd-kit/core';
+import { seedArchitectureCapabilities } from '@features/generation/core/architectureCapabilities.testing';
+import { type GenerationUiAdapter, GenerationUiProvider } from '@features/generation/ui/GenerationUiContext';
+import { system } from '@theme/system';
+import i18next from 'i18next';
+import { act } from 'react';
+import { createRoot, type Root } from 'react-dom/client';
+import { I18nextProvider, initReactI18next } from 'react-i18next';
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+
+import { GenerateReferenceImagesContent } from './GenerateReferenceImagesSection';
+
+/** A model that only lacks the right format explains why the reference section is gone; other models stay silent. */
+const i18n = i18next.createInstance();
+// The shipped copy, so a change to the hint's wording or placeholders fails here.
+await i18n.use(initReactI18next).init({
+  fallbackLng: 'en',
+  lng: 'en',
+  resources: { en: { translation: await fetch('/locales/en.json').then((response) => response.json()) } },
+});
+
+seedArchitectureCapabilities();
+
+const ADAPTER = {
+  gallery: { findImage: vi.fn() },
+  models: { getBaseLabel: (base: string) => (base === 'qwen-image-2-1' ? 'Qwen-Image-2.1' : base) },
+} as unknown as GenerationUiAdapter;
+
+const QWEN21_GGUF: GenerateModelConfig = {
+  base: 'qwen-image-2-1',
+  format: 'gguf_quantized',
+  key: 'qwen21-q4',
+  name: 'Qwen-Image-2.1 Q4',
+  type: 'main',
+};
+const NO_MODELS: [] = [];
+const COGVIEW4: GenerateModelConfig = { base: 'cogview4', key: 'cogview4', name: 'CogView4', type: 'main' };
+const QWEN21_REFERENCE: GenerateReferenceImage = {
+  config: {
+    image: { original: { image: { height: 512, image_name: 'edit.png', width: 512 } } },
+    type: 'qwen_image_2_1_reference_image',
+  },
+  id: 'edit',
+  isEnabled: true,
+};
+
+let host: HTMLDivElement | null = null;
+// DndContext renders its own screen-reader instructions, so assertions read only the section's output.
+const section = (): HTMLElement | null | undefined => host?.querySelector('[data-testid="section"]');
+let root: Root | null = null;
+(globalThis as typeof globalThis & { IS_REACT_ACT_ENVIRONMENT: boolean }).IS_REACT_ACT_ENVIRONMENT = true;
+
+const render = async (selectedModel: GenerateModelConfig, referenceImages: GenerateReferenceImage[] = []) => {
+  await act(() =>
+    root?.render(
+      <I18nextProvider i18n={i18n}>
+        <ChakraProvider value={system}>
+          <GenerationUiProvider adapter={ADAPTER}>
+            <DndContext>
+              <div data-testid="section">
+                <GenerateReferenceImagesContent
+                  models={NO_MODELS}
+                  referenceImages={referenceImages}
+                  selectedModel={selectedModel}
+                  onCommit={vi.fn()}
+                  onCommitImmediate={vi.fn()}
+                />
+              </div>
+            </DndContext>
+          </GenerationUiProvider>
+        </ChakraProvider>
+      </I18nextProvider>
+    )
+  );
+};
+
+beforeEach(() => {
+  host = document.createElement('div');
+  document.body.append(host);
+  root = createRoot(host);
+});
+
+afterEach(async () => {
+  await act(() => root?.unmount());
+  host?.remove();
+  host = null;
+  root = null;
+});
+
+describe('reference image format hint', () => {
+  it('names the Diffusers format where a GGUF Qwen-Image-2.1 model would show references', async () => {
+    await render(QWEN21_GGUF);
+
+    expect(section()?.textContent).toBe('Reference images need a Diffusers Qwen-Image-2.1 model.');
+    expect(section()?.querySelector('button')).toBeNull();
+  });
+
+  it('keeps the format hint next to the clear action for references the model cannot read', async () => {
+    await render(QWEN21_GGUF, [QWEN21_REFERENCE]);
+
+    expect(section()?.textContent).toContain('Reference images need a Diffusers Qwen-Image-2.1 model.');
+    expect(section()?.querySelector('button')?.textContent).toBe('Remove all');
+  });
+
+  it('renders nothing for a model whose base takes no references in any format', async () => {
+    await render(COGVIEW4);
+
+    expect(section()?.textContent).toBe('');
+  });
+});
diff --git a/invokeai/frontend/webv2/src/workbench/canvasMigration.ts b/invokeai/frontend/webv2/src/workbench/canvasMigration.ts
index b51c568a60d..cc1805ea6a6 100644
--- a/invokeai/frontend/webv2/src/workbench/canvasMigration.ts
+++ b/invokeai/frontend/webv2/src/workbench/canvasMigration.ts
@@ -192,7 +192,6 @@ const zReferenceImage = z.object({
     }),
     z.object({ image: zGeneratedImage.nullable(), type: z.literal('flux2_reference_image') }),
     z.object({ image: zGeneratedImage.nullable(), type: z.literal('qwen_image_reference_image') }),
-    z.object({ image: zGeneratedImage.nullable(), type: z.literal('qwen_image_2_1_reference_image') }),
     z.object({ image: zGeneratedImage.nullable(), type: z.literal('external_reference_image') }),
   ]),
   id: z.string(),
diff --git a/invokeai/frontend/webv2/src/workbench/image-actions/imageRecall.test.ts b/invokeai/frontend/webv2/src/workbench/image-actions/imageRecall.test.ts
index 800ef1f7e97..562f5ceedf7 100644
--- a/invokeai/frontend/webv2/src/workbench/image-actions/imageRecall.test.ts
+++ b/invokeai/frontend/webv2/src/workbench/image-actions/imageRecall.test.ts
@@ -341,6 +341,48 @@ describe('image recall', () => {
     expect(result?.fields).toContain('referenceImages');
   });
 
+  // Only the Diffusers pipeline reads Qwen-Image-2.1 references, so the recalled flavor survives there and nowhere else.
+  it('recalls Qwen-Image-2.1 reference images onto a Diffusers model and drops them on a GGUF one', () => {
+    const recall = (model: MainModelConfig) =>
+      buildImageRecallSettings({
+        currentValues: createValues({ model, modelKey: model.key }),
+        image,
+        kind: 'all',
+        metadata: {
+          positive_prompt: 'swap the jacket',
+          ref_images: [
+            {
+              config: {
+                image: { height: 640, image_name: 'edit.png', width: 480 },
+                type: 'qwen_image_2_1_reference_image',
+              },
+              id: 'edit-1',
+              isEnabled: true,
+            },
+          ],
+        },
+        models: [],
+        supportedModels: [model],
+        vaeModels: [],
+      });
+    const diffusers = recall({ base: 'qwen-image-2-1', format: 'diffusers', key: 'q21', name: 'Q21', type: 'main' });
+    const gguf = recall({ base: 'qwen-image-2-1', format: 'gguf_quantized', key: 'q21-q4', name: 'Q4', type: 'main' });
+
+    expect(diffusers?.values.referenceImages).toEqual([
+      {
+        config: {
+          image: { original: { image: { height: 640, image_name: 'edit.png', width: 480 } } },
+          type: 'qwen_image_2_1_reference_image',
+        },
+        id: 'edit-1',
+        isEnabled: true,
+      },
+    ]);
+    expect(diffusers?.fields).toContain('referenceImages');
+    expect(gguf?.values.referenceImages).toEqual([]);
+    expect(gguf?.fields).not.toContain('referenceImages');
+  });
+
   it('preserves canonical original and crop provenance from legacy metadata', () => {
     const crop = {
       box: { height: 256, width: 320, x: 12, y: 24 },
diff --git a/tests/app/invocations/test_qwen_image_2_1_nodes.py b/tests/app/invocations/test_qwen_image_2_1_nodes.py
index cb6e29a747e..4de2d880fac 100644
--- a/tests/app/invocations/test_qwen_image_2_1_nodes.py
+++ b/tests/app/invocations/test_qwen_image_2_1_nodes.py
@@ -12,13 +12,14 @@
 import torch
 from PIL import Image
 
-from invokeai.app.invocations.fields import ImageField, LatentsField, QwenImage21ConditioningField
+from invokeai.app.invocations.fields import ImageField, LatentsField, MetadataField, QwenImage21ConditioningField
 from invokeai.app.invocations.model import ModelIdentifierField, Qwen3VLEncoderField, TransformerField, VAEField
 from invokeai.app.invocations.qwen_image_2_1.qwen_image_2_1_denoise import (
-    KV_BYTES_PER_PREFIX_TOKEN,
     LATENT_CHANNELS,
+    PREFIX_CACHE_METADATA_KEY,
     QwenImage21DenoiseInvocation,
     clip_sigmas,
+    kv_bytes_per_prefix_token,
     pack_latents,
     prefix_cache_fits,
     prefix_length,
@@ -41,6 +42,8 @@
 )
 
 DENOISE = "invokeai.app.invocations.qwen_image_2_1.qwen_image_2_1_denoise"
+# K and V of one prefix token in the released model: 32 blocks of 32 heads x 128, two bytes each in bf16.
+RELEASED_KV_BYTES = 2 * 32 * 4096 * 2
 
 
 def test_latents_pack_one_token_per_pixel_in_raster_order() -> None:
@@ -230,11 +233,26 @@ def test_a_strength_on_base_names_whole_steps_and_always_runs_one(
     assert clipped == sigmas[first_step : first_step + len(clipped)]
 
 
+def _released_transformer(num_blocks: int = 32) -> SimpleNamespace:
+    return SimpleNamespace(
+        config=SimpleNamespace(num_attention_heads=32, attention_head_dim=128), transformer_blocks=[None] * num_blocks
+    )
+
+
+def test_the_prefix_cache_is_priced_from_the_loaded_transformers_shape() -> None:
+    assert kv_bytes_per_prefix_token(_released_transformer(), torch.bfloat16) == RELEASED_KV_BYTES == 2**19
+    # A shallower model keeps fewer blocks of K and V; a float32 fallback keeps each at twice the width.
+    assert kv_bytes_per_prefix_token(_released_transformer(16), torch.bfloat16) == RELEASED_KV_BYTES // 2
+    assert kv_bytes_per_prefix_token(_released_transformer(), torch.float32) == 2 * RELEASED_KV_BYTES
+
+
 def test_a_float32_fallback_reserves_twice_the_bfloat16_activations() -> None:
     estimate = QwenImage21DenoiseInvocation._estimate_working_memory
     base = 1024**3
-    bf16 = estimate(4096, 300, 300, True, True, 2) - base
-    assert estimate(4096, 300, 300, True, True, 4) - base == 2 * bf16
+    bf16_kv = kv_bytes_per_prefix_token(_released_transformer(), torch.bfloat16)
+    fp32_kv = kv_bytes_per_prefix_token(_released_transformer(), torch.float32)
+    bf16 = estimate(4096, 300, 300, True, True, 2, bf16_kv) - base
+    assert estimate(4096, 300, 300, True, True, 4, fp32_kv) - base == 2 * bf16
 
 
 @pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
@@ -247,7 +265,7 @@ def test_the_estimate_is_priced_at_the_compute_dtypes_width(
     estimate = QwenImage21DenoiseInvocation._estimate_working_memory
 
     def spy(*args):
-        widths.append(args[-1])
+        widths.append(args[5])
         return estimate(*args)
 
     monkeypatch.setattr(QwenImage21DenoiseInvocation, "_estimate_working_memory", staticmethod(spy))
@@ -460,18 +478,30 @@ def test_the_prefix_counts_each_reference_slot_as_four_latent_tokens() -> None:
 def test_the_prefix_caches_are_reserved_only_when_they_are_kept() -> None:
     estimate = QwenImage21DenoiseInvocation._estimate_working_memory
     prefix = 4200
-    cached = estimate(4096, prefix, prefix, True, True, 2)
-    recomputed = estimate(4096, prefix, prefix, True, False, 2)
+    cached = estimate(4096, prefix, prefix, True, True, 2, RELEASED_KV_BYTES)
+    recomputed = estimate(4096, prefix, prefix, True, False, 2, RELEASED_KV_BYTES)
     # K and V of every prefix token in all 32 blocks, per guidance branch: ~2 GiB per 1 MP reference.
-    assert cached - recomputed == 2 * prefix * KV_BYTES_PER_PREFIX_TOKEN
+    assert cached - recomputed == 2 * prefix * RELEASED_KV_BYTES
 
 
 def test_caches_larger_than_half_the_card_are_not_kept(monkeypatch: pytest.MonkeyPatch) -> None:
     monkeypatch.setattr(torch.cuda, "get_device_properties", lambda _device: SimpleNamespace(total_memory=24 * 2**30))
     cuda = torch.device("cuda")
     # Two references at CFG fit a 24 GB card; four do not.
-    assert prefix_cache_fits(2 * 2 * 4200 * KV_BYTES_PER_PREFIX_TOKEN, cuda)
-    assert not prefix_cache_fits(2 * 4 * 4200 * KV_BYTES_PER_PREFIX_TOKEN, cuda)
+    assert prefix_cache_fits(2 * 2 * 4200 * RELEASED_KV_BYTES, cuda)
+    assert not prefix_cache_fits(2 * 4 * 4200 * RELEASED_KV_BYTES, cuda)
+
+
+@pytest.mark.parametrize(("integrated", "fits"), [(False, False), (True, True)], ids=["discrete", "integrated"])
+def test_a_discrete_xpu_card_is_limited_like_any_dedicated_card(
+    monkeypatch: pytest.MonkeyPatch, integrated: bool, fits: bool
+) -> None:
+    # Four references at CFG hold ~16 GiB: more than half a 16 GB Arc card. An integrated GPU shares system RAM,
+    # which the cache does not budget as a card.
+    monkeypatch.setattr("invokeai.backend.util.devices.xpu_device_is_integrated", lambda _device: integrated)
+    monkeypatch.setattr(torch.xpu, "get_device_properties", lambda _device: SimpleNamespace(total_memory=16 * 2**30))
+    assert prefix_cache_fits(2 * 4 * 4200 * RELEASED_KV_BYTES, torch.device("xpu")) is fits
+    assert prefix_cache_fits(2 * 4 * 4200 * RELEASED_KV_BYTES, torch.device("cpu"))
 
 
 def test_without_room_for_the_caches_every_step_runs_the_whole_prefix(monkeypatch: pytest.MonkeyPatch) -> None:
@@ -483,6 +513,67 @@ def test_without_room_for_the_caches_every_step_runs_the_whole_prefix(monkeypatc
     assert {(cache, mode) for _prompt, cache, mode, _t in transformer.calls} == {(id(None), None)}
 
 
+def test_a_later_step_decodes_from_the_cache_its_prefill_filled_with_the_references() -> None:
+    transformer = _Transformer()
+    reference = torch.randn(1, LATENT_CHANNELS, 4, 8)
+    _denoise(reference_latents=[LatentsField(latents_name="ref")], steps=2)._run_diffusion(
+        _edit_context(transformer, [(2, 4)], {"ref": reference})
+    )
+
+    assert [(prompt, mode) for prompt, _cache, mode, _t in transformer.calls] == [("pos", "extract"), ("pos", "cached")]
+    # One cache for the run: the cached step reads what the prefill wrote.
+    assert len({cache for _prompt, cache, _mode, _t in transformer.calls}) == 1
+    # The transformer rebuilds the joint sequence on every step, so the cached step still needs the references in
+    # front of the target, and the same layout.
+    for seen in transformer.inputs:
+        assert seen.img_shapes == [[(1, 4, 8), (1, 4, 4)]]
+        assert torch.equal(seen.hidden_states[:, :32], pack_latents(reference))
+        assert seen.img_mask.tolist() == [[False] + [True] * 8 + [False] + [True] * 4]
+
+
+def test_large_references_before_a_small_target_price_the_last_references_attention(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    sides: list[int] = []
+
+    def score_bytes(*, seq_len: int, **_kwargs) -> int:
+        sides.append(seq_len)
+        return 0
+
+    monkeypatch.setattr(f"{DENOISE}.sdpa_score_matrix_bytes", score_bytes)
+    # Four 1 MP references (32x32 slots, 64x64 latents each) before a 256x256 target (16x16 latents).
+    grids = [(32, 32)] * 4
+    references = {f"ref{i}": torch.zeros(1, LATENT_CHANNELS, 64, 64) for i in range(4)}
+    _denoise(
+        reference_latents=[LatentsField(latents_name=name) for name in references], width=256, height=256, steps=1
+    )._run_diffusion(_edit_context(_Transformer(), grids, references))
+
+    # The prompt's layout: one text token, then each reference's 4096 latent tokens and one text token. The last
+    # reference's 4096 queries attend over every key up to its end: 4 * 4096 + 4 of them. The target's call is
+    # 256 x (16389 + 256), a sixteenth of that.
+    largest = 4096 * (4 * 4096 + 4)
+    assert len(sides) == 1
+    assert (sides[0] - 1) ** 2 < largest <= sides[0] ** 2
+
+
+@pytest.mark.parametrize("cache_fits", [True, False], ids=["cached", "recomputed"])
+def test_the_metadata_records_whether_the_prefix_was_cached(cache_fits: bool, monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setattr(f"{DENOISE}.prefix_cache_fits", lambda _bytes, _device: cache_fits)
+    context = _edit_context(_Transformer(), [(2, 4)], {"ref": torch.zeros(1, LATENT_CHANNELS, 4, 8)})
+    context.tensors.save = lambda tensor: "out"
+    node = _denoise(
+        reference_latents=LatentsField(latents_name="ref"),
+        cfg_scale=4.0,
+        metadata=MetadataField.model_validate({"seed": 7, "steps": 3}),
+    )
+
+    output = node.invoke(context)
+
+    # What the graph recorded passes through, with the mode this run took beside it.
+    assert output.metadata.root == {"seed": 7, "steps": 3, PREFIX_CACHE_METADATA_KEY: cache_fits}
+    assert (output.latents.latents_name, output.width, output.height) == ("out", 64, 64)
+
+
 def test_a_standalone_encoder_cannot_read_references() -> None:
     encoder = ModelIdentifierField(
         key="enc", hash="h", name="qwen3vl_8b", base=BaseModelType.Any, type=ModelType.Qwen3VLEncoder
@@ -504,16 +595,22 @@ def test_a_standalone_encoder_cannot_read_references() -> None:
         node.invoke(context)
 
 
-def test_the_processor_comes_from_the_qwen_image_2_1_pipeline_the_tokenizer_is_from() -> None:
+def _pipeline_encoder_node(num_references: int) -> QwenImage21TextEncoderInvocation:
     main = ModelIdentifierField(
         key="qwen21", hash="h", name="Qwen-Image-2.1", base=BaseModelType.QwenImage21, type=ModelType.Main
     )
-    tokenizer = main.model_copy(update={"submodel_type": SubModelType.Tokenizer})
-    node = QwenImage21TextEncoderInvocation.model_construct(
-        prompt="swap them",
-        qwen3_vl_encoder=Qwen3VLEncoderField(tokenizer=tokenizer, text_encoder=tokenizer, loras=[]),
-        reference_images=[ImageField(image_name="ref.png")],
+    return QwenImage21TextEncoderInvocation.model_construct(
+        prompt="Combine the subjects of all reference images.",
+        qwen3_vl_encoder=Qwen3VLEncoderField(
+            tokenizer=main.model_copy(update={"submodel_type": SubModelType.Tokenizer}),
+            text_encoder=main.model_copy(update={"submodel_type": SubModelType.TextEncoder}),
+            loras=[],
+        ),
+        reference_images=[ImageField(image_name=f"ref{i}.png") for i in range(num_references)],
     )
+
+
+def test_the_processor_comes_from_the_qwen_image_2_1_pipeline_the_tokenizer_is_from() -> None:
     context = SimpleNamespace(
         models=SimpleNamespace(
             get_config=lambda _identifier: SimpleNamespace(
@@ -521,24 +618,38 @@ def test_the_processor_comes_from_the_qwen_image_2_1_pipeline_the_tokenizer_is_f
             )
         )
     )
-    processor = node._processor_identifier(context)
+    tower, processor = _pipeline_encoder_node(1)._edit_identifiers(context)
+    assert (tower.key, tower.submodel_type) == ("qwen21", SubModelType.VisionTower)
     assert (processor.key, processor.submodel_type) == ("qwen21", SubModelType.Processor)
 
 
+def test_a_standalone_encoder_beside_the_pipelines_tokenizer_cannot_read_references() -> None:
+    # The tokenizer passes; the encoder, whose pipeline would have to bring the vision tower, does not.
+    def get_config(identifier):
+        if identifier.submodel_type is SubModelType.Tokenizer:
+            return SimpleNamespace(base=BaseModelType.QwenImage21, type=ModelType.Main, format=ModelFormat.Diffusers)
+        return SimpleNamespace(base=BaseModelType.Any, type=ModelType.Qwen3VLEncoder, format=ModelFormat.Checkpoint)
+
+    with pytest.raises(ValueError, match="vision tower"):
+        _pipeline_encoder_node(1)._edit_identifiers(SimpleNamespace(models=SimpleNamespace(get_config=get_config)))
+
+
+def test_more_references_than_the_architecture_reads_are_refused_before_anything_loads() -> None:
+    # The context has no images or models: reaching either would fail with another error.
+    with pytest.raises(ValueError, match="at most 4 reference images; this prompt has 5"):
+        _pipeline_encoder_node(5).invoke(SimpleNamespace())
+
+
 class _ReservationRecorded(Exception):
     pass
 
 
-@pytest.mark.parametrize("num_references", [0, 4])
-def test_the_encoder_reserves_room_for_the_attention_its_references_need(num_references: int) -> None:
+def _encode_until_reservation(num_references: int, image_size: int = 1024) -> tuple[list[int], list[SubModelType]]:
+    """Runs the node until the encoder would move to the device; returns what it reserved and what it loaded."""
     from invokeai.backend.qwen3_vl.qwen3_vl_assets import load_bundled_qwen3_vl_tokenizer
 
-    main = ModelIdentifierField(
-        key="qwen21", hash="h", name="Qwen-Image-2.1", base=BaseModelType.QwenImage21, type=ModelType.Main
-    )
-    tokenizer = main.model_copy(update={"submodel_type": SubModelType.Tokenizer})
-    encoder = main.model_copy(update={"submodel_type": SubModelType.TextEncoder})
     reserved: list[int] = []
+    loaded: list[SubModelType] = []
 
     @contextmanager
     def on_device(*, working_mem_bytes: int):
@@ -548,32 +659,139 @@ def on_device(*, working_mem_bytes: int):
 
     text_encoder = torch.nn.Module()
     text_encoder.language_model = torch.nn.Linear(2, 2)
-    text_encoder.visual = torch.nn.Linear(2, 2)
+    text_encoder.visual = torch.nn.Identity()
+    tower = torch.nn.Linear(2, 2)
+    # The released tower's attention geometry.
+    tower.config = SimpleNamespace(num_heads=16, hidden_size=1152)
     infos = {
         SubModelType.TextEncoder: SimpleNamespace(
             model=text_encoder, compute_device=torch.device("cpu"), model_on_device=on_device
         ),
+        SubModelType.VisionTower: SimpleNamespace(
+            model=tower, compute_device=torch.device("cpu"), model_on_device=lambda: nullcontext((None, tower))
+        ),
         SubModelType.Tokenizer: nullcontext(load_bundled_qwen3_vl_tokenizer()),
         SubModelType.Processor: SimpleNamespace(model_on_device=lambda: nullcontext((None, None))),
     }
-    node = QwenImage21TextEncoderInvocation.model_construct(
-        prompt="Combine the subjects of all reference images.",
-        qwen3_vl_encoder=Qwen3VLEncoderField(tokenizer=tokenizer, text_encoder=encoder, loras=[]),
-        reference_images=[ImageField(image_name=f"ref{i}.png") for i in range(num_references)],
+
+    def load(identifier):
+        loaded.append(identifier.submodel_type)
+        return infos[identifier.submodel_type]
+
+    context = SimpleNamespace(
+        images=SimpleNamespace(get_pil=lambda _name: Image.new("RGB", (image_size, image_size))),
+        models=SimpleNamespace(
+            get_config=lambda _identifier: SimpleNamespace(
+                base=BaseModelType.QwenImage21, type=ModelType.Main, format=ModelFormat.Diffusers
+            ),
+            load=load,
+        ),
+        util=SimpleNamespace(signal_progress=lambda _message: None),
     )
+    with pytest.raises(_ReservationRecorded):
+        _pipeline_encoder_node(num_references).invoke(context)
+    return reserved, loaded
+
+
+@pytest.mark.parametrize("num_references", [0, 2], ids=["text-only", "edit"])
+def test_only_an_edit_loads_the_vision_tower(num_references: int) -> None:
+    _, loaded = _encode_until_reservation(num_references, image_size=64)
+    # The encoder is the tower-less one either way: its language model is cached once for both.
+    assert loaded[0] is SubModelType.TextEncoder and SubModelType.TextEncoder not in loaded[1:]
+    assert (SubModelType.VisionTower in loaded) is bool(num_references)
+    assert (SubModelType.Processor in loaded) is bool(num_references)
+
+
+class _EncodeFailed(Exception):
+    pass
+
+
+@pytest.mark.parametrize("fail", [False, True], ids=["encoded", "failed"])
+def test_an_edit_encodes_with_the_tower_attached_and_detaches_it(fail: bool, monkeypatch: pytest.MonkeyPatch) -> None:
+    """End to end through the node with a tiny Qwen3-VL split the way the pipeline loader caches it."""
+    import copy
+
+    from transformers import Qwen2VLImageProcessor, Qwen3VLModel, Qwen3VLProcessor
+    from transformers.models.qwen3_vl.video_processing_qwen3_vl import Qwen3VLVideoProcessor
+
+    from invokeai.app.invocations.text_encoder import qwen_image_2_1_text_encoder as node_module
+    from invokeai.backend.model_manager.util.qwen3_vl import drop_qwen3vl_visual_tower
+    from invokeai.backend.qwen3_vl.qwen3_vl_assets import load_bundled_qwen3_vl_tokenizer
+    from invokeai.backend.qwen_image_2_1.text_encoding import encode_prompt
+    from invokeai.backend.qwen_image_2_1.vae import to_reference
+    from tests.backend.model_manager.load.qwen3vl_gguf_fixture import tiny_qwen3vl_config
+
+    monkeypatch.setattr(f"{node_module.__name__}.TorchDevice.choose_bfloat16_safe_dtype", lambda _d: torch.float32)
+    tokenizer = load_bundled_qwen3_vl_tokenizer()
+    processor = Qwen3VLProcessor(
+        image_processor=Qwen2VLImageProcessor(patch_size=16, merge_size=2, temporal_patch_size=2),
+        tokenizer=tokenizer,
+        video_processor=Qwen3VLVideoProcessor(),
+    )
+    config = tiny_qwen3vl_config()
+    config.text_config.vocab_size = len(tokenizer)
+    torch.manual_seed(0)
+    built_in = Qwen3VLModel(config).float().eval()
+    tower = copy.deepcopy(built_in.visual)
+    encoder = copy.deepcopy(built_in)
+    drop_qwen3vl_visual_tower(encoder)
+
+    def info(model):
+        return SimpleNamespace(
+            model=model, compute_device=torch.device("cpu"), model_on_device=lambda **_kw: nullcontext((None, model))
+        )
+
+    infos = {
+        SubModelType.TextEncoder: info(encoder),
+        SubModelType.VisionTower: info(tower),
+        SubModelType.Tokenizer: nullcontext(tokenizer),
+        SubModelType.Processor: info(processor),
+    }
+    saved: list[ConditioningFieldData] = []
+    reference = Image.new("RGB", (64, 64), (200, 30, 30))
     context = SimpleNamespace(
-        images=SimpleNamespace(get_pil=lambda _name: Image.new("RGB", (1024, 1024))),
+        images=SimpleNamespace(get_pil=lambda _name: reference),
         models=SimpleNamespace(
             get_config=lambda _identifier: SimpleNamespace(
                 base=BaseModelType.QwenImage21, type=ModelType.Main, format=ModelFormat.Diffusers
             ),
             load=lambda identifier: infos[identifier.submodel_type],
         ),
+        conditioning=SimpleNamespace(save=lambda data: saved.append(data) or "cond"),
         util=SimpleNamespace(signal_progress=lambda _message: None),
     )
+    node = _pipeline_encoder_node(1)
+    if fail:
 
-    with pytest.raises(_ReservationRecorded):
+        def failing_encode(*_args, **_kwargs):
+            raise _EncodeFailed
+
+        monkeypatch.setattr(node_module, "encode_prompt", failing_encode)
+        with pytest.raises(_EncodeFailed):
+            node.invoke(context)
+    else:
         node.invoke(context)
+        expected = encode_prompt(
+            built_in, tokenizer, node.prompt, torch.device("cpu"), processor=processor, images=[to_reference(reference)]
+        )
+        info_out = saved[0].conditionings[0]
+        assert torch.equal(info_out.prompt_embeds, expected.embeds)
+        assert torch.equal(info_out.image_pad_mask, expected.image_pad_mask)
+
+    # The cached encoder is left as it was loaded, whether the encode finished or not.
+    assert isinstance(encoder.visual, torch.nn.Identity)
+
+
+@pytest.mark.parametrize("num_references", [0, 4])
+def test_the_encoder_reserves_room_for_the_attention_its_references_need(
+    num_references: int, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    # The measurement below is a bf16 one; on a CPU without bf16 the node would price float32, twice as much.
+    monkeypatch.setattr(
+        "invokeai.app.invocations.text_encoder.qwen_image_2_1_text_encoder.TorchDevice.choose_bfloat16_safe_dtype",
+        lambda _device: torch.bfloat16,
+    )
+    reserved, _ = _encode_until_reservation(num_references)
 
     if num_references:
         # Measured on an RTX 4090: four 1-megapixel references peak 5.44 GiB above the weights, and the allocator
@@ -583,6 +801,18 @@ def on_device(*, working_mem_bytes: int):
         assert reserved[0] < 2**28
 
 
+def test_a_float32_encoder_reserves_twice_the_bfloat16_room(monkeypatch: pytest.MonkeyPatch) -> None:
+    # The encoder runs in float32 where the device has no bf16 (CPU, older MPS); its activations are twice as wide.
+    reserved = {}
+    for dtype in (torch.bfloat16, torch.float32):
+        monkeypatch.setattr(
+            "invokeai.app.invocations.text_encoder.qwen_image_2_1_text_encoder.TorchDevice.choose_bfloat16_safe_dtype",
+            lambda _device, dtype=dtype: dtype,
+        )
+        reserved[dtype] = _encode_until_reservation(4)[0][0]
+    assert reserved[torch.float32] == pytest.approx(2 * reserved[torch.bfloat16], rel=1e-6)
+
+
 class _VAE:
     """Records what it encodes and answers with bf16 latents, as the real VAE runs in bf16."""
 
diff --git a/tests/backend/model_manager/load/test_load_default_cpu_only.py b/tests/backend/model_manager/load/test_load_default_cpu_only.py
index d99eb5466d0..0aa27d8a04a 100644
--- a/tests/backend/model_manager/load/test_load_default_cpu_only.py
+++ b/tests/backend/model_manager/load/test_load_default_cpu_only.py
@@ -47,3 +47,13 @@ def test_config_without_cpu_only_attr_returns_none():
     # A config type that has neither `cpu_only` nor `default_settings` must be left on the
     # cache default (return None), not crash.
     assert _loader()._get_execution_device(SimpleNamespace(), None) is None
+
+
+def test_a_main_models_cpu_only_puts_its_encoder_and_vision_tower_on_the_cpu():
+    # Qwen-Image-2.1 attaches the tower to the encoder while it reads references, so the two must share a device;
+    # the denoiser stays on the cache default.
+    config = SimpleNamespace(default_settings=SimpleNamespace(cpu_only=True))
+    loader = _loader()
+    assert loader._get_execution_device(config, SubModelType.TextEncoder) == torch.device("cpu")
+    assert loader._get_execution_device(config, SubModelType.VisionTower) == torch.device("cpu")
+    assert loader._get_execution_device(config, SubModelType.Transformer) is None
diff --git a/tests/backend/model_manager/load/test_load_default_fp8.py b/tests/backend/model_manager/load/test_load_default_fp8.py
index 0ac15ed4ca9..236986cd000 100644
--- a/tests/backend/model_manager/load/test_load_default_fp8.py
+++ b/tests/backend/model_manager/load/test_load_default_fp8.py
@@ -183,6 +183,18 @@ def test_should_use_fp8_excludes_prompt_enhancer(submodel_type: SubModelType):
         assert loader._should_use_fp8(config, SubModelType.Transformer) is True
 
 
+def test_should_use_fp8_excludes_the_vision_tower():
+    """Qwen-Image-2.1's vision tower attaches to the text encoder for edits and follows its exclusion. It could
+    not take the cast either: `Qwen3VLModel` casts the pixels to `visual.dtype`, which a cast tower reports as
+    float8."""
+    loader = _make_loader(device="cuda")
+    config = _make_config(ModelType.Main, fp8=True, base=BaseModelType.QwenImage21)
+    with _device_holds_fp8():
+        assert loader._should_use_fp8(config, SubModelType.VisionTower) is False
+        # Sanity: the same config opts the transformer in, so the assertion above is about the exclusion.
+        assert loader._should_use_fp8(config, SubModelType.Transformer) is True
+
+
 class _RaisingModule(torch.nn.Module):
     """A module whose forward unconditionally raises — used to test that the FP8 wrapper's
     storage-dtype cleanup runs even when forward fails."""
diff --git a/tests/backend/model_manager/load/test_qwen3_vl_visual_tower.py b/tests/backend/model_manager/load/test_qwen3_vl_visual_tower.py
index c807f5c73ce..bd3fb1df4f2 100644
--- a/tests/backend/model_manager/load/test_qwen3_vl_visual_tower.py
+++ b/tests/backend/model_manager/load/test_qwen3_vl_visual_tower.py
@@ -3,7 +3,8 @@
 Krea-2 and Ideogram 4 condition on the language tower's hidden states; `Qwen3VLModel.forward`
 reaches `self.visual` only for `pixel_values`, which neither invocation passes, and Ideogram 4 does
 not go through `forward` at all. The tower is therefore never executed, and dropping it takes
-0.39-0.77 GiB off the 4B encoder depending on the container.
+0.39-0.77 GiB off the 4B encoder depending on the container. Qwen-Image-2.1 drops it for text
+prompts too, and loads the tower on its own for edits.
 
 These tests use a real (tiny) `Qwen3VLModel`, so what disappears is the architecture's actual vision
 tower rather than a stand-in. MiniMax H3 does run this tower and loads through
@@ -302,22 +303,95 @@ def test_the_folder_loader_drops_the_tower(tmp_path: Path) -> None:
     _assert_tower_gone(model)
 
 
-def test_the_qwen_image_2_1_pipeline_encoder_keeps_the_tower(monkeypatch, tmp_path: Path) -> None:
-    """The one encoder that keeps it: Qwen-Image-2.1 reads reference images through it, and the node refuses an
-    encoder without it."""
-    from transformers import Qwen3VLModel
+def _qwen_image_2_1_pipeline(
+    monkeypatch,
+    tmp_path: Path,
+    *,
+    conditional_generation: bool,
+    saved_dtype: torch.dtype = torch.float32,
+    max_shard_size: str | None = None,
+):
+    """A Qwen-Image-2.1 pipeline folder holding a tiny encoder, saved as `Qwen3VLForConditionalGeneration` (the
+    released layout, `model.visual.*`) or as `Qwen3VLModel` (`visual.*`); the loader over it, which loads in
+    float32; the saved encoder."""
+    from transformers import Qwen3VLForConditionalGeneration
 
     from invokeai.backend.model_manager.configs.main import Main_Diffusers_QwenImage21_Config
     from invokeai.backend.model_manager.load.model_loaders.qwen_image_2_1 import QwenImage21DiffusersModel
-    from invokeai.backend.qwen_image_2_1.text_encoding import has_vision_tower
 
-    encoder = _tiny_encoder()
+    torch.manual_seed(0)
+    saved = Qwen3VLForConditionalGeneration(tiny_qwen3vl_config()) if conditional_generation else _tiny_encoder()
+    saved = saved.to(saved_dtype)
+    shard_kwargs = {"max_shard_size": max_shard_size} if max_shard_size else {}
+    saved.save_pretrained(tmp_path / "text_encoder", **shard_kwargs)
+    monkeypatch.setattr(
+        "invokeai.backend.model_manager.load.model_loaders.qwen_image_2_1.TorchDevice.choose_bfloat16_safe_dtype",
+        lambda _device: torch.float32,
+    )
     config = Main_Diffusers_QwenImage21_Config.model_construct(path=str(tmp_path), repo_variant=None)
     loader = object.__new__(QwenImage21DiffusersModel)
     loader._apply_fp8_layerwise_casting = lambda model, _config, _submodel: model
-    monkeypatch.setattr(Qwen3VLModel, "from_pretrained", classmethod(lambda _cls, *_a, **_k: encoder))
+    encoder = saved.model if conditional_generation else saved
+    return loader, config, encoder
 
-    model = loader._load_model(config, SubModelType.TextEncoder)
 
-    assert has_vision_tower(model)
-    assert list(model.visual.parameters())
+def _assert_tower_loaded(tower, saved) -> None:
+    from transformers.models.qwen3_vl.modeling_qwen3_vl import Qwen3VLVisionModel
+
+    assert isinstance(tower, Qwen3VLVisionModel)
+    expected = saved.visual.state_dict()
+    loaded = tower.state_dict()
+    assert loaded.keys() == expected.keys()
+    for key, value in expected.items():
+        assert torch.equal(loaded[key], value), key
+
+
+@pytest.mark.parametrize("conditional_generation", [True, False], ids=["model.visual", "visual"])
+def test_the_qwen_image_2_1_pipeline_loads_the_tower_on_its_own(
+    monkeypatch, tmp_path: Path, conditional_generation: bool
+) -> None:
+    """Text prompts load the encoder without the tower, as every other loader does. Edits load the tower alone,
+    from the same folder and with the weights it was saved with, and attach it while they encode."""
+    loader, config, saved = _qwen_image_2_1_pipeline(
+        monkeypatch, tmp_path, conditional_generation=conditional_generation
+    )
+
+    _assert_tower_gone(loader._load_model(config, SubModelType.TextEncoder))
+    _assert_tower_loaded(loader._load_model(config, SubModelType.VisionTower), saved)
+
+
+def _shard_the_tower_and_leave_a_stray_file(tmp_path: Path) -> None:
+    """The released encoder is sharded; check the tower spans several shards, and leave a stray safetensors file
+    beside them (a leftover variant) that the index does not name, holding zeros under the tower's keys."""
+    from safetensors import safe_open
+    from safetensors.torch import save_file
+
+    folder = tmp_path / "text_encoder"
+    weight_map = json.loads((folder / "model.safetensors.index.json").read_text())["weight_map"]
+    tower_keys = [key for key in weight_map if key.startswith("model.visual.")]
+    assert len({weight_map[key] for key in tower_keys}) > 1
+    first_shard = folder / weight_map[tower_keys[0]]
+    with safe_open(first_shard, framework="pt") as f:
+        stray = {key: torch.zeros_like(f.get_tensor(key)) for key in f.keys() if key.startswith("model.visual.")}
+    save_file(stray, str(folder / "model.fp16.safetensors"))
+
+
+def test_a_sharded_tower_loads_from_the_shards_its_index_names(monkeypatch, tmp_path: Path) -> None:
+    loader, config, saved = _qwen_image_2_1_pipeline(
+        monkeypatch, tmp_path, conditional_generation=True, max_shard_size="40KB"
+    )
+    _shard_the_tower_and_leave_a_stray_file(tmp_path)
+
+    _assert_tower_loaded(loader._load_model(config, SubModelType.VisionTower), saved)
+
+
+def test_the_tower_reserves_its_own_size_at_the_dtype_it_loads_in(monkeypatch, tmp_path: Path) -> None:
+    """The cache makes room for this size before the load: the encoder folder's would evict ~17 GB for ~1 GB.
+    Saved in bf16, sharded, and loaded in float32: four bytes per element, counted once, from the index's shards."""
+    loader, config, saved = _qwen_image_2_1_pipeline(
+        monkeypatch, tmp_path, conditional_generation=True, saved_dtype=torch.bfloat16, max_shard_size="40KB"
+    )
+    _shard_the_tower_and_leave_a_stray_file(tmp_path)
+
+    elements = sum(v.nelement() for v in saved.visual.state_dict().values())
+    assert loader.get_size_fs(config, tmp_path, SubModelType.VisionTower) == 4 * elements
diff --git a/tests/backend/qwen_image_2_1/test_text_encoding.py b/tests/backend/qwen_image_2_1/test_text_encoding.py
index 349af916949..1c770c40d25 100644
--- a/tests/backend/qwen_image_2_1/test_text_encoding.py
+++ b/tests/backend/qwen_image_2_1/test_text_encoding.py
@@ -10,12 +10,15 @@
 from invokeai.backend.qwen3_vl.qwen3_vl_assets import load_bundled_qwen3_vl_tokenizer
 from invokeai.backend.qwen_image_2_1.text_encoding import (
     SYSTEM_BLOCK,
+    attached_vision_tower,
     encode_prompt,
     format_prompt,
     has_vision_tower,
     on_white,
     sequence_length,
     system_prefix_length,
+    vision_attention_bytes,
+    working_memory_bytes,
 )
 from tests.backend.model_manager.load.qwen3vl_gguf_fixture import tiny_qwen3vl_config
 
@@ -142,3 +145,110 @@ def test_transparency_is_flattened_over_white_for_the_vision_encoder() -> None:
     half_red = Image.new("RGBA", (1, 1), (255, 0, 0, 128))
     assert on_white(half_red).getpixel((0, 0)) == (255, 127, 127)
     assert on_white(Image.new("RGBA", (1, 1), (0, 0, 0, 0))).getpixel((0, 0)) == (255, 255, 255)
+
+
+def test_a_text_prompt_encodes_the_same_without_the_vision_tower(text_encoder, tokenizer) -> None:
+    """Text-only encodes load the encoder without its tower; they must read exactly what the full encoder reads."""
+    import copy
+
+    from invokeai.backend.model_manager.util.qwen3_vl import drop_qwen3vl_visual_tower
+
+    without_tower = copy.deepcopy(text_encoder)
+    drop_qwen3vl_visual_tower(without_tower)
+    assert has_vision_tower(text_encoder) and not has_vision_tower(without_tower)
+
+    for prompt in ("a fox in the snow", ""):
+        with_embeds, _, _ = encode_prompt(text_encoder, tokenizer, prompt, torch.device("cpu"))
+        without_embeds, _, _ = encode_prompt(without_tower, tokenizer, prompt, torch.device("cpu"))
+        assert torch.equal(with_embeds, without_embeds)
+
+
+def test_a_literal_image_placeholder_in_an_edit_prompt_is_refused(text_encoder, tokenizer, processor) -> None:
+    prompt = "put <|image_pad|> here"
+    reference = Image.new("RGB", (32, 32))
+    # Refused where the node counts the tokens, before the encoder loads, and again where it encodes.
+    with pytest.raises(ValueError, match=r"cannot contain <\|image_pad\|>"):
+        sequence_length(tokenizer, prompt, [reference])
+    with pytest.raises(ValueError, match=r"cannot contain <\|image_pad\|>"):
+        encode_prompt(text_encoder, tokenizer, prompt, torch.device("cpu"), processor=processor, images=[reference])
+    # Without references there is nothing for it to claim: it encodes as text.
+    encode_prompt(text_encoder, tokenizer, prompt, torch.device("cpu"))
+
+
+def test_the_encoder_reserve_scales_with_the_compute_dtype() -> None:
+    assert working_memory_bytes(4100, 4) == pytest.approx(2 * working_memory_bytes(4100, 2), abs=1)
+
+
+def test_the_vision_towers_attention_is_reserved_when_it_outgrows_the_language_models() -> None:
+    # One 1 MP reference: ~1.1k tokens for the language model, but ~4k patches for the tower, whose scores
+    # outweigh the language model's attention when they are materialized.
+    language_only = working_memory_bytes(1100, 2)
+    tower = 16 * 4096**2 * 17
+    assert working_memory_bytes(1100, 2, tower) >= 1.2 * tower
+    # The tower frees its scores before the language model starts: a tower term below the language model's
+    # attention adds nothing.
+    assert working_memory_bytes(1100, 2, 1024) == language_only
+
+
+def test_the_vision_tower_is_priced_for_its_largest_reference_at_four_patches_per_token(
+    text_encoder, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    calls: list[dict] = []
+
+    def score_bytes(**kwargs) -> int:
+        calls.append(kwargs)
+        return 123
+
+    monkeypatch.setattr("invokeai.backend.qwen_image_2_1.text_encoding.sdpa_score_matrix_bytes", score_bytes)
+    references = [Image.new("RGB", (64, 96)), Image.new("RGB", (128, 64)), Image.new("RGB", (32, 32))]
+    vision = text_encoder.visual.config
+
+    assert vision_attention_bytes(text_encoder.visual, references, torch.device("cpu"), torch.bfloat16) == 123
+    # The largest reference is 128x64: 4x2 vision tokens, 32 patches of 16px. The tower attends one image at a time.
+    assert len(calls) == 1
+    assert calls[0]["seq_len"] == 32
+    assert (calls[0]["num_heads"], calls[0]["head_dim"]) == (vision.num_heads, vision.hidden_size // vision.num_heads)
+    assert vision_attention_bytes(text_encoder.visual, [], torch.device("cpu"), torch.bfloat16) == 0
+
+
+def _split(text_encoder):
+    """The encoder as the pipeline loader caches it for an edit: without its tower, and the tower on its own."""
+    import copy
+
+    from invokeai.backend.model_manager.util.qwen3_vl import drop_qwen3vl_visual_tower
+
+    tower = copy.deepcopy(text_encoder.visual)
+    encoder = copy.deepcopy(text_encoder)
+    drop_qwen3vl_visual_tower(encoder)
+    return encoder, tower
+
+
+def test_an_attached_tower_reads_references_exactly_as_a_built_in_one(text_encoder, tokenizer, processor) -> None:
+    encoder, tower = _split(text_encoder)
+    references = [Image.new("RGB", (64, 96), (255, 0, 0)), Image.new("RGB", (128, 64), (0, 0, 255))]
+    built_in = _encode(text_encoder, tokenizer, processor, references)
+
+    with attached_vision_tower(encoder, tower):
+        attached = _encode(encoder, tokenizer, processor, references)
+
+    assert torch.equal(attached.embeds, built_in.embeds)
+    assert torch.equal(attached.image_pad_mask, built_in.image_pad_mask)
+    assert attached.reference_grids == built_in.reference_grids
+    # The cached encoder is left as it was loaded, so a later text prompt or another session never sees the tower.
+    assert isinstance(encoder.visual, torch.nn.Identity)
+    assert not has_vision_tower(encoder)
+
+
+def test_a_failed_encode_detaches_the_tower(text_encoder, tokenizer, processor) -> None:
+    encoder, tower = _split(text_encoder)
+    with pytest.raises(ValueError, match="image_pad"), attached_vision_tower(encoder, tower):
+        assert has_vision_tower(encoder)
+        encode_prompt(
+            encoder,
+            tokenizer,
+            "<|image_pad|>",
+            torch.device("cpu"),
+            processor=processor,
+            images=[Image.new("RGB", (32, 32))],
+        )
+    assert isinstance(encoder.visual, torch.nn.Identity)

From 381f461c175210c79659a7029cd3cfbf04e4e622 Mon Sep 17 00:00:00 2001
From: Alexander Eichhorn <alex@eichhorn.dev>
Date: Thu, 22 Oct 2026 07:37:47 +0200
Subject: [PATCH 4/4] fix(models): identify Qwen-Image-2.1 LoRAs only when they
 apply in full

Every module must be one complete low-rank, DoRA, LoKR, LoHA or full layer, so pure LoKR/LoHA/diff files install and partial ones stay unknown; Qwen Image no longer claims LTX-2 or 4096-wide LoKR files.
Sidecar working memory is priced per layer kind and also covers partly loaded merged formats; DoRA on an int8 transformer is refused before loading.
---
 .../03.Models/03.model-format-support.mdx     |   2 +-
 .../03.Models/Local Models/qwen-image-2-1.mdx |   7 +-
 .../qwen_image_2_1/qwen_image_2_1_denoise.py  | 138 ++++++-
 .../backend/model_manager/configs/lora.py     |  78 ++--
 .../qwen_image_2_1_lora_constants.py          | 100 +++--
 .../qwen_image_2_1_lora_conversion_utils.py   |  23 +-
 .../invocations/test_qwen_image_2_1_nodes.py  | 383 ++++++++++++++++--
 .../test_ltx2_lora_probe_independence.py      |  46 +--
 .../configs/test_qwen_image_2_1_config.py     | 172 ++++++--
 ...st_qwen_image_2_1_lora_conversion_utils.py | 131 ++++++
 10 files changed, 878 insertions(+), 202 deletions(-)

diff --git a/docs/src/content/docs/Users Guide/03.Models/03.model-format-support.mdx b/docs/src/content/docs/Users Guide/03.Models/03.model-format-support.mdx
index c85c0b9a37f..2ffe7373c2c 100644
--- a/docs/src/content/docs/Users Guide/03.Models/03.model-format-support.mdx	
+++ b/docs/src/content/docs/Users Guide/03.Models/03.model-format-support.mdx	
@@ -103,7 +103,7 @@ not check, so a quantized file there fails with an unclear error or decodes inco
 | FLUX.1 | ✓ (LyCORIS, Diffusers, OMI) | ✓ | Control LoRA, IP-Adapter, Redux |
 | FLUX.2, Z-Image | ✓ (LyCORIS, Diffusers) | FLUX.2 [dev] (single-file checkpoint), Z-Image | |
 | Qwen-Image, Krea-2, Wan 2.2, Anima | ✓ (LyCORIS/kohya, including LoKR where noted on the family page) | Qwen-Image (single-file checkpoint), Anima (ControlNet-LLLite) | |
-| Qwen-Image-2.1 | ✓ (ComfyUI, diffusers/PEFT and kohya low-rank, and DoRA except LyCORIS DoRA on ComfyUI's fused `gate_up`; LoKR and LoHA install as unknown) | ✗ | |
+| Qwen-Image-2.1 | ✓ (ComfyUI, diffusers/PEFT and kohya, including LoKR, LoHA and DoRA as noted on the family page) | ✗ | |
 | LTX-2, MiniMax H3 | ✓ (plain low-rank; LoKR, LoHA and DoRA are refused) | ✗ | |
 | SD 3, CogView4, Ideogram 4, ERNIE-Image | ✗ | ✗ | |
 
diff --git a/docs/src/content/docs/Users Guide/03.Models/Local Models/qwen-image-2-1.mdx b/docs/src/content/docs/Users Guide/03.Models/Local Models/qwen-image-2-1.mdx
index 2a5c966a9f4..9bbe9900fba 100644
--- a/docs/src/content/docs/Users Guide/03.Models/Local Models/qwen-image-2-1.mdx	
+++ b/docs/src/content/docs/Users Guide/03.Models/Local Models/qwen-image-2-1.mdx	
@@ -77,10 +77,11 @@ Image-to-image, inpainting and outpainting read the colors of the input image an
 Qwen-Image-2.1 LoRAs install like any other and appear under **Concepts** in the Generate panel. Files from ComfyUI (keys starting `diffusion_model.`), diffusers/PEFT trainers (`transformer.`) and Kohya-style trainers (`lora_unet_`) all load. ComfyUI's builds fuse each block's two MLP input projections into one `gate_up` layer; Invoke splits a LoRA on it to match the diffusers model.
 
 - LoRAs for [Qwen Image](/users-guide/models/local-models/qwen-image/) (2512, Edit 2511) do not fit Qwen-Image-2.1 and are not offered for it, nor its LoRAs for them.
-- They work on every format. On GGUF, int8 and fp8 transformers the LoRA runs alongside the packed weights rather than being merged into them. Full weight differences on the attention and text norms, which ComfyUI's Turbo LoRA carries, are merged on every format.
+- They work on every format, except that DoRA layers do not run on an int8 transformer: a generation with one there stops with a message before it starts. On GGUF, int8 and fp8 transformers the LoRA runs alongside the packed weights rather than being merged into them. Full weight differences on the attention and text norms, which ComfyUI's Turbo LoRA carries, are merged on every format.
 - A LoRA trained on the base model also applies to Turbo; how well it holds up at Turbo's 8 steps depends on the LoRA.
-- Low-rank LoRAs (`lora_A`/`lora_B` or `lora_down`/`lora_up`) are recognized, DoRA ones included — except a LyCORIS DoRA on ComfyUI's fused `gate_up`, whose magnitude spans both halves and cannot be split. LoKR and LoHA files are not: they install as an unknown model and do not appear under **Concepts**.
-- A LoRA is recognized only if every layer it targets exists in Qwen-Image-2.1, so none applies only in part. One case cannot be told apart: a FLUX.2 Klein 9B LoRA trained on nothing but the image attention of its first blocks has exactly the layers of a Qwen-Image-2.1 one, and installs as one.
+- Low-rank (`lora_A`/`lora_B` or `lora_down`/`lora_up`), DoRA, LoKR and LoHA LoRAs are recognized, and so are full weight differences (`diff`). On ComfyUI's fused `gate_up` only low-rank, PEFT DoRA and full layers work: a LoKR, LoHA or LyCORIS DoRA layer there mixes the two halves Invoke splits it into.
+- A LoRA is recognized as a Qwen-Image-2.1 one only if all of it applies: every layer it targets exists in Qwen-Image-2.1, is complete and, on `gate_up`, can be split. A file with any part that does not apply installs as an unknown model and does not appear under **Concepts**, so apart from the Klein case below no LoRA applies only in part.
+- A FLUX.2 Klein 9B LoRA on its double blocks' image attention has the same layer names and width as a Qwen-Image-2.1 one, and installs as one. If it also targets `proj_out` or `norm_out.linear`, it still installs, but those layers do not fit Qwen-Image-2.1: generation skips them or fails. A file on only those two layers is not recognized as a Qwen-Image-2.1 LoRA.
 
 ### Generation settings
 
diff --git a/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_denoise.py b/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_denoise.py
index 783a6087a0a..29fb0d3797e 100644
--- a/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_denoise.py
+++ b/invokeai/app/invocations/qwen_image_2_1/qwen_image_2_1_denoise.py
@@ -28,12 +28,22 @@
 from invokeai.backend.flux.sampling_utils import clip_timestep_schedule_fractional
 from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelFormat, QwenImage21VariantType
 from invokeai.backend.patches.layer_patcher import LayerPatcher, PatchSpec
+from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
+from invokeai.backend.patches.layers.dora_layer import DoRALayer
+from invokeai.backend.patches.layers.full_layer import FullLayer
+from invokeai.backend.patches.layers.loha_layer import LoHALayer
+from invokeai.backend.patches.layers.lokr_layer import LoKRLayer
+from invokeai.backend.patches.layers.lora_layer import LoRALayer
 from invokeai.backend.patches.lora_conversions.qwen_image_2_1_lora_conversion_utils import (
     QWEN_IMAGE_21_LORA_NORM_PREFIX,
     QWEN_IMAGE_21_LORA_TRANSFORMER_PREFIX,
 )
 from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
-from invokeai.backend.quantization.dequantizing_linear import peak_dequant_transient_bytes, requires_sidecar_patching
+from invokeai.backend.quantization.dequantizing_linear import (
+    DequantizingLinear,
+    peak_dequant_transient_bytes,
+    requires_sidecar_patching,
+)
 from invokeai.backend.quantization.fp8_scaled import count_fp8_weights
 from invokeai.backend.qwen_image_2_1.sampling import build_sigmas
 from invokeai.backend.rectified_flow.rectified_flow_inpaint_extension import RectifiedFlowInpaintExtension
@@ -111,12 +121,38 @@ def prefix_length(info: QwenImage21ConditioningInfo) -> int:
     return info.prompt_embeds.shape[1] + (TOKENS_PER_SLOT - 1) * slots
 
 
-# The widest Linear output a sidecar LoRA adds to: gate_layer and proj.
-_MLP_WIDTH = 12288
-
 # K and V of one prefix token in all 32 blocks: 2 x 4096 x 32 x 2 bytes in bf16.
 KV_BYTES_PER_PREFIX_TOKEN = 2 * 4096 * 32 * 2
 
+# `out_features x in_features` tensors a sidecar holds at once while it turns a layer of each kind into a weight
+# update (`autocast_linear_forward_sidecar_patches`): the weight cast or dequantized to the compute dtype, plus the
+# layer's own. A full layer scales its diff into a new tensor; LoKR builds kron(w1, w2), then scales it; LoHA holds
+# its two products and theirs. DoRA holds its scaled delta, the updated weight and their difference, 4 in all; the
+# input-dim (LyCORIS) magnitude's column norm, over a transposed view, measured another third of a copy on CUDA
+# (gate_layer, bf16), so DoRA is priced at 5. A low-rank layer forms none: it adds its output only.
+_SIDECAR_WEIGHT_COPIES: dict[type[BaseLayerPatch], int] = {
+    FullLayer: 2,
+    LoKRLayer: 3,
+    LoHALayer: 4,
+    DoRALayer: 5,
+}
+# The Linears that read the timestep embedding, which has two rows however long the sequence: the sampled
+# timestep's, and with `causal_condition` the t = 0 one the text and reference tokens modulate from.
+_TIMESTEP_ROWS = 2
+_PER_SAMPLE_LINEARS = frozenset(
+    {
+        "time_text_embed.timestep_embedder.linear_1",
+        "time_text_embed.timestep_embedder.linear_2",
+        "modulation.1",
+        "norm_out.linear",
+    }
+)
+
+
+def _elements(layer: BaseLayerPatch) -> int:
+    """Elements of a patch layer's tensors, which a sidecar holds in the compute dtype whatever it was loaded in."""
+    return sum(value.numel() for value in vars(layer).values() if isinstance(value, torch.Tensor))
+
 
 def prefix_cache_fits(cache_bytes: int, device: torch.device) -> bool:
     """Whether the prefix caches can stay on the GPU for the whole run.
@@ -377,8 +413,13 @@ def image_slot_mask(info: QwenImage21ConditioningInfo) -> torch.Tensor:
             transformer_info.model, image_seq_len, max(pos_prefix, neg_prefix or 0), device, dtype
         )
         working_memory += peak_dequant_transient_bytes(transformer_info.model, dtype)
+        self._refuse_dora_on_int8(context, transformer_info.model)
         working_memory += self._lora_working_memory(
-            context, transformer_info.model, transformer_config.format, image_seq_len + max(pos_prefix, neg_prefix or 0)
+            context,
+            transformer_info.model,
+            transformer_config.format,
+            image_seq_len + max(pos_prefix, neg_prefix or 0),
+            dtype,
         )
         log_attention_backends(logger, device)
 
@@ -530,20 +571,85 @@ def _attention_score_bytes(
         )
 
     def _lora_working_memory(
-        self, context: InvocationContext, transformer: torch.nn.Module, model_format: ModelFormat, seq_len: int
+        self,
+        context: InvocationContext,
+        transformer: torch.nn.Module,
+        model_format: ModelFormat,
+        seq_len: int,
+        dtype: torch.dtype,
     ) -> int:
-        """Room the LoRAs need while sampling: only where they run beside the weights, as sidecars.
-
-        A LoRA merged into the weights computes its update once, before the first step, in room the activations take
-        only afterwards. A sidecar -- for GGUF and int8 weights, and per layer for fp8 ones -- keeps its tensors on
-        the device and adds one output-sized update per forward.
+        """Room the LoRAs need while sampling, beside the activations.
+
+        A layer merged into the weights computes its update once, before the first step, in room the activations
+        take only afterwards. A layer run as a sidecar holds its tensors in the compute dtype, and each forward of its
+        module adds:
+
+        - an update of the module's output, one row per token (`_TIMESTEP_ROWS` for `_PER_SAMPLE_LINEARS`), with a
+          low-rank layer's `rank` columns beside it;
+        - for any layer but a low-rank one, the `out_features x in_features` tensors `_SIDECAR_WEIGHT_COPIES` counts
+          for its kind, one more from the second such layer on the module (the running sum) and two from the third
+          (the previous layer's result as well). An int8 Linear hands over only its weight's shape, so there one
+          copy is counted that is not made.
+
+        Modules run one after another, so only the largest such update counts. On GGUF, int8 and fp8 weights every
+        layer is a sidecar, kept on the device: all the sidecars' tensors are counted, plus that update. Other formats
+        merge, but the patcher wraps any layer partial loading leaves on the CPU, and that sidecar copies its tensors
+        to the device for each forward; which layers stay on the CPU is unknown before the transformer is locked, so
+        one module's worth is reserved: the largest of its tensors plus its update. Norm diffs are merged in every
+        format and a LoRA at weight 0 is not applied: neither is counted.
         """
-        if not self.transformer.loras:
-            return 0
-        if not requires_sidecar_patching(transformer, model_format) and not count_fp8_weights(transformer):
+        loras = [lora for lora in self.transformer.loras if lora.weight != 0]
+        if not loras:
             return 0
-        patch_bytes = sum(context.models.load(lora.lora).model.calc_size() for lora in self.transformer.loras)
-        return patch_bytes + seq_len * _MLP_WIDTH * 2
+        modules = dict(transformer.named_modules())
+        # Per patched module: its layers' elements, the largest rank among its low-rank layers, and the weight
+        # copies of each of its other layers.
+        elements: dict[str, int] = {}
+        ranks: dict[str, int] = {}
+        copies: dict[str, list[int]] = {}
+        for lora in loras:
+            for key, layer in context.models.load(lora.lora).model.layers.items():
+                # Keys are `<prefix>-<module path>`. A norm diff, under its own prefix, is merged directly in every
+                # format, so no sidecar holds one.
+                prefix, _, path = key.partition("-")
+                if f"{prefix}-" != QWEN_IMAGE_21_LORA_TRANSFORMER_PREFIX:
+                    continue
+                if path not in modules:
+                    # A block this transformer does not have: the patcher skips it too.
+                    continue
+                elements[path] = elements.get(path, 0) + _elements(layer)
+                if isinstance(layer, LoRALayer):
+                    ranks[path] = max(ranks.get(path, 0), layer.down.shape[0])
+                else:
+                    copies.setdefault(path, []).append(_SIDECAR_WEIGHT_COPIES[type(layer)])
+        updates: dict[str, int] = {}
+        for path in elements:
+            module = modules[path]
+            rows = _TIMESTEP_ROWS if path in _PER_SAMPLE_LINEARS else seq_len
+            summed = copies.get(path, [])
+            weight_copies = max(summed) + min(len(summed) - 1, 2) if summed else 0
+            updates[path] = (
+                rows * (module.out_features + ranks.get(path, 0))
+                + weight_copies * module.out_features * module.in_features
+            )
+        if requires_sidecar_patching(transformer, model_format) or count_fp8_weights(transformer):
+            return (sum(elements.values()) + max(updates.values(), default=0)) * dtype.itemsize
+        return max((elements[path] + updates[path] for path in elements), default=0) * dtype.itemsize
+
+    def _refuse_dora_on_int8(self, context: InvocationContext, transformer: torch.nn.Module) -> None:
+        """Fail before loading the transformer onto the device, not at the first step: a DoRA layer normalizes the
+        updated weight, and an int8 Linear's packed buffers never form one (`DoRALayer.get_parameters` raises)."""
+        loras = [lora for lora in self.transformer.loras if lora.weight != 0]
+        if not loras:
+            return
+        modules = dict(transformer.named_modules())
+        for lora in loras:
+            for key, layer in context.models.load(lora.lora).model.layers.items():
+                if isinstance(layer, DoRALayer) and isinstance(modules.get(key.partition("-")[2]), DequantizingLinear):
+                    raise ValueError(
+                        f"LoRA '{lora.lora.name}' has DoRA layers, which do not run on an int8 transformer. Use it "
+                        "with a Qwen-Image-2.1 transformer in another format, such as bf16, fp8 or GGUF."
+                    )
 
     def _lora_iterator(self, context: InvocationContext) -> Iterator[PatchSpec]:
         for lora in self.transformer.loras:
diff --git a/invokeai/backend/model_manager/configs/lora.py b/invokeai/backend/model_manager/configs/lora.py
index 0d29636ecff..c15730dc8de 100644
--- a/invokeai/backend/model_manager/configs/lora.py
+++ b/invokeai/backend/model_manager/configs/lora.py
@@ -875,6 +875,7 @@ def _validate_looks_like_lora(cls, mod: ModelOnDisk) -> None:
             and not has_krea2_keys
             and not has_flux_keys
             and not _has_qwen_image21_lora_keys(state_dict)
+            and not _has_ltx2_lora_keys(state_dict)
         ):
             return
 
@@ -908,11 +909,18 @@ def _get_base_or_raise(cls, mod: ModelOnDisk) -> BaseModelType:
             and not has_krea2_keys
             and not has_flux_keys
             and not _has_qwen_image21_lora_keys(state_dict)
+            and not _has_ltx2_lora_keys(state_dict)
         ):
             return BaseModelType.QwenImage
         raise NotAMatchError("model does not look like a Qwen Image Edit LoRA")
 
 
+def _has_ltx2_lora_keys(state_dict: dict[str | int, Any]) -> bool:
+    # LTX-2's published LoRAs, unprefixed or under `transformer.`, also start with `transformer_blocks.`; only its
+    # audio and cross-modal towers tell them apart, and without this both Qwen Image and LTX-2 would claim them.
+    return has_ltx2_lora_keys([key for key in state_dict if isinstance(key, str)])
+
+
 _QWEN_IMAGE21_HIDDEN_SIZE = 4096
 # Qwen-Image-2.1's gated MLP, by any of its names: diffusers' `gate_layer`/`proj`/`out`, ComfyUI's fused `gate_up`,
 # dotted or Kohya-flattened. Qwen-Image's MLP is `img_mlp.net.0.proj`/`img_mlp.net.2`, which none of these match.
@@ -930,16 +938,25 @@ def _get_base_or_raise(cls, mod: ModelOnDisk) -> BaseModelType:
 # `audio_to_video_attn.to_q` is as wide and would match the bare `attn.to_q`.
 _BLOCK_ATTENTION_PROJECTION = re.compile(r"transformer_blocks[._]\d+[._]attn[._]to_(?:[qkv]|out[._]0)[._]")
 _LOKR_FACTOR = re.compile(r"\.(lokr_w[12])(?:_b)?$")
+# Modules of Qwen Image's dual-stream blocks that no other family naming `transformer_blocks.N` has: its per-stream
+# modulation and MLPs, dotted or Kohya-flattened.
+_QWEN_IMAGE_ONLY_MODULE = re.compile(r"transformer_blocks[._]\d+[._](?:img_mod|txt_mod|img_mlp[._]net|txt_mlp)[._]")
+
+
+# The parameters that carry a layer's input width as their second dimension: the input-side matrix of a low-rank
+# (or DoRA) layer, either of LoHA's, and a full diff, which is the whole weight update.
+_INPUT_WIDTH_PARAMS = (".lora_A.weight", ".lora_down.weight", ".hada_w1_b", ".hada_w2_b", ".diff")
 
 
 def _block_attention_in_features(state_dict: dict[str | int, Any]) -> tuple[set[int], set[int]]:
-    """The input widths of the block-attention projections a LoRA targets: of its low-rank layers, and of its
-    LoKR ones.
+    """The input widths of the block-attention projections a LoRA targets: those its layers state directly, and
+    those of its LoKR layers.
 
-    A low-rank layer carries the width on its input-side matrix; a LoKR weight is the Kronecker product of two
-    factors, so its input width is the product of theirs (each given whole, or as the second matrix of a pair).
+    A low-rank, LoHA or full layer carries the width on one of its matrices; a LoKR weight is the Kronecker product
+    of two factors, so its input width is the product of theirs (each given whole, or as the second matrix of a
+    pair).
     """
-    low_rank: set[int] = set()
+    direct: set[int] = set()
     factored: set[int] = set()
     lokr: dict[str, dict[str, int]] = {}
     for key, value in state_dict.items():
@@ -948,12 +965,12 @@ def _block_attention_in_features(state_dict: dict[str | int, Any]) -> tuple[set[
         shape = getattr(value, "shape", ())
         if len(shape) != 2:
             continue
-        if key.endswith((".lora_A.weight", ".lora_down.weight")):
-            low_rank.add(shape[1])
+        if key.endswith(_INPUT_WIDTH_PARAMS):
+            direct.add(shape[1])
         elif factor := _LOKR_FACTOR.search(key):
             lokr.setdefault(key[: factor.start()], {})[factor.group(1)] = shape[1]
     factored.update(f["lokr_w1"] * f["lokr_w2"] for f in lokr.values() if len(f) == 2)
-    return low_rank, factored
+    return direct, factored
 
 
 def _has_qwen_image21_lora_keys(state_dict: dict[str | int, Any]) -> bool:
@@ -962,15 +979,21 @@ def _has_qwen_image21_lora_keys(state_dict: dict[str | int, Any]) -> bool:
     Its gated MLP (`gate_layer`, `proj` and `out` in diffusers/PEFT, ComfyUI's fused `gate_up`, and their Kohya
     spellings) is Qwen-Image-2.1's alone. An attention-only LoRA -- PEFT's usual `to_q/to_k/to_v` targets, or
     `to_out.0` -- names nothing Qwen-Image lacks, so its width decides: the projections read 4096 features here
-    and 3072 in Qwen-Image. A LoKR layer's width is a product of factor sizes, weaker evidence on its own, so it
-    counts only where every key also names a Qwen-Image-2.1 module.
+    and 3072 in Qwen-Image, so a 4096 one is never Qwen-Image's. A low-rank, LoHA or full layer states that width.
+    A LoKR layer's is a product of factor sizes, which can come to 4096 without any layer being that wide, so it
+    counts unless the file names a module only Qwen-Image has.
+
+    It tells the family apart, not whether the layers are whole: a 2.1 LoRA with a broken layer, or with a text
+    encoder's layer beside it, is still not Qwen-Image's; `_is_qwen_image21_lora` refuses it as a 2.1 one.
     """
     if any(isinstance(key, str) and any(m in key for m in _QWEN_IMAGE21_MLP_MARKERS) for key in state_dict):
         return True
-    low_rank, factored = _block_attention_in_features(state_dict)
-    if _QWEN_IMAGE21_HIDDEN_SIZE in low_rank:
+    direct, factored = _block_attention_in_features(state_dict)
+    if _QWEN_IMAGE21_HIDDEN_SIZE in direct:
         return True
-    return _QWEN_IMAGE21_HIDDEN_SIZE in factored and not qwen_image_21_unsupported_lora_keys(state_dict)
+    return _QWEN_IMAGE21_HIDDEN_SIZE in factored and not any(
+        isinstance(key, str) and _QWEN_IMAGE_ONLY_MODULE.search(key) for key in state_dict
+    )
 
 
 def _has_krea2_lora_keys(state_dict: dict[str | int, Any]) -> bool:
@@ -1180,34 +1203,29 @@ def _get_base_or_raise(cls, mod: ModelOnDisk) -> BaseModelType:
 
 
 def _is_qwen_image21_lora(state_dict: dict[str | int, Any]) -> bool:
-    """A Qwen-Image-2.1 LoRA that applies in full, and not one for a family whose module names it shares.
-
-    FLUX.2 Klein 9B is as wide (4096) and names its double blocks' attention the same. Its fingerprints (text
-    embedder widths, fused single-block projections) rule most of its LoRAs out, and every key having to name a
-    Qwen-Image-2.1 module rules out the rest that touch a module only a dual-stream model has (`add_q_proj`, `ff`).
-    A LoRA on nothing but the image attention of Klein's first eight blocks reads the same as a Qwen-Image-2.1 one.
+    """A Qwen-Image-2.1 LoRA that applies in full, every layer complete and on a module this transformer has.
+
+    That every key must name a Qwen-Image-2.1 module also rules out the families whose module names it shares, as
+    long as their LoRA touches one module 2.1 lacks. FLUX.2 Klein 9B is as wide (4096); its text embedders, single
+    blocks and text stream (`add_q_proj`, `ff`) are its own. A Klein LoRA on its double blocks' image attention has
+    a 2.1 LoRA's names and width, and reads as one. `proj_out` and `norm_out.linear` share the names but not the
+    output widths (Klein's are 128 and 8192, 2.1's 64 and 4096): beside that attention they are taken too and do
+    not fit, and on their own they are no 2.1 evidence.
     """
-    return (
-        _has_qwen_image21_lora_keys(state_dict)
-        and not _is_flux2_lora_state_dict(state_dict)
-        and not qwen_image_21_unsupported_lora_keys(state_dict)
-    )
+    return _has_qwen_image21_lora_keys(state_dict) and not qwen_image_21_unsupported_lora_keys(state_dict)
 
 
 class LoRA_LyCORIS_QwenImage21_Config(LoRA_LyCORIS_Config_Base, Config_Base):
     """Model config for Qwen-Image-2.1 LoRAs: ComfyUI (`diffusion_model.`, fused `gate_up`), diffusers/PEFT and
-    Kohya layouts, as plain low-rank layers."""
+    Kohya layouts, of low-rank, DoRA, LoKR, LoHA or full layers that apply to it in full."""
 
     base: Literal[BaseModelType.QwenImage21] = Field(default=BaseModelType.QwenImage21)
 
     @classmethod
     def _validate_looks_like_lora(cls, mod: ModelOnDisk) -> None:
-        state_dict = mod.load_state_dict()
-        if not (_is_qwen_image21_lora(state_dict) and _has_complete_lora_pair(state_dict)):
+        # An incomplete or misplaced layer is refused here, so it cannot install and then fail in conversion.
+        if not _is_qwen_image21_lora(mod.load_state_dict()):
             raise NotAMatchError("model does not match Qwen-Image-2.1 LoRA heuristics")
-        # An orphaned half would install here and fail in conversion at generation time.
-        if not _lora_weight_keys_are_all_paired(state_dict):
-            raise NotAMatchError("Qwen-Image-2.1 LoRA has an incomplete lora_A/B (or lora_down/up) weight pair")
 
     @classmethod
     def _get_base_or_raise(cls, mod: ModelOnDisk) -> BaseModelType:
diff --git a/invokeai/backend/patches/lora_conversions/qwen_image_2_1_lora_constants.py b/invokeai/backend/patches/lora_conversions/qwen_image_2_1_lora_constants.py
index c3604a9256b..a080d1873f9 100644
--- a/invokeai/backend/patches/lora_conversions/qwen_image_2_1_lora_constants.py
+++ b/invokeai/backend/patches/lora_conversions/qwen_image_2_1_lora_constants.py
@@ -9,6 +9,7 @@
 merged into the weight whatever the transformer's format: a norm is never quantized, and a sidecar cannot patch it.
 """
 
+import itertools
 import re
 from typing import Any
 
@@ -65,24 +66,48 @@
         "time_text_embed.timestep_embedder.linear_2",
     }
 )
-_BLOCK = re.compile(r"transformer_blocks\.(\d+)\.(.+)")
+# diffusers' `QwenImage21Transformer2DModel` default `num_layers`, which every released checkpoint has.
+_NUM_BLOCKS = 32
+# A block index as the transformer spells it, without leading zeros: `01` would be block 1 under a second name.
+_BLOCK = re.compile(r"transformer_blocks\.(0|[1-9]\d?)\.(.+)")
 # Kohya flattens the dots, which makes a name ambiguous on its own (`to_out_0`), so it is mapped, not re-dotted.
-_KOHYA_BLOCK = re.compile(r"transformer_blocks_(\d+)_(.+)")
+_KOHYA_BLOCK = re.compile(r"transformer_blocks_(0|[1-9]\d?)_(.+)")
 _KOHYA_BLOCK_MODULES = {module.replace(".", "_"): module for module in _BLOCK_MODULES | _BLOCK_NORMS}
 _KOHYA_TOP_MODULES = {module.replace(".", "_"): module for module in _TOP_MODULES | _TOP_NORMS}
 _KOHYA_PREFIX = "lora_unet_"
 
+
+def _layer_value_sets() -> frozenset[frozenset[str]]:
+    """Every set of value keys that builds one layer reading all of them: nothing missing, nothing it would ignore.
+
+    The kinds `any_lora_layer_from_state_dict` builds: low-rank, DoRA with either magnitude, LoKR with each operand
+    whole or factored, LoHA, and a full diff, which has no rank for an alpha to scale. (A LoKR with both operands
+    whole has none either and scales by 1 whatever its alpha; the alpha is accepted there, as it changes nothing.)
+    LyCORIS writes Tucker cores (`lokr_t2`, `hada_t1`/`hada_t2`) only for convolution kernels, and this transformer
+    is all Linears, so a layer with one is refused. No Linear here has a bias, so no bias key is recognized.
+    """
+    low_rank = frozenset({"lora_down.weight", "lora_up.weight"})
+    lokr_w1 = (frozenset({"lokr_w1"}), frozenset({"lokr_w1_a", "lokr_w1_b"}))
+    lokr_w2 = (frozenset({"lokr_w2"}), frozenset({"lokr_w2_a", "lokr_w2_b"}))
+    ranked = [
+        low_rank,
+        low_rank | {"dora_scale"},
+        low_rank | {"dora_magnitude"},
+        *(w1 | w2 for w1, w2 in itertools.product(lokr_w1, lokr_w2)),
+        frozenset({"hada_w1_a", "hada_w1_b", "hada_w2_a", "hada_w2_b"}),
+    ]
+    return frozenset([*ranked, *(layer | {"alpha"} for layer in ranked), frozenset({"diff"})])
+
+
+_LAYERS = _layer_value_sets()
+
 GATE_UP = "img_mlp.gate_up"
-# What a layer on the fused gate_up may hold to be split by its output rows: a low-rank update (its `up` rows),
-# a PEFT/ai-toolkit DoRA (also its output-dim magnitude), or a full one. LoKR and LoHA mix the rows, and LyCORIS's
-# input-dim `dora_scale` normalizes across both halves at once.
-GATE_UP_SPLITTABLE = (
-    frozenset({"lora_down.weight", "lora_up.weight"}),
-    frozenset({"lora_down.weight", "lora_up.weight", "alpha"}),
-    frozenset({"lora_down.weight", "lora_up.weight", "dora_magnitude"}),
-    frozenset({"lora_down.weight", "lora_up.weight", "alpha", "dora_magnitude"}),
-    frozenset({"diff"}),
-)
+# The values of a layer on the fused gate_up that split along its output rows. `lora_down` reads the input both
+# halves share and `alpha` is a scalar, so each half takes them whole.
+GATE_UP_ROW_KEYS = frozenset({"lora_up.weight", "dora_magnitude", "diff"})
+# What a gate_up layer may be to be split: low-rank, PEFT/ai-toolkit DoRA (an output-dim magnitude), or full. LoKR
+# and LoHA mix the rows, and LyCORIS's input-dim `dora_scale` normalizes across both halves at once.
+_GATE_UP_SPLITTABLE = frozenset(layer for layer in _LAYERS if layer <= GATE_UP_ROW_KEYS | {"lora_down.weight", "alpha"})
 
 
 def _module_path(name: str) -> str | None:
@@ -95,10 +120,12 @@ def _module_path(name: str) -> str | None:
         flat = name[len(_KOHYA_PREFIX) :]
         if block := _KOHYA_BLOCK.fullmatch(flat):
             module = _KOHYA_BLOCK_MODULES.get(block.group(2))
-            return f"transformer_blocks.{block.group(1)}.{module}" if module else None
+            exists = module is not None and int(block.group(1)) < _NUM_BLOCKS
+            return f"transformer_blocks.{block.group(1)}.{module}" if exists else None
         return _KOHYA_TOP_MODULES.get(flat)
     if block := _BLOCK.fullmatch(name):
-        return name if block.group(2) in _BLOCK_MODULES | _BLOCK_NORMS else None
+        exists = block.group(2) in _BLOCK_MODULES | _BLOCK_NORMS and int(block.group(1)) < _NUM_BLOCKS
+        return name if exists else None
     return name if name in _TOP_MODULES | _TOP_NORMS else None
 
 
@@ -118,22 +145,45 @@ def split_key(key: str) -> tuple[str, str] | None:
     return None
 
 
-def unsupported_keys(state_dict: dict[str | int, Any]) -> list[str]:
-    """The keys that keep this state dict from being applied to Qwen-Image-2.1 as a whole.
+def _splits_in_half(value: Any) -> bool:
+    shape = getattr(value, "shape", ())
+    return len(shape) > 0 and shape[0] % 2 == 0
+
 
-    A key that names no module of this transformer (another model's, or a text encoder's), anything but a full
-    `diff` on a norm, and a fused gate_up layer that cannot be split. Empty for a LoRA that applies in full.
+def unsupported_keys(state_dict: dict[str | int, Any]) -> list[str]:
+    """The keys that keep this state dict from being applied to Qwen-Image-2.1 as a whole; empty for a LoRA that
+    applies in full.
+
+    A key that names no module of this transformer (another model's, or a text encoder's), and every key that
+    gives a value another key of its module gives too (two prefixes, or two spellings of one parameter). All keys
+    of a module whose values are not exactly one layer (see `_layer_value_sets`): half a pair, an alpha or a
+    magnitude alone, two layer kinds at once. On a norm only a full `diff` applies. A fused gate_up must split into
+    two whole halves of its output rows, in a block that does not also name the gate_layer or proj it becomes.
     """
-    unsupported: list[str] = []
-    gate_up_layers: dict[str, set[str]] = {}
+    unsupported: set[str] = set()
+    modules: dict[str, dict[str, list[str]]] = {}
     for key in state_dict:
         if not isinstance(key, str) or (split := split_key(key)) is None:
-            unsupported.append(str(key))
+            unsupported.add(str(key))
             continue
         module, value_key = split
-        if is_norm(module) and value_key != "diff":
-            unsupported.append(key)
+        modules.setdefault(module, {}).setdefault(value_key, []).append(key)
+
+    for module, values in modules.items():
+        unsupported.update(key for spellings in values.values() if len(spellings) > 1 for key in spellings)
+        layer = frozenset(values)
+        if is_norm(module):
+            applies = layer == {"diff"}
         elif module.endswith(GATE_UP):
-            gate_up_layers.setdefault(module, set()).add(value_key)
-    unsupported += [module for module, values in gate_up_layers.items() if frozenset(values) not in GATE_UP_SPLITTABLE]
+            stem = module[: -len("gate_up")]
+            applies = (
+                layer in _GATE_UP_SPLITTABLE
+                and all(_splits_in_half(state_dict[values[row][0]]) for row in layer & GATE_UP_ROW_KEYS)
+                and f"{stem}gate_layer" not in modules
+                and f"{stem}proj" not in modules
+            )
+        else:
+            applies = layer in _LAYERS
+        if not applies:
+            unsupported.update(key for spellings in values.values() for key in spellings)
     return sorted(unsupported)
diff --git a/invokeai/backend/patches/lora_conversions/qwen_image_2_1_lora_conversion_utils.py b/invokeai/backend/patches/lora_conversions/qwen_image_2_1_lora_conversion_utils.py
index 24ec1103656..3108ce7241c 100644
--- a/invokeai/backend/patches/lora_conversions/qwen_image_2_1_lora_conversion_utils.py
+++ b/invokeai/backend/patches/lora_conversions/qwen_image_2_1_lora_conversion_utils.py
@@ -6,6 +6,7 @@
 from invokeai.backend.patches.layers.utils import any_lora_layer_from_state_dict
 from invokeai.backend.patches.lora_conversions.qwen_image_2_1_lora_constants import (
     GATE_UP,
+    GATE_UP_ROW_KEYS,
     QWEN_IMAGE_21_LORA_NORM_PREFIX,
     QWEN_IMAGE_21_LORA_TRANSFORMER_PREFIX,
     is_norm,
@@ -14,23 +15,18 @@
 )
 from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
 
-# What splits along with the output rows of a fused gate_up layer.
-_ROW_KEYS = ("lora_up.weight", "dora_magnitude", "diff")
-
 
 def _split_gate_up(module: str, values: dict[str, torch.Tensor]) -> dict[str, dict[str, torch.Tensor]]:
     """The fused gated-MLP input as diffusers' two Linears: `gate_layer` takes the first half of the output rows.
 
-    Every row-indexed tensor splits in two; the `down` matrix reads the shared input and goes to both halves.
+    Every row-indexed tensor splits in two (`unsupported_keys` has checked that the halves are whole); the `down`
+    matrix reads the shared input and goes to both halves.
     """
     stem = module[: -len("gate_up")]
     gate, proj = dict(values), dict(values)
-    for key in _ROW_KEYS:
-        if key in values:
-            rows = values[key]
-            if rows.shape[0] % 2:
-                raise ValueError(f"LoRA layer {module!r} has {rows.shape[0]} output rows; gate_up has two halves.")
-            gate[key], proj[key] = rows[: rows.shape[0] // 2], rows[rows.shape[0] // 2 :]
+    for key in GATE_UP_ROW_KEYS & values.keys():
+        rows = values[key]
+        gate[key], proj[key] = rows[: rows.shape[0] // 2], rows[rows.shape[0] // 2 :]
     if "lora_down.weight" in values:
         # A copy of its own, so the model cache, which sizes a patch by its tensors' elements, counts what it holds.
         proj["lora_down.weight"] = values["lora_down.weight"].clone()
@@ -48,9 +44,10 @@ def lora_model_from_qwen_image_21_state_dict(state_dict: dict[str, torch.Tensor]
         # Identification refuses these, so a file reaches here only by another path; applying the rest would make a
         # partial LoRA that passes for a whole one.
         raise ValueError(
-            f"{len(unsupported)} part(s) of this LoRA do not apply to Qwen-Image-2.1, e.g. {unsupported[0]!r}: "
-            "it targets layers this model does not have, a norm with anything but a full diff, or a LoKR, LoHA "
-            "or LyCORIS DoRA layer on the fused gate_up projection."
+            f"{len(unsupported)} key(s) of this LoRA do not apply to Qwen-Image-2.1, e.g. {unsupported[0]!r}. Each "
+            "key must name a layer this model has, and only once; each layer must be complete and of one kind. A "
+            "norm takes only a full diff, and the fused gate_up only a low-rank, PEFT DoRA or full layer with an even "
+            "number of rows, in a block that does not also name the gate_layer or proj it is split into."
         )
 
     grouped: dict[str, dict[str, torch.Tensor]] = {}
diff --git a/tests/app/invocations/test_qwen_image_2_1_nodes.py b/tests/app/invocations/test_qwen_image_2_1_nodes.py
index 495185c725b..ddfca5155bb 100644
--- a/tests/app/invocations/test_qwen_image_2_1_nodes.py
+++ b/tests/app/invocations/test_qwen_image_2_1_nodes.py
@@ -4,6 +4,8 @@
 an image-to-image run starts on each variant's schedule, and which components the loader refuses.
 """
 
+import itertools
+import weakref
 from contextlib import contextmanager, nullcontext
 from types import SimpleNamespace
 
@@ -13,6 +15,8 @@
 from diffusers.models.normalization import RMSNorm as DiffusersRMSNorm
 from diffusers.models.transformers.transformer_qwenimage21 import QwenImage21ZeroCenterRMSNorm
 from PIL import Image
+from torch.utils._python_dispatch import TorchDispatchMode
+from torch.utils._pytree import tree_leaves
 
 from invokeai.app.invocations.fields import ImageField, LatentsField, QwenImage21ConditioningField
 from invokeai.app.invocations.model import (
@@ -655,17 +659,20 @@ def on_device(**_kwargs):
     assert not torch.equal(expected, ((vae.z.float() - 0.3) / 1.7)[:, :, 0])
 
 
-def _lora_context(base: BaseModelType):
-    configs = {"lora": SimpleNamespace(name="a LoRA", base=base, type=ModelType.LoRA)}
+_LORA_NAME = "Manga to Photo"
+
+
+def _lora_context(base: BaseModelType, model_type: ModelType = ModelType.LoRA):
+    configs = {"lora": SimpleNamespace(name=_LORA_NAME, base=base, type=model_type)}
     return SimpleNamespace(
         models=SimpleNamespace(exists=lambda key: key in configs, get_config=lambda key: configs[key])
     )
 
 
-def _lora_field(weight: float = 0.8) -> LoRAField:
+def _lora_field(weight: float = 0.8, key: str = "lora") -> LoRAField:
     return LoRAField(
         lora=ModelIdentifierField(
-            key="lora", hash="h", name="a LoRA", base=BaseModelType.QwenImage21, type=ModelType.LoRA
+            key=key, hash="h", name=_LORA_NAME, base=BaseModelType.QwenImage21, type=ModelType.LoRA
         ),
         weight=weight,
     )
@@ -676,6 +683,14 @@ def _transformer_field() -> TransformerField:
     return TransformerField(transformer=model, loras=[])
 
 
+def _lora_loader(node_class: str, lora: LoRAField):
+    if node_class == "single":
+        return QwenImage21LoRALoaderInvocation.model_construct(
+            lora=lora.lora, weight=lora.weight, transformer=_transformer_field()
+        )
+    return QwenImage21LoRACollectionLoader.model_construct(loras=[lora], transformer=_transformer_field())
+
+
 def test_a_lora_collection_is_added_to_the_transformer_once() -> None:
     node = QwenImage21LoRACollectionLoader.model_construct(
         loras=[_lora_field(0.8), _lora_field(0.5)], transformer=_transformer_field()
@@ -688,19 +703,28 @@ def test_a_lora_collection_is_added_to_the_transformer_once() -> None:
 @pytest.mark.parametrize("node_class", ["single", "collection"])
 def test_a_qwen_image_lora_is_refused(node_class: str) -> None:
     # Its keys would match no layer, or the wrong ones, in Qwen-Image-2.1's transformer.
-    context = _lora_context(BaseModelType.QwenImage)
-    if node_class == "single":
-        node = QwenImage21LoRALoaderInvocation.model_construct(
-            lora=_lora_field().lora, weight=1.0, transformer=_transformer_field()
-        )
-    else:
-        node = QwenImage21LoRACollectionLoader.model_construct(loras=[_lora_field()], transformer=_transformer_field())
     with pytest.raises(ValueError, match="not Qwen-Image-2.1"):
-        node.invoke(context)
+        _lora_loader(node_class, _lora_field()).invoke(_lora_context(BaseModelType.QwenImage))
+
+
+@pytest.mark.parametrize("node_class", ["single", "collection"])
+def test_a_lora_that_is_not_installed_is_refused(node_class: str) -> None:
+    with pytest.raises(ValueError) as refused:
+        _lora_loader(node_class, _lora_field(key="gone")).invoke(_lora_context(BaseModelType.QwenImage21))
+    assert "gone" in str(refused.value) and "Unknown" in str(refused.value)
+
+
+@pytest.mark.parametrize("node_class", ["single", "collection"])
+def test_a_model_that_is_not_a_lora_is_refused(node_class: str) -> None:
+    # A Qwen-Image-2.1 model of another type, so the base alone would let it through.
+    with pytest.raises(ValueError) as refused:
+        _lora_loader(node_class, _lora_field()).invoke(_lora_context(BaseModelType.QwenImage21, ModelType.Main))
+    message = str(refused.value)
+    assert _LORA_NAME in message and ModelType.Main.value in message and "not a LoRA" in message
 
 
 class _LinearTransformer(torch.nn.Module):
-    """A transformer with one real Linear and one real norm a LoRA can target, reading both on every forward."""
+    """A transformer with real Linears and norms a LoRA can target; each forward reads to_q and the norms."""
 
     config = SimpleNamespace(causal_condition=True, num_attention_heads=2, attention_head_dim=4)
 
@@ -710,8 +734,11 @@ def __init__(self) -> None:
         block.attn = torch.nn.Module()
         block.attn.to_q = torch.nn.Linear(8, 8, bias=False)
         torch.nn.init.zeros_(block.attn.to_q.weight)
+        block.attn.to_k = torch.nn.Linear(8, 8, bias=False)
         block.attn.norm_q = DiffusersRMSNorm(8, eps=1e-6)
         self.transformer_blocks = torch.nn.ModuleList([block])
+        # Reads the timestep embedding, as 2.1's shared modulation does.
+        self.modulation = torch.nn.Sequential(torch.nn.SiLU(), torch.nn.Linear(8, 32, bias=False))
         # The text norm has no custom wrapper, so only a forced direct patch can reach it.
         self.txt_in = torch.nn.Module()
         self.txt_in.text_norm = QwenImage21ZeroCenterRMSNorm(8)
@@ -728,10 +755,27 @@ def forward(self, *, hidden_states, encoder_hidden_states, kv_cache_mode, **_kwa
         return (torch.zeros(1, encoder_hidden_states.shape[1] + hidden_states.shape[1], hidden_states.shape[2]),)
 
 
-@pytest.mark.parametrize("model_format", [ModelFormat.Diffusers, ModelFormat.GGUFQuantized], ids=["merged", "sidecar"])
-def test_loras_reach_the_transformer_from_the_first_step_and_leave_it_unchanged(
-    model_format: ModelFormat, monkeypatch: pytest.MonkeyPatch
-) -> None:
+class _ReservingTransformerInfo(_TransformerInfo):
+    """Records the working memory the transformer is loaded with."""
+
+    def __init__(self, transformer) -> None:
+        super().__init__(transformer)
+        self.reserved: list[int] = []
+
+    @contextmanager
+    def model_on_device(self, *, working_mem_bytes: int, **_kwargs):
+        self.reserved.append(working_mem_bytes)
+        yield ({}, self.model)
+
+
+_UP, _DOWN = torch.full((8, 2), 0.5), torch.full((2, 8), 0.25)
+
+
+def _run_with_a_lora(
+    model_format: ModelFormat, weight: float | None, monkeypatch: pytest.MonkeyPatch
+) -> tuple[_LinearTransformer, int]:
+    """Two steps on a `_LinearTransformer` as the model cache holds it, with a LoRA on its to_q and both norms at
+    `weight` (None: no LoRA). Returns the transformer and the working memory it was loaded with."""
     from invokeai.backend.model_manager.load.model_cache.torch_module_autocast.torch_module_autocast import (
         apply_custom_layers_to_model,
     )
@@ -745,30 +789,38 @@ def test_loras_reach_the_transformer_from_the_first_step_and_leave_it_unchanged(
     transformer = _LinearTransformer()
     # As the model cache holds it: GGUF's sidecar path wraps these layers.
     apply_custom_layers_to_model(transformer)
-    up, down = torch.full((8, 2), 0.5), torch.full((2, 8), 0.25)
     patch = lora_model_from_qwen_image_21_state_dict(
         {
-            "diffusion_model.transformer_blocks.0.attn.to_q.lora_A.weight": down,
-            "diffusion_model.transformer_blocks.0.attn.to_q.lora_B.weight": up,
+            "diffusion_model.transformer_blocks.0.attn.to_q.lora_A.weight": _DOWN,
+            "diffusion_model.transformer_blocks.0.attn.to_q.lora_B.weight": _UP,
             "diffusion_model.transformer_blocks.0.attn.norm_q.diff": torch.full((8,), 0.25),
             "diffusion_model.txt_in.text_norm.diff": torch.full((8,), -0.5),
         }
     )
     lora_info = SimpleNamespace(model=patch, model_in_ram=nullcontext)
-    lora = ModelIdentifierField(key="lora", hash="h", name="l", base=BaseModelType.QwenImage21, type=ModelType.LoRA)
+    transformer_info = _ReservingTransformerInfo(transformer)
     context = _context(_Transformer(), QwenImage21VariantType.Base)
     context.models = SimpleNamespace(
-        load=lambda identifier: lora_info if identifier.key == "lora" else _TransformerInfo(transformer),
+        load=lambda identifier: lora_info if identifier.key == "lora" else transformer_info,
         get_config=lambda _identifier: SimpleNamespace(variant=QwenImage21VariantType.Base, format=model_format),
     )
     node = _denoise(steps=2)
-    node.transformer.loras.append(LoRAField(lora=lora, weight=0.5))
+    if weight is not None:
+        node.transformer.loras.append(_lora_field(weight))
 
     node._run_diffusion(context)
+    return transformer, transformer_info.reserved[0]
+
+
+@pytest.mark.parametrize("model_format", [ModelFormat.Diffusers, ModelFormat.GGUFQuantized], ids=["merged", "sidecar"])
+def test_loras_reach_the_transformer_from_the_first_step_and_leave_it_unchanged(
+    model_format: ModelFormat, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    transformer, _ = _run_with_a_lora(model_format, 0.5, monkeypatch)
 
     # The zero base weight plus the LoRA at weight 0.5 (scale 1, as no alpha is given), on both steps -- the prefill
     # that fills the prefix cache included.
-    expected = (up @ down * 0.5) @ torch.ones(8)
+    expected = (_UP @ _DOWN * 0.5) @ torch.ones(8)
     assert [mode for mode, _ in transformer.seen] == ["extract", "cached"]
     for _, out in transformer.seen:
         torch.testing.assert_close(out[0], expected)
@@ -784,18 +836,263 @@ def test_loras_reach_the_transformer_from_the_first_step_and_leave_it_unchanged(
     assert not transformer.txt_in.text_norm.weight.any()
 
 
-def test_loras_reserve_room_only_where_they_run_beside_the_weights() -> None:
-    patch = SimpleNamespace(calc_size=lambda: 100 * 2**20)
-    lora = ModelIdentifierField(key="lora", hash="h", name="l", base=BaseModelType.QwenImage21, type=ModelType.LoRA)
-    context = SimpleNamespace(models=SimpleNamespace(load=lambda _identifier: SimpleNamespace(model=patch)))
+def test_a_sidecar_lora_widens_the_room_the_transformer_is_loaded_with(monkeypatch: pytest.MonkeyPatch) -> None:
+    _, without = _run_with_a_lora(ModelFormat.GGUFQuantized, None, monkeypatch)
+    _, at_zero = _run_with_a_lora(ModelFormat.GGUFQuantized, 0.0, monkeypatch)
+    _, applied = _run_with_a_lora(ModelFormat.GGUFQuantized, 0.5, monkeypatch)
+    # A LoRA at weight 0 is never applied, so it holds nothing.
+    assert at_zero == without
+    assert applied > without
+
+
+def _patch(module: str = "transformer_blocks.0.attn.to_q", **values: torch.Tensor):
+    """A converted LoRA with the given values on one module; to_q and to_k are 8 x 8 in `_LinearTransformer`."""
+    from invokeai.backend.patches.lora_conversions.qwen_image_2_1_lora_conversion_utils import (
+        lora_model_from_qwen_image_21_state_dict,
+    )
+
+    return lora_model_from_qwen_image_21_state_dict(
+        {f"diffusion_model.{module}.{name}": value for name, value in values.items()}
+    )
+
+
+def _low_rank(rank: int, module: str = "transformer_blocks.0.attn.to_q", out_features: int = 8):
+    return _patch(module, **{"lora_A.weight": torch.zeros(rank, 8), "lora_B.weight": torch.zeros(out_features, rank)})
+
+
+def _reserve(
+    patch,
+    transformer: torch.nn.Module,
+    model_format: ModelFormat = ModelFormat.GGUFQuantized,
+    seq_len: int = 100,
+    dtype: torch.dtype = torch.float32,
+    weight: float = 1.0,
+) -> int:
+    """What the denoise reserves for one LoRA, or for each of a list of them, on `transformer`."""
+    patches = patch if isinstance(patch, list) else [patch]
     node = _denoise()
-    node.transformer.loras.append(LoRAField(lora=lora, weight=1.0))
-    plain = torch.nn.Linear(4, 4)
+    node.transformer.loras.extend(_lora_field(weight, key=f"lora{i}") for i in range(len(patches)))
+    context = SimpleNamespace(
+        models=SimpleNamespace(load=lambda identifier: SimpleNamespace(model=patches[int(identifier.key[4:])]))
+    )
+    return node._lora_working_memory(context, transformer, model_format, seq_len, dtype)
+
+
+def test_a_sidecar_lora_reserves_its_tensors_and_an_update_per_token() -> None:
+    transformer = _LinearTransformer()
+    for dtype in (torch.float32, torch.bfloat16):
+        # Beside the weights, the LoRA's 64 elements (8 x 4 twice), at the compute dtype's width: they are stored in
+        # float32, and the patcher casts a sidecar to the compute dtype.
+        assert _reserve(_low_rank(4), transformer, seq_len=0, dtype=dtype) == 64 * dtype.itemsize
+        # And each forward adds, per token, a row of to_q's 8 outputs and one of the LoRA's 4 ranks.
+        longer = _reserve(_low_rank(4), transformer, seq_len=300, dtype=dtype)
+        assert longer - _reserve(_low_rank(4), transformer, seq_len=100, dtype=dtype) == 200 * (8 + 4) * dtype.itemsize
+    # Not applied at weight 0; and a norm diff is merged on every format.
+    assert _reserve(_low_rank(4), transformer, weight=0.0) == 0
+    assert _reserve(_patch("transformer_blocks.0.attn.norm_q", diff=torch.zeros(8)), transformer) == 0
+
+
+def test_a_linear_that_reads_the_timestep_embedding_adds_two_rows_however_long_the_sequence() -> None:
+    transformer = _LinearTransformer()
+    modulation = _low_rank(4, "modulation.1", out_features=32)
+    assert _reserve(modulation, transformer, seq_len=300) == _reserve(modulation, transformer, seq_len=100)
+    # Its 160 elements (4 x 8 and 32 x 4), and two rows of its 32 outputs and 4 ranks: the embedding of the sampled
+    # timestep and, under `causal_condition`, of t = 0.
+    assert _reserve(modulation, transformer) == (160 + 2 * (32 + 4)) * 4
+
+
+def test_sidecars_hold_every_modules_tensors_but_one_modules_update_at_a_time() -> None:
+    transformer = _LinearTransformer()
+    to_q, to_k = _low_rank(4), _low_rank(4, "transformer_blocks.0.attn.to_k")
+    # to_k's 64 elements join to_q's; its update, as large as to_q's, comes only after to_q's is gone.
+    assert _reserve([to_q, to_k], transformer) - _reserve(to_q, transformer) == 64 * 4
+
+
+def test_a_merged_format_reserves_one_modules_sidecar_for_partial_loading() -> None:
+    # The patcher wraps a layer partial loading leaves on the CPU whatever the format; that sidecar copies its tensors
+    # to the device for each forward, one module after another.
+    transformer = _LinearTransformer()
+    to_q, to_k = _low_rank(4), _low_rank(4, "transformer_blocks.0.attn.to_k")
+    merged = _reserve([to_q, to_k], transformer, ModelFormat.Diffusers)
+    assert 0 < merged < _reserve([to_q, to_k], transformer, ModelFormat.GGUFQuantized)
+    assert merged == _reserve(to_q, transformer, ModelFormat.Diffusers)
+
+
+def test_an_fp8_transformer_reserves_every_sidecars_tensors() -> None:
+    # fp8 weights cannot take an update in place, so the patcher wraps every layer on them, as on a GGUF.
+    transformer = _LinearTransformer()
+    loras = [_low_rank(4), _low_rank(4, "transformer_blocks.0.attn.to_k")]
+    plain = _reserve(loras, transformer, ModelFormat.Checkpoint)
+    for to in (transformer.transformer_blocks[0].attn.to_q, transformer.transformer_blocks[0].attn.to_k):
+        to.weight = torch.nn.Parameter(to.weight.to(torch.float8_e4m3fn), requires_grad=False)
+    fp8 = _reserve(loras, transformer, ModelFormat.Checkpoint)
+    assert fp8 > plain
+    assert fp8 == _reserve(loras, transformer, ModelFormat.GGUFQuantized)
+
+
+class _PeakAllocation(TorchDispatchMode):
+    """The most bytes that tensors made while `counting` is on -- and views of them -- hold at once."""
 
-    # Merged once, before the first step: nothing to hold while sampling.
-    assert node._lora_working_memory(context, plain, ModelFormat.Diffusers, 4096) == 0
-    # A sidecar holds its tensors and adds one gate_layer-wide update per forward.
-    assert node._lora_working_memory(context, plain, ModelFormat.GGUFQuantized, 4096) == 100 * 2**20 + 4096 * 12288 * 2
+    def __init__(self) -> None:
+        super().__init__()
+        self.counting = True
+        self.peak = 0
+        self._made: set[int] = set()
+        self._tensors: weakref.WeakValueDictionary[int, torch.Tensor] = weakref.WeakValueDictionary()
+        self._ids = itertools.count()
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        inputs = {t.untyped_storage().data_ptr() for t in tree_leaves((args, kwargs)) if isinstance(t, torch.Tensor)}
+        out = func(*args, **(kwargs or {}))
+        if self.counting:
+            for tensor in (t for t in tree_leaves(out) if isinstance(t, torch.Tensor)):
+                storage = tensor.untyped_storage().data_ptr()
+                if storage not in inputs:
+                    self._made.add(storage)
+                # A view keeps its storage alive after the tensor it was taken from is gone.
+                if storage in self._made:
+                    self._tensors[next(self._ids)] = tensor
+            live = {t.untyped_storage().data_ptr(): t.untyped_storage().nbytes() for t in self._tensors.values()}
+            self.peak = max(self.peak, sum(live.values()))
+        return out
+
+
+_W = 64  # A Linear wide enough that only weight-sized tensors count against half a copy of its weight.
+# Weight copies priced above what a CPU forward allocates: the CUDA column norm of LyCORIS's DoRA, priced for both
+# orientations (see `_SIDECAR_WEIGHT_COPIES`).
+_CUDA_ONLY_COPIES = {"peft-dora": 1, "lycoris-dora": 1}
+
+
+def _sidecar_kind(kind: str):
+    r = 2
+    values = {
+        "low-rank": {"lora_A.weight": torch.randn(r, _W), "lora_B.weight": torch.randn(_W, r)},
+        "full": {"diff": torch.randn(_W, _W)},
+        "lokr": {
+            "lokr_w1": torch.randn(4, 4),
+            "lokr_w2_a": torch.randn(_W // 4, r),
+            "lokr_w2_b": torch.randn(r, _W // 4),
+        },
+        "loha": {
+            f"hada_w{i}_{s}": torch.randn(_W, r) if s == "a" else torch.randn(r, _W) for i in (1, 2) for s in "ab"
+        },
+        "peft-dora": {
+            "lora_A.weight": torch.randn(r, _W),
+            "lora_B.weight": torch.randn(_W, r),
+            "lora_magnitude_vector.weight": torch.rand(_W) + 1,
+        },
+        "lycoris-dora": {
+            "lora_A.weight": torch.randn(r, _W),
+            "lora_B.weight": torch.randn(_W, r),
+            "dora_scale": torch.rand(1, _W) + 1,
+        },
+    }[kind]
+    return _patch(**values)
+
+
+@pytest.mark.parametrize(
+    "kinds",
+    [
+        ["low-rank"],
+        ["full"],
+        ["lokr"],
+        ["loha"],
+        ["peft-dora"],
+        ["lycoris-dora"],
+        ["lokr", "lokr"],
+        ["low-rank", "lokr"],
+        ["full", "full", "full"],
+        ["lokr", "lokr", "lokr"],
+        ["full", "lokr", "loha"],
+    ],
+    ids=lambda kinds: "+".join(kinds),
+)
+def test_a_sidecar_reserves_what_its_forward_allocates(kinds: list[str], monkeypatch: pytest.MonkeyPatch) -> None:
+    """Measured: what a sidecar forward on an fp8 weight allocates for its LoRAs -- the layer's own forward aside,
+    which the activation estimate covers -- is what the denoise reserves for them, within half a weight copy."""
+    from invokeai.backend.model_manager.load.model_cache.torch_module_autocast.torch_module_autocast import (
+        apply_custom_layers_to_model,
+    )
+    from invokeai.backend.patches.layer_patcher import LayerPatcher
+    from invokeai.backend.patches.lora_conversions.qwen_image_2_1_lora_conversion_utils import (
+        QWEN_IMAGE_21_LORA_TRANSFORMER_PREFIX,
+    )
+
+    torch.manual_seed(0)
+    transformer = torch.nn.Module()
+    block = torch.nn.Module()
+    block.attn = torch.nn.Module()
+    block.attn.to_q = torch.nn.Linear(_W, _W, bias=False)
+    block.attn.to_q.weight = torch.nn.Parameter(torch.randn(_W, _W).to(torch.float8_e4m3fn), requires_grad=False)
+    transformer.transformer_blocks = torch.nn.ModuleList([block])
+    apply_custom_layers_to_model(transformer)
+    to_q = transformer.transformer_blocks[0].attn.to_q
+    patches = [_sidecar_kind(kind) for kind in kinds]
+    seq_len = 2
+
+    mode = _PeakAllocation()
+    own_forward = to_q._autocast_forward
+
+    def uncounted_forward(x: torch.Tensor) -> torch.Tensor:
+        mode.counting = False
+        try:
+            return own_forward(x)
+        finally:
+            mode.counting = True
+
+    monkeypatch.setattr(to_q, "_autocast_forward", uncounted_forward)
+    x = torch.randn(1, seq_len, _W)
+    with (
+        torch.no_grad(),
+        LayerPatcher.apply_smart_model_patches(
+            model=transformer,
+            patches=[(patch, 1.0) for patch in patches],
+            prefix=QWEN_IMAGE_21_LORA_TRANSFORMER_PREFIX,
+            dtype=torch.float32,
+        ),
+    ):
+        with mode:
+            to_q(x)
+
+    reserved = _reserve(patches, transformer, ModelFormat.Checkpoint, seq_len=seq_len) - sum(
+        patch.calc_size() for patch in patches
+    )
+    weight_copy = _W * _W * torch.float32.itemsize
+    beyond_cpu = max(_CUDA_ONLY_COPIES.get(kind, 0) for kind in kinds) * weight_copy
+    assert abs(reserved - beyond_cpu - mode.peak) < weight_copy / 2, (reserved / weight_copy, mode.peak / weight_copy)
+
+
+def test_a_dora_lora_on_an_int8_transformer_is_refused_before_the_transformer_is_loaded() -> None:
+    from invokeai.backend.model_manager.load.model_cache.torch_module_autocast.torch_module_autocast import (
+        apply_custom_layers_to_model,
+    )
+    from invokeai.backend.quantization.int8_convrot import Int8ConvrotLinear
+
+    transformer = _LinearTransformer()
+    weight = torch.zeros(8, 8, dtype=torch.int8)
+    transformer.transformer_blocks[0].attn.to_q = Int8ConvrotLinear(weight, torch.ones(8, 1), convrot=False)
+    apply_custom_layers_to_model(transformer)
+    low_rank = {"lora_A.weight": torch.zeros(2, 8), "lora_B.weight": torch.zeros(8, 2)}
+    dora = _patch(**low_rank, **{"lora_magnitude_vector.weight": torch.ones(8)})
+    transformer_info = _ReservingTransformerInfo(transformer)
+    context = _context(_Transformer(), QwenImage21VariantType.Base)
+    loaded = {"lora": SimpleNamespace(model=dora)}
+    context.models = SimpleNamespace(
+        load=lambda identifier: loaded.get(identifier.key, transformer_info),
+        get_config=lambda _identifier: SimpleNamespace(
+            variant=QwenImage21VariantType.Base, format=ModelFormat.Checkpoint
+        ),
+    )
+    node = _denoise(steps=1)
+    node.transformer.loras.append(_lora_field(1.0))
+
+    with pytest.raises(ValueError) as refused:
+        node._run_diffusion(context)
+    assert _LORA_NAME in str(refused.value) and "int8" in str(refused.value)
+    assert transformer_info.reserved == []
+    # A low-rank LoRA runs there as a sidecar.
+    loaded["lora"] = SimpleNamespace(model=_patch(**low_rank))
+    node._refuse_dora_on_int8(context, transformer)
 
 
 def test_a_lora_already_on_the_transformer_is_not_applied_again() -> None:
@@ -812,10 +1109,16 @@ def test_a_lora_already_on_the_transformer_is_not_applied_again() -> None:
         single.invoke(_lora_context(BaseModelType.QwenImage21))
 
 
-def test_another_familys_lora_is_named_without_the_qwen_image_hint() -> None:
-    node = QwenImage21LoRALoaderInvocation.model_construct(
-        lora=_lora_field().lora, weight=1.0, transformer=_transformer_field()
-    )
+@pytest.mark.parametrize(
+    ("base", "hinted"),
+    [(BaseModelType.StableDiffusionXL, False), (BaseModelType.QwenImage, True)],
+    ids=["sdxl", "qwen"],
+)
+def test_another_familys_lora_is_named_with_the_qwen_image_hint_only_for_qwen_image(
+    base: BaseModelType, hinted: bool
+) -> None:
     with pytest.raises(ValueError) as refused:
-        node.invoke(_lora_context(BaseModelType.StableDiffusionXL))
-    assert str(refused.value) == "LoRA 'a LoRA' is for sdxl models, not Qwen-Image-2.1."
+        _lora_loader("single", _lora_field()).invoke(_lora_context(base))
+    message = str(refused.value)
+    assert f"for {base.value} models" in message
+    assert ("Qwen Image LoRAs" in message) is hinted
diff --git a/tests/backend/model_manager/configs/test_ltx2_lora_probe_independence.py b/tests/backend/model_manager/configs/test_ltx2_lora_probe_independence.py
index d00edffcf5a..6b186463bea 100644
--- a/tests/backend/model_manager/configs/test_ltx2_lora_probe_independence.py
+++ b/tests/backend/model_manager/configs/test_ltx2_lora_probe_independence.py
@@ -19,19 +19,9 @@
 import pytest
 import torch
 
+from invokeai.backend.model_manager.configs.base import Config_Base
 from invokeai.backend.model_manager.configs.identification_utils import NotAMatchError
-from invokeai.backend.model_manager.configs.lora import (
-    LoRA_LyCORIS_Anima_Config,
-    LoRA_LyCORIS_Flux2_Config,
-    LoRA_LyCORIS_FLUX_Config,
-    LoRA_LyCORIS_Krea2_Config,
-    LoRA_LyCORIS_LTX2_Config,
-    LoRA_LyCORIS_MiniMaxH3_Config,
-    LoRA_LyCORIS_QwenImage21_Config,
-    LoRA_LyCORIS_QwenImage_Config,
-    LoRA_LyCORIS_Wan_Config,
-    LoRA_LyCORIS_ZImage_Config,
-)
+from invokeai.backend.model_manager.configs.lora import LoRA_LyCORIS_LTX2_Config
 from invokeai.backend.model_manager.taxonomy import BaseModelType
 
 
@@ -159,29 +149,15 @@ def test_an_ltx2_lora_identifies_as_ltx2(prefix: str) -> None:
     assert result.base is BaseModelType.LTX2
 
 
-@pytest.mark.parametrize(
-    "other",
-    [
-        LoRA_LyCORIS_Wan_Config,
-        LoRA_LyCORIS_MiniMaxH3_Config,
-        LoRA_LyCORIS_Anima_Config,
-        LoRA_LyCORIS_Krea2_Config,
-        # These four also precede the LTX-2 entry in the union, so they are the ones whose order
-        # actually decides which config claims a file. Omitting them left the exclusivity this
-        # file's docstring promises resting on the four that happen to come after.
-        LoRA_LyCORIS_FLUX_Config,
-        LoRA_LyCORIS_Flux2_Config,
-        LoRA_LyCORIS_QwenImage_Config,
-        LoRA_LyCORIS_ZImage_Config,
-        # As wide (4096) and also named transformer_blocks.N.attn.*, but attn1 and audio towers are LTX-2's.
-        LoRA_LyCORIS_QwenImage21_Config,
-    ],
-    ids=lambda cls: cls.__name__,
-)
-def test_an_ltx2_lora_is_rejected_by_every_other_probe(other) -> None:
-    matched, _ = _probe(other, _ltx2_distilled_keys())
-
-    assert not matched
+@pytest.mark.parametrize("prefix", ["", "diffusion_model.", "transformer.", "base_model.model.transformer."])
+def test_an_ltx2_lora_is_claimed_by_the_ltx2_probe_alone(prefix: str) -> None:
+    """Every LoRA probe, whatever the prefix. Unprefixed or under ``transformer.`` its keys start
+    with ``transformer_blocks.`` as Qwen Image's do; as wide as Qwen-Image-2.1 (4096), it names
+    ``transformer_blocks.N.attn1``. Only the audio and cross-modal towers set it apart."""
+    sd = _ltx2_distilled_keys(prefix)
+    probes = [cls for cls in Config_Base.CONFIG_CLASSES if cls.__name__.startswith("LoRA_")]
+
+    assert [cls.__name__ for cls in probes if _probe(cls, sd)[0]] == [LoRA_LyCORIS_LTX2_Config.__name__]
 
 
 @pytest.mark.parametrize(
diff --git a/tests/backend/model_manager/configs/test_qwen_image_2_1_config.py b/tests/backend/model_manager/configs/test_qwen_image_2_1_config.py
index 7f87bf96d0a..1ed1c9743df 100644
--- a/tests/backend/model_manager/configs/test_qwen_image_2_1_config.py
+++ b/tests/backend/model_manager/configs/test_qwen_image_2_1_config.py
@@ -206,6 +206,10 @@ def _lora_on(*modules: str, kohya: bool = False) -> dict[str, torch.Tensor]:
     "attention-output": _lora_on("attn.to_out.0"),
     "mlp-halves": _lora_on("img_mlp.proj", "img_mlp.out"),
     "mlp-halves-kohya": _lora_on("img_mlp.proj", "img_mlp.out", kohya=True),
+    # Each half on its own: `proj` is a name Qwen-Image's MLP has only inside `img_mlp.net.0.proj`.
+    "mlp-proj": _lora_on("img_mlp.proj"),
+    "mlp-proj-kohya": _lora_on("img_mlp.proj", kohya=True),
+    "mlp-out": _lora_on("img_mlp.out"),
 }
 
 
@@ -233,9 +237,9 @@ def test_a_lora_on_the_attention_output_or_the_mlp_halves_is_qwen_image_2_1(
 @pytest.mark.parametrize("split_w2", [False, True], ids=["whole", "split-w2"])
 def test_an_attention_only_lokr_is_told_apart_by_its_width(split_w2: bool, tmp_path: Path) -> None:
     assert _probe(LoRA_LyCORIS_QwenImage_Config, _attention_lokr(3072, split_w2), tmp_path)
-    # Not Qwen-Image's; and as a LoKR, not installable as Qwen-Image-2.1's either: it is left unknown.
+    assert not _probe(LoRA_LyCORIS_QwenImage21_Config, _attention_lokr(3072, split_w2), tmp_path)
     assert not _probe(LoRA_LyCORIS_QwenImage_Config, _attention_lokr(4096, split_w2), tmp_path)
-    assert not _probe(LoRA_LyCORIS_QwenImage21_Config, _attention_lokr(4096, split_w2), tmp_path)
+    assert _probe(LoRA_LyCORIS_QwenImage21_Config, _attention_lokr(4096, split_w2), tmp_path)
     # A factored width is weaker evidence: beside a module only Qwen-Image has, it stays Qwen-Image's.
     with_qwen_image_mlp = _attention_lokr(4096) | {
         "transformer.transformer_blocks.0.img_mlp.net.0.proj.lokr_w1": torch.zeros(4, 4),
@@ -244,12 +248,57 @@ def test_an_attention_only_lokr_is_told_apart_by_its_width(split_w2: bool, tmp_p
     assert _probe(LoRA_LyCORIS_QwenImage_Config, with_qwen_image_mlp, tmp_path)
 
 
+def _z(*shape: int) -> torch.Tensor:
+    """Zeros of the shape, in one element of storage: identification reads shapes, never values."""
+    return torch.zeros(()).expand(shape)
+
+
+def _attention_layers(kind: str, width: int) -> dict[str, torch.Tensor]:
+    """A LoRA of one layer kind on the image attention of a model `width` features wide."""
+    sd: dict[str, torch.Tensor] = {}
+    for projection in ("to_q", "to_k", "to_v"):
+        prefix = f"transformer.transformer_blocks.0.attn.{projection}"
+        if kind == "loha":
+            for factor in ("w1", "w2"):
+                sd[f"{prefix}.hada_{factor}_a"] = _z(width, 4)
+                sd[f"{prefix}.hada_{factor}_b"] = _z(4, width)
+        elif kind == "full":
+            sd[f"{prefix}.diff"] = _z(width, width)
+        elif kind == "lycoris-dora":
+            sd |= {f"{prefix}.lora_A.weight": _z(4, width), f"{prefix}.lora_B.weight": _z(width, 4)}
+            sd[f"{prefix}.dora_scale"] = _z(1, width)
+    return sd
+
+
+@pytest.mark.parametrize("kind", ["loha", "full", "lycoris-dora"])
+def test_an_attention_only_lora_of_another_kind_is_told_apart_by_its_width(kind: str, tmp_path: Path) -> None:
+    # A LoHA states its input width on either `_b` matrix, a full layer as its second dimension.
+    assert _probe(LoRA_LyCORIS_QwenImage21_Config, _attention_layers(kind, 4096), tmp_path)
+    assert not _probe(LoRA_LyCORIS_QwenImage21_Config, _attention_layers(kind, 3072), tmp_path)
+
+
+_OTHER_KINDS = {
+    "lokr": _attention_lokr(4096),
+    "lokr-factored-w1": {
+        "transformer.transformer_blocks.0.attn.to_q.lokr_w1_a": _z(4, 2),
+        "transformer.transformer_blocks.0.attn.to_q.lokr_w1_b": _z(2, 4),
+        "transformer.transformer_blocks.0.attn.to_q.lokr_w2": _z(1024, 1024),
+    },
+    "loha": _attention_layers("loha", 4096),
+    "full": _attention_layers("full", 4096),
+    "lycoris-dora": _attention_layers("lycoris-dora", 4096),
+    "peft-dora-gate-up": _comfy_gate_up_lora()
+    | {"diffusion_model.transformer_blocks.0.img_mlp.gate_up.lora_magnitude_vector.weight": _z(24576)},
+}
+
+
 @pytest.mark.parametrize(
     "sd",
-    [_comfy_gate_up_lora(), _attention_lora(4096), *_OUTPUT_OR_MLP.values()],
-    ids=["gate_up", "attention", *_OUTPUT_OR_MLP.keys()],
+    [_comfy_gate_up_lora(), _attention_lora(4096), *_OUTPUT_OR_MLP.values(), *_OTHER_KINDS.values()],
+    ids=["gate_up", "attention", *_OUTPUT_OR_MLP.keys(), *_OTHER_KINDS.keys()],
 )
-def test_a_qwen_image_2_1_lora_is_claimed_by_no_other_lora_config(sd: dict[str, torch.Tensor], tmp_path: Path) -> None:
+def test_a_qwen_image_2_1_lora_is_claimed_by_its_config_alone(sd: dict[str, torch.Tensor], tmp_path: Path) -> None:
+    assert _probe(LoRA_LyCORIS_QwenImage21_Config, sd, tmp_path)
     # Identification tries every config; two that accept one file leave the outcome to iteration order.
     others = [
         c
@@ -260,21 +309,18 @@ def test_a_qwen_image_2_1_lora_is_claimed_by_no_other_lora_config(sd: dict[str,
     assert [c.__name__ for c in others if _probe(c, sd, tmp_path)] == []
 
 
-def _klein_double_block(module: str) -> dict[str, torch.Tensor]:
-    prefix = f"transformer.transformer_blocks.0.attn.{module}"
-    return {f"{prefix}.lora_A.weight": torch.zeros(4, 4096), f"{prefix}.lora_B.weight": torch.zeros(4096, 4)}
+def _pair(name: str, out_features: int, in_features: int = 4096) -> dict[str, torch.Tensor]:
+    return {f"{name}.lora_A.weight": _z(4, in_features), f"{name}.lora_B.weight": _z(out_features, 4)}
 
 
 @pytest.mark.parametrize(
     "extra",
     [
         # Its text embedder's width.
-        {
-            "transformer.context_embedder.lora_A.weight": torch.zeros(4, 12288),
-            "transformer.context_embedder.lora_B.weight": torch.zeros(4096, 4),
-        },
+        _pair("transformer.context_embedder", 4096, 12288),
         # Its double blocks' text stream, which a single-stream Qwen-Image-2.1 block does not have.
-        _klein_double_block("add_q_proj") | _klein_double_block("to_add_out"),
+        _pair("transformer.transformer_blocks.0.attn.add_q_proj", 4096)
+        | _pair("transformer.transformer_blocks.0.attn.to_add_out", 4096),
     ],
     ids=["text-embedder", "text-stream"],
 )
@@ -283,35 +329,83 @@ def test_a_flux2_klein_9b_lora_is_not_qwen_image_2_1(extra: dict[str, torch.Tens
     assert not _probe(LoRA_LyCORIS_QwenImage21_Config, _attention_lora(4096) | extra, tmp_path)
 
 
-def test_image_attention_alone_reads_as_qwen_image_2_1(tmp_path: Path) -> None:
-    """A known ambiguity, pinned: a LoRA on only `to_q/to_k/to_v` of Klein 9B's first blocks has exactly the keys
-    and shapes of a Qwen-Image-2.1 one, and is filed as the latter."""
-    assert _probe(LoRA_LyCORIS_QwenImage21_Config, _attention_lora(4096), tmp_path)
+def test_a_klein_lora_on_its_double_blocks_image_attention_reads_as_qwen_image_2_1(tmp_path: Path) -> None:
+    """A known ambiguity, pinned: a FLUX.2 Klein 9B LoRA on its double blocks' image attention has a 2.1 LoRA's
+    names and width. Its `proj_out` and `norm_out.linear` share the names but not the output widths (128 and 8192
+    against 64 and 4096): beside that attention they are taken along, on their own they are no 2.1 evidence."""
+    output_layers = _pair("transformer.proj_out", 128) | _pair("transformer.norm_out.linear", 8192)
+    assert _probe(LoRA_LyCORIS_QwenImage21_Config, _attention_lora(4096) | output_layers, tmp_path)
+    assert not _probe(LoRA_LyCORIS_QwenImage21_Config, output_layers, tmp_path)
 
 
-@pytest.mark.parametrize(
-    "extra",
-    [
+# Qwen-Image-2.1 LoRAs that apply in full, with the prefix their layout uses.
+_WHOLE_LORAS = {
+    "comfy-gate-up": (_comfy_gate_up_lora, "diffusion_model."),
+    "peft-attention": (lambda: _attention_lora(4096), "transformer."),
+    # Its width is a product of factor sizes, the weakest evidence: its module names alone make it 2.1's.
+    "lokr-attention": (lambda: _attention_lokr(4096, split_w2=True), "transformer."),
+}
+
+
+def _part_that_does_not_apply(defect: str, whole: dict[str, torch.Tensor], prefix: str) -> dict[str, torch.Tensor]:
+    # Block 2: a module of no whole LoRA, so the part is not merged into one of its layers.
+    block_0, block_2 = f"{prefix}transformer_blocks.0", f"{prefix}transformer_blocks.2"
+    other_prefix = "transformer." if prefix == "diffusion_model." else "diffusion_model."
+    return {
         # A text encoder's layer: no Qwen-Image-2.1 transformer module.
-        {
-            "text_encoder.layers.0.mlp.fc1.lora_A.weight": torch.zeros(4, 4096),
-            "text_encoder.layers.0.mlp.fc1.lora_B.weight": torch.zeros(4096, 4),
-        },
+        "foreign-layer": lambda: _pair("text_encoder.layers.0.mlp.fc1", 4096),
+        "block-out-of-range": lambda: _pair(f"{prefix}transformer_blocks.40.attn.to_q", 4096),
         # LoKR on the fused gate_up, which cannot be split into gate_layer and proj.
-        {
-            "diffusion_model.transformer_blocks.1.img_mlp.gate_up.lokr_w1": torch.zeros(4, 4),
-            "diffusion_model.transformer_blocks.1.img_mlp.gate_up.lokr_w2": torch.zeros(6144, 1024),
+        "lokr-gate-up": lambda: {
+            f"{block_2}.img_mlp.gate_up.lokr_w1": _z(4, 4),
+            f"{block_2}.img_mlp.gate_up.lokr_w2": _z(6144, 1024),
         },
-    ],
-    ids=["foreign-layer", "lokr-gate-up"],
-)
-def test_a_lora_that_would_apply_only_in_part_is_not_installed_as_one(
-    extra: dict[str, torch.Tensor], tmp_path: Path
+        "half-pair": lambda: {f"{block_2}.attn.to_q.lora_A.weight": _z(4, 4096)},
+        "incomplete-lokr": lambda: {f"{block_2}.attn.to_q.lokr_w1": _z(4, 4)},
+        "alpha-only": lambda: {f"{block_2}.attn.to_q.alpha": torch.tensor(4.0)},
+        # The whole LoRA's first block once more, under the other prefix.
+        "two-prefixes": lambda: {
+            other_prefix + key.removeprefix(prefix): value for key, value in whole.items() if key.startswith(block_0)
+        },
+        # LyCORIS's DoRA on a LoKR layer, which no layer class builds.
+        "lokr-dora-scale": lambda: {
+            f"{block_2}.attn.to_q.lokr_w1": _z(4, 4),
+            f"{block_2}.attn.to_q.lokr_w2": _z(1024, 1024),
+            f"{block_2}.attn.to_q.dora_scale": _z(1, 4096),
+        },
+        # A gate_up beside the gate_layer it is split into.
+        "gate-up-and-gate-layer": lambda: _pair(f"{block_0}.img_mlp.gate_up", 24576)
+        | _pair(f"{block_0}.img_mlp.gate_layer", 12288),
+        "diff-and-pair": lambda: _pair(f"{block_2}.attn.to_q", 4096) | {f"{block_2}.attn.to_q.diff": _z(4096, 4096)},
+        "odd-row-gate-up": lambda: _pair(f"{block_2}.img_mlp.gate_up", 24575),
+    }[defect]()
+
+
+_DEFECTS = [
+    "foreign-layer",
+    "block-out-of-range",
+    "lokr-gate-up",
+    "half-pair",
+    "incomplete-lokr",
+    "alpha-only",
+    "two-prefixes",
+    "lokr-dora-scale",
+    "gate-up-and-gate-layer",
+    "diff-and-pair",
+    "odd-row-gate-up",
+]
+
+
+@pytest.mark.parametrize("defect", _DEFECTS)
+@pytest.mark.parametrize("layout", _WHOLE_LORAS)
+def test_a_qwen_image_2_1_lora_that_applies_only_in_part_is_claimed_by_no_config(
+    layout: str, defect: str, tmp_path: Path
 ) -> None:
-    # Refused at identification, rather than installed and refused at the first generation.
-    assert not _probe(LoRA_LyCORIS_QwenImage21_Config, _comfy_gate_up_lora() | extra, tmp_path)
-
-
-def test_a_lora_with_an_orphaned_half_is_refused(tmp_path: Path) -> None:
-    sd = _comfy_gate_up_lora() | {"diffusion_model.transformer_blocks.1.attn.to_q.lora_A.weight": torch.zeros(4, 4096)}
-    assert not _probe(LoRA_LyCORIS_QwenImage21_Config, sd, tmp_path)
+    """Claimed by no LoRA config, so it installs as an unknown model: not as a 2.1 LoRA that fails at the first
+    generation, nor as another family's."""
+    whole, prefix = _WHOLE_LORAS[layout]
+    sd = whole()
+    sd |= _part_that_does_not_apply(defect, sd, prefix)
+    probes = [c for c in Config_Base.CONFIG_CLASSES if c.__name__.startswith("LoRA_")]
+    assert LoRA_LyCORIS_QwenImage21_Config in probes
+    assert [c.__name__ for c in probes if _probe(c, sd, tmp_path)] == []
diff --git a/tests/backend/patches/lora_conversions/test_qwen_image_2_1_lora_conversion_utils.py b/tests/backend/patches/lora_conversions/test_qwen_image_2_1_lora_conversion_utils.py
index 307a52f1121..0cf7ffd173a 100644
--- a/tests/backend/patches/lora_conversions/test_qwen_image_2_1_lora_conversion_utils.py
+++ b/tests/backend/patches/lora_conversions/test_qwen_image_2_1_lora_conversion_utils.py
@@ -179,3 +179,134 @@ def test_a_lora_with_layers_this_model_lacks_is_refused_whole() -> None:
     )
     with pytest.raises(ValueError, match="add_q_proj"):
         convert(sd)
+
+
+_TO_Q = "diffusion_model.transformer_blocks.0.attn.to_q"
+
+
+def _update(layer, weight: torch.Tensor) -> torch.Tensor:
+    """The weight update a layer adds to `weight` at strength 1, as the patcher asks it for one."""
+    return layer.get_parameters({"weight": weight}, weight=1.0)["weight"]
+
+
+@pytest.mark.parametrize("kind", ["lokr", "lokr-factored", "lokr-factored-w1", "loha", "full"])
+def test_a_layer_of_any_kind_converts_to_the_update_it_defines(kind: str) -> None:
+    # On a 6x6 projection. LoKR is kron(w1, w2); LoHA the elementwise product of two low-rank products; a full layer
+    # the update itself. An alpha scales by alpha / rank, the rank being the inner size of a factored operand.
+    generator = torch.Generator().manual_seed(0)
+
+    def randn(*shape: int) -> torch.Tensor:
+        return torch.randn(*shape, generator=generator)
+
+    w1, w2, w2_a, w2_b = randn(2, 2), randn(3, 3), randn(3, 1), randn(1, 3)
+    w1_a, w1_b = randn(2, 1), randn(1, 2)
+    h1_a, h1_b, h2_a, h2_b = randn(6, 2), randn(2, 6), randn(6, 2), randn(2, 6)
+    diff = randn(6, 6)
+    values, expected = {
+        "lokr": ({"lokr_w1": w1, "lokr_w2": w2}, torch.kron(w1, w2)),
+        "lokr-factored": (
+            {"lokr_w1": w1, "lokr_w2_a": w2_a, "lokr_w2_b": w2_b, "alpha": torch.tensor(0.5)},
+            torch.kron(w1, w2_a @ w2_b) * 0.5,
+        ),
+        "lokr-factored-w1": (
+            {"lokr_w1_a": w1_a, "lokr_w1_b": w1_b, "lokr_w2": w2, "alpha": torch.tensor(0.5)},
+            torch.kron(w1_a @ w1_b, w2) * 0.5,
+        ),
+        "loha": (
+            {"hada_w1_a": h1_a, "hada_w1_b": h1_b, "hada_w2_a": h2_a, "hada_w2_b": h2_b, "alpha": torch.tensor(1.0)},
+            (h1_a @ h1_b) * (h2_a @ h2_b) * 0.5,
+        ),
+        "full": ({"diff": diff}, diff),
+    }[kind]
+
+    layers = convert({f"{_TO_Q}.{name}": value for name, value in values.items()}).layers
+    assert list(layers) == [f"{PREFIX}transformer_blocks.0.attn.to_q"]
+    torch.testing.assert_close(_update(layers[f"{PREFIX}transformer_blocks.0.attn.to_q"], torch.zeros(6, 6)), expected)
+
+
+_INCOMPLETE_OR_MIXED = {
+    "alpha-only": ["alpha"],
+    "magnitude-only": ["lora_magnitude_vector.weight"],
+    "half-pair": ["lora_A.weight"],
+    "both-magnitudes": ["lora_A.weight", "lora_B.weight", "dora_scale", "lora_magnitude_vector.weight"],
+    "lokr-half-operand": ["lokr_w1", "lokr_w2_a"],
+    "lokr-operand-twice": ["lokr_w1", "lokr_w1_a", "lokr_w1_b", "lokr_w2"],
+    # Tucker cores, which LyCORIS writes for convolution kernels only.
+    "lokr-tucker-core": ["lokr_w1", "lokr_w2_a", "lokr_w2_b", "lokr_t2"],
+    "loha-tucker-cores": ["hada_w1_a", "hada_w1_b", "hada_w2_a", "hada_w2_b", "hada_t1", "hada_t2"],
+    "loha-three-of-four": ["hada_w1_a", "hada_w1_b", "hada_w2_a"],
+    "diff-plus-pair": ["diff", "lora_A.weight", "lora_B.weight"],
+    # A full layer has no rank to scale by: the alpha would be ignored.
+    "diff-with-alpha": ["diff", "alpha"],
+}
+
+
+@pytest.mark.parametrize("params", _INCOMPLETE_OR_MIXED.values(), ids=_INCOMPLETE_OR_MIXED.keys())
+def test_a_module_that_is_not_exactly_one_layer_is_refused(params: list[str]) -> None:
+    # Beside a complete layer on to_k, so it is the to_q module that is refused, not the file for being empty.
+    sd = _pair("diffusion_model.transformer_blocks.0.attn.to_k", 6, 6)
+    sd |= {f"{_TO_Q}.{param}": torch.ones(()) for param in params}
+    with pytest.raises(ValueError, match=r"attn\.to_q"):
+        convert(sd)
+
+
+@pytest.mark.parametrize(
+    "spellings",
+    [
+        _pair(_TO_Q, 6, 6) | _pair("transformer.transformer_blocks.0.attn.to_q", 6, 6),
+        _pair(_TO_Q, 6, 6) | _pair("lora_unet_transformer_blocks_0_attn_to_q", 6, 6, a="lora_down", b="lora_up"),
+        _pair(_TO_Q, 6, 6) | {f"{_TO_Q}.lora_down.weight": torch.zeros(RANK, 6)},
+        _pair(_TO_Q, 6, 6)
+        | {f"{_TO_Q}.lora_magnitude_vector.weight": torch.ones(6), f"{_TO_Q}.magnitude": torch.ones(6)},
+    ],
+    ids=["two-prefixes", "kohya-and-dotted", "lora_A-and-lora_down", "two-magnitude-spellings"],
+)
+def test_a_value_given_twice_is_refused(spellings: dict) -> None:
+    # Which of the two would be applied would come down to the order of the file's keys.
+    with pytest.raises(ValueError, match="to_q"):
+        convert(spellings)
+
+
+@pytest.mark.parametrize("half", ["gate_layer", "proj"])
+def test_a_gate_up_beside_the_half_it_is_split_into_is_refused(half: str) -> None:
+    # The split would overwrite the layer the file names directly.
+    gate_up = _pair("diffusion_model.transformer_blocks.0.img_mlp.gate_up", 8, 6)
+    with pytest.raises(ValueError, match="gate_up"):
+        convert(gate_up | _pair(f"diffusion_model.transformer_blocks.0.img_mlp.{half}", 4, 6))
+    # Another block's halves are not in the way.
+    layers = convert(gate_up | _pair(f"diffusion_model.transformer_blocks.1.img_mlp.{half}", 4, 6)).layers
+    assert set(layers) == {
+        f"{PREFIX}transformer_blocks.0.img_mlp.gate_layer",
+        f"{PREFIX}transformer_blocks.0.img_mlp.proj",
+        f"{PREFIX}transformer_blocks.1.img_mlp.{half}",
+    }
+
+
+@pytest.mark.parametrize(
+    "gate_up_layer",
+    [_pair("m", 7, 6), {"m.diff": torch.zeros(7, 6)}, _pair("m", 8, 6) | {"m.magnitude": torch.ones(7)}],
+    ids=["low-rank", "full", "magnitude"],
+)
+def test_a_gate_up_layer_with_an_odd_number_of_rows_is_refused(gate_up_layer: dict) -> None:
+    # gate_layer and proj are the two halves of its rows; 7 has none.
+    module = "diffusion_model.transformer_blocks.0.img_mlp.gate_up"
+    with pytest.raises(ValueError, match="gate_up"):
+        convert({f"{module}.{key.removeprefix('m.')}": value for key, value in gate_up_layer.items()})
+
+
+@pytest.mark.parametrize("block", ["32", "40", "99", "01", "00"])
+@pytest.mark.parametrize("layout", ["dotted", "kohya"])
+def test_a_block_the_transformer_does_not_have_is_refused(block: str, layout: str) -> None:
+    # Its 32 blocks, numbered without leading zeros: `01` would be block 1 under a second name, and a gate_up in
+    # block `0` would not collide with the gate_layer of a block `00`.
+    if layout == "dotted":
+        sd = _pair(f"diffusion_model.transformer_blocks.{block}.attn.to_q", 6, 6)
+    else:
+        sd = _pair(f"lora_unet_transformer_blocks_{block}_attn_to_q", 6, 6, a="lora_down", b="lora_up")
+    with pytest.raises(ValueError, match=f"transformer_blocks[._]{block}[._]"):
+        convert(sd)
+
+
+def test_the_last_block_is_the_thirty_second() -> None:
+    sd = _pair("diffusion_model.transformer_blocks.31.attn.to_q", 6, 6)
+    assert list(convert(sd).layers) == [f"{PREFIX}transformer_blocks.31.attn.to_q"]