From 1675e4e35401bb61ff1ba35afbadba5a91bf0b2a Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Fri, 2 Oct 2026 00:51:12 +0200 Subject: [PATCH 1/5] feat(anima): support Anima-2.9B and Anima-3.8B finetunes - Read the DiT depth from the checkpoint instead of hard-coding 28 blocks (Anima-2.9B silently lost 12 of 40 blocks); load int8_tensorwise builds - Anima-3.8B v1.1: AnimaVariantType, bundled semantic connector recomputed per denoise step, new Qwen3.5 encoder model type with vendored tokenizer - Migration 2026_10_01_add_anima_variant for installed Anima records - webv2: Qwen3.5 encoder slot and graph wiring for the anima_qwen35 variant - Starter models, user guide, and integration guide section on extending an existing architecture; package-data guard test --- .../03.Models/Local Models/anima.mdx | 37 +- .../contributing/new-model-integration.mdx | 119 +++++- .../app/invocations/anima/anima_denoise.py | 189 ++++++---- .../invocations/anima/anima_model_loader.py | 41 ++- invokeai/app/invocations/fields.py | 1 + invokeai/app/invocations/model.py | 7 + .../text_encoder/anima_text_encoder.py | 64 +++- .../model_records/model_records_base.py | 4 + .../migration_2026_10_01_add_anima_variant.py | 99 +++++ invokeai/backend/anima/anima_transformer.py | 70 +++- invokeai/backend/anima/conditioning_data.py | 4 + invokeai/backend/anima/semantic_connector.py | 347 ++++++++++++++++++ invokeai/backend/architectures/defs/anima.py | 14 +- .../backend/architectures/facets/variant.py | 11 +- .../backend/model_manager/configs/factory.py | 2 + .../backend/model_manager/configs/main.py | 47 ++- .../model_manager/configs/qwen3_5_encoder.py | 82 +++++ .../model_manager/configs/qwen3_encoder.py | 20 + .../model_manager/load/model_loaders/anima.py | 192 +++++++--- .../load/model_loaders/qwen3_5_encoder.py | 117 ++++++ .../model_manager/starter_models/__init__.py | 8 + .../model_manager/starter_models/anima.py | 56 +++ invokeai/backend/model_manager/taxonomy.py | 35 ++ invokeai/backend/qwen3_5/__init__.py | 1 + .../qwen3_5/qwen3_5_4b_text_config.json | 76 ++++ invokeai/backend/qwen3_5/qwen3_5_encoder.py | 122 ++++++ .../qwen3_5/tokenizer/tokenizer.json.gz | Bin 0 -> 3516036 bytes .../qwen3_5/tokenizer/tokenizer_config.json | 305 +++++++++++++++ .../diffusion/conditioning_data.py | 11 + invokeai/frontend/api/openapi.json | 308 +++++++++++++++- invokeai/frontend/api/schema.ts | 172 ++++++++- .../architectureCapabilities.json | 56 +++ .../__snapshots__/generateGraphNodeTypes.json | 16 +- .../generation/core/baseGenerationPolicies.ts | 26 ++ .../core/canvas/addRegionalGuidance.ts | 4 +- .../generation/core/componentCompatibility.ts | 4 + .../src/features/generation/core/graph.ts | 15 + .../generation/core/graphCoverage.test.ts | 5 +- .../generation/core/graphCoverage.testing.ts | 6 + .../src/features/generation/core/settings.ts | 4 + .../src/features/generation/core/types.ts | 2 + .../ui/GenerateComponentsSection.tsx | 1 + .../src/features/models/core/relationships.ts | 3 + .../src/features/models/core/taxonomy.ts | 6 + .../webv2/src/features/models/core/types.ts | 1 + .../models/ui/detail/CpuOnlySetting.tsx | 1 + .../workflow/core/modelRequirements.ts | 1 + .../importGalleryImages.test.ts | 1 + .../image-actions/imageRecall.test.ts | 2 + .../workbench/image-actions/imageRecall.ts | 2 + .../src/workbench/workbenchState.test.ts | 1 + pyproject.toml | 1 + .../test_anima_qwen3_5_conditioning.py | 159 ++++++++ ..._migration_2026_10_01_add_anima_variant.py | 107 ++++++ .../backend/anima/test_semantic_connector.py | 179 +++++++++ tests/backend/architectures/test_variants.py | 21 +- .../test_anima_finetune_identification.py | 124 +++++++ .../load/state_dicts/anima_2_9b_keys.py | 181 +++++++++ .../state_dicts/anima_3_8b_connector_keys.py | 218 +++++++++++ .../model_manager/load/test_anima_depth.py | 109 ++++++ .../load/test_anima_fp8_wiring.py | 4 + tests/backend/qwen3_5/test_qwen3_5_encoder.py | 130 +++++++ tests/test_package_data.py | 47 +++ 63 files changed, 3813 insertions(+), 185 deletions(-) create mode 100644 invokeai/app/services/shared/sqlite_migrator/migrations/migration_2026_10_01_add_anima_variant.py create mode 100644 invokeai/backend/anima/semantic_connector.py create mode 100644 invokeai/backend/model_manager/configs/qwen3_5_encoder.py create mode 100644 invokeai/backend/model_manager/load/model_loaders/qwen3_5_encoder.py create mode 100644 invokeai/backend/qwen3_5/__init__.py create mode 100644 invokeai/backend/qwen3_5/qwen3_5_4b_text_config.json create mode 100644 invokeai/backend/qwen3_5/qwen3_5_encoder.py create mode 100644 invokeai/backend/qwen3_5/tokenizer/tokenizer.json.gz create mode 100644 invokeai/backend/qwen3_5/tokenizer/tokenizer_config.json create mode 100644 tests/app/invocations/test_anima_qwen3_5_conditioning.py create mode 100644 tests/app/services/shared/sqlite_migrator/migrations/test_migration_2026_10_01_add_anima_variant.py create mode 100644 tests/backend/anima/test_semantic_connector.py create mode 100644 tests/backend/model_manager/configs/test_anima_finetune_identification.py create mode 100644 tests/backend/model_manager/load/state_dicts/anima_2_9b_keys.py create mode 100644 tests/backend/model_manager/load/state_dicts/anima_3_8b_connector_keys.py create mode 100644 tests/backend/model_manager/load/test_anima_depth.py create mode 100644 tests/backend/qwen3_5/test_qwen3_5_encoder.py create mode 100644 tests/test_package_data.py diff --git a/docs/src/content/docs/Users Guide/03.Models/Local Models/anima.mdx b/docs/src/content/docs/Users Guide/03.Models/Local Models/anima.mdx index 3ca2830fbac..2260a444125 100644 --- a/docs/src/content/docs/Users Guide/03.Models/Local Models/anima.mdx +++ b/docs/src/content/docs/Users Guide/03.Models/Local Models/anima.mdx @@ -1,7 +1,7 @@ --- title: Anima description: Generate anime-style images with Anima, a 2B Cosmos Predict2 diffusion transformer with an LLM adapter, including its schedulers, regional prompting and ControlNet-LLLite adapters. -lastUpdated: 2026-09-30 +lastUpdated: 2026-10-02 sidebar: order: 1 --- @@ -12,7 +12,8 @@ built-in **LLM adapter** that translates the encoder's output for the transforme 16-channel Wan 2.1 / Qwen Image VAE. InvokeAI supports Anima for text-to-image, and on the Canvas for image-to-image, inpainting, outpainting -and regional prompts. +and regional prompts. Two community finetunes are supported as well: **Anima-2.9B** and **Anima-3.8B** +(see [below](#community-finetunes)). ## License @@ -53,6 +54,34 @@ three are listed. FLUX VAEs are **not** compatible. Anima also needs a T5-XXL tokenizer, which ships with InvokeAI; no T5 model has to be installed. +## Community finetunes + +Both finetunes keep Anima's VAE, schedulers and Canvas support, and install from the Starter Models. + +| Model | What changes | Components | +| ----- | ------------ | ---------- | +| **Anima-2.9B** (Preview v1) | 40 transformer blocks instead of 28, trained on 1.7M more images (cutoff July 2026). An int8 build (~3.1 GB) halves the memory of the bf16 one (~5.8 GB); it is not faster. | Same as Anima | +| **Anima-3.8B** (v1.1) | 52 blocks, plus a **semantic connector** bundled in the checkpoint that adds a second text encoder, **Qwen3.5 4B**, for better prompt adherence, multi-character binding and mixed natural-language/tag prompts. | Qwen3 0.6B **and** Qwen3.5 4B encoder, VAE | + +With an Anima-3.8B model selected, the **Components** section shows a third picker, **Qwen3.5 Encoder**, +and generation needs it. Its starter model (~4.8 GB) installs together with Anima-3.8B. The connector +runs once per denoising step, so Anima-3.8B is slower per step than Anima-2.9B (about 1.3 against 1.7 +iterations per second at 832×1216 on an RTX 4090), and its Qwen3.5 encoder adds memory while the prompt +is encoded. **Reset all to model defaults** sets 40 steps at CFG 6, the settings of the author's reference +workflow. + +Only the v1.1 checkpoint of Anima-3.8B is supported. The earlier v1.0 transformer needs a separate adapter +file and is refused at install with a message that says so. + +Both finetunes are derivatives of Anima and fall under the same non-commercial license. + +:::caution[LoRAs and LLLite adapters] +LoRAs and ControlNet-LLLite adapters trained on Anima address its transformer blocks by position. The +finetunes insert their extra blocks between the original ones, so from the third block on, such an +adapter patches different blocks than it was trained on. It loads and runs, but expect weaker or +different results than on Anima itself. +::: + ## Generation settings Selecting an Anima model applies these defaults: @@ -110,8 +139,8 @@ Base 1.0; the pose adapter is notably weak. Only the Inpainting and Sketch adapt | Node | Purpose | | ---------------------------- | --------------------------------------------------------- | -| **Main Model - Anima** | Loads the transformer, Qwen3 encoder and VAE | -| **Prompt - Anima** | Encodes a prompt, with an optional regional mask | +| **Main Model - Anima** | Loads the transformer, Qwen3 encoder and VAE, and for Anima-3.8B the Qwen3.5 encoder | +| **Prompt - Anima** | Encodes a prompt, with an optional regional mask; connect the Qwen3.5 encoder for Anima-3.8B | | **Denoise - Anima** | Runs sampling; accepts img2img latents, masks and LLLite adapters | | **Image to Latents - Anima** | VAE encode | | **Latents to Image - Anima** | VAE decode | diff --git a/docs/src/content/docs/contributing/new-model-integration.mdx b/docs/src/content/docs/contributing/new-model-integration.mdx index 44811bfc1b2..be2fe41cf81 100644 --- a/docs/src/content/docs/contributing/new-model-integration.mdx +++ b/docs/src/content/docs/contributing/new-model-integration.mdx @@ -1,7 +1,7 @@ --- title: New Model Type Integration Checklist description: A step-by-step checklist for integrating a new model architecture into InvokeAI, from the model manager to the webv2 frontend and the tests that enforce completeness. -lastUpdated: 2026-09-25 +lastUpdated: 2026-10-02 --- import { Steps, FileTree } from '@astrojs/starlight/components'; @@ -20,6 +20,14 @@ The examples use a hypothetical architecture, `NewModel`, with the base value `n - **Krea-2**: a single-stream DiT with a Qwen3-VL encoder, installable as Diffusers, single-file or GGUF. - **Z-Image**: variants with different defaults, LoRA and control. - **Qwen-Image**: reference images and an edit variant. +- **Anima**: depth-expanded finetunes (Anima-2.9B, Anima-3.8B) and a variant with a second text encoder. +::: + +:::tip[Extending an architecture that already exists?] +A finetune with a different depth, a new variant or an extra encoder for an architecture InvokeAI already +supports is a different job: most of this checklist does not apply, and some traps apply only there -- +installed models whose stored records must keep validating, and adapters trained on the original layout. +Start with [Extending an existing architecture](#14-extending-an-existing-architecture). ::: :::caution[Frontend scope] @@ -109,13 +117,21 @@ File: `invokeai/backend/model_manager/taxonomy.py` - `variant_type_adapter` in `taxonomy.py` - `ModelRecordChanges.variant` in `invokeai/app/services/model_records/model_records_base.py` - `tests/backend/architectures/test_variants.py` checks that the three agree and that the values are unique. + `tests/backend/architectures/test_variants.py` checks that the three agree and that the values are unique. A variant enum on a `base=Any` config (an encoder's) belongs to no architecture, so it also goes into `BASE_AGNOSTIC_VARIANT_ENUMS` in that test. + + :::caution[Adding a variant to an architecture with installed models] + The model config's `variant` field must be **required** (`Field()` with no default), and existing records need a [migration](#persisted-records-and-migrations). Both shortcuts break every installed model of that class: + - Without a migration, a stored record without `variant` fails validation and is skipped on read; the models vanish from the model list. + - With a default, `Config_Base.get_tag` adds the variant to the class's discriminator tag, which a stored record's dict does not carry, so no record of that class deserializes at all. + ::: 3. **Add encoder types (only for a new text encoder).** - There is no generic text-encoder type. Each encoder family has its own `ModelType` member, and a `ModelFormat` member for its folder layout. Existing examples are `Qwen3Encoder`, `Qwen3VLEncoder`, `QwenVLEncoder`, `MistralEncoder` and `T5Encoder`. + There is no generic text-encoder type. Each encoder family has its own `ModelType` member. A matching `ModelFormat` member is needed only if the encoder ships as a folder; a single-file encoder uses `ModelFormat.Checkpoint`. Existing examples are `Qwen3Encoder`, `Qwen3VLEncoder`, `Qwen35Encoder`, `QwenVLEncoder`, `MistralEncoder` and `T5Encoder`. + + Reuse an existing type whenever the encoder is a model InvokeAI already supports. Krea-2, Ideogram 4 and MiniMax H3 all use `ModelType.Qwen3VLEncoder`, told apart by `Qwen3VLVariantType`. Check whether the encoder weights are a stock checkpoint before adding a type: Anima-3.8B's `qwen35_4b.safetensors` ships its last layer without the MLP and replaces the LM head with a projection, and the conditioning was trained on exactly that. - Reuse an existing type whenever the encoder is a model InvokeAI already supports. Krea-2, Ideogram 4 and MiniMax H3 all use `ModelType.Qwen3VLEncoder`, told apart by `Qwen3VLVariantType`. Check whether the encoder weights are a stock checkpoint before adding a type. + Same width is not same family. A new family needs its own type when its architecture differs, even where an existing variant matches its hidden size: the Qwen3.5 4B and the Qwen3 4B are both 2560 wide. :::tip[Checklist: Taxonomy]{icon="approve-check"} @@ -296,6 +312,8 @@ Configs identify a model on disk. Every non-abstract subclass of `Config_Base` r ... ``` + Widths and key prefixes are not enough where two families share them. A `model.`-prefixed Qwen3.5 export satisfies every heuristic of the Qwen3 encoder configs; only its `linear_attn` layers tell it apart, so `Qwen35Encoder_Checkpoint_Config` requires them and the Qwen3 configs reject them. + **Configs must exclude each other.** Identification iterates `Config_Base.CONFIG_CLASSES`, which is a set, so the order in the `AnyModelConfig` union decides nothing. When two configs match the same file, `matches_sort_key` breaks the tie, which amounts to chance. For every existing config whose heuristic the new files could satisfy, add a negative check on one side or both. The same applies to LoRA and VAE configs, whose heuristics are often loose, for example "any key starting with `transformer_blocks.`". Cover each exclusion with a detection test. 3. **VAE config (only for a new VAE)** (`configs/vae.py`) @@ -335,8 +353,23 @@ Configs identify a model on disk. Every non-abstract subclass of `Config_Base` r ```python title="invokeai/backend/model_manager/configs/factory.py" Annotated[Main_Diffusers_NewModel_Config, Main_Diffusers_NewModel_Config.get_tag()], ``` + +6. **Refuse what is recognized but not supported** + + Raise `InvalidMatchError` for a file the config recognizes and cannot run, with a message that says what to install instead. `NotAMatchError` would let it fall through to another config or to `unknown`, and installing it as a working model would be worse. Anima refuses the Anima-3.8B v1.0 transformer this way: it loads cleanly as a 52-block Anima, but needs an adapter file InvokeAI does not load; its header (`qwen35_joint_dit_blocks`) is what gives it away. +### Persisted records and migrations + +Every installed model is stored as its config's JSON in the `models` table. `ModelRecordServiceSQL` *skips* a stored config that no longer validates, with a log line and nothing in the UI: the model just disappears from the model list. So any change that makes stored records invalid -- a new required field, a renamed enum value, a narrowed `Literal` -- needs a migration in `invokeai/app/services/shared/sqlite_migrator/migrations/` that rewrites them: + +- Name it `migration__.py` with a `build_migration(...)` that returns a `Migration`. Its `depends_on` names the newest migration on the main line. Discovery is automatic. +- Request `app_config` in `build_migration` if the callback has to open model files; stored paths may be relative to `app_config.models_path`. Fall back to a safe value when a file is gone. +- Touch only the records the change concerns (type, base and format), and leave records that already carry the field alone. +- Test it against an in-memory `models` table, as `test_migration_2026_10_01_add_anima_variant.py` does. + +`migration_2026_09_16_add_qwen3_vl_encoder_variant` (a new required encoder variant) and `migration_2026_10_01_add_anima_variant` (a new required main-model variant that reads each checkpoint's header) are complete examples. + :::tip[Checklist: Model configs]{icon="approve-check"} - [ ] Main configs per format (Diffusers, checkpoint, GGUF) - [ ] Detection helper and variant detection @@ -344,6 +377,8 @@ Configs identify a model on disk. Every non-abstract subclass of `Config_Base` r - [ ] VAE config and exclusions in the existing VAE detectors (if the VAE is new) - [ ] Encoder configs (if the encoder type is new) - [ ] Every new config in the `AnyModelConfig` union +- [ ] `InvalidMatchError` for recognized files that cannot run +- [ ] A migration for every change that would invalidate stored records ::: --- @@ -387,18 +422,21 @@ Loaders turn a config into an in-memory model. Every module in this folder is im Community single-file checkpoints often use key names that differ from diffusers, for example fused projections. Verify the conversion numerically against the diffusers module on a small config. + **Read structural hyperparameters from the checkpoint.** A loader that hard-codes the depth or a width and loads with `strict=False` builds the wrong model without an error when a finetune changes them: it fills the first N blocks of a deeper checkpoint and drops the rest as unexpected keys, which `log_unexpected_keys` reports at DEBUG only. `reject_incomplete_load` does not catch this either -- nothing is *missing*. Anima's loader took Anima-2.9B's first 28 of 40 blocks this way and generated degraded images. Count blocks from the state dict (`count_anima_dit_blocks`), refuse gaps, and read further hyperparameters from the safetensors header where the checkpoint records them (`read_safetensors_metadata`). + 3. **VAE loader (only for a new VAE)** Build the diffusers VAE class and load the converted state dict. `vae.py` and `flux.py` handle existing VAEs from native and ComfyUI layouts. 4. **Text encoder loader (only for a new encoder type)** - Register with `base=BaseModelType.Any` and the encoder's `ModelType`, and serve both `SubModelType.TextEncoder` and `SubModelType.Tokenizer`. Vendor tokenizers and configs the loader needs, as `invokeai/backend/qwen2_5_vl/` does, so a single-file install loads without network access. Add the vendored files to `[tool.setuptools.package-data]` in `pyproject.toml`, or a wheel install fails with `FileNotFoundError`. + Register with `base=BaseModelType.Any` and the encoder's `ModelType`, and serve both `SubModelType.TextEncoder` and `SubModelType.Tokenizer`. Vendor tokenizers and configs the loader needs, as `invokeai/backend/qwen2_5_vl/` does, so a single-file install loads without network access. Add the vendored files to `[tool.setuptools.package-data]` in `pyproject.toml`, or a wheel install fails with `FileNotFoundError`; `tests/test_package_data.py` checks every `*.json` and `*.json.gz` under `invokeai/backend`. :::tip[Checklist: Model loaders]{icon="approve-check"} - [ ] Loader per registered `(base, type, format)` - [ ] Key conversion for single-file and GGUF layouts, verified numerically against diffusers +- [ ] Depth and other structural hyperparameters read from the checkpoint, not hard-coded - [ ] Quantized formats via the existing `invokeai/backend/quantization/` helpers - [ ] FP8 Storage: the loader casts, or declares `Unimplemented` / `NotApplicable` at registration - [ ] VAE and encoder loaders (if new) @@ -588,6 +626,7 @@ Packages are discovered automatically. A directory of node modules without an `_ - **Backend package (optional).** Put reusable math in `invokeai/backend/new_model/`: noise, packing, position IDs, schedules and text encoding. FLUX, FLUX.2 and Krea-2 have one; Qwen-Image and Z-Image keep the loop in the denoise node. Use a package once code is shared between nodes or needs its own tests. - **Schedulers.** Flow-matching architectures use diffusers' `FlowMatchEulerDiscreteScheduler`, or the shared maps in `invokeai/backend/flux/schedulers.py`: `FLUX_SCHEDULER_MAP`, `ZIMAGE_SCHEDULER_MAP` and `ERNIE_IMAGE_SCHEDULER_MAP`, each with name values and labels. Declare which set the UI offers in `FeaturesFacet.scheduler_set`. Set `scheduler_applies_to_graph=True` only if the denoise node really takes a scheduler field. Reproduce the reference sigma schedule and shift (`mu`, time-shift type, terminal shift) exactly, and test it against the reference scheduler. - **External noise (optional).** Only if the denoise node accepts a noise tensor: extend `LatentNoiseType` and its shape and grid logic in `invokeai/app/invocations/latent_noise.py`, and extend `noise_type` in `invocations/noise.py`. Validate with `validate_noise_tensor_shape`. +- **Timestep-dependent conditioning.** Most architectures turn the text embeddings into the transformer's context once, before the loop. If a module between encoder and transformer reads the timestep -- Anima-3.8B's semantic connector does -- the context has to be recomputed at every step, for the positive, the negative and every regional conditioning. Pass the timestep exactly as the reference does: the connector scales sigma by 1000 before embedding it, so it gets a float32 sigma, where bf16's rounding would move its high frequencies by radians. A mask that leaves a query no key (an empty prompt encoded as one masked token) needs a defined result; `masked_sdpa` makes it zero instead of leaving it to the SDPA backend. - **Inpainting.** Rectified-flow models use `RectifiedFlowInpaintExtension` (`invokeai/backend/rectified_flow/rectified_flow_inpaint_extension.py`). - **Previews.** `LatentSpaceFacet` provides them; there is nothing to add in the step callback. @@ -767,10 +806,14 @@ Paths below are relative to `invokeai/frontend/webv2/src/features/generation/cor - Add a `case` to `getBaseComponentSectionPolicy` in `baseGenerationPolicies.ts`. Its `default` returns an empty policy silently. - Add encoder filters in `componentCompatibility.ts`. If a main model can bring its own components, update `isBundledMainForBase`. VAE acceptance comes from the backend. + - A slot that only one variant needs goes into the policy conditionally: `getBaseComponentSectionPolicy` receives the model, so branch on `model.variant`, as FLUX.2 dev and Anima-3.8B do. - A **new** settings key must be added in all of these places: - `GenerateSettings` (`types.ts`) - `GenerateComponentValueKey`, `COMPONENT_SETTING_LABELS`, `getComponentPolicyContext` and `getDefaultGenerateSettings` (`baseGenerationPolicies.ts`) - - `normalizeGenerateSettings` and `cloneGenerateWidgetValues` (`settings.ts`) + - the second `getComponentPolicyContext` in `src/features/generation/ui/GenerateComponentsSection.tsx` + - `normalizeGenerateSettings`, `cloneGenerateWidgetValues` and `syncGenerateWidgetValuesWithModels` (`settings.ts`) + - `RecalledComponentSetting` and `RECALLED_COMPONENTS` (`src/workbench/image-actions/imageRecall.ts`), under the metadata key the builder writes + - the test fixtures that build a complete `GenerateWidgetValues` literal (`imageRecall.test.ts`, `workbenchState.test.ts`, `importGalleryImages.test.ts`). Vitest does not type-check them; `lint:tsc` does. 5. **Canvas** @@ -789,11 +832,17 @@ Paths below are relative to `invokeai/frontend/webv2/src/features/generation/cor - `src/features/models/core/baseIdentity.ts`: `MODEL_BASES`, plus its pinned test - `src/features/models/core/types.ts`: `ModelBase` - - `src/features/models/core/taxonomy.ts`: variant values + - `src/features/models/core/taxonomy.ts`: variant values -- labels in `MODEL_VARIANT_LABELS`, main-model variants in `MAIN_VARIANTS_BY_BASE`, the variants of other types in `VARIANTS_BY_TYPE` - `src/features/models/core/relationships.ts`: which encoder and VAE types may link to the base - `src/features/workflow/core/modelRequirements.ts`: `BASE_LABELS` - New user-facing strings go in `invokeai/frontend/webv2/public/locales/en.json`. + A new **model type** (an encoder family) has its own set: + - `ModelTaxonomyType` in `types.ts`, and its label in the type list at the top of `taxonomy.ts` + - `NULL_BASE_ALLOWANCES` and `SINGLETON_LINK_TYPES` in `relationships.ts` + - the type label in `modelRequirements.ts` + - `CPU_ONLY_TYPES` in `src/features/models/ui/detail/CpuOnlySetting.tsx` if the config has `cpu_only` + 8. **Per-family UI (only for controls unique to the architecture)** `src/features/generation/ui/GenerateRenderSection.tsx` branches on the family for controls such as Krea-2 seed variance and Wan settings. Controls every architecture has come from capabilities; they need no code. @@ -804,17 +853,18 @@ Paths below are relative to `invokeai/frontend/webv2/src/features/generation/cor pnpm -C invokeai/frontend/webv2 test src/features/generation/core/graphCoverage.test.ts -u ``` - This rewrites `__snapshots__/generateGraphNodeTypes.json`. Then raise `SUPPORTED_BASE_COUNTS["generate"]` in `tests/app/invocations/test_frontend_graph_node_types.py`, and `LITERAL_FLOORS` if needed. That Python test checks every node type, edge field and literal value the builders emit against the backend's invocation registry. + This rewrites `__snapshots__/generateGraphNodeTypes.json`. Graph coverage and the remix round trip compile each base in the shapes listed in `graphCoverage.testing.ts`; a variant that changes the graph needs its own entry in `SHAPE_OVERRIDES`, and a component variant a slot filters for needs to be in `CANDIDATE_VARIANTS`, or neither test ever builds that graph. Then raise `SUPPORTED_BASE_COUNTS["generate"]` in `tests/app/invocations/test_frontend_graph_node_types.py`, and `LITERAL_FLOORS` if needed. That Python test checks every node type, edge field and literal value the builders emit against the backend's invocation registry. :::tip[Checklist: webv2]{icon="approve-check"} - [ ] Capabilities fixture regenerated - [ ] `contracts.ts`, `supportedBases.ts` and its test - [ ] Builder in `graph.ts` and registered in `GRAPH_BUILDERS` -- [ ] Component slot policy, encoder filters, new settings keys everywhere +- [ ] Component slot policy (per variant where only one needs it), encoder filters, new settings keys everywhere - [ ] `CANVAS_I2L_NODE_TYPES` and `BASE_CASES` - [ ] Recall - [ ] Model registry, labels, `en.json` +- [ ] `SHAPE_OVERRIDES` / `CANDIDATE_VARIANTS` for variant-dependent graphs - [ ] Graph snapshot regenerated, `SUPPORTED_BASE_COUNTS` raised - [ ] Changed interactions verified in the browser (`--webv2`) ::: @@ -857,19 +907,23 @@ Several tests pin hand-maintained tables, so a new architecture has to extend th | `tests/backend/architectures/test_latent_space.py` | `DECLARED_LATENT_SPACES` | | `tests/backend/architectures/test_default_settings.py` | Rows in `DEFAULT_SETTINGS_MATRIX` | | `tests/backend/architectures/test_conditioning.py` | The pinned numbers of conditioning types and architectures | -| `tests/backend/architectures/test_variants.py` | Nothing if the variant lists and unique values are right; it checks them | +| `tests/backend/architectures/test_variants.py` | A `base=Any` variant enum in `BASE_AGNOSTIC_VARIANT_ENUMS`; an architecture that gains variants out of the pinned list of variant-less ones. Otherwise it checks the variant lists and unique values itself | | `tests/backend/architectures/test_modality.py` | Nothing if `GENERATION_MODES` matches `ModalityFacet` | | `tests/backend/architectures/test_capabilities_fixture.py` | Regenerate the webv2 fixture (see section 11) | | `tests/app/invocations/test_frontend_graph_node_types.py` | `SUPPORTED_BASE_COUNTS`, possibly `LITERAL_FLOORS` | | `tests/backend/model_manager/load/test_diffusers_0XX_compatibility.py` | New classes, when the dependency is bumped | +| `tests/test_package_data.py` | Nothing if `package-data` covers the vendored files; it checks them | +| `src/features/generation/core/graphCoverage.testing.ts` (webv2) | `SHAPE_OVERRIDES` and `CANDIDATE_VARIANTS` for variant-dependent graphs | +| `src/workbench/image-actions/imageRecall.test.ts` (webv2) | The recorded component setting in the remix round-trip list | Beyond those tables, test what can silently go wrong: - detection of every format, including negative cases against similar architectures - key conversion against the diffusers module - the text-encoder template - the sampling schedule against the reference scheduler +- every migration, against an in-memory `models` table -Use real lightweight modules with small configs rather than mocks. +Use real lightweight modules with small configs rather than mocks. For layout tests, capture the key names and shapes of the real checkpoint into a fixture under `tests/backend/model_manager/load/state_dicts/` and build the model on the `meta` device: real extents are billions of elements. Where a module is ported rather than taken from `diffusers`/`transformers`, compare it once against the reference implementation on the real weights before writing the small-config tests. **CI gates for generated artifacts.** New or changed invocations change the OpenAPI schema. CI (`openapi-checks.yml`, `typegen-checks.yml`) requires the shared package's `invokeai/frontend/api/openapi.json` and `invokeai/frontend/api/schema.ts` to be current. Regenerate both from `invokeai/frontend/api` in the repository's Python environment after installing that package's locked dependencies: @@ -889,6 +943,49 @@ python ../../../scripts/generate_openapi_schema.py | pnpm typegen --- +## 14. Extending an existing architecture + +Finetunes and variants of a supported architecture reuse almost everything above. Decide first what the new checkpoints actually change: + +| What changes | Treat it as | Work | +| --- | --- | --- | +| Only the weights (finetune, merge) | nothing | at most a starter model | +| A structural hyperparameter nothing outside the loader depends on, such as depth | loader detection, **not** a variant | read it from the checkpoint ([5.2](#5-model-loaders)), test against the real key layout | +| What the graph or UI needs: an extra encoder, a different conditioning path, different defaults | a **variant** | the checklist below | +| The latent space, the VAE family or the transformer family | a new base | the rest of this guide | + +The scaffolder does not apply; it creates files for a new base. + +Anima-2.9B (40 blocks instead of 28) needed only the second row. Anima-3.8B (52 blocks, plus a connector that reads a second encoder) needed the third: + + +1. **Taxonomy.** The variant enum, with values unique across all enums, listed in the three places of [2.2](#2-taxonomy). Values describe what differs (`anima_qwen35`: conditioned on Qwen3.5 as well), not a release name, so the next finetune with the same requirements fits. + +2. **Config and migration.** A required `variant` field, detection from the checkpoint, `override_fields.pop("variant", None)` before detection, and a [migration](#persisted-records-and-migrations) for installed records. A model installed before the variant existed may already be the new kind; read its header in the migration rather than assume. + +3. **Declaration.** `VariantFacet`, per-variant `DefaultSettingsFacet` entries, the regenerated capabilities fixture. The model's defaults reach the UI through **Reset all to model defaults**; switching models keeps the user's settings. + +4. **Invocations.** New inputs optional, so existing workflows keep validating; node version bumps. The model loader checks the main model's variant and fails with a message naming what is missing, rather than letting the denoise node meet a `None`. + +5. **Frontend.** The per-variant slot and builder branch ([11.4](#11-frontend-webv2)), the variant in `MAIN_VARIANTS_BY_BASE` and `MODEL_VARIANT_LABELS`, `SHAPE_OVERRIDES` for both variants. + +6. **Unsupported releases.** Refuse a recognized checkpoint you do not support with `InvalidMatchError` and a pointer to the one you do. + +7. **Existing adapters.** LoRAs and control adapters address blocks by name, and names carry the block index. A depth expansion that inserts blocks between the original ones shifts every index after the first insertion, so an adapter trained on the original loads and runs against the wrong blocks. Say so in the user guide; a remap needs the insertion positions, which only some checkpoints record. + + +:::tip[Checklist: Extending an architecture]{icon="approve-check"} +- [ ] Decided: weights only, loader detection, variant or new base +- [ ] Structural hyperparameters read from the checkpoint, tested against a real-captured key fixture +- [ ] Variant: required field, detection, migration with a test, `VariantFacet`, defaults per variant +- [ ] Optional node inputs, variant check with a clear error in the model loader +- [ ] Per-variant slot and graph branch, variant labels, `SHAPE_OVERRIDES` +- [ ] `InvalidMatchError` for recognized but unsupported releases +- [ ] Adapter compatibility documented +::: + +--- + ## Summary: Minimal integration A minimal txt2img integration with a Diffusers main model, reusing an existing VAE and encoder: diff --git a/invokeai/app/invocations/anima/anima_denoise.py b/invokeai/app/invocations/anima/anima_denoise.py index 5e3c40736d4..df3a13cec46 100644 --- a/invokeai/app/invocations/anima/anima_denoise.py +++ b/invokeai/app/invocations/anima/anima_denoise.py @@ -482,16 +482,58 @@ def _load_text_conditionings( t5xxl_ids=cond_info.t5xxl_ids, t5xxl_weights=cond_info.t5xxl_weights, mask=mask, + qwen35_states=cond_info.qwen35_states, + qwen35_mask=cond_info.qwen35_mask, ) ) return text_conditionings + @staticmethod + def _run_llm_adapter( + transformer, + tc: AnimaTextConditioning, + dtype: torch.dtype, + timesteps: torch.Tensor | None, + ) -> torch.Tensor: + """Run the LLM Adapter -- or, on Anima-3.8B, the semantic connector -- for one conditioning. + + Args: + transformer: The AnimaTransformer instance (must be on device). + tc: The conditioning. + dtype: Inference dtype. + timesteps: The flow timestep, float32, shape (1,). Only the semantic connector reads it; it + is None for every other model, whose context does not change between steps. + + Returns: + Context of shape (1, 512, 1024). + """ + qwen3_embeds = tc.qwen3_embeds.unsqueeze(0) # (1, seq_len, 1024) + t5xxl_ids = tc.t5xxl_ids.unsqueeze(0) # (1, seq_len) + t5xxl_weights = None + if tc.t5xxl_weights is not None: + t5xxl_weights = tc.t5xxl_weights.unsqueeze(0).unsqueeze(-1).to(dtype=dtype) # (1, seq_len, 1) + semantic_states = None + semantic_mask = None + if timesteps is not None and tc.qwen35_states is not None: + # (num_layers, seq_len, 2560) -> one (1, seq_len, 2560) tensor per layer. + semantic_states = [state.unsqueeze(0).to(dtype=dtype) for state in tc.qwen35_states] + semantic_mask = tc.qwen35_mask.unsqueeze(0) if tc.qwen35_mask is not None else None + return transformer.preprocess_text_embeds( + qwen3_embeds.to(dtype=dtype), + t5xxl_ids, + t5xxl_weights=t5xxl_weights, + semantic_states=semantic_states, + semantic_mask=semantic_mask, + timesteps=timesteps, + ) + def _run_llm_adapter_for_regions( self, transformer, text_conditionings: list[AnimaTextConditioning], dtype: torch.dtype, + timesteps: torch.Tensor | None = None, ) -> AnimaRegionalTextConditioning: """Run the LLM Adapter separately for each regional conditioning and concatenate. @@ -499,6 +541,7 @@ def _run_llm_adapter_for_regions( transformer: The AnimaTransformer instance (must be on device). text_conditionings: List of per-region conditioning data. dtype: Inference dtype. + timesteps: See `_run_llm_adapter`. Returns: AnimaRegionalTextConditioning with concatenated context and masks. @@ -509,20 +552,8 @@ def _run_llm_adapter_for_regions( cur_len = 0 for tc in text_conditionings: - qwen3_embeds = tc.qwen3_embeds.unsqueeze(0) # (1, seq_len, 1024) - t5xxl_ids = tc.t5xxl_ids.unsqueeze(0) # (1, seq_len) - t5xxl_weights = None - if tc.t5xxl_weights is not None: - t5xxl_weights = tc.t5xxl_weights.unsqueeze(0).unsqueeze(-1) # (1, seq_len, 1) - - # Run the LLM Adapter to produce context for this region - context = transformer.preprocess_text_embeds( - qwen3_embeds.to(dtype=dtype), - t5xxl_ids, - t5xxl_weights=t5xxl_weights.to(dtype=dtype) if t5xxl_weights is not None else None, - ) # context shape: (1, 512, 1024) — squeeze batch dim - context_2d = context.squeeze(0) # (512, 1024) + context_2d = self._run_llm_adapter(transformer, tc, dtype, timesteps).squeeze(0) # (512, 1024) context_embeds_list.append(context_2d) context_ranges.append(Range(start=cur_len, end=cur_len + context_2d.shape[0])) @@ -537,6 +568,26 @@ def _run_llm_adapter_for_regions( context_ranges=context_ranges, ) + @staticmethod + def _check_qwen3_5_conditioning( + context: InvocationContext, + uses_connector: bool, + positive: list[AnimaTextConditioning], + negative: list[AnimaTextConditioning], + ) -> None: + """Refuse Anima-3.8B conditioning without Qwen3.5 states; note Qwen3.5 states nothing will read.""" + conditionings = [*positive, *negative] + if uses_connector: + if any(tc.qwen35_states is None for tc in conditionings): + raise ValueError( + "This Anima model bundles a Qwen3.5 semantic connector, but a prompt was encoded without " + "Qwen3.5. Connect the model loader's Qwen3.5 Encoder output to every Anima prompt node." + ) + elif any(tc.qwen35_states is not None for tc in conditionings): + context.logger.warning( + "This Anima model has no semantic connector; the prompt's Qwen3.5 encoding is unused." + ) + def _run_diffusion(self, context: InvocationContext) -> torch.Tensor: device = TorchDevice.choose_torch_device() inference_dtype = TorchDevice.choose_anima_inference_dtype(device) @@ -717,57 +768,61 @@ def _run_diffusion(self, context: InvocationContext) -> torch.Tensor: ) ) - # Run LLM Adapter for each regional conditioning to produce context vectors. - # This must happen with the transformer on device since it uses the adapter weights. - if has_regional: - pos_regional = self._run_llm_adapter_for_regions(transformer, pos_text_conditionings, inference_dtype) - pos_context = pos_regional.context_embeds.unsqueeze(0) # (1, total_ctx_len, 1024) + # Anima-3.8B's semantic connector conditions the context on the timestep, so its context + # is recomputed at every step; every other Anima model's context is computed once here. + uses_connector = bool(getattr(transformer, "has_semantic_connector", False)) + self._check_qwen3_5_conditioning( + context, uses_connector, pos_text_conditionings, neg_text_conditionings or [] + ) - # Build regional prompting extension with cross-attention mask - regional_extension = AnimaRegionalPromptingExtension.from_regional_conditioning( - pos_regional, img_seq_len + def build_contexts( + sigma: float | None, + ) -> tuple[torch.Tensor, torch.Tensor | None, AnimaRegionalTextConditioning | None]: + # float32, as the reference passes it: the connector's timestep embedding scales sigma by + # 1000, where bf16's rounding of sigma would move its high frequencies by radians. + timesteps = ( + torch.tensor([sigma * ANIMA_MULTIPLIER], device=device, dtype=torch.float32) + if sigma is not None + else None ) - - # For negative, concatenate all regions without masking (matches Z-Image behavior) - neg_context = None - if do_cfg and neg_text_conditionings is not None: - neg_regional = self._run_llm_adapter_for_regions( - transformer, neg_text_conditionings, inference_dtype + # Must run with the transformer on device since it uses the adapter weights. + if has_regional: + pos_regional = self._run_llm_adapter_for_regions( + transformer, pos_text_conditionings, inference_dtype, timesteps ) - neg_context = neg_regional.context_embeds.unsqueeze(0) - else: - # Single conditioning — run LLM Adapter via normal forward path - tc = pos_text_conditionings[0] - pos_qwen3_embeds = tc.qwen3_embeds.unsqueeze(0) - pos_t5xxl_ids = tc.t5xxl_ids.unsqueeze(0) - pos_t5xxl_weights = None - if tc.t5xxl_weights is not None: - pos_t5xxl_weights = tc.t5xxl_weights.unsqueeze(0).unsqueeze(-1) - - # Pre-compute context via LLM Adapter - pos_context = transformer.preprocess_text_embeds( - pos_qwen3_embeds.to(dtype=inference_dtype), - pos_t5xxl_ids, - t5xxl_weights=pos_t5xxl_weights.to(dtype=inference_dtype) - if pos_t5xxl_weights is not None - else None, - ) - - neg_context = None + pos = pos_regional.context_embeds.unsqueeze(0) # (1, total_ctx_len, 1024) + # For negative, concatenate all regions without masking (matches Z-Image behavior) + neg = None + if do_cfg and neg_text_conditionings is not None: + neg_regional = self._run_llm_adapter_for_regions( + transformer, neg_text_conditionings, inference_dtype, timesteps + ) + neg = neg_regional.context_embeds.unsqueeze(0) + return pos, neg, pos_regional + pos = self._run_llm_adapter(transformer, pos_text_conditionings[0], inference_dtype, timesteps) + neg = None if do_cfg and neg_text_conditionings is not None: - ntc = neg_text_conditionings[0] - neg_qwen3 = ntc.qwen3_embeds.unsqueeze(0) - neg_ids = ntc.t5xxl_ids.unsqueeze(0) - neg_weights = None - if ntc.t5xxl_weights is not None: - neg_weights = ntc.t5xxl_weights.unsqueeze(0).unsqueeze(-1) - neg_context = transformer.preprocess_text_embeds( - neg_qwen3.to(dtype=inference_dtype), - neg_ids, - t5xxl_weights=neg_weights.to(dtype=inference_dtype) if neg_weights is not None else None, - ) - - regional_extension = None + neg = self._run_llm_adapter(transformer, neg_text_conditionings[0], inference_dtype, timesteps) + return pos, neg, None + + first_sigma = sigmas[0] if uses_connector else None + pos_context, neg_context, pos_regional = build_contexts(first_sigma) + context_sigma = first_sigma + + def contexts_at(sigma: float) -> tuple[torch.Tensor, torch.Tensor | None]: + nonlocal pos_context, neg_context, context_sigma + if uses_connector and sigma != context_sigma: + pos_context, neg_context, _ = build_contexts(sigma) + context_sigma = sigma + return pos_context, neg_context + + # The regional cross-attention mask depends only on how many context tokens each region + # contributes (512 each), not on their values, so it is built once. + regional_extension = ( + AnimaRegionalPromptingExtension.from_regional_conditioning(pos_regional, img_seq_len) + if pos_regional is not None + else None + ) # Apply regional prompting patch if we have regional masks exit_stack.enter_context(patch_anima_for_regional_prompting(transformer, regional_extension)) @@ -802,11 +857,12 @@ def _run_transformer(ctx: torch.Tensor, x: torch.Tensor, t: torch.Tensor) -> tor timestep = torch.tensor( [it.sigma_curr * ANIMA_MULTIPLIER], device=device, dtype=inference_dtype ).expand(latents.shape[0]) + step_pos_context, step_neg_context = contexts_at(it.sigma_curr) - noise_pred_cond = _run_transformer(pos_context, latents, timestep).float() + noise_pred_cond = _run_transformer(step_pos_context, latents, timestep).float() - if do_cfg and neg_context is not None: - noise_pred_uncond = _run_transformer(neg_context, latents, timestep).float() + if do_cfg and step_neg_context is not None: + noise_pred_uncond = _run_transformer(step_neg_context, latents, timestep).float() noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond) else: noise_pred = noise_pred_cond @@ -858,11 +914,12 @@ def _run_transformer(ctx: torch.Tensor, x: torch.Tensor, t: torch.Tensor) -> tor timestep = torch.tensor( [sigma_curr * ANIMA_MULTIPLIER], device=device, dtype=inference_dtype ).expand(latents.shape[0]) + step_pos_context, step_neg_context = contexts_at(sigma_curr) - noise_pred_cond = _run_transformer(pos_context, latents, timestep).float() + noise_pred_cond = _run_transformer(step_pos_context, latents, timestep).float() - if do_cfg and neg_context is not None: - noise_pred_uncond = _run_transformer(neg_context, latents, timestep).float() + if do_cfg and step_neg_context is not None: + noise_pred_uncond = _run_transformer(step_neg_context, latents, timestep).float() noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond) else: noise_pred = noise_pred_cond diff --git a/invokeai/app/invocations/anima/anima_model_loader.py b/invokeai/app/invocations/anima/anima_model_loader.py index 11ac96e7897..dd98ddced44 100644 --- a/invokeai/app/invocations/anima/anima_model_loader.py +++ b/invokeai/app/invocations/anima/anima_model_loader.py @@ -1,3 +1,5 @@ +from typing import Optional + from invokeai.app.invocations.baseinvocation import ( BaseInvocation, BaseInvocationOutput, @@ -9,12 +11,13 @@ from invokeai.app.invocations.model import ( ModelIdentifierField, Qwen3EncoderField, + Qwen35EncoderField, TransformerField, VAEField, ) from invokeai.app.services.shared.invocation_context import InvocationContext from invokeai.backend.architectures import accepted_vae_bases -from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType, SubModelType +from invokeai.backend.model_manager.taxonomy import AnimaVariantType, BaseModelType, ModelType, SubModelType @invocation_output("anima_model_loader_output") @@ -23,6 +26,11 @@ class AnimaModelLoaderOutput(BaseInvocationOutput): transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer") qwen3_encoder: Qwen3EncoderField = OutputField(description=FieldDescriptions.qwen3_encoder, title="Qwen3 Encoder") + qwen3_5_encoder: Optional[Qwen35EncoderField] = OutputField( + default=None, + description=f"{FieldDescriptions.qwen3_5_encoder}. Set only for an Anima-3.8B model.", + title="Qwen3.5 Encoder", + ) vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE") @@ -31,7 +39,7 @@ class AnimaModelLoaderOutput(BaseInvocationOutput): title="Main Model - Anima", tags=["model", "anima"], category="model", - version="1.4.0", + version="1.5.0", classification=Classification.Prototype, ) class AnimaModelLoaderInvocation(BaseInvocation): @@ -41,6 +49,7 @@ class AnimaModelLoaderInvocation(BaseInvocation): - Transformer: Cosmos Predict2 DiT + LLM Adapter (from single-file checkpoint) - Qwen3 Encoder: Qwen3 0.6B (standalone single-file) - VAE: AutoencoderKLQwenImage / Wan 2.1 VAE (standalone single-file) + - Qwen3.5 Encoder: Qwen3.5 4B, for Anima-3.8B only, whose bundled semantic connector reads it The T5-XXL tokenizer needed for LLM Adapter token IDs is bundled in the package, so no T5-XXL encoder model needs to be installed. @@ -69,6 +78,14 @@ class AnimaModelLoaderInvocation(BaseInvocation): title="Qwen3 Encoder", ) + qwen3_5_encoder_model: Optional[ModelIdentifierField] = InputField( + default=None, + description="Standalone Qwen3.5 4B Encoder model. Required by Anima-3.8B, ignored by every other Anima model.", + input=Input.Direct, + ui_model_type=ModelType.Qwen35Encoder, + title="Qwen3.5 Encoder", + ) + def invoke(self, context: InvocationContext) -> AnimaModelLoaderOutput: # Transformer always comes from the main model transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer}) @@ -83,5 +100,25 @@ def invoke(self, context: InvocationContext) -> AnimaModelLoaderOutput: return AnimaModelLoaderOutput( transformer=TransformerField(transformer=transformer, loras=[]), qwen3_encoder=Qwen3EncoderField(tokenizer=qwen3_tokenizer, text_encoder=qwen3_encoder), + qwen3_5_encoder=self._qwen3_5_encoder(context), vae=VAEField(vae=vae), ) + + def _qwen3_5_encoder(self, context: InvocationContext) -> Optional[Qwen35EncoderField]: + """The Qwen3.5 encoder if the main model reads one, else None. Refuses a model that needs one and has none.""" + variant = getattr(context.models.get_config(self.model), "variant", None) + if variant != AnimaVariantType.Qwen35: + if self.qwen3_5_encoder_model is not None: + context.logger.warning( + f"{self.model.name} is not conditioned on Qwen3.5; ignoring the selected Qwen3.5 encoder." + ) + return None + if self.qwen3_5_encoder_model is None: + raise ValueError( + f"{self.model.name} bundles a Qwen3.5 semantic connector and needs a Qwen3.5 4B encoder. " + "Install one (qwen35_4b.safetensors) and select it as the Qwen3.5 Encoder." + ) + return Qwen35EncoderField( + tokenizer=self.qwen3_5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer}), + text_encoder=self.qwen3_5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder}), + ) diff --git a/invokeai/app/invocations/fields.py b/invokeai/app/invocations/fields.py index 4225422f645..e85bb75bb74 100644 --- a/invokeai/app/invocations/fields.py +++ b/invokeai/app/invocations/fields.py @@ -163,6 +163,7 @@ class FieldDescriptions: glm_encoder = "GLM (THUDM) tokenizer and text encoder" qwen3_encoder = "Qwen3 tokenizer and text encoder" qwen3_vl_encoder = "Qwen3-VL tokenizer and text encoder" + qwen3_5_encoder = "Qwen3.5 tokenizer and text encoder" mistral_encoder = "Mistral tokenizer/processor and text encoder" clip_embed_model = "CLIP Embed loader" clip_g_model = "CLIP-G Embed loader" diff --git a/invokeai/app/invocations/model.py b/invokeai/app/invocations/model.py index 8528aa29161..737cab9e2f5 100644 --- a/invokeai/app/invocations/model.py +++ b/invokeai/app/invocations/model.py @@ -162,6 +162,13 @@ class Qwen3VLEncoderField(BaseModel): loras: List[LoRAField] = Field(default_factory=list, description="LoRAs to apply on model loading") +class Qwen35EncoderField(BaseModel): + """Field for the Qwen3.5 text encoder Anima-3.8B's semantic connector reads.""" + + tokenizer: ModelIdentifierField = Field(description="Info to load tokenizer submodel") + text_encoder: ModelIdentifierField = Field(description="Info to load text_encoder submodel") + + class WanT5EncoderField(BaseModel): """Field for the UMT5-XXL text encoder used by Wan 2.2 models.""" diff --git a/invokeai/app/invocations/text_encoder/anima_text_encoder.py b/invokeai/app/invocations/text_encoder/anima_text_encoder.py index 7c446e17f48..3774bb5a248 100644 --- a/invokeai/app/invocations/text_encoder/anima_text_encoder.py +++ b/invokeai/app/invocations/text_encoder/anima_text_encoder.py @@ -7,6 +7,10 @@ Both outputs are stored together in AnimaConditioningInfo and used by the LLM Adapter inside the transformer during denoising. +Anima-3.8B additionally reads Qwen3.5 4B hidden states (layers 7, 15, 23, 31) through the semantic +connector bundled in its transformer. They are encoded here when a Qwen3.5 encoder is connected and +stored beside the Qwen3 ones; the connector itself runs inside the denoising loop, per step. + Key differences from Z-Image text encoder: - Anima uses Qwen3 0.6B (base model, NOT instruct) — no chat template - Anima additionally tokenizes with T5-XXL tokenizer to get token IDs @@ -28,13 +32,15 @@ TensorField, UIComponent, ) -from invokeai.app.invocations.model import Qwen3EncoderField +from invokeai.app.invocations.model import Qwen3EncoderField, Qwen35EncoderField from invokeai.app.invocations.primitives import AnimaConditioningOutput from invokeai.app.services.shared.invocation_context import InvocationContext +from invokeai.backend.anima.semantic_connector import QWEN35_LAYER_INDICES from invokeai.backend.patches.layer_patcher import LayerPatcher, PatchSpec from invokeai.backend.patches.lora_conversions.anima_lora_constants import ANIMA_LORA_QWEN3_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.qwen3_5.qwen3_5_encoder import Qwen35Encoder from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ( AnimaConditioningInfo, ConditioningFieldData, @@ -52,13 +58,16 @@ # Qwen3 0.6B supports 32K context but the LLM Adapter doesn't need that much. QWEN3_MAX_SEQ_LEN = 8192 +# The reference encoder for Anima-3.8B's semantic connector truncates the Qwen3.5 prompt here. +QWEN3_5_MAX_SEQ_LEN = 1024 + @invocation( "anima_text_encoder", title="Prompt - Anima", tags=["prompt", "conditioning", "anima"], category="conditioning", - version="1.4.0", + version="1.5.0", classification=Classification.Prototype, idle_gpu_offloadable=True, ) @@ -80,10 +89,17 @@ class AnimaTextEncoderInvocation(BaseInvocation): default=None, description="A mask defining the region that this conditioning prompt applies to.", ) + qwen3_5_encoder: Qwen35EncoderField | None = InputField( + default=None, + title="Qwen3.5 Encoder", + description=f"{FieldDescriptions.qwen3_5_encoder}. Connect it for Anima-3.8B, which reads both encoders.", + input=Input.Connection, + ) @torch.no_grad() def invoke(self, context: InvocationContext) -> AnimaConditioningOutput: qwen3_embeds, t5xxl_ids, t5xxl_weights = self._encode_prompt(context) + qwen35_states, qwen35_mask = self._encode_qwen3_5(context) if self.qwen3_5_encoder else (None, None) # Move to CPU for storage qwen3_embeds = qwen3_embeds.detach().to("cpu") @@ -96,6 +112,8 @@ def invoke(self, context: InvocationContext) -> AnimaConditioningOutput: qwen3_embeds=qwen3_embeds, t5xxl_ids=t5xxl_ids, t5xxl_weights=t5xxl_weights, + qwen35_states=qwen35_states, + qwen35_mask=qwen35_mask, ) ] ) @@ -219,6 +237,48 @@ def _encode_prompt( return qwen3_embeds, t5xxl_ids, None + def _encode_qwen3_5(self, context: InvocationContext) -> tuple[torch.Tensor, torch.Tensor]: + """Encode the prompt with Qwen3.5 the way Anima-3.8B's reference encoder does. + + Returns: + Tuple of (states, mask), on the CPU. + - states: Shape (num_layers, seq_len, 2560), one row per layer in `QWEN35_LAYER_INDICES`. + - mask: Shape (seq_len,), True for real tokens. + """ + assert self.qwen3_5_encoder is not None + context.util.signal_progress("Running Qwen3.5 text encoder") + tokenizer_info = context.models.load(self.qwen3_5_encoder.tokenizer) + with tokenizer_info.model_on_device() as (_, tokenizer): + if not isinstance(tokenizer, PreTrainedTokenizerBase): + raise TypeError(f"Expected PreTrainedTokenizerBase for tokenizer, got {type(tokenizer).__name__}.") + # No template, no special tokens -- the prompt's tokens as they are. + token_ids = tokenizer.encode(self.prompt, add_special_tokens=False) + pad_token_id = tokenizer.pad_token_id + if len(token_ids) > QWEN3_5_MAX_SEQ_LEN: + logger.warning(f"Prompt was truncated to {QWEN3_5_MAX_SEQ_LEN} tokens for the Qwen3.5 encoder.") + token_ids = token_ids[:QWEN3_5_MAX_SEQ_LEN] + # An empty prompt becomes one padding token that nothing attends to, as in the reference, so the + # connector adds no Qwen3.5 signal for it. (The reference also masks every token from the first + # occurrence of id 151643 on -- the Qwen3 padding id, which in Qwen3.5's vocabulary is the + # ordinary token " 내용". That is not reproduced.) + is_empty = not token_ids + if is_empty: + if pad_token_id is None: + raise ValueError("The Qwen3.5 tokenizer has no padding token to encode an empty prompt with.") + token_ids = [pad_token_id] + mask = torch.full((len(token_ids),), not is_empty, dtype=torch.bool) + + encoder_info = context.models.load(self.qwen3_5_encoder.text_encoder) + with encoder_info.model_on_device() as (_, encoder): + if not isinstance(encoder, Qwen35Encoder): + raise TypeError(f"Expected a Qwen3.5 encoder, got {type(encoder).__name__}.") + input_ids = torch.tensor([token_ids], device=encoder_info.compute_device) + # The connector was trained on what Anima-3.8B's encoder checkpoint computes, and that file + # ships its last tapped layer without the MLP. + states = encoder(input_ids, QWEN35_LAYER_INDICES, last_layer_attention_only=True) + stacked = torch.cat(states, dim=0).detach().to("cpu") + return stacked, mask + def _lora_iterator(self, context: InvocationContext) -> Iterator[PatchSpec]: """Iterate over LoRA models to apply to the Qwen3 text encoder.""" for lora in self.qwen3_encoder.loras: diff --git a/invokeai/app/services/model_records/model_records_base.py b/invokeai/app/services/model_records/model_records_base.py index 582cc1ef01a..4fa27d97d2a 100644 --- a/invokeai/app/services/model_records/model_records_base.py +++ b/invokeai/app/services/model_records/model_records_base.py @@ -21,6 +21,7 @@ from invokeai.backend.model_manager.configs.lora import LoraModelDefaultSettings from invokeai.backend.model_manager.configs.main import MainModelDefaultSettings from invokeai.backend.model_manager.taxonomy import ( + AnimaVariantType, BaseModelType, ClipVariantType, Flux2VariantType, @@ -36,6 +37,7 @@ PiDDecoderVariantType, Qwen3VariantType, Qwen3VLVariantType, + Qwen35VariantType, QwenImageVariantType, SchedulerPredictionType, WanLoRAVariantType, @@ -152,6 +154,8 @@ def validate_source_url(cls, v: Any) -> Optional[str]: | WanLoRAVariantType | Qwen3VariantType | Qwen3VLVariantType + | Qwen35VariantType + | AnimaVariantType | Krea2VariantType | MiniMaxH3VariantType | LTX2VariantType diff --git a/invokeai/app/services/shared/sqlite_migrator/migrations/migration_2026_10_01_add_anima_variant.py b/invokeai/app/services/shared/sqlite_migrator/migrations/migration_2026_10_01_add_anima_variant.py new file mode 100644 index 00000000000..66cc4975688 --- /dev/null +++ b/invokeai/app/services/shared/sqlite_migrator/migrations/migration_2026_10_01_add_anima_variant.py @@ -0,0 +1,99 @@ +"""Record the variant on Anima main models installed before Anima had more than one. + +`Main_Checkpoint_Anima_Config.variant` became a required field when Anima-3.8B joined: its bundled +semantic connector needs a Qwen3.5 encoder beside the Qwen3 one, and the frontend reads the +variant to ask for it. Records written before that carry no `variant`, and a stored config that +fails to validate is *skipped* on read (`ModelRecordServiceSQL._select_models`) -- every installed +Anima model would silently vanish from the model list. + +Nearly every such record is the Qwen3-only variant, because Anima-3.8B could not load before. It +could be *installed*, though: its checkpoint identified as an ordinary Anima. So the checkpoint's +header is read where the file is still there, and a bundled connector decides; a record whose file +cannot be read gets the Qwen3-only variant, which is what it was being loaded as. + +Mirrors `2026_09_16_add_qwen3_vl_encoder_variant`. +""" + +import json +import sqlite3 +from logging import Logger +from pathlib import Path +from typing import Any + +from safetensors import safe_open + +from invokeai.app.services.config.config_default import InvokeAIAppConfig +from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration +from invokeai.backend.model_manager.taxonomy import AnimaVariantType, BaseModelType, ModelFormat, ModelType + +# Kept literal rather than imported from the config module: a migration must keep meaning what it +# meant when it was written, whatever the config later renames. +_CONNECTOR_SEGMENT = "anima_v2_connector." + + +def _bundles_semantic_connector(path: Path) -> bool: + with safe_open(path, framework="pt", device="cpu") as checkpoint: + return any(key.startswith(_CONNECTOR_SEGMENT) or f".{_CONNECTOR_SEGMENT}" in key for key in checkpoint.keys()) + + +class AddAnimaVariantCallback: + def __init__(self, app_config: InvokeAIAppConfig, logger: Logger) -> None: + self._models_path = app_config.models_path + self._logger = logger + + def __call__(self, cursor: sqlite3.Cursor) -> None: + cursor.execute("SELECT id, config FROM models;") + rows = cursor.fetchall() + + migrated: dict[str, int] = {} + for model_id, config_json in rows: + try: + config: dict[str, Any] = json.loads(config_json) + except json.JSONDecodeError as e: + self._logger.error("Invalid config JSON for model %s: %s", model_id, e) + raise + + # Only the single-file main model gained the field. Anima's LLLite control adapters share + # the base and have no variant at all. + if ( + config.get("type") != ModelType.Main.value + or config.get("base") != BaseModelType.Anima.value + or config.get("format") != ModelFormat.Checkpoint.value + or "variant" in config + ): + continue + + variant = self._variant_of(config) + config["variant"] = variant.value + cursor.execute("UPDATE models SET config = ? WHERE id = ?;", (json.dumps(config), model_id)) + migrated[variant.value] = migrated.get(variant.value, 0) + 1 + + if migrated: + self._logger.info(f"Recorded the variant on Anima model config(s): {migrated}") + + def _variant_of(self, config: dict[str, Any]) -> AnimaVariantType: + raw_path = config.get("path") + if not isinstance(raw_path, str) or not raw_path: + return AnimaVariantType.Qwen3 + path = Path(raw_path) + if not path.is_absolute(): + path = self._models_path / path + try: + if _bundles_semantic_connector(path): + return AnimaVariantType.Qwen35 + except Exception as e: + self._logger.warning( + "Could not read the header of Anima model %s (%s); recording it as %s.", + config.get("name", raw_path), + e, + AnimaVariantType.Qwen3.value, + ) + return AnimaVariantType.Qwen3 + + +def build_migration(app_config: InvokeAIAppConfig, logger: Logger) -> Migration: + return Migration( + id="2026_10_01_add_anima_variant", + depends_on="2026_09_26_add_workflow_revision", + callback=AddAnimaVariantCallback(app_config=app_config, logger=logger), + ) diff --git a/invokeai/backend/anima/anima_transformer.py b/invokeai/backend/anima/anima_transformer.py index 8f42d30b8fe..231ecbc3153 100644 --- a/invokeai/backend/anima/anima_transformer.py +++ b/invokeai/backend/anima/anima_transformer.py @@ -16,7 +16,7 @@ import logging import math -from typing import Optional, Tuple +from typing import TYPE_CHECKING, Optional, Tuple import torch import torch.nn.functional as F @@ -24,6 +24,9 @@ from einops.layers.torch import Rearrange from torch import nn +if TYPE_CHECKING: + from invokeai.backend.anima.semantic_connector import AnimaSemanticConnectorConfig + logger = logging.getLogger(__name__) @@ -785,6 +788,27 @@ def _apply_rope(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch. return (x * cos.unsqueeze(1)) + (_rotate_half(x) * sin.unsqueeze(1)) +def masked_sdpa( + q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, attn_mask: Optional[torch.Tensor] = None +) -> torch.Tensor: + """`scaled_dot_product_attention` where a query row with no key to attend to yields zeros. + + Only the math backend defines that case (as zeros); the fused kernels may return NaN for it. + It is reachable: an empty prompt is encoded as one masked padding token, so every key of that + context is masked. Such rows are computed against an unmasked key set and then zeroed. + + Args: + attn_mask: Boolean, True where attention is allowed, broadcastable to (B, H, Lq, Lk). + """ + if attn_mask is None or attn_mask.dtype != torch.bool: + return F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + has_key = attn_mask.any(dim=-1, keepdim=True) + if bool(has_key.all()): + return F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + out = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask | ~has_key) + return out * has_key.to(out.dtype) + + class LLMAdapterRotaryEmbedding(nn.Module): """Rotary position embedding for the LLM Adapter's attention layers.""" @@ -842,7 +866,7 @@ def forward( q = _apply_rope(q, *pos_q) k = _apply_rope(k, *pos_k) - y = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + y = masked_sdpa(q, k, v, attn_mask) y = y.transpose(1, 2).reshape(x.shape[0], x.shape[1], -1).contiguous() return self.o_proj(y) @@ -1018,29 +1042,65 @@ class AnimaTransformer(MiniTrainDIT): "final_layer", ] - def __init__(self, *args, **kwargs): + def __init__(self, *args, semantic_connector: Optional["AnimaSemanticConnectorConfig"] = None, **kwargs): super().__init__(*args, **kwargs) self.llm_adapter = LLMAdapter() + if semantic_connector is not None: + from invokeai.backend.anima.semantic_connector import AnimaSemanticConnector + + # Named as in the Anima-3.8B bundle (`net.anima_v2_connector.*`), so its keys load as-is. + self.anima_v2_connector = AnimaSemanticConnector(semantic_connector) + # The resampler's timestep path plays the role `t_embedder` plays for the DiT: its output + # modulates every resampler block. Kept out of FP8 Storage on that analogy -- unmeasured. + # Declared on the instance so a model without the connector does not carry dead patterns. + self._skip_layerwise_casting_patterns = [ + *type(self)._skip_layerwise_casting_patterns, + r"^anima_v2_connector\.semantic_resampler\.(time_mlp|blocks\.\d+\.time_modulation)", + ] + + @property + def has_semantic_connector(self) -> bool: + """True for Anima-3.8B: the context depends on Qwen3.5 hidden states and on the timestep.""" + return hasattr(self, "anima_v2_connector") def preprocess_text_embeds( self, text_embeds: torch.Tensor, text_ids: Optional[torch.Tensor], t5xxl_weights: Optional[torch.Tensor] = None, + *, + semantic_states: Optional[list[torch.Tensor]] = None, + semantic_mask: Optional[torch.Tensor] = None, + timesteps: Optional[torch.Tensor] = None, ) -> torch.Tensor: - """Run the LLM Adapter to produce conditioning for the DiT. + """Run the LLM Adapter (or, on Anima-3.8B, the semantic connector) to produce conditioning for the DiT. Args: text_embeds: Qwen3 hidden states. Shape: (batch, seq_len, 1024). text_ids: T5-XXL token IDs. Shape: (batch, seq_len). If None, returns text_embeds directly. t5xxl_weights: Optional per-token weights. Shape: (batch, seq_len, 1). + semantic_states: Qwen3.5 hidden states, one per connector layer. Each (batch, seq_len, 2560). + Required by, and only read by, a transformer with the semantic connector. + semantic_mask: True for valid Qwen3.5 tokens. Shape: (batch, seq_len). + timesteps: The flow timestep (sigma). Shape: (batch,). Required with the semantic connector, + whose output changes with it. Returns: Conditioning tensor. Shape: (batch, 512, 1024), zero-padded if needed. """ if text_ids is None: return text_embeds - out = self.llm_adapter(text_embeds, text_ids) + if self.has_semantic_connector: + if semantic_states is None or timesteps is None: + raise ValueError( + "This Anima model bundles a Qwen3.5 semantic connector and needs Qwen3.5 conditioning " + "and the timestep. Encode the prompt with a Qwen3.5 encoder connected." + ) + out = self.anima_v2_connector( + self.llm_adapter, text_embeds, text_ids, semantic_states, semantic_mask, timesteps + ) + else: + out = self.llm_adapter(text_embeds, text_ids) if t5xxl_weights is not None: out = out * t5xxl_weights if out.shape[1] < 512: diff --git a/invokeai/backend/anima/conditioning_data.py b/invokeai/backend/anima/conditioning_data.py index b96c807835d..8d2186a1e54 100644 --- a/invokeai/backend/anima/conditioning_data.py +++ b/invokeai/backend/anima/conditioning_data.py @@ -39,6 +39,10 @@ class AnimaTextConditioning: t5xxl_ids: torch.Tensor t5xxl_weights: torch.Tensor | None = None mask: torch.Tensor | None = None + qwen35_states: torch.Tensor | None = None + """Qwen3.5 hidden states for Anima-3.8B's semantic connector. Shape: (num_layers, seq_len, 2560).""" + qwen35_mask: torch.Tensor | None = None + """True for valid Qwen3.5 tokens. Shape: (seq_len,).""" @dataclass diff --git a/invokeai/backend/anima/semantic_connector.py b/invokeai/backend/anima/semantic_connector.py new file mode 100644 index 00000000000..35e889cc689 --- /dev/null +++ b/invokeai/backend/anima/semantic_connector.py @@ -0,0 +1,347 @@ +"""Anima-3.8B's semantic connector: Qwen3.5 conditioning on top of Anima's native LLM adapter. + +Anima-3.8B v1.1 bundles this connector into its checkpoint under `anima_v2_connector.`. It replaces +the plain `LLMAdapter` call: the native adapter's six blocks still run, unchanged and in order, and +after each one two residual cross-attentions add Qwen3.5 information -- + +- the *quality anchor* attends to a per-block mix of four Qwen3.5 hidden layers (7, 15, 23, 31); +- the *v2 injection* attends to a 64-token bank that a timestep-aware perceiver resampler distills + from the same four layers. + +The resampler is conditioned on the diffusion timestep, so unlike the native adapter's output the +connector's output changes from step to step and has to be recomputed inside the denoising loop. + +Ported from the reference ComfyUI extension (https://github.com/GumGum10/comfyui-anima-3-8B, MIT), +`semantic_connector_v2.py` and `progressive_cross_adapter.py`. Module and parameter names follow +the checkpoint so the bundled weights load without a key map. Training-only machinery +(initialization, trainability, gradient checkpointing) is left out. +""" + +import math +from collections.abc import Sequence +from dataclasses import dataclass +from typing import Optional, Tuple + +import torch +import torch.nn.functional as F +from torch import nn + +from invokeai.backend.anima.anima_transformer import LLMAdapter, LLMAdapterAttention, masked_sdpa + +#: The Qwen3.5 4B hidden layers the connector was trained on. +QWEN35_LAYER_INDICES: tuple[int, ...] = (7, 15, 23, 31) +#: Qwen3.5 4B hidden size. +QWEN35_HIDDEN_SIZE = 2560 + + +@dataclass(frozen=True) +class AnimaSemanticConnectorConfig: + """The connector's hyperparameters. The bundle records them in its header (see `from_metadata`).""" + + num_queries: int = 64 + resampler_blocks: int = 6 + resampler_dim: int = 2048 + resampler_heads: int = 16 + mlp_hidden_dim: int = 5632 + semantic_source_dim: int = QWEN35_HIDDEN_SIZE + layer_indices: tuple[int, ...] = QWEN35_LAYER_INDICES + + #: The architecture name the bundle declares for the connector it carries. + ARCHITECTURE = "anima_qwen35_quality_anchored_semantic_connector_v2" + + @classmethod + def from_metadata(cls, metadata: dict[str, str]) -> "AnimaSemanticConnectorConfig": + """Read the hyperparameters from a v1.1 bundle's safetensors header. + + Raises: + ValueError: if the header declares a connector architecture other than the one ported here. + """ + architecture = metadata.get("anima_v2_adapter_architecture") + if architecture is not None and architecture != cls.ARCHITECTURE: + raise ValueError(f"Unsupported Anima semantic connector architecture {architecture!r}.") + + def number(name: str, default: int) -> int: + return int(metadata.get(f"anima_v2_adapter_{name}", default)) + + layer_indices = QWEN35_LAYER_INDICES + if (raw := metadata.get("anima_v2_adapter_layer_indices")) is not None: + layer_indices = tuple(int(v) for v in raw.strip("[]").split(",") if v.strip()) + return cls( + num_queries=number("semantic_query_tokens", cls.num_queries), + resampler_blocks=number("semantic_resampler_blocks", cls.resampler_blocks), + resampler_dim=number("semantic_resampler_dim", cls.resampler_dim), + resampler_heads=number("semantic_resampler_heads", cls.resampler_heads), + mlp_hidden_dim=number("semantic_resampler_mlp_hidden_dim", cls.mlp_hidden_dim), + layer_indices=layer_indices, + ) + + +def sinusoidal_timestep_embedding(timesteps: torch.Tensor, dim: int, max_period: int = 10_000) -> torch.Tensor: + """Embed Anima's continuous [0, 1] flow timestep. Scaled by 1000 like the reference.""" + half = dim // 2 + frequencies = torch.exp( + -math.log(max_period) * torch.arange(half, device=timesteps.device, dtype=torch.float32) / max(half, 1) + ) + angles = timesteps.float().reshape(-1, 1) * 1_000.0 * frequencies.reshape(1, -1) + embedding = torch.cat((angles.cos(), angles.sin()), dim=-1) + if dim % 2: + embedding = F.pad(embedding, (0, 1)) + return embedding + + +class ResamplerAttention(nn.Module): + """Plain multi-head attention with independently sized query and context streams. No norms, no RoPE.""" + + def __init__(self, query_dim: int, context_dim: int, num_heads: int): + super().__init__() + if query_dim % num_heads: + raise ValueError("query_dim must be divisible by num_heads") + self.num_heads = num_heads + self.head_dim = query_dim // num_heads + self.q_proj = nn.Linear(query_dim, query_dim, bias=False) + self.k_proj = nn.Linear(context_dim, query_dim, bias=False) + self.v_proj = nn.Linear(context_dim, query_dim, bias=False) + self.o_proj = nn.Linear(query_dim, query_dim, bias=False) + + def forward( + self, query: torch.Tensor, context: torch.Tensor, context_mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: + batch, query_tokens, query_dim = query.shape + context_tokens = context.shape[1] + + def heads(value: torch.Tensor, tokens: int) -> torch.Tensor: + return value.reshape(batch, tokens, self.num_heads, self.head_dim).transpose(1, 2) + + q = heads(self.q_proj(query), query_tokens) + k = heads(self.k_proj(context), context_tokens) + v = heads(self.v_proj(context), context_tokens) + mask = None + if context_mask is not None: + mask = context_mask.to(torch.bool).reshape(batch, 1, 1, context_tokens) + attended = masked_sdpa(q, k, v, mask) + return self.o_proj(attended.transpose(1, 2).reshape(batch, query_tokens, query_dim)) + + +class TimestepModulatedNorm(nn.Module): + """Parameter-free LayerNorm whose scale and shift come from the timestep.""" + + def __init__(self, dim: int): + super().__init__() + self.norm = nn.LayerNorm(dim, elementwise_affine=False) + + def forward(self, value: torch.Tensor, scale: torch.Tensor, shift: torch.Tensor) -> torch.Tensor: + return self.norm(value) * (1.0 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class SemanticResamplerBlock(nn.Module): + """One timestep-aware block: cross-attention to Qwen3.5, self-attention among queries, SwiGLU.""" + + def __init__(self, dim: int, qwen_dim: int, num_heads: int, mlp_hidden_dim: int): + super().__init__() + self.cross_norm = TimestepModulatedNorm(dim) + self.self_norm = TimestepModulatedNorm(dim) + self.mlp_norm = TimestepModulatedNorm(dim) + self.source_norm = nn.LayerNorm(qwen_dim) + self.cross_attention = ResamplerAttention(dim, qwen_dim, num_heads) + self.self_attention = ResamplerAttention(dim, dim, num_heads) + self.mlp_in = nn.Linear(dim, 2 * mlp_hidden_dim, bias=False) + self.mlp_out = nn.Linear(mlp_hidden_dim, dim, bias=False) + # Scale and shift for each of the three sub-blocks. + self.time_modulation = nn.Linear(dim, 6 * dim, bias=True) + + def forward( + self, + queries: torch.Tensor, + qwen_features: torch.Tensor, + timestep_embedding: torch.Tensor, + qwen_mask: Optional[torch.Tensor], + ) -> torch.Tensor: + modulation = self.time_modulation(F.silu(timestep_embedding)) + cross_scale, cross_shift, self_scale, self_shift, mlp_scale, mlp_shift = modulation.chunk(6, dim=-1) + queries = queries + self.cross_attention( + self.cross_norm(queries, cross_scale, cross_shift), self.source_norm(qwen_features), qwen_mask + ) + normalized = self.self_norm(queries, self_scale, self_shift) + queries = queries + self.self_attention(normalized, normalized) + normalized = self.mlp_norm(queries, mlp_scale, mlp_shift) + gate, value = self.mlp_in(normalized).chunk(2, dim=-1) + return queries + self.mlp_out(F.silu(gate) * value) + + +class TimestepAwareSemanticResampler(nn.Module): + """Compresses four Qwen3.5 layer streams into a bank of semantic query tokens, per timestep.""" + + def __init__( + self, + qwen_dim: int, + output_dim: int, + num_layers: int, + num_queries: int, + num_blocks: int, + model_dim: int, + num_heads: int, + mlp_hidden_dim: int, + ): + super().__init__() + self.num_layers = num_layers + self.model_dim = model_dim + self.query_tokens = nn.Parameter(torch.empty(1, num_queries, model_dim)) + self.layer_embeddings = nn.Parameter(torch.empty(num_layers, 1, qwen_dim)) + self.time_mlp = nn.Sequential(nn.Linear(model_dim, model_dim), nn.SiLU(), nn.Linear(model_dim, model_dim)) + self.blocks = nn.ModuleList( + [SemanticResamplerBlock(model_dim, qwen_dim, num_heads, mlp_hidden_dim) for _ in range(num_blocks)] + ) + self.output_norm = nn.LayerNorm(model_dim) + self.output_projection = nn.Linear(model_dim, output_dim, bias=False) + + def forward( + self, + hidden_states: Sequence[torch.Tensor], + timesteps: torch.Tensor, + source_mask: Optional[torch.Tensor], + ) -> torch.Tensor: + if len(hidden_states) != self.num_layers: + raise ValueError(f"Expected {self.num_layers} Qwen3.5 layers, got {len(hidden_states)}") + batch = hidden_states[0].shape[0] + qwen_features = torch.cat( + [hidden + self.layer_embeddings[index].unsqueeze(0) for index, hidden in enumerate(hidden_states)], dim=1 + ) + qwen_mask = torch.cat([source_mask] * self.num_layers, dim=1) if source_mask is not None else None + + time = sinusoidal_timestep_embedding(timesteps, self.model_dim).to(dtype=qwen_features.dtype) + time = self.time_mlp(time) + queries = self.query_tokens.expand(batch, -1, -1) + for block in self.blocks: + queries = block(queries, qwen_features, time, qwen_mask) + return self.output_projection(self.output_norm(queries)) + + +class QualityAnchor(nn.Module): + """The frozen v1 cross-attentions to Qwen3.5 that the v2 connector was trained on top of. + + One residual cross-attention per native adapter block, each reading its own learned softmax mix + of the four Qwen3.5 layers (`layer_mix_logits`). + """ + + def __init__(self, model_dim: int, num_heads: int, num_blocks: int, semantic_source_dim: int, num_layers: int): + super().__init__() + head_dim = model_dim // num_heads + self.query_norms = nn.ModuleList([nn.RMSNorm(model_dim, eps=1e-6) for _ in range(num_blocks)]) + self.source_norms = nn.ModuleList([nn.RMSNorm(semantic_source_dim, eps=1e-6) for _ in range(num_blocks)]) + self.semantic_attentions = nn.ModuleList( + [LLMAdapterAttention(model_dim, semantic_source_dim, num_heads, head_dim) for _ in range(num_blocks)] + ) + self.layer_mix_logits = nn.Parameter(torch.zeros(num_blocks, num_layers)) + + @staticmethod + def mixed_source(hidden_states: Sequence[torch.Tensor], mix: torch.Tensor, block_index: int) -> torch.Tensor: + return sum(hidden * mix[block_index, layer_index] for layer_index, hidden in enumerate(hidden_states)) # type: ignore[return-value] + + +class AnimaSemanticConnector(nn.Module): + """Quality-anchored semantic connector v2. Wraps a native `LLMAdapter` it does not own. + + The adapter is passed to `forward` instead of being held: it belongs to the transformer, which + patches it with LoRAs and moves it between devices, and a second registration would serialize and + move it twice. + """ + + def __init__( + self, + config: AnimaSemanticConnectorConfig, + model_dim: int = 1024, + num_heads: int = 16, + num_adapter_blocks: int = 6, + ): + super().__init__() + self.config = config + num_layers = len(config.layer_indices) + head_dim = model_dim // num_heads + self.quality_anchor = QualityAnchor( + model_dim, num_heads, num_adapter_blocks, config.semantic_source_dim, num_layers + ) + self.semantic_resampler = TimestepAwareSemanticResampler( + qwen_dim=config.semantic_source_dim, + output_dim=model_dim, + num_layers=num_layers, + num_queries=config.num_queries, + num_blocks=config.resampler_blocks, + model_dim=config.resampler_dim, + num_heads=config.resampler_heads, + mlp_hidden_dim=config.mlp_hidden_dim, + ) + self.v2_query_norms = nn.ModuleList([nn.RMSNorm(model_dim, eps=1e-6) for _ in range(num_adapter_blocks)]) + self.v2_semantic_norms = nn.ModuleList([nn.RMSNorm(model_dim, eps=1e-6) for _ in range(num_adapter_blocks)]) + self.v2_attentions = nn.ModuleList( + [LLMAdapterAttention(model_dim, model_dim, num_heads, head_dim) for _ in range(num_adapter_blocks)] + ) + + @staticmethod + def _attention_mask(mask: Optional[torch.Tensor]) -> Optional[torch.Tensor]: + if mask is None: + return None + mask = mask.to(torch.bool) + return mask[:, None, None, :] if mask.ndim == 2 else mask + + def forward( + self, + native_adapter: LLMAdapter, + native_source: torch.Tensor, + target_input_ids: torch.Tensor, + semantic_hidden_states: Sequence[torch.Tensor], + semantic_mask: Optional[torch.Tensor], + timesteps: torch.Tensor, + ) -> torch.Tensor: + """Produce the DiT's cross-attention context for one denoising step. + + Args: + native_adapter: The transformer's own `LLMAdapter`. + native_source: Qwen3 0.6B hidden states. Shape: (B, L_qwen3, 1024). + target_input_ids: T5-XXL token IDs. Shape: (B, L_t5). + semantic_hidden_states: Qwen3.5 hidden states, one per entry of `config.layer_indices`. + Each of shape (B, L_qwen35, 2560). + semantic_mask: True for valid Qwen3.5 tokens. Shape: (B, L_qwen35). A fully masked row + (the reference's encoding of an empty prompt) contributes nothing. + timesteps: The flow timestep (sigma). Shape: (B,). + + Returns: + Context of shape (B, L_t5, 1024), before Anima's padding to 512 tokens. + """ + if len(semantic_hidden_states) != len(self.config.layer_indices): + raise ValueError( + f"Expected {len(self.config.layer_indices)} Qwen3.5 layers, got {len(semantic_hidden_states)}" + ) + semantic_attention_mask = self._attention_mask(semantic_mask) + semantic_bank = self.semantic_resampler(semantic_hidden_states, timesteps, semantic_mask) + + x = native_adapter.embed(target_input_ids).to(dtype=native_source.dtype) + rotary = native_adapter.rotary_emb + + def positions(length: int) -> Tuple[torch.Tensor, torch.Tensor]: + return rotary(x, torch.arange(length, device=x.device, dtype=torch.long).unsqueeze(0)) + + query_rope = positions(x.shape[1]) + native_rope = positions(native_source.shape[1]) + anchor_rope = positions(semantic_hidden_states[0].shape[1]) + bank_rope = positions(semantic_bank.shape[1]) + anchor_mix = self.quality_anchor.layer_mix_logits.float().softmax(dim=-1).to(x.dtype) + + anchor = self.quality_anchor + for index, native_block in enumerate(native_adapter.blocks): + x = native_block(x, context=native_source, pos_target=query_rope, pos_source=native_rope) + anchor_source = anchor.source_norms[index](anchor.mixed_source(semantic_hidden_states, anchor_mix, index)) + x = x + anchor.semantic_attentions[index]( + anchor.query_norms[index](x), + context=anchor_source, + attn_mask=semantic_attention_mask, + pos_q=query_rope, + pos_k=anchor_rope, + ) + x = x + self.v2_attentions[index]( + self.v2_query_norms[index](x), + context=self.v2_semantic_norms[index](semantic_bank), + pos_q=query_rope, + pos_k=bank_rope, + ) + + return native_adapter.norm(native_adapter.out_proj(x)) diff --git a/invokeai/backend/architectures/defs/anima.py b/invokeai/backend/architectures/defs/anima.py index fec89d0d6e4..d19c83f1fa5 100644 --- a/invokeai/backend/architectures/defs/anima.py +++ b/invokeai/backend/architectures/defs/anima.py @@ -6,9 +6,10 @@ from invokeai.backend.architectures.facets.latent_space import WAN21_16, LatentSpaceFacet from invokeai.backend.architectures.facets.modality import ModalityFacet from invokeai.backend.architectures.facets.vae import VaeCompatibility, VaeFacet +from invokeai.backend.architectures.facets.variant import VariantFacet from invokeai.backend.architectures.registry import register from invokeai.backend.model_manager.configs.default_settings import MainModelDefaultSettings -from invokeai.backend.model_manager.taxonomy import BaseModelType +from invokeai.backend.model_manager.taxonomy import AnimaVariantType, BaseModelType, ModelType from invokeai.backend.stable_diffusion.diffusion.conditioning_data import AnimaConditioningInfo # Anima uses the Wan 2.1 VAE. @@ -17,7 +18,14 @@ LatentSpaceFacet(WAN21_16), ConditioningFacet(AnimaConditioningInfo), DefaultSettingsFacet( - {None: MainModelDefaultSettings(scheduler="euler", steps=35, cfg_scale=4.5, width=1024, height=1024)} + { + # The author's reference workflow for Anima-3.8B v1.1 samples at CFG 6 for 40 steps; the + # model card recommends CFG 4-7 and 28-50 steps. + AnimaVariantType.Qwen35: MainModelDefaultSettings( + scheduler="euler", steps=40, cfg_scale=6.0, width=1024, height=1024 + ), + None: MainModelDefaultSettings(scheduler="euler", steps=35, cfg_scale=4.5, width=1024, height=1024), + } ), ModalityFacet(frozenset({"txt2img", "img2img", "inpaint", "outpaint"}), metadata_slug="anima"), FeaturesFacet( @@ -48,4 +56,6 @@ } ) ), + # Variant values must be globally unique; see taxonomy.py. + VariantFacet({ModelType.Main: AnimaVariantType}), ) diff --git a/invokeai/backend/architectures/facets/variant.py b/invokeai/backend/architectures/facets/variant.py index 0976571f8db..98f6840499d 100644 --- a/invokeai/backend/architectures/facets/variant.py +++ b/invokeai/backend/architectures/facets/variant.py @@ -8,10 +8,11 @@ layer patcher. - The PiD decoder's resolution presets are one enum shared across five bases. -Three variant enums cannot live here at all: `ClipVariantType`, `Qwen3VariantType` and -`MistralVariantType` sit on `base=Any` configs, and `Any` is a sentinel the registry refuses to -register. They are named explicitly in `tests/backend/architectures/test_variants.py` so the -completeness check against `AnyVariant` stays total rather than quietly partial. +The text-encoder variant enums (`ClipVariantType`, `Qwen3VariantType`, `Qwen3VLVariantType`, +`Qwen35VariantType`, `MistralVariantType`) cannot live here at all: they sit on `base=Any` configs, +and `Any` is a sentinel the registry refuses to register. They are named explicitly in +`tests/backend/architectures/test_variants.py` so the completeness check against `AnyVariant` stays +total rather than quietly partial. This facet is deliberately *not* wired into `configs/factory.py`. That module validates a bare variant string against `variant_type_adapter` without passing the base (`build_common_fields`), so @@ -34,7 +35,7 @@ class VariantFacet(Facet): """The variant enums an architecture's models are labelled with, by model type. - Optional: four registered architectures (CogView4, ERNIE-Image, Ideogram 4, Anima) model no + Optional: three registered architectures (CogView4, ERNIE-Image, Ideogram 4) model no variants at all, and declaring an empty facet would be indistinguishable from declaring nothing. """ diff --git a/invokeai/backend/model_manager/configs/factory.py b/invokeai/backend/model_manager/configs/factory.py index a3260ecc65c..6aa10f0bd97 100644 --- a/invokeai/backend/model_manager/configs/factory.py +++ b/invokeai/backend/model_manager/configs/factory.py @@ -132,6 +132,7 @@ PiDDecoder_Checkpoint_SD3_Config, PiDDecoder_Checkpoint_SDXL_Config, ) +from invokeai.backend.model_manager.configs.qwen3_5_encoder import Qwen35Encoder_Checkpoint_Config from invokeai.backend.model_manager.configs.qwen3_encoder import ( Qwen3Encoder_Checkpoint_Config, Qwen3Encoder_GGUF_Config, @@ -537,6 +538,7 @@ def has_model_export(module: Any, name: Any, expected_bases: tuple[type, ...]) - # satisfies the text-only Qwen3 GGUF heuristic in full. Annotated[Qwen3VLEncoder_GGUF_Config, Qwen3VLEncoder_GGUF_Config.get_tag()], Annotated[Qwen3VLEncoder_Qwen3VLEncoder_Config, Qwen3VLEncoder_Qwen3VLEncoder_Config.get_tag()], + Annotated[Qwen35Encoder_Checkpoint_Config, Qwen35Encoder_Checkpoint_Config.get_tag()], # Qwen3 Encoder Annotated[Qwen3Encoder_Qwen3Encoder_Config, Qwen3Encoder_Qwen3Encoder_Config.get_tag()], Annotated[Qwen3Encoder_Checkpoint_Config, Qwen3Encoder_Checkpoint_Config.get_tag()], diff --git a/invokeai/backend/model_manager/configs/main.py b/invokeai/backend/model_manager/configs/main.py index 4e446818809..bb1c7153c76 100644 --- a/invokeai/backend/model_manager/configs/main.py +++ b/invokeai/backend/model_manager/configs/main.py @@ -43,6 +43,7 @@ from invokeai.backend.model_manager.configs.qwen3_encoder import _SDNQ_LOADABLE_QWEN_ARCHITECTURES from invokeai.backend.model_manager.model_on_disk import ModelOnDisk from invokeai.backend.model_manager.taxonomy import ( + AnimaVariantType, BaseModelType, Flux2VariantType, FluxVariantType, @@ -1286,6 +1287,22 @@ def _has_anima_keys(state_dict: dict[str | int, Any]) -> bool: return False +#: Where Anima-3.8B v1.1 bundles its semantic connector, relative to the transformer root. The +#: bundle records it in its header as `anima_v2_connector_prefix` (`net.anima_v2_connector.`). +ANIMA_V2_CONNECTOR_KEY_PREFIX = "anima_v2_connector." + +#: Anima's own wrapper namespaces, as `_has_anima_keys` accepts them. +_ANIMA_KEY_PREFIXES = ("", "net.", "model.diffusion_model.") + + +def _get_anima_variant(state_dict: dict[str | int, Any]) -> AnimaVariantType: + """An Anima checkpoint that bundles the semantic connector needs Qwen3.5 as well as Qwen3.""" + connector_prefixes = tuple(f"{prefix}{ANIMA_V2_CONNECTOR_KEY_PREFIX}" for prefix in _ANIMA_KEY_PREFIXES) + if any(isinstance(key, str) and key.startswith(connector_prefixes) for key in state_dict): + return AnimaVariantType.Qwen35 + return AnimaVariantType.Qwen3 + + class Main_Diffusers_ZImage_Config(Diffusers_Config_Base, Main_Config_Base, Config_Base): """Model config for Z-Image diffusers models (Z-Image-Turbo, Z-Image-Base).""" @@ -2884,6 +2901,11 @@ class Main_Checkpoint_Anima_Config(Checkpoint_Config_Base, Main_Config_Base, Con base: Literal[BaseModelType.Anima] = Field(default=BaseModelType.Anima) format: Literal[ModelFormat.Checkpoint] = Field(default=ModelFormat.Checkpoint) + # Required, and therefore not part of the discriminator tag. A default would put it into the tag + # (`Config_Base.get_tag`), which a stored record's dict does not carry, and every Anima record + # would stop deserializing. Records written before the field existed get it from + # `migration_2026_10_01_add_anima_variant`. + variant: AnimaVariantType = Field(description="Which text encoders the model is conditioned on.") @classmethod def from_model_on_disk(cls, mod: ModelOnDisk, override_fields: dict[str, Any]) -> Self: @@ -2892,8 +2914,10 @@ def from_model_on_disk(cls, mod: ModelOnDisk, override_fields: dict[str, Any]) - raise_for_override_fields(cls, override_fields) cls._validate_looks_like_anima_model(mod) + cls._reject_unbundled_qwen35_dit(mod) - return cls(**override_fields) + variant = override_fields.pop("variant", None) or _get_anima_variant(mod.load_state_dict()) + return cls(**override_fields, variant=variant) @classmethod def _validate_looks_like_anima_model(cls, mod: ModelOnDisk) -> None: @@ -2901,6 +2925,27 @@ def _validate_looks_like_anima_model(cls, mod: ModelOnDisk) -> None: if not has_anima_keys: raise NotAMatchError("state dict does not look like an Anima model") + @classmethod + def _reject_unbundled_qwen35_dit(cls, mod: ModelOnDisk) -> None: + """Refuse the Anima-3.8B v1.0 DiT, which needs an adapter file this release does not load. + + v1.0 trained 12 inserted blocks jointly with a separate Qwen3.5 cross-attention adapter; on its + own it loads cleanly as a 52-block Anima and generates with a conditioning signal its new + blocks were never trained without. v1.1 bundles a newer connector into the checkpoint and is + the release path, so the v1.0 file is refused here rather than installed as something it is + not. Its header names the joint training; a plain depth-expanded finetune carries no such key. + """ + metadata = mod.metadata() + if "qwen35_joint_dit_blocks" not in metadata: + return + if _get_anima_variant(mod.load_state_dict()) is AnimaVariantType.Qwen35: + return + raise InvalidMatchError( + "This is the Anima-3.8B v1.0 transformer, which only works together with its separate " + "Qwen3.5 adapter file. Install the Anima-3.8B v1.1 checkpoint instead " + "(Anima-3.8B-v1.1.safetensors), which bundles its connector." + ) + class Main_SDNQ_FLUX_Config(Checkpoint_Config_Base, Main_Config_Base, Config_Base): """Model config for SDNQ-quantized FLUX transformer models.""" diff --git a/invokeai/backend/model_manager/configs/qwen3_5_encoder.py b/invokeai/backend/model_manager/configs/qwen3_5_encoder.py new file mode 100644 index 00000000000..d06a32716b0 --- /dev/null +++ b/invokeai/backend/model_manager/configs/qwen3_5_encoder.py @@ -0,0 +1,82 @@ +"""Identification of Qwen3.5 text encoders (single-file). + +Qwen3.5 is a family of its own -- Gated DeltaNet linear attention interleaved with gated full +attention -- so it cannot share `ModelType.Qwen3Encoder` even where widths coincide: the Qwen3.5 4B +and the Qwen3 4B are both 2560 wide. `linear_attn` keys are what tell them apart, and the Qwen3 +configs reject them for the same reason this one requires them. +""" + +from typing import Any, Literal, Optional, Self + +from pydantic import Field + +from invokeai.backend.model_manager.configs.base import Checkpoint_Config_Base, Config_Base +from invokeai.backend.model_manager.configs.identification_utils import ( + NotAMatchError, + raise_for_override_fields, + raise_if_not_file, +) +from invokeai.backend.model_manager.model_on_disk import ModelOnDisk +from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelFormat, ModelType, Qwen35VariantType +from invokeai.backend.quantization.gguf.ggml_tensor import GGMLTensor + +#: Where a Qwen3.5 language model's `layers.` and `embed_tokens.` sit: bare (ComfyUI text-encoder +#: exports, Anima-3.8B's `qwen35_4b.safetensors`), under a causal LM's `model.`, or under the +#: multimodal checkpoint's `model.language_model.`. +QWEN3_5_KEY_PREFIXES = ("", "model.", "model.language_model.") + +_QWEN3_5_HIDDEN_SIZES = {2560: Qwen35VariantType.Qwen35_4B} + + +def has_qwen3_5_linear_attention(state_dict: dict[str | int, Any]) -> bool: + """True if the state dict holds Gated DeltaNet (`linear_attn`) layers -- Qwen3.5, never Qwen3.""" + return any(isinstance(key, str) and ".linear_attn." in key for key in state_dict) + + +def qwen3_5_key_prefix(state_dict: dict[str | int, Any]) -> Optional[str]: + """The prefix the language model's keys live under, or None if this is not a Qwen3.5 model. + + Layer 0 is a linear-attention layer in every Qwen3.5 size (full attention is every fourth). + """ + for prefix in QWEN3_5_KEY_PREFIXES: + if f"{prefix}layers.0.linear_attn.in_proj_qkv.weight" in state_dict and f"{prefix}embed_tokens.weight" in ( + state_dict + ): + return prefix + return None + + +def get_qwen3_5_variant(state_dict: dict[str | int, Any], prefix: str) -> Optional[Qwen35VariantType]: + embed = state_dict.get(f"{prefix}embed_tokens.weight") + shape = getattr(embed, "shape", None) + if shape is None or len(shape) != 2: + return None + return _QWEN3_5_HIDDEN_SIZES.get(int(shape[1])) + + +class Qwen35Encoder_Checkpoint_Config(Checkpoint_Config_Base, Config_Base): + """Configuration for single-file Qwen3.5 text encoders (safetensors).""" + + base: Literal[BaseModelType.Any] = Field(default=BaseModelType.Any) + type: Literal[ModelType.Qwen35Encoder] = Field(default=ModelType.Qwen35Encoder) + format: Literal[ModelFormat.Checkpoint] = Field(default=ModelFormat.Checkpoint) + cpu_only: bool | None = Field(default=None, description="Whether this model should run on CPU only") + variant: Qwen35VariantType = Field(description="Qwen3.5 model size variant") + + @classmethod + def from_model_on_disk(cls, mod: ModelOnDisk, override_fields: dict[str, Any]) -> Self: + raise_if_not_file(mod) + + raise_for_override_fields(cls, override_fields) + + state_dict = mod.load_state_dict() + prefix = qwen3_5_key_prefix(state_dict) + if prefix is None: + raise NotAMatchError("state dict does not look like a Qwen3.5 language model (no linear_attn layer 0)") + if any(isinstance(v, GGMLTensor) for v in state_dict.values()): + raise NotAMatchError("state dict looks like GGUF quantized") + + variant = override_fields.pop("variant", None) or get_qwen3_5_variant(state_dict, prefix) + if variant is None: + raise NotAMatchError("hidden size does not match a known Qwen3.5 variant") + return cls(**override_fields, variant=variant) diff --git a/invokeai/backend/model_manager/configs/qwen3_encoder.py b/invokeai/backend/model_manager/configs/qwen3_encoder.py index ea465061919..33de104f8b0 100644 --- a/invokeai/backend/model_manager/configs/qwen3_encoder.py +++ b/invokeai/backend/model_manager/configs/qwen3_encoder.py @@ -197,6 +197,17 @@ def _has_qwen3_specific_keys(tensor_names: Iterable[str | int]) -> bool: return False +def _has_qwen3_5_layers(tensor_names: Iterable[str | int]) -> bool: + """Check for Qwen3.5's Gated DeltaNet layers, which a Qwen3 model never has. + + Qwen3.5 is as wide as Qwen3 at 4B (2560) and keeps the `model.layers.*` / `q_norm` naming on its + full-attention layers, so it satisfies every heuristic above and would install as a Qwen3 4B + encoder the Qwen3 loader cannot build. Its linear-attention layers are named `linear_attn` in + PyTorch exports and `ssm_*` in llama.cpp ones. + """ + return any(isinstance(key, str) and (".linear_attn." in key or ".ssm_" in key) for key in tensor_names) + + def _get_qwen3_variant_from_state_dict(state_dict: dict[str | int, Any]) -> Optional[Qwen3VariantType]: """Determine Qwen3 variant (0.6B, 4B, or 8B) from state dict based on hidden_size. @@ -315,6 +326,9 @@ def _validate_looks_like_qwen3_model(cls, mod: ModelOnDisk) -> None: raise NotAMatchError( "state dict bundles a Qwen-VL visual tower; this is a Qwen-VL encoder, not a text-only Qwen3 encoder" ) + # Reject Qwen3.5: same width as Qwen3 at 4B, a different architecture (`Qwen35Encoder_*`). + if _has_qwen3_5_layers(state_dict): + raise NotAMatchError("state dict has Qwen3.5 linear-attention layers; this is a Qwen3.5 encoder, not Qwen3") @classmethod def _validate_does_not_look_like_gguf_quantized(cls, mod: ModelOnDisk) -> None: @@ -496,6 +510,9 @@ def _validate_looks_like_qwen3_model(cls, mod: ModelOnDisk) -> None: raise NotAMatchError( "state dict bundles a Qwen-VL visual tower; this is a Qwen-VL encoder, not a text-only Qwen3 encoder" ) + # Reject Qwen3.5: same width as Qwen3 at 4B, a different architecture (`Qwen35Encoder_*`). + if _has_qwen3_5_layers(state_dict): + raise NotAMatchError("state dict has Qwen3.5 linear-attention layers; this is a Qwen3.5 encoder, not Qwen3") # Reject Qwen3-VL language towers. The visual-tower check above cannot see them: llama.cpp # keeps the visual tower in a separate ``mmproj-*.gguf``, so a Qwen3-VL GGUF is structurally # identical to a text-only Qwen3 of the same width (both 36 layers at hidden 2560 for the @@ -568,6 +585,9 @@ def _validate_looks_like_qwen3_model(cls, mod: ModelOnDisk) -> None: raise NotAMatchError( "state dict bundles a Qwen-VL visual tower; this is a Qwen-VL encoder, not a text-only Qwen3 encoder" ) + # Reject Qwen3.5: same width as Qwen3 at 4B, a different architecture (`Qwen35Encoder_*`). + if _has_qwen3_5_layers(state_dict): + raise NotAMatchError("state dict has Qwen3.5 linear-attention layers; this is a Qwen3.5 encoder, not Qwen3") if not _has_qwen3_specific_keys(state_dict): raise NotAMatchError( "state dict lacks Qwen3 QK-normalization (q_norm/k_norm) weights; looks like a Qwen2 model, " diff --git a/invokeai/backend/model_manager/load/model_loaders/anima.py b/invokeai/backend/model_manager/load/model_loaders/anima.py index 4c77dd2ad5f..dd1ab6a7a3f 100644 --- a/invokeai/backend/model_manager/load/model_loaders/anima.py +++ b/invokeai/backend/model_manager/load/model_loaders/anima.py @@ -1,7 +1,8 @@ """Class for Anima model loading in InvokeAI.""" +from collections.abc import Mapping from pathlib import Path -from typing import Optional +from typing import Any, Optional import accelerate @@ -9,7 +10,7 @@ from invokeai.backend.model_manager.configs.base import Checkpoint_Config_Base from invokeai.backend.model_manager.configs.controlnet import ControlNet_Checkpoint_Anima_Config from invokeai.backend.model_manager.configs.factory import AnyModelConfig -from invokeai.backend.model_manager.configs.main import Main_Checkpoint_Anima_Config +from invokeai.backend.model_manager.configs.main import ANIMA_V2_CONNECTOR_KEY_PREFIX, Main_Checkpoint_Anima_Config from invokeai.backend.model_manager.load.fp8_capability import NotApplicable from invokeai.backend.model_manager.load.load_default import ModelLoader, _model_declared_skip_patterns from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry @@ -21,6 +22,7 @@ SubModelType, ) from invokeai.backend.quantization.fp8_scaled import ( + Fp8ScaledLayer, attach_fp8_scales, cast_state_dict, dequantize_fp8_scaled, @@ -34,6 +36,13 @@ strip_layer_path_prefix, warn_on_unattached_scales, ) +from invokeai.backend.quantization.int8_convrot import ( + drop_unconsumed_quantization_sidecars, + extract_int8_convrot_markers, + install_int8_convrot_layers, + reject_unmarked_int8_weights, + resolve_quantized_module_paths, +) from invokeai.backend.quantization.load_plan import reserve_for_load from invokeai.backend.util.devices import TorchDevice from invokeai.backend.util.logging import InvokeAILogger @@ -86,9 +95,10 @@ def _filter_non_model_keys(sd: dict) -> dict: } -# Anima's fixed transformer architecture. Kept at module level so tests can instantiate the real -# module graph (e.g. to pin `_skip_layerwise_casting_patterns` to actual dotted module paths) -# without duplicating these values. +# Anima's transformer architecture at the depth of the official release. Kept at module level so tests +# can instantiate the real module graph (e.g. to pin `_skip_layerwise_casting_patterns` to actual +# dotted module paths) without duplicating these values. `num_blocks` is the only value that differs +# between redistributions -- see `anima_transformer_config`. ANIMA_TRANSFORMER_CONFIG = { "max_img_h": 240, "max_img_w": 240, @@ -118,6 +128,39 @@ def _filter_non_model_keys(sd: dict) -> dict: } +def count_anima_dit_blocks(sd: Mapping[str, Any]) -> int: + """Number of DiT blocks in a prefix-stripped Anima state dict. + + Depth-expanded finetunes keep every other dimension of the official 28-block model: Anima-2.9B + has 40 blocks and Anima-3.8B 52, both grown by LLaMA-Pro style interleaved insertion. A model + built at the official depth would load the first 28 of them and drop the rest as unexpected + keys -- reported only at DEBUG, with the surviving blocks in the wrong order, so the result is + a degraded image rather than an error. + + Raises: + ValueError: if the state dict has no DiT blocks or the block indices have gaps. + """ + indices = { + int(parts[1]) + for key in sd + if isinstance(key, str) and key.startswith("blocks.") + for parts in [key.split(".", 2)] + if parts[1].isdigit() + } + if not indices: + raise ValueError("Anima checkpoint has no DiT blocks (no `blocks..` keys after the prefix strip).") + count = max(indices) + 1 + if len(indices) != count: + missing = sorted(set(range(count)) - indices) + raise ValueError(f"Anima checkpoint has gaps in its DiT blocks: {missing[:10]} missing of 0-{count - 1}.") + return count + + +def anima_transformer_config(sd: Mapping[str, Any]) -> dict[str, Any]: + """`ANIMA_TRANSFORMER_CONFIG` at the depth of the checkpoint in `sd` (prefix-stripped).""" + return {**ANIMA_TRANSFORMER_CONFIG, "num_blocks": count_anima_dit_blocks(sd)} + + @ModelLoaderRegistry.register(base=BaseModelType.Anima, type=ModelType.Main, format=ModelFormat.Checkpoint) class AnimaCheckpointModel(ModelLoader): """Class to load Anima transformer models from single-file checkpoints. @@ -150,6 +193,7 @@ def _load_from_singlefile( from safetensors.torch import load_file from invokeai.backend.anima.anima_transformer import AnimaTransformer + from invokeai.backend.anima.semantic_connector import AnimaSemanticConnectorConfig if not isinstance(config, Main_Checkpoint_Anima_Config): raise TypeError( @@ -170,59 +214,101 @@ def _load_from_singlefile( target_device = TorchDevice.choose_torch_device() model_dtype = TorchDevice.choose_anima_inference_dtype(target_device) - # ComfyUI 'scaled fp8': an fp8 weight plus a `weight_scale`. `_filter_non_model_keys` above - # keeps those keys, and `load_state_dict` below rejects the checkpoint outright over them -- - # 500 unexpected keys on a plain scaled export, 749 on one that also ships `comfy_quant` - # markers. Such a checkpoint therefore does not load at all today. - # - # Anima keeps `q_proj`/`k_proj`/`v_proj` separate and the only key rewrite is a prefix strip, - # so a sibling scale travels with its weight and nothing has to be split. - keep_fp8 = self._keep_fp8_weights(config, SubModelType.Transformer) - header_hints = parse_quantization_metadata(read_safetensors_metadata(model_path, logger)) - # The header names layers in the checkpoint's own scheme -- `net.`-prefixed on every Anima - # redistribution measured -- while the scales are read after `_strip_anima_bundle_prefix` - # has run. Without this the per-layer flags, `full_precision_matrix_mult` above all, match - # nothing and are silently ignored. - layer_hints = { - **extract_comfy_quant_hints(sd), - **strip_layer_path_prefix(header_hints), - } - fp8_layers = extract_fp8_scaled_layers(sd, layer_hints=layer_hints) - - # Create an empty AnimaTransformer with Anima's default architecture parameters + # Two ComfyUI side-channel formats share the `weight` + `weight_scale` layout, and a checkpoint + # carries one or the other (Anima-2.9B ships an `int8_tensorwise` build beside its bf16 one). + # The int8 markers come out first: the fp8 path below reads `.comfy_quant` as layer hints and + # would take an int8 weight for an unscaled one. The rejection sits outside the branch so an + # int8 weight whose marker is missing or unreadable is refused instead of cast as raw codes. + int8_markers = extract_int8_convrot_markers(sd) + reject_unmarked_int8_weights(sd, int8_markers, "Anima") + + metadata = read_safetensors_metadata(model_path, logger) + # Anima-3.8B bundles a semantic connector whose hyperparameters live in the header. Without + # it in the model the connector's tensors would fall to `strict=False` and the 52-block DiT + # would generate from the native adapter alone, which its new blocks were not trained on. + semantic_connector = None + if any(key.startswith(ANIMA_V2_CONNECTOR_KEY_PREFIX) for key in sd): + semantic_connector = AnimaSemanticConnectorConfig.from_metadata(metadata) + + keep_fp8 = False + fp8_layers: dict[str, Fp8ScaledLayer] = {} + if int8_markers: + sd = drop_unconsumed_quantization_sidecars(sd) + # The prefix strip is the only key rewrite, so the markers already name the model's modules. + quantized = resolve_quantized_module_paths(int8_markers, key_map={}) + else: + # ComfyUI 'scaled fp8': an fp8 weight plus a `weight_scale`. `_filter_non_model_keys` above + # keeps those keys, and `load_state_dict` below rejects the checkpoint outright over them -- + # 500 unexpected keys on a plain scaled export, 749 on one that also ships `comfy_quant` + # markers. Such a checkpoint therefore does not load at all today. + # + # Anima keeps `q_proj`/`k_proj`/`v_proj` separate and the only key rewrite is a prefix strip, + # so a sibling scale travels with its weight and nothing has to be split. + keep_fp8 = self._keep_fp8_weights(config, SubModelType.Transformer) + header_hints = parse_quantization_metadata(metadata) + # The header names layers in the checkpoint's own scheme -- `net.`-prefixed on every Anima + # redistribution measured -- while the scales are read after `_strip_anima_bundle_prefix` + # has run. Without this the per-layer flags, `full_precision_matrix_mult` above all, match + # nothing and are silently ignored. + layer_hints = { + **extract_comfy_quant_hints(sd), + **strip_layer_path_prefix(header_hints), + } + fp8_layers = extract_fp8_scaled_layers(sd, layer_hints=layer_hints) + + # Build at the checkpoint's own depth: 28 blocks for the official release, more for the + # depth-expanded finetunes (Anima-2.9B: 40, Anima-3.8B: 52). with accelerate.init_empty_weights(): - model = AnimaTransformer(**ANIMA_TRANSFORMER_CONFIG) + model = AnimaTransformer(**anima_transformer_config(sd), semantic_connector=semantic_connector) + if semantic_connector is not None: + logger.info(f"Anima: {len(model.blocks)}-block transformer with a bundled Qwen3.5 semantic connector") skip_patterns = _model_declared_skip_patterns(model) - # Reserve before anything below widens a weight -- the fold and the split both do, and - # reserving afterwards lets either peak land on a cache that was only ever sized for the - # file. `scaled_layers` is what keeps the prediction honest where the weights are kept: the - # split also widens layers whose scale layout `scaled_mm` cannot apply, and without the - # mapping the prediction would charge those 1 byte/element and arrive at 2. Where they are - # not kept the prediction charges every float at `model_dtype`, folded yet or not, so the - # number is the same on either side of the fold -- what changes is when the room exists. - # Building the model first costs nothing: `init_empty_weights` leaves every param on meta. - reserve_for_load( - self._ram_cache.make_room, - sd, - model_dtype, - keep_fp8=keep_fp8, - model=model, - skip_patterns=skip_patterns, - fp8_layers=fp8_layers, - nvfp4_payloads={}, - ) + if int8_markers: + quantized = install_int8_convrot_layers( + model, + sd, + quantized, + model_dtype, + architecture="Anima", + reserve=self._ram_cache.make_room, + skip_patterns=skip_patterns, + ) + logger.info( + f"Anima: kept {len(quantized)} of {len(int8_markers)} layer(s) in int8 " + "(int8_tensorwise checkpoint, dequantized per forward)" + ) + kept = 0 + else: + # Reserve before anything below widens a weight -- the fold and the split both do, and + # reserving afterwards lets either peak land on a cache that was only ever sized for the + # file. `scaled_layers` is what keeps the prediction honest where the weights are kept: the + # split also widens layers whose scale layout `scaled_mm` cannot apply, and without the + # mapping the prediction would charge those 1 byte/element and arrive at 2. Where they are + # not kept the prediction charges every float at `model_dtype`, folded yet or not, so the + # number is the same on either side of the fold -- what changes is when the room exists. + # Building the model first costs nothing: `init_empty_weights` leaves every param on meta. + reserve_for_load( + self._ram_cache.make_room, + sd, + model_dtype, + keep_fp8=keep_fp8, + model=model, + skip_patterns=skip_patterns, + fp8_layers=fp8_layers, + nvfp4_payloads={}, + ) - if fp8_layers and not keep_fp8: - # Neither the matmul nor FP8 Storage asked for them, so keeping them quantized would - # dequantize on every forward to save memory nobody wanted saved. Fold the scale in. - dequantize_fp8_scaled(sd, fp8_layers, model_dtype) - fp8_layers = {} + if fp8_layers and not keep_fp8: + # Neither the matmul nor FP8 Storage asked for them, so keeping them quantized would + # dequantize on every forward to save memory nobody wanted saved. Fold the scale in. + dequantize_fp8_scaled(sd, fp8_layers, model_dtype) + fp8_layers = {} - # Layers the cast would dequantize anyway are folded here too, scale applied, so the cast - # never strips a scale that can no longer be put back. - fp8_layers = split_fp8_scaled_layers(sd, fp8_layers, model_dtype, model=model, skip_patterns=skip_patterns) - kept = cast_state_dict(sd, model_dtype, keep_fp8=keep_fp8, model=model, skip_patterns=skip_patterns) + # Layers the cast would dequantize anyway are folded here too, scale applied, so the cast + # never strips a scale that can no longer be put back. + fp8_layers = split_fp8_scaled_layers(sd, fp8_layers, model_dtype, model=model, skip_patterns=skip_patterns) + kept = cast_state_dict(sd, model_dtype, keep_fp8=keep_fp8, model=model, skip_patterns=skip_patterns) load_result = model.load_state_dict(sd, assign=True, strict=False) log_unexpected_keys("Anima transformer checkpoint", load_result.unexpected_keys) diff --git a/invokeai/backend/model_manager/load/model_loaders/qwen3_5_encoder.py b/invokeai/backend/model_manager/load/model_loaders/qwen3_5_encoder.py new file mode 100644 index 00000000000..9e14c312611 --- /dev/null +++ b/invokeai/backend/model_manager/load/model_loaders/qwen3_5_encoder.py @@ -0,0 +1,117 @@ +"""Loader for single-file Qwen3.5 text encoders.""" + +from pathlib import Path +from typing import Any, Optional + +import accelerate +import torch +from torch import nn + +from invokeai.backend.model_manager.configs.factory import AnyModelConfig +from invokeai.backend.model_manager.configs.qwen3_5_encoder import Qwen35Encoder_Checkpoint_Config, qwen3_5_key_prefix +from invokeai.backend.model_manager.load.load_default import ModelLoader, _model_declared_skip_patterns +from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry +from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType +from invokeai.backend.quantization.fp8_scaled import cast_state_dict, reject_quantized_side_channel +from invokeai.backend.quantization.load_plan import reserve_for_load +from invokeai.backend.util.devices import TorchDevice +from invokeai.backend.util.state_dict_loading import log_unexpected_keys, reject_incomplete_load + + +class _ZeroMLP(nn.Module): + """Stands in for an MLP sub-block the checkpoint does not ship, so the layer adds nothing there.""" + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return torch.zeros_like(x) + + +def _language_model_state_dict(sd: dict[str, Any], prefix: str) -> dict[str, Any]: + """The decoder's own tensors, keyed as `Qwen35Encoder` names them. + + Everything else is dropped: a multimodal export's vision tower, the LM head, the final norm and, + in Anima-3.8B's `qwen35_4b.safetensors`, the 2560->1024 projection head stored under `norm.*` -- + none of which an encoder that returns intermediate hidden states runs. + """ + return { + key[len(prefix) :]: tensor + for key, tensor in sd.items() + if key.startswith(prefix) and key[len(prefix) :].startswith(("embed_tokens.", "layers.")) + } + + +@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.Qwen35Encoder, format=ModelFormat.Checkpoint) +class Qwen35EncoderCheckpointLoader(ModelLoader): + """Loads a single-file Qwen3.5 text encoder and the vendored Qwen3.5 tokenizer.""" + + def _load_model(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> AnyModel: + if not isinstance(config, Qwen35Encoder_Checkpoint_Config): + raise ValueError("Only Qwen35Encoder_Checkpoint_Config models are supported here.") + + match submodel_type: + case SubModelType.TextEncoder: + return self._load_text_encoder(config) + case SubModelType.Tokenizer: + from invokeai.backend.qwen3_5.qwen3_5_encoder import load_bundled_qwen3_5_tokenizer + + # Single-file checkpoints ship no tokenizer. + return load_bundled_qwen3_5_tokenizer() + + raise ValueError( + f"Only TextEncoder and Tokenizer submodels are supported. Received: {submodel_type.value if submodel_type else 'None'}" + ) + + def _load_text_encoder(self, config: Qwen35Encoder_Checkpoint_Config) -> AnyModel: + from safetensors.torch import load_file + + from invokeai.backend.qwen3_5.qwen3_5_encoder import Qwen35Encoder, qwen3_5_4b_text_config + + model_path = Path(config.path) + target_device = TorchDevice.choose_torch_device() + model_dtype = TorchDevice.choose_bfloat16_safe_dtype(target_device) + + sd = load_file(model_path) + # Anima-3.8B's encoder ships raw fp8 projections (no scale), which the cast below handles + # exactly. A scaled, int8 or nvfp4 build would load *wrong* here rather than fail, so refuse it + # until one exists to support. + reject_quantized_side_channel(sd, f"Qwen3.5 encoder checkpoint {model_path.name}") + prefix = qwen3_5_key_prefix(sd) + if prefix is None: + raise ValueError(f"{model_path.name} is not a Qwen3.5 language model checkpoint.") + sd = _language_model_state_dict(sd, prefix) + + text_config = qwen3_5_4b_text_config() + with accelerate.init_empty_weights(): + model = Qwen35Encoder(text_config) + + # Anima-3.8B's encoder ships its last layer without the MLP sub-block (and its norm), because + # nothing it was used for reads past that layer's attention. Give such a layer an MLP that adds + # nothing, so the layer computes exactly what the file encodes instead of loading half-empty. + for index, layer in enumerate(model.layers): + if not any(key.startswith(f"layers.{index}.mlp.") for key in sd): + layer.mlp = _ZeroMLP() + layer.post_attention_layernorm = nn.Identity() + self._logger.info(f"Qwen3.5 encoder: layer {index} ships without an MLP; running it attention-only.") + + keep_fp8 = self._keep_fp8_weights(config, SubModelType.TextEncoder) + skip_patterns = _model_declared_skip_patterns(model) + reserve_for_load( + self._ram_cache.make_room, + sd, + model_dtype, + keep_fp8=keep_fp8, + model=model, + skip_patterns=skip_patterns, + fp8_layers={}, + nvfp4_payloads={}, + ) + kept = cast_state_dict(sd, model_dtype, keep_fp8=keep_fp8, model=model, skip_patterns=skip_patterns) + + result = model.load_state_dict(sd, strict=False, assign=True) + log_unexpected_keys("Qwen3.5 encoder checkpoint", result.unexpected_keys) + reject_incomplete_load(model, what=f"Qwen3.5 encoder checkpoint {model_path.name}") + sd.clear() + + if kept: + self._logger.info(f"Qwen3.5 encoder: kept {kept} raw fp8 weight(s) quantized ({self._fp8_kept_reason()}).") + model.eval() + return self._apply_fp8_layerwise_casting(model, config, SubModelType.TextEncoder) diff --git a/invokeai/backend/model_manager/starter_models/__init__.py b/invokeai/backend/model_manager/starter_models/__init__.py index b8cc4198fea..25900a1c62b 100644 --- a/invokeai/backend/model_manager/starter_models/__init__.py +++ b/invokeai/backend/model_manager/starter_models/__init__.py @@ -10,6 +10,9 @@ """ from invokeai.backend.model_manager.starter_models.anima import ( + anima_2_9b, + anima_2_9b_int8, + anima_3_8b, anima_base, anima_lllite_depth_preview3, anima_lllite_inpainting, @@ -17,6 +20,7 @@ anima_lllite_pose_preview3, anima_lllite_scribble_preview3, anima_lllite_sketch, + anima_qwen3_5_encoder, anima_vae, ) from invokeai.backend.model_manager.starter_models.cogview4 import cogview4 @@ -496,7 +500,11 @@ alibabacloud_wan26_t2i, alibabacloud_qwen_image_edit_max, anima_base, + anima_2_9b, + anima_2_9b_int8, + anima_3_8b, anima_qwen3_encoder, + anima_qwen3_5_encoder, anima_vae, anima_lllite_inpainting, anima_lllite_sketch, diff --git a/invokeai/backend/model_manager/starter_models/anima.py b/invokeai/backend/model_manager/starter_models/anima.py index 417a7c0c177..768b1daf796 100644 --- a/invokeai/backend/model_manager/starter_models/anima.py +++ b/invokeai/backend/model_manager/starter_models/anima.py @@ -3,9 +3,11 @@ from invokeai.backend.model_manager.starter_models.common import anima_qwen3_encoder from invokeai.backend.model_manager.starter_models.types import StarterModel from invokeai.backend.model_manager.taxonomy import ( + AnimaVariantType, BaseModelType, ModelFormat, ModelType, + Qwen35VariantType, ) anima_vae = StarterModel( @@ -24,9 +26,63 @@ description="Anima Base 1.0 - 2B parameter anime-focused text-to-image model built on Cosmos Predict2 DiT. ~4.5GB", type=ModelType.Main, format=ModelFormat.Checkpoint, + variant=AnimaVariantType.Qwen3, dependencies=[anima_qwen3_encoder, anima_vae], ) +_DERIVATIVE_LICENSE = ( + "A derivative of Anima under the CircleStone Labs Non-Commercial License: the model may not be used " + "commercially (generated images may)." +) + +anima_2_9b = StarterModel( + name="Anima-2.9B (Preview v1)", + base=BaseModelType.Anima, + source="https://huggingface.co/Gazingstars123/Anima-2.9B/resolve/main/Anima-2.9B-preview-v1.safetensors", + description="Community finetune of Anima Base, depth-expanded from 28 to 40 blocks and trained on 1.7M more " + f"anime/illustration images (knowledge cutoff July 2026). Recommended: 28-50 steps, CFG 3.5-5. " + f"{_DERIVATIVE_LICENSE} ~5.8GB", + type=ModelType.Main, + format=ModelFormat.Checkpoint, + variant=AnimaVariantType.Qwen3, + dependencies=[anima_qwen3_encoder, anima_vae], +) + +anima_2_9b_int8 = StarterModel( + name="Anima-2.9B (Preview v1, int8)", + base=BaseModelType.Anima, + source="https://huggingface.co/Gazingstars123/Anima-2.9B/resolve/main/Anima-2.9B-preview-v1_int8_convrot.safetensors", + description="Anima-2.9B in ComfyUI's int8_tensorwise format: half the memory of the bf16 build, dequantized " + f"per forward (no speedup). {_DERIVATIVE_LICENSE} ~3.1GB", + type=ModelType.Main, + format=ModelFormat.Checkpoint, + variant=AnimaVariantType.Qwen3, + dependencies=[anima_qwen3_encoder, anima_vae], +) + +anima_qwen3_5_encoder = StarterModel( + name="Anima-3.8B Qwen3.5 4B Encoder", + base=BaseModelType.Any, + source="https://huggingface.co/lylogummy/Anima-3.8B/resolve/main/text_encoders/qwen35_4b.safetensors", + description="Qwen3.5 4B text encoder (raw fp8) that Anima-3.8B's semantic connector reads. Apache-2.0. ~4.8GB", + type=ModelType.Qwen35Encoder, + format=ModelFormat.Checkpoint, + variant=Qwen35VariantType.Qwen35_4B, +) + +anima_3_8b = StarterModel( + name="Anima-3.8B (v1.1)", + base=BaseModelType.Anima, + source="https://huggingface.co/lylogummy/Anima-3.8B/resolve/main/difussion_models/Anima-3.8B-v1.1.safetensors", + description="Community expansion of Anima-2.9B to 52 blocks with a bundled Qwen3.5 semantic connector, for " + "prompt adherence, multi-character binding and mixed natural-language/tag prompts. Needs both the Qwen3 0.6B " + f"and the Qwen3.5 4B encoder. Recommended: 28-50 steps, CFG 4-7. {_DERIVATIVE_LICENSE} ~8.8GB", + type=ModelType.Main, + format=ModelFormat.Checkpoint, + variant=AnimaVariantType.Qwen35, + dependencies=[anima_qwen3_encoder, anima_qwen3_5_encoder, anima_vae], +) + anima_lllite_inpainting = StarterModel( name="Anima LLLite Inpainting", base=BaseModelType.Anima, diff --git a/invokeai/backend/model_manager/taxonomy.py b/invokeai/backend/model_manager/taxonomy.py index 54821ab52fa..e4cfdb3da10 100644 --- a/invokeai/backend/model_manager/taxonomy.py +++ b/invokeai/backend/model_manager/taxonomy.py @@ -94,6 +94,7 @@ class ModelType(str, Enum): Qwen3Encoder = "qwen3_encoder" QwenVLEncoder = "qwen_vl_encoder" Qwen3VLEncoder = "qwen3_vl_encoder" + Qwen35Encoder = "qwen3_5_encoder" MistralEncoder = "mistral_encoder" WanT5Encoder = "wan_t5_encoder" Gemma2Encoder = "gemma2_encoder" @@ -282,6 +283,34 @@ class Qwen3VLVariantType(str, Enum): of its layers for a 53248-wide feature vector.""" +class Qwen35VariantType(str, Enum): + """Qwen3.5 text encoder variants, by language-model width. + + A family of its own: Qwen3.5 interleaves Gated DeltaNet linear-attention layers with gated full + attention, so its checkpoints share neither architecture nor key layout with `Qwen3VariantType`, + even at the same width. + """ + + Qwen35_4B = "qwen3_5_4b" + """Qwen3.5 4B (hidden_size=2560, 32 layers). Anima-3.8B's semantic encoder, read at layers 7/15/23/31.""" + + +class AnimaVariantType(str, Enum): + """Anima model variants, by how the transformer is conditioned. + + The depth of the DiT (28 blocks for the official release, 40 for Anima-2.9B, 52 for Anima-3.8B) + is not a variant: the loader reads it from the checkpoint and nothing else depends on it. + """ + + Qwen3 = "anima_qwen3" + """Conditioned on Qwen3 0.6B through the native LLM adapter. Every release by CircleStone Labs + and Anima-2.9B.""" + + Qwen35 = "anima_qwen35" + """Additionally conditioned on Qwen3.5 4B through a bundled, timestep-aware semantic connector + (Anima-3.8B v1.1). Needs both encoders, and the connector runs at every denoising step.""" + + class MiniMaxH3VariantType(str, Enum): """MiniMax H3 model variants (task-specific transformer checkpoints sharing every other component).""" @@ -446,6 +475,8 @@ class FluxLoRAFormat(str, Enum): WanLoRAVariantType, Qwen3VariantType, Qwen3VLVariantType, + Qwen35VariantType, + AnimaVariantType, Krea2VariantType, MiniMaxH3VariantType, LTX2VariantType, @@ -463,6 +494,8 @@ class FluxLoRAFormat(str, Enum): | WanLoRAVariantType | Qwen3VariantType | Qwen3VLVariantType + | Qwen35VariantType + | AnimaVariantType | Krea2VariantType | MiniMaxH3VariantType | LTX2VariantType @@ -479,6 +512,8 @@ class FluxLoRAFormat(str, Enum): | WanLoRAVariantType | Qwen3VariantType | Qwen3VLVariantType + | Qwen35VariantType + | AnimaVariantType | Krea2VariantType | MiniMaxH3VariantType | LTX2VariantType diff --git a/invokeai/backend/qwen3_5/__init__.py b/invokeai/backend/qwen3_5/__init__.py new file mode 100644 index 00000000000..64a0c29154b --- /dev/null +++ b/invokeai/backend/qwen3_5/__init__.py @@ -0,0 +1 @@ +"""Qwen3.5 text backbone used as a hidden-state encoder (Anima-3.8B's semantic encoder).""" diff --git a/invokeai/backend/qwen3_5/qwen3_5_4b_text_config.json b/invokeai/backend/qwen3_5/qwen3_5_4b_text_config.json new file mode 100644 index 00000000000..2160becee10 --- /dev/null +++ b/invokeai/backend/qwen3_5/qwen3_5_4b_text_config.json @@ -0,0 +1,76 @@ +{ + "attention_bias": false, + "attention_dropout": 0.0, + "attn_output_gate": true, + "dtype": "bfloat16", + "eos_token_id": 248044, + "full_attention_interval": 4, + "head_dim": 256, + "hidden_act": "silu", + "hidden_size": 2560, + "initializer_range": 0.02, + "intermediate_size": 9216, + "layer_types": [ + "linear_attention", + "linear_attention", + "linear_attention", + "full_attention", + "linear_attention", + "linear_attention", + "linear_attention", + "full_attention", + "linear_attention", + "linear_attention", + "linear_attention", + "full_attention", + "linear_attention", + "linear_attention", + "linear_attention", + "full_attention", + "linear_attention", + "linear_attention", + "linear_attention", + "full_attention", + "linear_attention", + "linear_attention", + "linear_attention", + "full_attention", + "linear_attention", + "linear_attention", + "linear_attention", + "full_attention", + "linear_attention", + "linear_attention", + "linear_attention", + "full_attention" + ], + "linear_conv_kernel_dim": 4, + "linear_key_head_dim": 128, + "linear_num_key_heads": 16, + "linear_num_value_heads": 32, + "linear_value_head_dim": 128, + "max_position_embeddings": 262144, + "mlp_only_layers": [], + "model_type": "qwen3_5_text", + "mtp_num_hidden_layers": 1, + "mtp_use_dedicated_embeddings": false, + "num_attention_heads": 16, + "num_hidden_layers": 32, + "num_key_value_heads": 4, + "rms_norm_eps": 1e-06, + "tie_word_embeddings": true, + "use_cache": true, + "vocab_size": 248320, + "mamba_ssm_dtype": "float32", + "rope_parameters": { + "mrope_interleaved": true, + "mrope_section": [ + 11, + 11, + 10 + ], + "rope_type": "default", + "rope_theta": 10000000, + "partial_rotary_factor": 0.25 + } +} diff --git a/invokeai/backend/qwen3_5/qwen3_5_encoder.py b/invokeai/backend/qwen3_5/qwen3_5_encoder.py new file mode 100644 index 00000000000..580d8f7b205 --- /dev/null +++ b/invokeai/backend/qwen3_5/qwen3_5_encoder.py @@ -0,0 +1,122 @@ +"""Qwen3.5's text backbone, run as an encoder that returns intermediate hidden states. + +Qwen3.5 interleaves Gated DeltaNet linear-attention layers with gated full attention (every fourth +layer). The decoder layers come from `transformers` (`qwen3_5`); this module only owns the loop, so +it can stop at the deepest layer a caller reads and hand back that layer's output without the final +norm `Qwen3_5TextModel.forward` applies. + +Anima-3.8B reads layers 7, 15, 23 and 31 of the 4B model. Its encoder checkpoint +(`qwen35_4b.safetensors`) is not a stock export: it ships layer 31 without its MLP and replaces the +LM head with a 2560->1024 projection Anima never reads. The connector was trained on what that file +computes, so `forward` can run the deepest requested layer attention-only -- matching the file +exactly, and making a stock checkpoint's layer-31 MLP irrelevant rather than wrong. + +The tokenizer is vendored from `Qwen/Qwen3.5-4B` (Apache-2.0) at revision +851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a, the revision the reference ComfyUI extension ships, so a +single-file install encodes offline. +""" + +import json +from functools import lru_cache +from pathlib import Path +from typing import Any + +import torch +from torch import nn +from transformers import PreTrainedTokenizerBase +from transformers.models.qwen3_5.configuration_qwen3_5 import Qwen3_5TextConfig +from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5DecoderLayer, Qwen3_5TextRotaryEmbedding + +from invokeai.backend.util.bundled_tokenizer import load_gzipped_tokenizer_dir + +_PACKAGE_DIR = Path(__file__).parent +_TOKENIZER_DIR = _PACKAGE_DIR / "tokenizer" +QWEN3_5_4B_TEXT_CONFIG_PATH = _PACKAGE_DIR / "qwen3_5_4b_text_config.json" + + +@lru_cache(maxsize=1) +def load_bundled_qwen3_5_tokenizer() -> PreTrainedTokenizerBase: + """Load the vendored Qwen3.5 fast tokenizer. Result is cached for the process.""" + return load_gzipped_tokenizer_dir(_TOKENIZER_DIR) + + +def qwen3_5_4b_text_config() -> Qwen3_5TextConfig: + """The Qwen3.5 4B text config, as published in `Qwen/Qwen3.5-4B`'s `config.json`.""" + with open(QWEN3_5_4B_TEXT_CONFIG_PATH, "r", encoding="utf-8") as f: + raw: dict[str, Any] = json.load(f) + config = Qwen3_5TextConfig(**raw) + # The decoder layers dispatch attention through this; a config built by hand leaves it unset. + config._attn_implementation = "sdpa" + return config + + +class Qwen35Encoder(nn.Module): + """Qwen3.5 decoder stack that returns the outputs of selected layers. + + Holds `embed_tokens` and `layers` under the names a checkpoint uses, so a bare-keyed state dict + loads directly. Weights for layers past the deepest one any caller reads may be absent: they are + never run. + """ + + def __init__(self, config: Qwen3_5TextConfig): + super().__init__() + self.config = config + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size) + self.layers = nn.ModuleList([Qwen3_5DecoderLayer(config, i) for i in range(config.num_hidden_layers)]) + self.rotary_emb = Qwen3_5TextRotaryEmbedding(config=config) + + @property + def dtype(self) -> torch.dtype: + return self.embed_tokens.weight.dtype + + @torch.no_grad() + def forward( + self, + input_ids: torch.Tensor, + layer_indices: tuple[int, ...], + last_layer_attention_only: bool = False, + ) -> list[torch.Tensor]: + """Run the stack up to `max(layer_indices)` and return those layers' outputs, in order. + + Args: + input_ids: Token IDs, unpadded. Shape: (batch, seq_len). + layer_indices: Zero-based decoder layers whose output to return. + last_layer_attention_only: Run the deepest requested layer without its MLP sub-block and + return its post-attention residual. What Anima-3.8B's encoder checkpoint computes. + + Returns: + One tensor of shape (batch, seq_len, hidden_size) per entry of `layer_indices`. + """ + if not layer_indices: + raise ValueError("layer_indices must not be empty") + last = max(layer_indices) + if last >= len(self.layers): + raise ValueError(f"Layer {last} requested from a {len(self.layers)}-layer Qwen3.5 model") + + hidden_states = self.embed_tokens(input_ids) + position_ids = torch.arange(input_ids.shape[1], device=input_ids.device).unsqueeze(0) + # Text-only: the three mRoPE axes share one position, which reduces to plain 1-D RoPE. + position_embeddings = self.rotary_emb(hidden_states, position_ids.unsqueeze(0).expand(3, -1, -1)) + + outputs: dict[int, torch.Tensor] = {} + for index in range(last + 1): + layer = self.layers[index] + if index == last and last_layer_attention_only: + hidden_states = hidden_states + self._token_mixer(layer, hidden_states, position_embeddings) + else: + # No mask: a single unpadded prompt, so full attention is plain causal attention and + # the linear-attention layers have nothing to zero. + hidden_states = layer(hidden_states, position_embeddings=position_embeddings, attention_mask=None) + if index in layer_indices: + outputs[index] = hidden_states + return [outputs[index] for index in layer_indices] + + @staticmethod + def _token_mixer( + layer: Qwen3_5DecoderLayer, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor] + ) -> torch.Tensor: + normed = layer.input_layernorm(hidden_states) + if layer.layer_type == "linear_attention": + return layer.linear_attn(hidden_states=normed, cache_params=None, attention_mask=None) + mixed, _ = layer.self_attn(hidden_states=normed, position_embeddings=position_embeddings, attention_mask=None) + return mixed diff --git a/invokeai/backend/qwen3_5/tokenizer/tokenizer.json.gz b/invokeai/backend/qwen3_5/tokenizer/tokenizer.json.gz new file mode 100644 index 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"normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "248066": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "248067": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "248068": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "248069": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "248070": { + "content": "<|audio_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "248071": { + "content": "<|audio_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "248072": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "248073": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "248074": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "248075": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "248076": { + "content": "<|audio_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "additional_special_tokens": [ + "<|im_start|>", + "<|im_end|>", + "<|object_ref_start|>", + "<|object_ref_end|>", + "<|box_start|>", + "<|box_end|>", + "<|quad_start|>", + "<|quad_end|>", + "<|vision_start|>", + "<|vision_end|>", + "<|vision_pad|>", + "<|image_pad|>", + "<|video_pad|>" + ], + "bos_token": null, + "chat_template": "{%- set image_count = namespace(value=0) %}\n{%- set video_count = namespace(value=0) %}\n{%- macro render_content(content, do_vision_count, is_system_content=false) %}\n {%- if content is string %}\n {{- content }}\n {%- elif content is iterable and content is not mapping %}\n {%- for item in content %}\n {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}\n {%- if is_system_content %}\n {{- raise_exception('System message cannot contain images.') }}\n {%- endif %}\n {%- if do_vision_count %}\n {%- set image_count.value = image_count.value + 1 %}\n {%- endif %}\n {%- if add_vision_id %}\n {{- 'Picture ' ~ image_count.value ~ ': ' }}\n {%- endif %}\n {{- '<|vision_start|><|image_pad|><|vision_end|>' }}\n {%- elif 'video' in item or item.type == 'video' %}\n {%- if is_system_content %}\n {{- raise_exception('System message cannot contain videos.') }}\n {%- endif %}\n {%- if do_vision_count %}\n {%- set video_count.value = video_count.value + 1 %}\n {%- endif %}\n {%- if add_vision_id %}\n {{- 'Video ' ~ video_count.value ~ ': ' }}\n {%- endif %}\n {{- '<|vision_start|><|video_pad|><|vision_end|>' }}\n {%- elif 'text' in item %}\n {{- item.text }}\n {%- else %}\n {{- raise_exception('Unexpected item type in content.') }}\n {%- endif %}\n {%- endfor %}\n {%- elif content is none or content is undefined %}\n {{- '' }}\n {%- else %}\n {{- raise_exception('Unexpected content type.') }}\n {%- endif %}\n{%- endmacro %}\n{%- if not messages %}\n {{- raise_exception('No messages provided.') }}\n{%- endif %}\n{%- if tools and tools is iterable and tools is not mapping %}\n {{- '<|im_start|>system\\n' }}\n {{- \"# Tools\\n\\nYou have access to the following functions:\\n\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\" }}\n {{- '\\n\\nIf you choose to call a function ONLY reply in the following format with NO suffix:\\n\\n\\n\\n\\nvalue_1\\n\\n\\nThis is the value for the second parameter\\nthat can span\\nmultiple lines\\n\\n\\n\\n\\n\\nReminder:\\n- Function calls MUST follow the specified format: an inner block must be nested within XML tags\\n- Required parameters MUST be specified\\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\\n' }}\n {%- if messages[0].role == 'system' %}\n {%- set content = render_content(messages[0].content, false, true)|trim %}\n {%- if content %}\n {{- '\\n\\n' + content }}\n {%- endif %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {%- set content = render_content(messages[0].content, false, true)|trim %}\n {{- '<|im_start|>system\\n' + content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" %}\n {%- set content = render_content(message.content, false)|trim %}\n {%- if not(content.startswith('') and content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if ns.multi_step_tool %}\n {{- raise_exception('No user query found in messages.') }}\n{%- endif %}\n{%- for message in messages %}\n {%- set content = render_content(message.content, true)|trim %}\n {%- if message.role == \"system\" %}\n {%- if not loop.first %}\n {{- raise_exception('System message must be at the beginning.') }}\n {%- endif %}\n {%- elif message.role == \"user\" %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- set reasoning_content = reasoning_content|trim %}\n {%- if loop.index0 > ns.last_query_index %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content + '\\n\\n\\n' + content }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {%- if loop.first %}\n {%- if content|trim %}\n {{- '\\n\\n\\n\\n' }}\n {%- else %}\n {{- '\\n\\n' }}\n {%- endif %}\n {%- else %}\n {{- '\\n\\n\\n' }}\n {%- endif %}\n {%- if tool_call.arguments is defined %}\n {%- for args_name, args_value in tool_call.arguments|items %}\n {{- '\\n' }}\n {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}\n {{- args_value }}\n {{- '\\n\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.previtem and loop.previtem.role != \"tool\" %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if not loop.last and loop.nextitem.role != \"tool\" %}\n {{- '<|im_end|>\\n' }}\n {%- elif loop.last %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- else %}\n {{- raise_exception('Unexpected message role.') }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n\\n' }}\n {%- else %}\n {{- '\\n' }}\n {%- endif %}\n{%- endif %}", + "clean_up_tokenization_spaces": false, + "eos_token": "<|im_end|>", + "errors": "replace", + "model_max_length": 262144, + "pad_token": "<|endoftext|>", + "split_special_tokens": false, + "tokenizer_class": "Qwen2Tokenizer", + "unk_token": null, + "add_bos_token": false, + "pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", + "extra_special_tokens": { + "audio_bos_token": "<|audio_start|>", + "audio_eos_token": "<|audio_end|>", + "audio_token": "<|audio_pad|>", + "image_token": "<|image_pad|>", + "video_token": "<|video_pad|>", + "vision_bos_token": "<|vision_start|>", + "vision_eos_token": "<|vision_end|>" + } +} \ No newline at end of file diff --git a/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py b/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py index bbdfd186eb6..a3e357463f2 100644 --- a/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py +++ b/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py @@ -173,11 +173,22 @@ class AnimaConditioningInfo: t5xxl_weights: Optional[torch.Tensor] = None """Per-token weights for prompt weighting. Shape: (seq_len,). None means uniform weight.""" + qwen35_states: Optional[torch.Tensor] = None + """Qwen3.5 4B hidden states for Anima-3.8B's semantic connector, one row per tapped layer (7, 15, + 23, 31). Shape: (num_layers, seq_len, 2560). None when the prompt was encoded without Qwen3.5.""" + + qwen35_mask: Optional[torch.Tensor] = None + """True for valid Qwen3.5 tokens. Shape: (seq_len,). All False for an empty prompt.""" + def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None): self.qwen3_embeds = self.qwen3_embeds.to(device=device, dtype=dtype) self.t5xxl_ids = self.t5xxl_ids.to(device=device) if self.t5xxl_weights is not None: self.t5xxl_weights = self.t5xxl_weights.to(device=device, dtype=dtype) + if self.qwen35_states is not None: + self.qwen35_states = self.qwen35_states.to(device=device, dtype=dtype) + if self.qwen35_mask is not None: + self.qwen35_mask = self.qwen35_mask.to(device=device) return self diff --git a/invokeai/frontend/api/openapi.json b/invokeai/frontend/api/openapi.json index 658f57011d1..6a3c8162b47 100644 --- a/invokeai/frontend/api/openapi.json +++ b/invokeai/frontend/api/openapi.json @@ -1679,6 +1679,9 @@ { "$ref": "#/components/schemas/Qwen3VLEncoder_Qwen3VLEncoder_Config" }, + { + "$ref": "#/components/schemas/Qwen35Encoder_Checkpoint_Config" + }, { "$ref": "#/components/schemas/Qwen3Encoder_Qwen3Encoder_Config" }, @@ -2146,6 +2149,9 @@ { "$ref": "#/components/schemas/Qwen3VLEncoder_Qwen3VLEncoder_Config" }, + { + "$ref": "#/components/schemas/Qwen35Encoder_Checkpoint_Config" + }, { "$ref": "#/components/schemas/Qwen3Encoder_Qwen3Encoder_Config" }, @@ -2613,6 +2619,9 @@ { "$ref": "#/components/schemas/Qwen3VLEncoder_Qwen3VLEncoder_Config" }, + { + "$ref": "#/components/schemas/Qwen35Encoder_Checkpoint_Config" + }, { "$ref": "#/components/schemas/Qwen3Encoder_Qwen3Encoder_Config" }, @@ -3125,6 +3134,9 @@ { "$ref": "#/components/schemas/Qwen3VLEncoder_Qwen3VLEncoder_Config" }, + { + "$ref": "#/components/schemas/Qwen35Encoder_Checkpoint_Config" + }, { "$ref": "#/components/schemas/Qwen3Encoder_Qwen3Encoder_Config" }, @@ -3664,6 +3676,9 @@ { "$ref": "#/components/schemas/Qwen3VLEncoder_Qwen3VLEncoder_Config" }, + { + "$ref": "#/components/schemas/Qwen35Encoder_Checkpoint_Config" + }, { "$ref": "#/components/schemas/Qwen3Encoder_Qwen3Encoder_Config" }, @@ -5060,6 +5075,9 @@ { "$ref": "#/components/schemas/Qwen3VLEncoder_Qwen3VLEncoder_Config" }, + { + "$ref": "#/components/schemas/Qwen35Encoder_Checkpoint_Config" + }, { "$ref": "#/components/schemas/Qwen3Encoder_Qwen3Encoder_Config" }, @@ -18444,7 +18462,7 @@ "category": "model", "class": "invocation", "classification": "prototype", - "description": "Loads an Anima model, outputting its submodels.\n\nAnima uses:\n- Transformer: Cosmos Predict2 DiT + LLM Adapter (from single-file checkpoint)\n- Qwen3 Encoder: Qwen3 0.6B (standalone single-file)\n- VAE: AutoencoderKLQwenImage / Wan 2.1 VAE (standalone single-file)\n\nThe T5-XXL tokenizer needed for LLM Adapter token IDs is bundled in the package,\nso no T5-XXL encoder model needs to be installed.", + "description": "Loads an Anima model, outputting its submodels.\n\nAnima uses:\n- Transformer: Cosmos Predict2 DiT + LLM Adapter (from single-file checkpoint)\n- Qwen3 Encoder: Qwen3 0.6B (standalone single-file)\n- VAE: AutoencoderKLQwenImage / Wan 2.1 VAE (standalone single-file)\n- Qwen3.5 Encoder: Qwen3.5 4B, for Anima-3.8B only, whose bundled semantic connector reads it\n\nThe T5-XXL tokenizer needed for LLM Adapter token IDs is bundled in the package,\nso no T5-XXL encoder model needs to be installed.", "node_pack": "invokeai", "properties": { "id": { @@ -18500,6 +18518,24 @@ "title": "Qwen3 Encoder", "ui_model_type": ["qwen3_encoder"] }, + "qwen3_5_encoder_model": { + "anyOf": [ + { + "$ref": "#/components/schemas/ModelIdentifierField" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Standalone Qwen3.5 4B Encoder model. Required by Anima-3.8B, ignored by every other Anima model.", + "field_kind": "input", + "input": "direct", + "orig_default": null, + "orig_required": false, + "title": "Qwen3.5 Encoder", + "ui_model_type": ["qwen3_5_encoder"] + }, "type": { "const": "anima_model_loader", "default": "anima_model_loader", @@ -18512,7 +18548,7 @@ "tags": ["model", "anima"], "title": "Main Model - Anima", "type": "object", - "version": "1.4.0", + "version": "1.5.0", "output": { "$ref": "#/components/schemas/AnimaModelLoaderOutput" } @@ -18535,6 +18571,21 @@ "title": "Qwen3 Encoder", "ui_hidden": false }, + "qwen3_5_encoder": { + "anyOf": [ + { + "$ref": "#/components/schemas/Qwen35EncoderField" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Qwen3.5 tokenizer and text encoder. Set only for an Anima-3.8B model.", + "field_kind": "output", + "title": "Qwen3.5 Encoder", + "ui_hidden": false + }, "vae": { "$ref": "#/components/schemas/VAEField", "description": "VAE", @@ -18550,7 +18601,7 @@ "type": "string" } }, - "required": ["output_meta", "transformer", "qwen3_encoder", "vae", "type", "type"], + "required": ["output_meta", "transformer", "qwen3_encoder", "qwen3_5_encoder", "vae", "type", "type"], "title": "AnimaModelLoaderOutput", "type": "object" }, @@ -18634,6 +18685,23 @@ "orig_default": null, "orig_required": false }, + "qwen3_5_encoder": { + "anyOf": [ + { + "$ref": "#/components/schemas/Qwen35EncoderField" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Qwen3.5 tokenizer and text encoder. Connect it for Anima-3.8B, which reads both encoders.", + "field_kind": "input", + "input": "connection", + "orig_default": null, + "orig_required": false, + "title": "Qwen3.5 Encoder" + }, "type": { "const": "anima_text_encoder", "default": "anima_text_encoder", @@ -18646,11 +18714,17 @@ "tags": ["prompt", "conditioning", "anima"], "title": "Prompt - Anima", "type": "object", - "version": "1.4.0", + "version": "1.5.0", "output": { "$ref": "#/components/schemas/AnimaConditioningOutput" } }, + "AnimaVariantType": { + "type": "string", + "enum": ["anima_qwen3", "anima_qwen35"], + "title": "AnimaVariantType", + "description": "Anima model variants, by how the transformer is conditioned.\n\nThe depth of the DiT (28 blocks for the official release, 40 for Anima-2.9B, 52 for Anima-3.8B)\nis not a variant: the loader reads it from the checkpoint and nothing else depends on it." + }, "AnyModelConfig": { "oneOf": [ { @@ -18953,6 +19027,9 @@ { "$ref": "#/components/schemas/Qwen3VLEncoder_Qwen3VLEncoder_Config" }, + { + "$ref": "#/components/schemas/Qwen35Encoder_Checkpoint_Config" + }, { "$ref": "#/components/schemas/Qwen3Encoder_Qwen3Encoder_Config" }, @@ -69439,6 +69516,10 @@ "const": "checkpoint", "title": "Format", "default": "checkpoint" + }, + "variant": { + "$ref": "#/components/schemas/AnimaVariantType", + "description": "Which text encoders the model is conditioned on." } }, "type": "object", @@ -69459,7 +69540,8 @@ "default_settings", "config_path", "base", - "format" + "format", + "variant" ], "title": "Main_Checkpoint_Anima_Config", "description": "Model config for Anima single-file checkpoint models (safetensors).\n\nAnima is built on NVIDIA Cosmos Predict2 DiT with a custom LLM Adapter\nthat bridges Qwen3 0.6B text encoder outputs to the DiT." @@ -83468,6 +83550,9 @@ { "$ref": "#/components/schemas/Qwen3VLEncoder_Qwen3VLEncoder_Config" }, + { + "$ref": "#/components/schemas/Qwen35Encoder_Checkpoint_Config" + }, { "$ref": "#/components/schemas/Qwen3Encoder_Qwen3Encoder_Config" }, @@ -84181,6 +84266,9 @@ { "$ref": "#/components/schemas/Qwen3VLEncoder_Qwen3VLEncoder_Config" }, + { + "$ref": "#/components/schemas/Qwen35Encoder_Checkpoint_Config" + }, { "$ref": "#/components/schemas/Qwen3Encoder_Qwen3Encoder_Config" }, @@ -84779,6 +84867,9 @@ { "$ref": "#/components/schemas/Qwen3VLEncoder_Qwen3VLEncoder_Config" }, + { + "$ref": "#/components/schemas/Qwen35Encoder_Checkpoint_Config" + }, { "$ref": "#/components/schemas/Qwen3Encoder_Qwen3Encoder_Config" }, @@ -85233,6 +85324,9 @@ { "$ref": "#/components/schemas/Qwen3VLEncoder_Qwen3VLEncoder_Config" }, + { + "$ref": "#/components/schemas/Qwen35Encoder_Checkpoint_Config" + }, { "$ref": "#/components/schemas/Qwen3Encoder_Qwen3Encoder_Config" }, @@ -85684,6 +85778,12 @@ { "$ref": "#/components/schemas/Qwen3VLVariantType" }, + { + "$ref": "#/components/schemas/Qwen35VariantType" + }, + { + "$ref": "#/components/schemas/AnimaVariantType" + }, { "$ref": "#/components/schemas/Krea2VariantType" }, @@ -85839,6 +85939,7 @@ "qwen3_encoder", "qwen_vl_encoder", "qwen3_vl_encoder", + "qwen3_5_encoder", "mistral_encoder", "wan_t5_encoder", "gemma2_encoder", @@ -86168,6 +86269,9 @@ { "$ref": "#/components/schemas/Qwen3VLEncoder_Qwen3VLEncoder_Config" }, + { + "$ref": "#/components/schemas/Qwen35Encoder_Checkpoint_Config" + }, { "$ref": "#/components/schemas/Qwen3Encoder_Qwen3Encoder_Config" }, @@ -89944,6 +90048,182 @@ "title": "QueueItemsRetriedEvent", "type": "object" }, + "Qwen35EncoderField": { + "description": "Field for the Qwen3.5 text encoder Anima-3.8B's semantic connector reads.", + "properties": { + "tokenizer": { + "$ref": "#/components/schemas/ModelIdentifierField", + "description": "Info to load tokenizer submodel" + }, + "text_encoder": { + "$ref": "#/components/schemas/ModelIdentifierField", + "description": "Info to load text_encoder submodel" + } + }, + "required": ["tokenizer", "text_encoder"], + "title": "Qwen35EncoderField", + "type": "object" + }, + "Qwen35Encoder_Checkpoint_Config": { + "properties": { + "key": { + "type": "string", + "title": "Key", + "description": "A unique key for this model." + }, + "hash": { + "type": "string", + "title": "Hash", + "description": "The hash of the model file(s)." + }, + "path": { + "type": "string", + "title": "Path", + "description": "Path to the model on the filesystem. Relative paths are relative to the Invoke root directory." + }, + "file_size": { + "type": "integer", + "title": "File Size", + "description": "The size of the model in bytes." + }, + "name": { + "type": "string", + "title": "Name", + "description": "Name of the model." + }, + "description": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Description", + "description": "Model description" + }, + "source": { + "type": "string", + "title": "Source", + "description": "The original source of the model (path, URL or repo_id)." + }, + "source_type": { + "$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" + }, + "config_path": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Config Path", + "description": "Path to the config for this model, if any." + }, + "base": { + "type": "string", + "const": "any", + "title": "Base", + "default": "any" + }, + "type": { + "type": "string", + "const": "qwen3_5_encoder", + "title": "Type", + "default": "qwen3_5_encoder" + }, + "format": { + "type": "string", + "const": "checkpoint", + "title": "Format", + "default": "checkpoint" + }, + "cpu_only": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "title": "Cpu Only", + "description": "Whether this model should run on CPU only" + }, + "variant": { + "$ref": "#/components/schemas/Qwen35VariantType", + "description": "Qwen3.5 model size variant" + } + }, + "type": "object", + "required": [ + "key", + "hash", + "path", + "file_size", + "name", + "description", + "source", + "source_type", + "source_api_response", + "source_url", + "cover_image", + "config_path", + "base", + "type", + "format", + "cpu_only", + "variant" + ], + "title": "Qwen35Encoder_Checkpoint_Config", + "description": "Configuration for single-file Qwen3.5 text encoders (safetensors)." + }, + "Qwen35VariantType": { + "type": "string", + "enum": ["qwen3_5_4b"], + "title": "Qwen35VariantType", + "description": "Qwen3.5 text encoder variants, by language-model width.\n\nA family of its own: Qwen3.5 interleaves Gated DeltaNet linear-attention layers with gated full\nattention, so its checkpoints share neither architecture nor key layout with `Qwen3VariantType`,\neven at the same width." + }, "Qwen3EncoderField": { "description": "Field for Qwen3 text encoder used by Z-Image models.", "properties": { @@ -98741,6 +99021,12 @@ { "$ref": "#/components/schemas/Qwen3VLVariantType" }, + { + "$ref": "#/components/schemas/Qwen35VariantType" + }, + { + "$ref": "#/components/schemas/AnimaVariantType" + }, { "$ref": "#/components/schemas/Krea2VariantType" }, @@ -98925,6 +99211,12 @@ { "$ref": "#/components/schemas/Qwen3VLVariantType" }, + { + "$ref": "#/components/schemas/Qwen35VariantType" + }, + { + "$ref": "#/components/schemas/AnimaVariantType" + }, { "$ref": "#/components/schemas/Krea2VariantType" }, @@ -100193,6 +100485,12 @@ { "$ref": "#/components/schemas/Qwen3VLVariantType" }, + { + "$ref": "#/components/schemas/Qwen35VariantType" + }, + { + "$ref": "#/components/schemas/AnimaVariantType" + }, { "$ref": "#/components/schemas/Krea2VariantType" }, diff --git a/invokeai/frontend/api/schema.ts b/invokeai/frontend/api/schema.ts index c4dbf1c0622..fddf71a2711 100644 --- a/invokeai/frontend/api/schema.ts +++ b/invokeai/frontend/api/schema.ts @@ -5206,6 +5206,7 @@ export type components = { * - Transformer: Cosmos Predict2 DiT + LLM Adapter (from single-file checkpoint) * - Qwen3 Encoder: Qwen3 0.6B (standalone single-file) * - VAE: AutoencoderKLQwenImage / Wan 2.1 VAE (standalone single-file) + * - Qwen3.5 Encoder: Qwen3.5 4B, for Anima-3.8B only, whose bundled semantic connector reads it * * The T5-XXL tokenizer needed for LLM Adapter token IDs is bundled in the package, * so no T5-XXL encoder model needs to be installed. @@ -5243,6 +5244,12 @@ export type components = { * @description Standalone Qwen3 0.6B Encoder model. */ qwen3_encoder_model: components["schemas"]["ModelIdentifierField"]; + /** + * Qwen3.5 Encoder + * @description Standalone Qwen3.5 4B Encoder model. Required by Anima-3.8B, ignored by every other Anima model. + * @default null + */ + qwen3_5_encoder_model?: components["schemas"]["ModelIdentifierField"] | null; /** * type * @default anima_model_loader @@ -5265,6 +5272,12 @@ export type components = { * @description Qwen3 tokenizer and text encoder */ qwen3_encoder: components["schemas"]["Qwen3EncoderField"]; + /** + * Qwen3.5 Encoder + * @description Qwen3.5 tokenizer and text encoder. Set only for an Anima-3.8B model. + * @default null + */ + qwen3_5_encoder: components["schemas"]["Qwen35EncoderField"] | null; /** * VAE * @description VAE @@ -5320,6 +5333,12 @@ export type components = { * @default null */ mask?: components["schemas"]["TensorField"] | null; + /** + * Qwen3.5 Encoder + * @description Qwen3.5 tokenizer and text encoder. Connect it for Anima-3.8B, which reads both encoders. + * @default null + */ + qwen3_5_encoder?: components["schemas"]["Qwen35EncoderField"] | null; /** * type * @default anima_text_encoder @@ -5327,7 +5346,16 @@ export type components = { */ type: "anima_text_encoder"; }; - 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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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"]["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"]; + /** + * AnimaVariantType + * @description Anima model variants, by how the transformer is conditioned. + * + * The depth of the DiT (28 blocks for the official release, 40 for Anima-2.9B, 52 for Anima-3.8B) + * is not a variant: the loader reads it from the checkpoint and nothing else depends on it. + * @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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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 @@ -28787,6 +28815,8 @@ export type components = { * @constant */ format: "checkpoint"; + /** @description Which text encoders the model is conditioned on. */ + variant: components["schemas"]["AnimaVariantType"]; }; /** * Main_Checkpoint_ErnieImage_Config @@ -36051,7 +36081,7 @@ 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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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"]["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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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 @@ -36217,7 +36247,7 @@ 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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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"]["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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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 @@ -36303,7 +36333,7 @@ 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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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"]["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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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 @@ -36330,7 +36360,7 @@ 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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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"]["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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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 @@ -36462,7 +36492,7 @@ export type components = { * Variant * @description The variant of the model. */ - variant?: components["schemas"]["ModelVariantType"] | components["schemas"]["ClipVariantType"] | components["schemas"]["FluxVariantType"] | components["schemas"]["Flux2VariantType"] | components["schemas"]["ZImageVariantType"] | components["schemas"]["QwenImageVariantType"] | components["schemas"]["WanVariantType"] | components["schemas"]["WanLoRAVariantType"] | components["schemas"]["Qwen3VariantType"] | components["schemas"]["Qwen3VLVariantType"] | components["schemas"]["Krea2VariantType"] | components["schemas"]["MiniMaxH3VariantType"] | components["schemas"]["LTX2VariantType"] | components["schemas"]["MistralVariantType"] | components["schemas"]["PiDDecoderVariantType"] | null; + variant?: components["schemas"]["ModelVariantType"] | components["schemas"]["ClipVariantType"] | components["schemas"]["FluxVariantType"] | components["schemas"]["Flux2VariantType"] | components["schemas"]["ZImageVariantType"] | components["schemas"]["QwenImageVariantType"] | components["schemas"]["WanVariantType"] | components["schemas"]["WanLoRAVariantType"] | components["schemas"]["Qwen3VariantType"] | components["schemas"]["Qwen3VLVariantType"] | components["schemas"]["Qwen35VariantType"] | components["schemas"]["AnimaVariantType"] | components["schemas"]["Krea2VariantType"] | components["schemas"]["MiniMaxH3VariantType"] | components["schemas"]["LTX2VariantType"] | components["schemas"]["MistralVariantType"] | components["schemas"]["PiDDecoderVariantType"] | null; /** @description The prediction type of the model. */ prediction_type?: components["schemas"]["SchedulerPredictionType"] | null; /** @@ -36544,7 +36574,7 @@ export type components = { * @description Model type. * @enum {string} */ - ModelType: "onnx" | "main" | "vae" | "lora" | "control_lora" | "controlnet" | "embedding" | "ip_adapter" | "clip_vision" | "clip_embed" | "t2i_adapter" | "t5_encoder" | "qwen3_encoder" | "qwen_vl_encoder" | "qwen3_vl_encoder" | "mistral_encoder" | "wan_t5_encoder" | "gemma2_encoder" | "gemma4_encoder" | "spandrel_image_to_image" | "siglip" | "flux_redux" | "llava_onevision" | "prompt_enhancer" | "text_llm" | "external_image_generator" | "pid_decoder" | "ltx2_duration_head" | "unknown"; + ModelType: "onnx" | "main" | "vae" | "lora" | "control_lora" | "controlnet" | "embedding" | "ip_adapter" | "clip_vision" | "clip_embed" | "t2i_adapter" | "t5_encoder" | "qwen3_encoder" | "qwen_vl_encoder" | "qwen3_vl_encoder" | "qwen3_5_encoder" | "mistral_encoder" | "wan_t5_encoder" | "gemma2_encoder" | "gemma4_encoder" | "spandrel_image_to_image" | "siglip" | "flux_redux" | "llava_onevision" | "prompt_enhancer" | "text_llm" | "external_image_generator" | "pid_decoder" | "ltx2_duration_head" | "unknown"; /** * ModelVariantType * @description Variant type. @@ -36557,7 +36587,7 @@ 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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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"]["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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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 @@ -38680,6 +38710,114 @@ export type components = { [key: string]: number[]; }; }; + /** + * Qwen35EncoderField + * @description Field for the Qwen3.5 text encoder Anima-3.8B's semantic connector reads. + */ + Qwen35EncoderField: { + /** @description Info to load tokenizer submodel */ + tokenizer: components["schemas"]["ModelIdentifierField"]; + /** @description Info to load text_encoder submodel */ + text_encoder: components["schemas"]["ModelIdentifierField"]; + }; + /** + * Qwen35Encoder_Checkpoint_Config + * @description Configuration for single-file Qwen3.5 text encoders (safetensors). + */ + Qwen35Encoder_Checkpoint_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; + /** + * Config Path + * @description Path to the config for this model, if any. + */ + config_path: string | null; + /** + * Base + * @default any + * @constant + */ + base: "any"; + /** + * Type + * @default qwen3_5_encoder + * @constant + */ + type: "qwen3_5_encoder"; + /** + * Format + * @default checkpoint + * @constant + */ + format: "checkpoint"; + /** + * Cpu Only + * @description Whether this model should run on CPU only + */ + cpu_only: boolean | null; + /** @description Qwen3.5 model size variant */ + variant: components["schemas"]["Qwen35VariantType"]; + }; + /** + * Qwen35VariantType + * @description Qwen3.5 text encoder variants, by language-model width. + * + * A family of its own: Qwen3.5 interleaves Gated DeltaNet linear-attention layers with gated full + * attention, so its checkpoints share neither architecture nor key layout with `Qwen3VariantType`, + * even at the same width. + * @enum {string} + */ + Qwen35VariantType: "qwen3_5_4b"; /** * Qwen3EncoderField * @description Field for Qwen3 text encoder used by Z-Image models. @@ -43297,7 +43435,7 @@ export type components = { type: components["schemas"]["ModelType"]; format?: components["schemas"]["ModelFormat"] | null; /** Variant */ - variant?: components["schemas"]["ModelVariantType"] | components["schemas"]["ClipVariantType"] | components["schemas"]["FluxVariantType"] | components["schemas"]["Flux2VariantType"] | components["schemas"]["ZImageVariantType"] | components["schemas"]["QwenImageVariantType"] | components["schemas"]["WanVariantType"] | components["schemas"]["WanLoRAVariantType"] | components["schemas"]["Qwen3VariantType"] | components["schemas"]["Qwen3VLVariantType"] | components["schemas"]["Krea2VariantType"] | components["schemas"]["MiniMaxH3VariantType"] | components["schemas"]["LTX2VariantType"] | components["schemas"]["MistralVariantType"] | components["schemas"]["PiDDecoderVariantType"] | null; + variant?: components["schemas"]["ModelVariantType"] | components["schemas"]["ClipVariantType"] | components["schemas"]["FluxVariantType"] | components["schemas"]["Flux2VariantType"] | components["schemas"]["ZImageVariantType"] | components["schemas"]["QwenImageVariantType"] | components["schemas"]["WanVariantType"] | components["schemas"]["WanLoRAVariantType"] | components["schemas"]["Qwen3VariantType"] | components["schemas"]["Qwen3VLVariantType"] | components["schemas"]["Qwen35VariantType"] | components["schemas"]["AnimaVariantType"] | components["schemas"]["Krea2VariantType"] | components["schemas"]["MiniMaxH3VariantType"] | components["schemas"]["LTX2VariantType"] | components["schemas"]["MistralVariantType"] | components["schemas"]["PiDDecoderVariantType"] | null; /** * Is Installed * @default false @@ -43342,7 +43480,7 @@ export type components = { type: components["schemas"]["ModelType"]; format?: components["schemas"]["ModelFormat"] | null; /** Variant */ - variant?: components["schemas"]["ModelVariantType"] | components["schemas"]["ClipVariantType"] | components["schemas"]["FluxVariantType"] | components["schemas"]["Flux2VariantType"] | components["schemas"]["ZImageVariantType"] | components["schemas"]["QwenImageVariantType"] | components["schemas"]["WanVariantType"] | components["schemas"]["WanLoRAVariantType"] | components["schemas"]["Qwen3VariantType"] | components["schemas"]["Qwen3VLVariantType"] | components["schemas"]["Krea2VariantType"] | components["schemas"]["MiniMaxH3VariantType"] | components["schemas"]["LTX2VariantType"] | components["schemas"]["MistralVariantType"] | components["schemas"]["PiDDecoderVariantType"] | null; + variant?: components["schemas"]["ModelVariantType"] | components["schemas"]["ClipVariantType"] | components["schemas"]["FluxVariantType"] | components["schemas"]["Flux2VariantType"] | components["schemas"]["ZImageVariantType"] | components["schemas"]["QwenImageVariantType"] | components["schemas"]["WanVariantType"] | components["schemas"]["WanLoRAVariantType"] | components["schemas"]["Qwen3VariantType"] | components["schemas"]["Qwen3VLVariantType"] | components["schemas"]["Qwen35VariantType"] | components["schemas"]["AnimaVariantType"] | components["schemas"]["Krea2VariantType"] | components["schemas"]["MiniMaxH3VariantType"] | components["schemas"]["LTX2VariantType"] | components["schemas"]["MistralVariantType"] | components["schemas"]["PiDDecoderVariantType"] | null; /** * Is Installed * @default false @@ -44036,7 +44174,7 @@ export type components = { path_or_prefix: string; model_type: components["schemas"]["ModelType"]; /** Variant */ - variant?: components["schemas"]["ModelVariantType"] | components["schemas"]["ClipVariantType"] | components["schemas"]["FluxVariantType"] | components["schemas"]["Flux2VariantType"] | components["schemas"]["ZImageVariantType"] | components["schemas"]["QwenImageVariantType"] | components["schemas"]["WanVariantType"] | components["schemas"]["WanLoRAVariantType"] | components["schemas"]["Qwen3VariantType"] | components["schemas"]["Qwen3VLVariantType"] | components["schemas"]["Krea2VariantType"] | components["schemas"]["MiniMaxH3VariantType"] | components["schemas"]["LTX2VariantType"] | components["schemas"]["MistralVariantType"] | components["schemas"]["PiDDecoderVariantType"] | null; + variant?: components["schemas"]["ModelVariantType"] | components["schemas"]["ClipVariantType"] | components["schemas"]["FluxVariantType"] | components["schemas"]["Flux2VariantType"] | components["schemas"]["ZImageVariantType"] | components["schemas"]["QwenImageVariantType"] | components["schemas"]["WanVariantType"] | components["schemas"]["WanLoRAVariantType"] | components["schemas"]["Qwen3VariantType"] | components["schemas"]["Qwen3VLVariantType"] | components["schemas"]["Qwen35VariantType"] | components["schemas"]["AnimaVariantType"] | components["schemas"]["Krea2VariantType"] | components["schemas"]["MiniMaxH3VariantType"] | components["schemas"]["LTX2VariantType"] | components["schemas"]["MistralVariantType"] | components["schemas"]["PiDDecoderVariantType"] | null; }; /** * Subtract Integers @@ -52298,7 +52436,7 @@ 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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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"]["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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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 */ @@ -52330,7 +52468,7 @@ 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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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"]["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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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 */ @@ -52382,7 +52520,7 @@ 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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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"]["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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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 */ @@ -52496,7 +52634,7 @@ 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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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"]["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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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 */ @@ -52569,7 +52707,7 @@ 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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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"]["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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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 */ @@ -53335,7 +53473,7 @@ 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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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"]["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_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_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_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_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_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"]["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"]["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/__fixtures__/architectureCapabilities.json b/invokeai/frontend/webv2/src/features/generation/core/__fixtures__/architectureCapabilities.json index b854efb1231..7af37b5ae7c 100644 --- a/invokeai/frontend/webv2/src/features/generation/core/__fixtures__/architectureCapabilities.json +++ b/invokeai/frontend/webv2/src/features/generation/core/__fixtures__/architectureCapabilities.json @@ -55,6 +55,62 @@ ] } }, + { + "base": "anima", + "variant": "anima_qwen35", + "features": { + "negative_prompt": { + "visible": true, + "usage": "cfg-gated" + }, + "dimension_grid": 8, + "guidance_label": "CFG", + "guidance_min": 1.0, + "guidance_max": null, + "scheduler_set": "anima", + "scheduler_applies_to_graph": true, + "control_kinds": [], + "max_reference_images": 0, + "reference_images_require_variant": null, + "supports_regional_guidance": true, + "regional_negative": false, + "clip_skip_max": null, + "supports_seamless": false, + "supports_cfg_rescale": false, + "sd_vae_override": false, + "color_compensation": false, + "vae_precision": false + }, + "defaults": { + "vae": null, + "vae_precision": null, + "scheduler": "euler", + "steps": 40, + "cfg_scale": 6.0, + "cfg_rescale_multiplier": null, + "width": 1024, + "height": 1024, + "guidance": null, + "cpu_only": null, + "fp8_storage": null + }, + "vae": { + "accepted": [ + { + "base": "anima", + "latent_channels": null + }, + { + "base": "qwen-image", + "latent_channels": null + }, + { + "base": "wan", + "latent_channels": 16 + } + ] + } + }, { "base": "cogview4", "variant": null, 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 7fbb0822030..88a3f8d4de9 100644 --- a/invokeai/frontend/webv2/src/features/generation/core/__snapshots__/generateGraphNodeTypes.json +++ b/invokeai/frontend/webv2/src/features/generation/core/__snapshots__/generateGraphNodeTypes.json @@ -247,11 +247,12 @@ }, "anima": { "componentsFilled": { - "diffusers": [ + "qwen3": [ "qwen3EncoderModel", "vae" ], - "standalone-components": [ + "qwen35": [ + "qwen35EncoderModel", "qwen3EncoderModel", "vae" ] @@ -284,7 +285,8 @@ 0 ], "guidance_scale": [ - 4.5 + 4.5, + 6 ], "height": [ 1024 @@ -296,7 +298,8 @@ "euler" ], "steps": [ - 35 + 35, + 40 ], "use_cache": [ true @@ -332,6 +335,7 @@ true ], "model": [], + "qwen3_5_encoder_model": [], "qwen3_encoder_model": [], "use_cache": [ true @@ -339,6 +343,7 @@ "vae_model": [] }, "outputs": [ + "qwen3_5_encoder", "qwen3_encoder", "transformer", "vae" @@ -347,6 +352,7 @@ "anima_text_encoder": { "inputs": [ "prompt", + "qwen3_5_encoder", "qwen3_encoder" ], "literalInputs": { @@ -509,6 +515,7 @@ 3.5, 4, 4.5, + 6, 7 ], "clip_embed_model": [], @@ -583,6 +590,7 @@ "model": [], "qwen_image_qwen_vl_encoder": [], "qwen_image_vae": [], + "qwen3_5_encoder": [], "qwen3_encoder": [], "qwen3_source": [], "qwen3_vl_encoder": [], diff --git a/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.ts b/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.ts index af0b37053ac..3868f08f143 100644 --- a/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.ts +++ b/invokeai/frontend/webv2/src/features/generation/core/baseGenerationPolicies.ts @@ -31,6 +31,7 @@ import type { import { getCompatibleDiffusersComponentSource, isBundledMainForBase, + isAnimaQwen35Encoder, isAnimaQwen3Encoder, isClipVariant, isDiffusersMainForBase, @@ -524,6 +525,7 @@ export const getDefaultGenerateSettings = (model?: GenerateModelConfig): Generat qwen3EncoderModel: null, qwenVLEncoderModel: null, qwen3VLEncoderModel: null, + qwen35EncoderModel: null, wanT5EncoderModel: null, ideogram4UnconditionalModel: null, wanLowNoiseModel: null, @@ -592,6 +594,7 @@ export type GenerateComponentValueKey = | 'qwen3EncoderModel' | 'qwenVLEncoderModel' | 'qwen3VLEncoderModel' + | 'qwen35EncoderModel' | 'wanT5EncoderModel' | 'wanLowNoiseModel' | 'ideogram4UnconditionalModel' @@ -636,6 +639,7 @@ const TYPE_MISTRAL: ModelTaxonomyType[] = ['mistral_encoder']; const TYPE_QWEN3: ModelTaxonomyType[] = ['qwen3_encoder']; const TYPE_QWEN_VL: ModelTaxonomyType[] = ['qwen_vl_encoder']; const TYPE_QWEN3_VL: ModelTaxonomyType[] = ['qwen3_vl_encoder']; +const TYPE_QWEN35: ModelTaxonomyType[] = ['qwen3_5_encoder']; const TYPE_WAN_T5: ModelTaxonomyType[] = ['wan_t5_encoder']; const TYPE_PID_DECODER: ModelTaxonomyType[] = ['pid_decoder']; const TYPE_GEMMA2: ModelTaxonomyType[] = ['gemma2_encoder']; @@ -699,6 +703,16 @@ const qwen3VlEncoderSlot = (helpText: string, filter: GenerateComponentFilter): filter, }); +const qwen35EncoderSlot = (helpText: string): ComponentSlotPolicy => + slot({ + key: 'qwen35EncoderModel', + label: 'Qwen3.5 Encoder', + modelTypes: TYPE_QWEN35, + valueKind: 'component', + helpText, + filter: wrapFilter(isAnimaQwen35Encoder), + }); + /** Expose PiD slots before enabling it; Z-Image uses FLUX's decoder. */ const pidDecoderSlot = (): ComponentSlotPolicy => slot({ @@ -1107,6 +1121,16 @@ const getBaseComponentSectionPolicy = ( required: () => true, missingMessage: 'Generate needs a Qwen3 Encoder for Anima models.', }, + // Anima-3.8B reads a second encoder through its bundled semantic connector; no other Anima does. + ...(model.variant === 'anima_qwen35' + ? [ + { + ...qwen35EncoderSlot('Anima-3.8B reads Qwen3.5 4B beside Qwen3 0.6B. Required for this model.'), + required: () => true, + missingMessage: 'Generate needs a Qwen3.5 Encoder for Anima-3.8B.', + }, + ] + : []), { ...vaeSlot( 'Anima decodes with the 16-channel Wan 2.1 VAE, which may be installed under the Anima, Qwen-Image, or Wan base, so all three are listed. Required for Anima models.', @@ -1153,6 +1177,7 @@ const getComponentPolicyContext = (model: GenerateModelConfig, settings: Generat qwen3EncoderModel: settings.qwen3EncoderModel, qwenVLEncoderModel: settings.qwenVLEncoderModel, qwen3VLEncoderModel: settings.qwen3VLEncoderModel, + qwen35EncoderModel: settings.qwen35EncoderModel, wanT5EncoderModel: settings.wanT5EncoderModel, wanLowNoiseModel: settings.wanLowNoiseModel, ideogram4UnconditionalModel: settings.ideogram4UnconditionalModel, @@ -1171,6 +1196,7 @@ const COMPONENT_SETTING_LABELS: Record = { qwen3EncoderModel: 'Qwen3 Encoder', qwenVLEncoderModel: 'Qwen VL Encoder', qwen3VLEncoderModel: 'Qwen3-VL Encoder', + qwen35EncoderModel: 'Qwen3.5 Encoder', wanT5EncoderModel: 'Wan T5 Encoder', wanLowNoiseModel: 'Low-noise expert', ideogram4UnconditionalModel: 'Transformer (Unconditional)', diff --git a/invokeai/frontend/webv2/src/features/generation/core/canvas/addRegionalGuidance.ts b/invokeai/frontend/webv2/src/features/generation/core/canvas/addRegionalGuidance.ts index b8891ab0afd..4939bba2532 100644 --- a/invokeai/frontend/webv2/src/features/generation/core/canvas/addRegionalGuidance.ts +++ b/invokeai/frontend/webv2/src/features/generation/core/canvas/addRegionalGuidance.ts @@ -157,8 +157,10 @@ const copyEncoderFields = (base: RegionalGuidanceBase, modelVariant?: string | n case 'krea-2': return ['qwen3_vl_encoder']; case 'z-image': - case 'anima': return ['qwen3_encoder']; + case 'anima': + // Copied only where the global prompt has it: Anima-3.8B's second encoder. + return ['qwen3_encoder', 'qwen3_5_encoder']; case 'sd-1': case 'sd-2': return ['clip']; diff --git a/invokeai/frontend/webv2/src/features/generation/core/componentCompatibility.ts b/invokeai/frontend/webv2/src/features/generation/core/componentCompatibility.ts index b0c17622634..85c4bf40a3b 100644 --- a/invokeai/frontend/webv2/src/features/generation/core/componentCompatibility.ts +++ b/invokeai/frontend/webv2/src/features/generation/core/componentCompatibility.ts @@ -74,6 +74,10 @@ export const isClipVariant = export const isAnimaQwen3Encoder: GenerateComponentFilter = (model) => model.type === 'qwen3_encoder' && model.variant === 'qwen3_06b'; +/** Anima-3.8B's semantic connector was trained on the Qwen3.5 4B. */ +export const isAnimaQwen35Encoder: GenerateComponentFilter = (model) => + model.type === 'qwen3_5_encoder' && model.variant === 'qwen3_5_4b'; + export const isNonAnimaQwen3Encoder: GenerateComponentFilter = (model) => model.type === 'qwen3_encoder' && model.variant !== 'qwen3_06b'; diff --git a/invokeai/frontend/webv2/src/features/generation/core/graph.ts b/invokeai/frontend/webv2/src/features/generation/core/graph.ts index 87a4168e40f..02be3aae118 100644 --- a/invokeai/frontend/webv2/src/features/generation/core/graph.ts +++ b/invokeai/frontend/webv2/src/features/generation/core/graph.ts @@ -29,6 +29,7 @@ import { } from './baseGenerationPolicies'; import { getCompatibleDiffusersComponentSource, + isAnimaQwen35Encoder, isAnimaQwen3Encoder, isBundledMainForBase, isErnieImageMistralEncoder, @@ -1125,6 +1126,11 @@ const buildAnimaGraph = ( settings.qwen3EncoderModel && isAnimaQwen3Encoder(settings.qwen3EncoderModel) ? settings.qwen3EncoderModel : null, 'Qwen3 Encoder' ); + // Anima-3.8B's bundled semantic connector also reads Qwen3.5; no other Anima model does. + const qwen35EncoderModel = + model.variant === 'anima_qwen35' + ? requireComponent(getCompatibleComponent(settings.qwen35EncoderModel, isAnimaQwen35Encoder), 'Qwen3.5 Encoder') + : null; const graph: BackendGraphContract = { edges: [], id: createId('anima_graph'), nodes: {} }; const { negativePrompt, positivePrompt, seed } = addPromptAndSeedNodes(graph); const scheduler = coerceSchedulerForGraph(model, settings.scheduler); @@ -1134,6 +1140,7 @@ const buildAnimaGraph = ( id: 'model_loader', model, qwen3_encoder_model: qwen3EncoderModel, + ...(qwen35EncoderModel ? { qwen3_5_encoder_model: qwen35EncoderModel } : {}), type: 'anima_model_loader', vae_model: vaeModel, }); @@ -1162,6 +1169,10 @@ const buildAnimaGraph = ( addEdge(graph, loraSource, 'transformer', denoise, 'transformer'); addEdge(graph, loraSource, 'qwen3_encoder', posCond, 'qwen3_encoder'); + if (qwen35EncoderModel) { + // Straight from the loader: the LoRA collection loader passes Qwen3.5 through untouched, so it has no output for it. + addEdge(graph, modelLoader, 'qwen3_5_encoder', posCond, 'qwen3_5_encoder'); + } addEdge(graph, modelLoader, 'vae', output, 'vae'); addEdge(graph, positivePrompt, 'value', posCond, 'prompt'); addEdge(graph, posCond, 'conditioning', posCondCollect, 'item'); @@ -1169,6 +1180,9 @@ const buildAnimaGraph = ( if (negCond && negCondCollect) { addEdge(graph, loraSource, 'qwen3_encoder', negCond, 'qwen3_encoder'); + if (qwen35EncoderModel) { + addEdge(graph, modelLoader, 'qwen3_5_encoder', negCond, 'qwen3_5_encoder'); + } addEdge(graph, negativePrompt, 'value', negCond, 'prompt'); addEdge(graph, negCond, 'conditioning', negCondCollect, 'item'); addEdge(graph, negCondCollect, 'collection', denoise, 'negative_conditioning'); @@ -1178,6 +1192,7 @@ const buildAnimaGraph = ( addEdge(graph, denoise, 'latents', output, 'latents'); addMetadata(graph, output, settings, model, 'anima_txt2img', projectSettings, { qwen3_encoder: qwen3EncoderModel, + ...(qwen35EncoderModel ? { qwen3_5_encoder: qwen35EncoderModel } : {}), scheduler, vae: vaeModel, }); diff --git a/invokeai/frontend/webv2/src/features/generation/core/graphCoverage.test.ts b/invokeai/frontend/webv2/src/features/generation/core/graphCoverage.test.ts index e506673ec1f..d825794bee5 100644 --- a/invokeai/frontend/webv2/src/features/generation/core/graphCoverage.test.ts +++ b/invokeai/frontend/webv2/src/features/generation/core/graphCoverage.test.ts @@ -108,8 +108,9 @@ describe('generate graph coverage', () => { // Require nonempty cross-base coverage after runtime seeding. expect(checked).toEqual( expect.arrayContaining([ - 'anima/standalone-components:qwen-image/undefined', - 'anima/standalone-components:wan/16', + 'anima/qwen3:qwen-image/undefined', + 'anima/qwen3:wan/16', + 'anima/qwen35:qwen-image/undefined', 'krea-2/standalone-components:anima/undefined', 'qwen-image/standalone-components:anima/undefined', 'z-image/standalone-components:flux/undefined', 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 eb2d03de4cf..6340317d653 100644 --- a/invokeai/frontend/webv2/src/features/generation/core/graphCoverage.testing.ts +++ b/invokeai/frontend/webv2/src/features/generation/core/graphCoverage.testing.ts @@ -28,6 +28,11 @@ const DEFAULT_SHAPES: readonly ModelShape[] = [ /** Check per-base shape overrides against supported bases. */ export const SHAPE_OVERRIDES: Partial> = { + // Anima ships single files only; Anima-3.8B's variant adds a second encoder. + anima: [ + { label: 'qwen3', overrides: { format: 'checkpoint', variant: 'anima_qwen3' } }, + { label: 'qwen35', overrides: { format: 'checkpoint', variant: 'anima_qwen35' } }, + ], // Ideogram's standalone fixture is a conditional checkpoint, not GGUF. 'ideogram-4': [ { label: 'diffusers', overrides: { format: 'diffusers' } }, @@ -58,6 +63,7 @@ const CANDIDATE_VARIANTS = [ 'ministral3_3b', 'qwen3_vl_4b', 'qwen3_vl_8b', + 'qwen3_5_4b', ] as const; /** Ideogram 4's two transformer branches; every other main leaves the field unset. */ const CANDIDATE_BRANCHES = [undefined, 'conditional', 'unconditional'] as const; diff --git a/invokeai/frontend/webv2/src/features/generation/core/settings.ts b/invokeai/frontend/webv2/src/features/generation/core/settings.ts index 2399ad20e42..9e223a9825c 100644 --- a/invokeai/frontend/webv2/src/features/generation/core/settings.ts +++ b/invokeai/frontend/webv2/src/features/generation/core/settings.ts @@ -469,6 +469,7 @@ export const cloneGenerateWidgetValues = ( qwen3EncoderModel: values.qwen3EncoderModel ? { ...values.qwen3EncoderModel } : null, qwenVLEncoderModel: values.qwenVLEncoderModel ? { ...values.qwenVLEncoderModel } : null, qwen3VLEncoderModel: values.qwen3VLEncoderModel ? { ...values.qwen3VLEncoderModel } : null, + qwen35EncoderModel: values.qwen35EncoderModel ? { ...values.qwen35EncoderModel } : null, wanT5EncoderModel: values.wanT5EncoderModel ? { ...values.wanT5EncoderModel } : null, wanLowNoiseModel: values.wanLowNoiseModel ? { ...values.wanLowNoiseModel } : null, ideogram4UnconditionalModel: values.ideogram4UnconditionalModel ? { ...values.ideogram4UnconditionalModel } : null, @@ -559,6 +560,7 @@ export const syncGenerateWidgetValuesWithModels = ( mistralEncoderModel: syncModelIdentifierWithModels(values.mistralEncoderModel, modelsByKey), qwen3EncoderModel: syncModelIdentifierWithModels(values.qwen3EncoderModel, modelsByKey), qwenVLEncoderModel: syncModelIdentifierWithModels(values.qwenVLEncoderModel, modelsByKey), + qwen35EncoderModel: syncModelIdentifierWithModels(values.qwen35EncoderModel, modelsByKey), referenceImages: syncReferenceImagesWithModels(values.referenceImages, models), t5EncoderModel: syncModelIdentifierWithModels(values.t5EncoderModel, modelsByKey), vae: isVaeModelConfig(vae) ? vae : values.vae, @@ -574,6 +576,7 @@ export const syncGenerateWidgetValuesWithModels = ( nextValues.mistralEncoderModel === values.mistralEncoderModel && nextValues.qwen3EncoderModel === values.qwen3EncoderModel && nextValues.qwenVLEncoderModel === values.qwenVLEncoderModel && + nextValues.qwen35EncoderModel === values.qwen35EncoderModel && nextValues.referenceImages === values.referenceImages && nextValues.componentSourceModel === values.componentSourceModel && nextValues.modelKey === values.modelKey @@ -750,6 +753,7 @@ export const normalizeGenerateSettings = (values: unknown): GenerateSettings | n qwen3EncoderModel: getModelIdentifierOrNull(values.qwen3EncoderModel), qwenVLEncoderModel: getModelIdentifierOrNull(values.qwenVLEncoderModel), qwen3VLEncoderModel: getModelIdentifierOrNull(values.qwen3VLEncoderModel), + qwen35EncoderModel: getModelIdentifierOrNull(values.qwen35EncoderModel), wanT5EncoderModel: getModelIdentifierOrNull(values.wanT5EncoderModel), wanLowNoiseModel: getMainModelOrNull(values.wanLowNoiseModel), ideogram4UnconditionalModel: getMainModelOrNull(values.ideogram4UnconditionalModel), diff --git a/invokeai/frontend/webv2/src/features/generation/core/types.ts b/invokeai/frontend/webv2/src/features/generation/core/types.ts index 8631409294c..e2aff4fbb4a 100644 --- a/invokeai/frontend/webv2/src/features/generation/core/types.ts +++ b/invokeai/frontend/webv2/src/features/generation/core/types.ts @@ -229,6 +229,8 @@ export interface GenerateSettings { qwenVLEncoderModel: ComponentModelConfig | null; /** Krea-2's text encoder. Distinct from `qwenVLEncoderModel` (Qwen2.5-VL). */ qwen3VLEncoderModel: ComponentModelConfig | null; + /** Anima-3.8B's second text encoder, read by its bundled semantic connector. */ + qwen35EncoderModel: ComponentModelConfig | null; /** Wan 2.2's UMT5-XXL text encoder. */ wanT5EncoderModel: ComponentModelConfig | null; /** The low-noise expert is optional; the selected expert can span the full schedule. */ diff --git a/invokeai/frontend/webv2/src/features/generation/ui/GenerateComponentsSection.tsx b/invokeai/frontend/webv2/src/features/generation/ui/GenerateComponentsSection.tsx index ac05164395c..f4252f9c4e1 100644 --- a/invokeai/frontend/webv2/src/features/generation/ui/GenerateComponentsSection.tsx +++ b/invokeai/frontend/webv2/src/features/generation/ui/GenerateComponentsSection.tsx @@ -36,6 +36,7 @@ const getComponentPolicyContext = (model: GenerateModelConfig, settings: Generat qwen3EncoderModel: settings.qwen3EncoderModel, qwenVLEncoderModel: settings.qwenVLEncoderModel, qwen3VLEncoderModel: settings.qwen3VLEncoderModel, + qwen35EncoderModel: settings.qwen35EncoderModel, wanT5EncoderModel: settings.wanT5EncoderModel, wanLowNoiseModel: settings.wanLowNoiseModel, ideogram4UnconditionalModel: settings.ideogram4UnconditionalModel, diff --git a/invokeai/frontend/webv2/src/features/models/core/relationships.ts b/invokeai/frontend/webv2/src/features/models/core/relationships.ts index d5fdf769c74..e185ff768c6 100644 --- a/invokeai/frontend/webv2/src/features/models/core/relationships.ts +++ b/invokeai/frontend/webv2/src/features/models/core/relationships.ts @@ -25,6 +25,8 @@ export const NULL_BASE_ALLOWANCES: Readonly = new Set = { '5b': 'Wan 2.2 5B LoRA', a14b: 'Wan 2.2 A14B LoRA', + anima_qwen3: 'Anima', + anima_qwen35: 'Anima + Qwen3.5 (Anima-3.8B)', cow_mistral3_small: 'cow-mistral3-small (FLUX.2)', depth: 'Depth', dev: 'FLUX Dev', @@ -131,6 +134,7 @@ export const MODEL_VARIANT_LABELS: Record = { qwen3_06b: 'Qwen3 0.6B', qwen3_4b: 'Qwen3 4B', qwen3_8b: 'Qwen3 8B', + qwen3_5_4b: 'Qwen3.5 4B (Anima-3.8B)', qwen3_vl_4b: 'Qwen3-VL 4B (Krea-2)', qwen3_vl_8b: 'Qwen3-VL 8B (Ideogram 4)', ref2va: 'MiniMax H3 Ref2VA', @@ -150,6 +154,7 @@ export const getModelVariantLabel = (variant: string): string => MODEL_VARIANT_L // Mirrors the backend's per-class variant enums (taxonomy.py): main models // key their variants off the base, and a few non-main types carry their own. const MAIN_VARIANTS_BY_BASE: Record = { + anima: ['anima_qwen3', 'anima_qwen35'], flux: ['schnell', 'dev', 'dev_fill'], flux2: ['klein_4b', 'klein_4b_base', 'klein_9b', 'klein_9b_base', 'dev'], 'krea-2': ['krea2_turbo', 'krea2_base'], @@ -169,6 +174,7 @@ const VARIANTS_BY_TYPE: Record = { mistral_encoder: ['cow_mistral3_small', 'mistral3_24b', 'ministral3_3b'], pid_decoder: ['res2k_sr4x', 'res2kto4k_sr4x'], qwen3_encoder: ['qwen3_4b', 'qwen3_8b', 'qwen3_06b'], + qwen3_5_encoder: ['qwen3_5_4b'], // Required variant configs need explicit choices; a fallback None would fail database validation. qwen3_vl_encoder: ['qwen3_vl_4b', 'qwen3_vl_8b'], }; diff --git a/invokeai/frontend/webv2/src/features/models/core/types.ts b/invokeai/frontend/webv2/src/features/models/core/types.ts index deb851c51b5..fc09a8111f5 100644 --- a/invokeai/frontend/webv2/src/features/models/core/types.ts +++ b/invokeai/frontend/webv2/src/features/models/core/types.ts @@ -46,6 +46,7 @@ export type ModelTaxonomyType = | 'qwen3_encoder' | 'qwen_vl_encoder' | 'qwen3_vl_encoder' + | 'qwen3_5_encoder' | 'wan_t5_encoder' | 'mistral_encoder' | 'gemma2_encoder' diff --git a/invokeai/frontend/webv2/src/features/models/ui/detail/CpuOnlySetting.tsx b/invokeai/frontend/webv2/src/features/models/ui/detail/CpuOnlySetting.tsx index 8fe8340cef7..cba8fe20972 100644 --- a/invokeai/frontend/webv2/src/features/models/ui/detail/CpuOnlySetting.tsx +++ b/invokeai/frontend/webv2/src/features/models/ui/detail/CpuOnlySetting.tsx @@ -25,6 +25,7 @@ const CPU_ONLY_TYPES: ReadonlySet = new Set([ 'gemma2_encoder', 'mistral_encoder', 'qwen3_vl_encoder', + 'qwen3_5_encoder', ]); export const supportsCpuOnlySetting = (model: Pick): boolean => CPU_ONLY_TYPES.has(model.type); diff --git a/invokeai/frontend/webv2/src/features/workflow/core/modelRequirements.ts b/invokeai/frontend/webv2/src/features/workflow/core/modelRequirements.ts index 87f82ed5520..db97a6fa583 100644 --- a/invokeai/frontend/webv2/src/features/workflow/core/modelRequirements.ts +++ b/invokeai/frontend/webv2/src/features/workflow/core/modelRequirements.ts @@ -69,6 +69,7 @@ const MODEL_TYPE_LABELS: Record = { pid_decoder: 'PID decoder', qwen3_encoder: 'Qwen3 encoder', qwen3_vl_encoder: 'Qwen3 VL encoder', + qwen3_5_encoder: 'Qwen3.5 encoder', qwen_vl_encoder: 'Qwen VL encoder', siglip: 'SigLIP', spandrel_image_to_image: 'upscaler', diff --git a/invokeai/frontend/webv2/src/workbench/canvas-operations/importGalleryImages.test.ts b/invokeai/frontend/webv2/src/workbench/canvas-operations/importGalleryImages.test.ts index 6b44e6e8bad..891ff98dfb2 100644 --- a/invokeai/frontend/webv2/src/workbench/canvas-operations/importGalleryImages.test.ts +++ b/invokeai/frontend/webv2/src/workbench/canvas-operations/importGalleryImages.test.ts @@ -108,6 +108,7 @@ const setModel = (project: Project, base: GenerateWidgetValues['model']['base']) qwen3EncoderModel: null, qwenVLEncoderModel: null, qwen3VLEncoderModel: null, + qwen35EncoderModel: null, wanT5EncoderModel: null, wanLowNoiseModel: null, ideogram4UnconditionalModel: null, 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 fcd3c97ec21..b96af23f17b 100644 --- a/invokeai/frontend/webv2/src/workbench/image-actions/imageRecall.test.ts +++ b/invokeai/frontend/webv2/src/workbench/image-actions/imageRecall.test.ts @@ -110,6 +110,7 @@ const createValues = (overrides: Partial = {}): GenerateWi qwen3EncoderModel: null, qwenVLEncoderModel: null, qwen3VLEncoderModel: null, + qwen35EncoderModel: null, wanT5EncoderModel: null, wanLowNoiseModel: null, ideogram4UnconditionalModel: null, @@ -918,6 +919,7 @@ describe('remix round trip', () => { 'ideogram4UnconditionalModel', 'mistralEncoderModel', 'pidDecoderModel', + 'qwen35EncoderModel', 'qwen3EncoderModel', 'qwen3VLEncoderModel', 'qwenVLEncoderModel', diff --git a/invokeai/frontend/webv2/src/workbench/image-actions/imageRecall.ts b/invokeai/frontend/webv2/src/workbench/image-actions/imageRecall.ts index cded1adbaa0..ea297e0f8c9 100644 --- a/invokeai/frontend/webv2/src/workbench/image-actions/imageRecall.ts +++ b/invokeai/frontend/webv2/src/workbench/image-actions/imageRecall.ts @@ -410,6 +410,7 @@ type RecalledComponentSetting = keyof Pick< | 'pidDecoderModel' | 'qwen3EncoderModel' | 'qwen3VLEncoderModel' + | 'qwen35EncoderModel' | 'qwenVLEncoderModel' | 't5EncoderModel' | 'wanLowNoiseModel' @@ -439,6 +440,7 @@ const RECALLED_COMPONENTS: readonly RecalledComponent[] = [ { metadataKey: 'pid_decoder', setting: 'pidDecoderModel' }, { metadataKey: 'qwen3_encoder', setting: 'qwen3EncoderModel' }, { metadataKey: 'qwen3_vl_encoder', setting: 'qwen3VLEncoderModel' }, + { metadataKey: 'qwen3_5_encoder', setting: 'qwen35EncoderModel' }, { metadataKey: 'qwen_image_qwen_vl_encoder', setting: 'qwenVLEncoderModel' }, { metadataKey: 't5_encoder', setting: 't5EncoderModel' }, { metadataKey: 'wan_t5_encoder_model', setting: 'wanT5EncoderModel' }, diff --git a/invokeai/frontend/webv2/src/workbench/workbenchState.test.ts b/invokeai/frontend/webv2/src/workbench/workbenchState.test.ts index c6d4d403311..4486b6b23ee 100644 --- a/invokeai/frontend/webv2/src/workbench/workbenchState.test.ts +++ b/invokeai/frontend/webv2/src/workbench/workbenchState.test.ts @@ -236,6 +236,7 @@ const createGenerateValues = (overrides: Partial = {}): Ge qwen3EncoderModel: null, qwenVLEncoderModel: null, qwen3VLEncoderModel: null, + qwen35EncoderModel: null, wanT5EncoderModel: null, wanLowNoiseModel: null, ideogram4UnconditionalModel: null, diff --git a/pyproject.toml b/pyproject.toml index 6975b28d4fa..36b23f1ea67 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -329,6 +329,7 @@ version = { attr = "invokeai.version.__version__" } "invokeai.app.assets" = ["**/*.png"] "invokeai.backend.qwen3" = ["tokenizer/*.json", "tokenizer/*.json.gz"] "invokeai.backend.qwen3_vl" = ["tokenizer/*.json", "tokenizer/*.json.gz", "config/*.json"] +"invokeai.backend.qwen3_5" = ["*.json", "tokenizer/*.json", "tokenizer/*.json.gz"] "invokeai.backend.qwen2_5_vl" = ["*.json", "tokenizer/*.json", "tokenizer/*.json.gz"] # Read at load time by the MiniMax H3 single-file encoder loader. Without this the file is # absent from a wheel install and that loader raises FileNotFoundError. diff --git a/tests/app/invocations/test_anima_qwen3_5_conditioning.py b/tests/app/invocations/test_anima_qwen3_5_conditioning.py new file mode 100644 index 00000000000..6844b236269 --- /dev/null +++ b/tests/app/invocations/test_anima_qwen3_5_conditioning.py @@ -0,0 +1,159 @@ +"""Anima-3.8B's Qwen3.5 conditioning through the prompt and denoise nodes. + +The semantic connector depends on the timestep, so the denoise node must recompute its context at every +step, with that step's sigma -- in float32, because the connector scales sigma by 1000 before embedding +it -- while every other Anima model keeps computing its context once. +""" + +from contextlib import contextmanager, nullcontext +from types import SimpleNamespace +from unittest.mock import MagicMock + +import pytest +import torch + +from invokeai.app.invocations.anima.anima_denoise import AnimaDenoiseInvocation +from invokeai.app.invocations.fields import AnimaConditioningField +from invokeai.app.invocations.text_encoder.anima_text_encoder import AnimaTextEncoderInvocation +from invokeai.backend.anima.semantic_connector import QWEN35_LAYER_INDICES +from invokeai.backend.stable_diffusion.diffusion.conditioning_data import AnimaConditioningInfo, ConditioningFieldData + + +class _Loaded: + def __init__(self, model, compute_device=torch.device("cpu")): + self.model = model + self.compute_device = compute_device + + @contextmanager + def model_on_device(self, working_mem_bytes=None): + yield (None, self.model) + + +# --- prompt node -------------------------------------------------------------------------------------- + + +class _FakeQwen35Tokenizer: + pad_token_id = 248044 + + def encode(self, prompt: str, add_special_tokens: bool) -> list[int]: + assert add_special_tokens is False + return [] if not prompt else [5, 6, 7] + + +class _FakeQwen35Encoder(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.calls: list[tuple[list[int], tuple[int, ...], bool]] = [] + + def forward(self, input_ids, layer_indices, last_layer_attention_only=False): + self.calls.append((input_ids[0].tolist(), layer_indices, last_layer_attention_only)) + return [torch.full((1, input_ids.shape[1], 8), float(i)) for i in layer_indices] + + +def _encode_qwen3_5(monkeypatch, prompt: str) -> tuple[torch.Tensor, torch.Tensor, _FakeQwen35Encoder]: + module = "invokeai.app.invocations.text_encoder.anima_text_encoder" + monkeypatch.setattr(f"{module}.PreTrainedTokenizerBase", _FakeQwen35Tokenizer) + monkeypatch.setattr(f"{module}.Qwen35Encoder", _FakeQwen35Encoder) + encoder = _FakeQwen35Encoder() + context = MagicMock() + context.models.load.side_effect = [_Loaded(_FakeQwen35Tokenizer()), _Loaded(encoder)] + invocation = AnimaTextEncoderInvocation.model_construct( + prompt=prompt, qwen3_5_encoder=SimpleNamespace(tokenizer=object(), text_encoder=object()) + ) + states, mask = invocation._encode_qwen3_5(context) + return states, mask, encoder + + +def test_qwen3_5_is_read_at_the_trained_layers_with_an_attention_only_last_layer(monkeypatch) -> None: + states, mask, encoder = _encode_qwen3_5(monkeypatch, "1girl, Miku") + + assert encoder.calls == [([5, 6, 7], QWEN35_LAYER_INDICES, True)] + assert states.shape == (len(QWEN35_LAYER_INDICES), 3, 8) + assert mask.tolist() == [True, True, True] + + +def test_an_empty_prompt_is_one_masked_padding_token(monkeypatch) -> None: + states, mask, encoder = _encode_qwen3_5(monkeypatch, "") + + assert encoder.calls[0][0] == [_FakeQwen35Tokenizer.pad_token_id] + assert states.shape[1] == 1 + assert mask.tolist() == [False] + + +# --- denoise node ------------------------------------------------------------------------------------- + + +class _FakeTransformer(torch.nn.Module): + """Records what the adapter is asked for; predicts zero velocity.""" + + def __init__(self, connector: bool) -> None: + super().__init__() + self.has_semantic_connector = connector + self.adapter_calls: list[tuple[torch.Tensor | None, bool]] = [] + self.patch_spatial = 2 + + def preprocess_text_embeds(self, text_embeds, text_ids, t5xxl_weights=None, *, semantic_states=None, + semantic_mask=None, timesteps=None): # fmt: skip + self.adapter_calls.append((timesteps, semantic_states is not None)) + return torch.zeros(1, 512, 1024) + + def forward(self, x, timesteps, context, **kwargs): + return torch.zeros_like(x) + + +def _conditioning(with_qwen3_5: bool) -> ConditioningFieldData: + return ConditioningFieldData( + conditionings=[ + AnimaConditioningInfo( + qwen3_embeds=torch.zeros(3, 1024), + t5xxl_ids=torch.zeros(3, dtype=torch.long), + qwen35_states=torch.zeros(4, 2, 2560) if with_qwen3_5 else None, + qwen35_mask=torch.ones(2, dtype=torch.bool) if with_qwen3_5 else None, + ) + ] + ) + + +def _denoise(monkeypatch, *, connector: bool, with_qwen3_5: bool, steps: int = 4) -> _FakeTransformer: + module = "invokeai.app.invocations.anima.anima_denoise" + monkeypatch.setattr(f"{module}.TorchDevice.choose_torch_device", lambda: torch.device("cpu")) + monkeypatch.setattr(f"{module}.TorchDevice.choose_anima_inference_dtype", lambda _d: torch.bfloat16) + monkeypatch.setattr(f"{module}.LayerPatcher.apply_smart_model_patches", lambda **_kw: nullcontext()) + monkeypatch.setattr(f"{module}.patch_anima_for_regional_prompting", lambda *_a: nullcontext()) + transformer = _FakeTransformer(connector) + context = MagicMock() + context.models.load.return_value = _Loaded(transformer) + context.conditioning.load.side_effect = lambda _name: _conditioning(with_qwen3_5) + + invocation = AnimaDenoiseInvocation.model_construct( + latents=None, noise=None, denoise_mask=None, denoising_start=0.0, denoising_end=1.0, add_noise=True, + transformer=SimpleNamespace(transformer=object(), loras=[]), + positive_conditioning=AnimaConditioningField(conditioning_name="pos"), + negative_conditioning=AnimaConditioningField(conditioning_name="neg"), + guidance_scale=6.0, width=64, height=64, steps=steps, seed=0, control_lllite=None, scheduler="euler", + ) # fmt: skip + invocation._run_diffusion(context) + return transformer + + +def test_the_connector_context_is_recomputed_at_every_step_in_float32(monkeypatch) -> None: + transformer = _denoise(monkeypatch, connector=True, with_qwen3_5=True, steps=4) + sigmas = AnimaDenoiseInvocation.model_construct()._get_sigmas(4) + + # Positive and negative, once per step; the first step reuses what was built before the loop. + timesteps = [t for t, _ in transformer.adapter_calls] + assert len(timesteps) == 2 * 4 + assert all(t is not None and t.dtype == torch.float32 for t in timesteps) + assert [round(t.item(), 6) for t in timesteps[::2]] == [round(s, 6) for s in sigmas[:4]] + assert all(has_states for _, has_states in transformer.adapter_calls) + + +def test_a_model_without_the_connector_computes_its_context_once(monkeypatch) -> None: + transformer = _denoise(monkeypatch, connector=False, with_qwen3_5=False) + + assert transformer.adapter_calls == [(None, False), (None, False)] + + +def test_the_connector_without_qwen3_5_conditioning_is_refused(monkeypatch) -> None: + with pytest.raises(ValueError, match="encoded without Qwen3.5"): + _denoise(monkeypatch, connector=True, with_qwen3_5=False) diff --git a/tests/app/services/shared/sqlite_migrator/migrations/test_migration_2026_10_01_add_anima_variant.py b/tests/app/services/shared/sqlite_migrator/migrations/test_migration_2026_10_01_add_anima_variant.py new file mode 100644 index 00000000000..6c12251fe32 --- /dev/null +++ b/tests/app/services/shared/sqlite_migrator/migrations/test_migration_2026_10_01_add_anima_variant.py @@ -0,0 +1,107 @@ +"""The Anima variant backfill. + +Without it every Anima model installed before the variant field existed fails validation on read and is +*skipped* -- the models disappear from the model list with nothing in the UI to say why. +""" + +import json +import sqlite3 +from logging import getLogger +from pathlib import Path +from types import SimpleNamespace + +import pytest +import torch +from safetensors.torch import save_file + +from invokeai.app.services.shared.sqlite_migrator.migrations.migration_2026_10_01_add_anima_variant import ( + build_migration, +) +from invokeai.backend.model_manager.taxonomy import AnimaVariantType, BaseModelType, ModelFormat, ModelType + + +@pytest.fixture +def cursor() -> sqlite3.Cursor: + connection = sqlite3.connect(":memory:") + connection.execute("CREATE TABLE models (id TEXT PRIMARY KEY, config TEXT NOT NULL);") + return connection.cursor() + + +def _insert(cursor: sqlite3.Cursor, model_id: str, config: dict) -> None: + cursor.execute("INSERT INTO models (id, config) VALUES (?, ?);", (model_id, json.dumps(config))) + + +def _config(cursor: sqlite3.Cursor, model_id: str) -> dict: + cursor.execute("SELECT config FROM models WHERE id = ?;", (model_id,)) + return json.loads(cursor.fetchone()[0]) + + +def _run(cursor: sqlite3.Cursor, models_path: Path) -> None: + app_config = SimpleNamespace(models_path=models_path) + build_migration(app_config, getLogger(__name__)).callback(cursor) # type: ignore[arg-type,misc] + + +def _anima_main(path: str) -> dict: + return { + "type": ModelType.Main.value, + "base": BaseModelType.Anima.value, + "format": ModelFormat.Checkpoint.value, + "name": Path(path).stem, + "path": path, + } + + +def test_a_legacy_anima_model_becomes_qwen3(cursor: sqlite3.Cursor, tmp_path: Path) -> None: + checkpoint = tmp_path / "anima-base-v1.0.safetensors" + save_file({"net.blocks.0.mlp.layer1.weight": torch.zeros(1)}, checkpoint) + _insert(cursor, "base", _anima_main(str(checkpoint))) + + _run(cursor, tmp_path) + + assert _config(cursor, "base")["variant"] == AnimaVariantType.Qwen3.value + + +def test_an_installed_3_8b_bundle_becomes_qwen35(cursor: sqlite3.Cursor, tmp_path: Path) -> None: + # It identified as a plain Anima before the variant existed; its header still says what it is. + # A relative path resolves against the models directory, as the record service resolves it. + (tmp_path / "anima").mkdir() + save_file( + {"net.blocks.0.mlp.layer1.weight": torch.zeros(1), "net.anima_v2_connector.query_tokens": torch.zeros(1)}, + tmp_path / "anima" / "Anima-3.8B-v1.1.safetensors", + ) + _insert(cursor, "bundle", _anima_main("anima/Anima-3.8B-v1.1.safetensors")) + + _run(cursor, tmp_path) + + assert _config(cursor, "bundle")["variant"] == AnimaVariantType.Qwen35.value + + +def test_a_missing_file_becomes_qwen3(cursor: sqlite3.Cursor, tmp_path: Path) -> None: + _insert(cursor, "gone", _anima_main(str(tmp_path / "gone.safetensors"))) + + _run(cursor, tmp_path) + + assert _config(cursor, "gone")["variant"] == AnimaVariantType.Qwen3.value + + +def test_a_recorded_variant_is_left_alone(cursor: sqlite3.Cursor, tmp_path: Path) -> None: + _insert(cursor, "done", {**_anima_main(str(tmp_path / "x.safetensors")), "variant": AnimaVariantType.Qwen35.value}) + + _run(cursor, tmp_path) + + assert _config(cursor, "done")["variant"] == AnimaVariantType.Qwen35.value + + +def test_other_models_are_untouched(cursor: sqlite3.Cursor, tmp_path: Path) -> None: + # Anima's LLLite adapters share the base and carry no variant; other mains mean something else by it. + _insert( + cursor, + "lllite", + {"type": ModelType.ControlNet.value, "base": BaseModelType.Anima.value, "format": "checkpoint"}, + ) + _insert(cursor, "krea", {"type": ModelType.Main.value, "base": BaseModelType.Krea2.value, "format": "checkpoint"}) + + _run(cursor, tmp_path) + + assert "variant" not in _config(cursor, "lllite") + assert "variant" not in _config(cursor, "krea") diff --git a/tests/backend/anima/test_semantic_connector.py b/tests/backend/anima/test_semantic_connector.py new file mode 100644 index 00000000000..2b7b2277f1a --- /dev/null +++ b/tests/backend/anima/test_semantic_connector.py @@ -0,0 +1,179 @@ +"""Anima-3.8B's semantic connector: layout against the real bundle, and the behaviour the port relies on. + +The port was checked numerically against the reference implementation on the real weights (bit-identical +in fp32). These tests pin what that check cannot keep pinned: that the module tree still matches the +checkpoint's key layout, that the header is read into the right hyperparameters, and the edge cases the +denoise loop depends on -- the timestep dependence, and an empty prompt's fully masked Qwen3.5 source. +""" + +import re + +import accelerate +import pytest +import torch + +from invokeai.backend.anima.anima_transformer import AnimaTransformer, LLMAdapter, masked_sdpa +from invokeai.backend.anima.semantic_connector import AnimaSemanticConnector, AnimaSemanticConnectorConfig +from invokeai.backend.model_manager.load.model_loaders.anima import ( + ANIMA_TRANSFORMER_CONFIG, + _filter_non_model_keys, + _strip_anima_bundle_prefix, + anima_transformer_config, +) +from tests.backend.model_manager.load.state_dicts.anima_2_9b_keys import state_dict_keys as anima_2_9b_keys +from tests.backend.model_manager.load.state_dicts.anima_3_8b_connector_keys import ( + ANIMA_3_8B_NUM_BLOCKS, + connector_keys, + metadata, +) + +_CONNECTOR_PREFIX = "net.anima_v2_connector." +_BLOCK_KEY = re.compile(r"^net\.blocks\.(\d+)\.(.+)$") + + +class TestConfigFromMetadata: + def test_real_bundle_header(self) -> None: + config = AnimaSemanticConnectorConfig.from_metadata(metadata) + assert config == AnimaSemanticConnectorConfig( + num_queries=64, + resampler_blocks=6, + resampler_dim=2048, + resampler_heads=16, + mlp_hidden_dim=5632, + layer_indices=(7, 15, 23, 31), + ) + + def test_a_header_without_connector_keys_gets_the_trained_defaults(self) -> None: + assert AnimaSemanticConnectorConfig.from_metadata({}) == AnimaSemanticConnectorConfig() + + def test_a_different_connector_architecture_is_refused(self) -> None: + with pytest.raises(ValueError, match="Unsupported Anima semantic connector"): + AnimaSemanticConnectorConfig.from_metadata( + {"anima_v2_adapter_architecture": "anima_progressive_qwen35_cross_adapter_v1"} + ) + + +def test_connector_module_tree_matches_the_real_bundle() -> None: + with accelerate.init_empty_weights(): + connector = AnimaSemanticConnector(AnimaSemanticConnectorConfig.from_metadata(metadata)) + expected = {k.removeprefix(_CONNECTOR_PREFIX): shape for k, shape in connector_keys.items()} + actual = {k: list(v.shape) for k, v in connector.state_dict().items()} + assert actual == expected + + +def test_transformer_built_from_the_bundle_takes_every_key() -> None: + """52 blocks plus the connector, from the key layout the loader sees after its prefix passes.""" + block_suffixes = { + m.group(2): shape for k, shape in anima_2_9b_keys.items() if (m := _BLOCK_KEY.match(k)) and m.group(1) == "0" + } + sd = {k: torch.empty(s, device="meta") for k, s in anima_2_9b_keys.items() if not _BLOCK_KEY.match(k)} + for index in range(ANIMA_3_8B_NUM_BLOCKS): + for suffix, shape in block_suffixes.items(): + sd[f"net.blocks.{index}.{suffix}"] = torch.empty(shape, device="meta") + sd.update({k: torch.empty(s, device="meta") for k, s in connector_keys.items()}) + sd = _filter_non_model_keys(_strip_anima_bundle_prefix(sd)) + + with accelerate.init_empty_weights(): + model = AnimaTransformer( + **anima_transformer_config(sd), semantic_connector=AnimaSemanticConnectorConfig.from_metadata(metadata) + ) + result = model.load_state_dict(sd, strict=False, assign=True) + + assert len(model.blocks) == ANIMA_3_8B_NUM_BLOCKS + assert model.has_semantic_connector + assert result.unexpected_keys == [] + assert result.missing_keys == [] + + +def test_only_a_transformer_with_the_connector_declares_its_skip_pattern() -> None: + with accelerate.init_empty_weights(): + plain = AnimaTransformer(**ANIMA_TRANSFORMER_CONFIG) + expanded = AnimaTransformer(**ANIMA_TRANSFORMER_CONFIG, semantic_connector=AnimaSemanticConnectorConfig()) + assert not plain.has_semantic_connector + assert plain._skip_layerwise_casting_patterns is AnimaTransformer._skip_layerwise_casting_patterns + skipped = { + name + for name, module in expanded.named_modules() + if isinstance(module, torch.nn.Linear) + and any( + re.search(p, name) + for p in expanded._skip_layerwise_casting_patterns[len(plain._skip_layerwise_casting_patterns) :] + ) + } + assert skipped == { + "anima_v2_connector.semantic_resampler.time_mlp.0", + "anima_v2_connector.semantic_resampler.time_mlp.2", + *(f"anima_v2_connector.semantic_resampler.blocks.{i}.time_modulation" for i in range(6)), + } + + +class TestMaskedSdpa: + def test_rows_with_keys_match_plain_sdpa(self) -> None: + q, k, v = (torch.randn(1, 2, 3, 8) for _ in range(3)) + mask = torch.tensor([True, False, True, True]).reshape(1, 1, 1, 4) + k, v = torch.randn(1, 2, 4, 8), torch.randn(1, 2, 4, 8) + expected = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask) + assert torch.allclose(masked_sdpa(q, k, v, mask), expected) + + def test_a_fully_masked_row_attends_to_nothing(self) -> None: + q, k, v = torch.randn(2, 2, 3, 8), torch.randn(2, 2, 4, 8), torch.randn(2, 2, 4, 8) + mask = torch.tensor([[True] * 4, [False] * 4]).reshape(2, 1, 1, 4) + out = masked_sdpa(q, k, v, mask) + assert torch.equal(out[1], torch.zeros_like(out[1])) + assert torch.allclose(out[0], torch.nn.functional.scaled_dot_product_attention(q[:1], k[:1], v[:1])) + + +def _tiny_connector() -> tuple[LLMAdapter, AnimaSemanticConnector]: + torch.manual_seed(0) + config = AnimaSemanticConnectorConfig( + num_queries=4, + resampler_blocks=2, + resampler_dim=32, + resampler_heads=4, + mlp_hidden_dim=48, + semantic_source_dim=24, + ) + adapter = LLMAdapter(vocab_size=50, dim=32, num_layers=2, num_heads=4) + connector = AnimaSemanticConnector(config, model_dim=32, num_heads=4, num_adapter_blocks=2) + for parameter in [*adapter.parameters(), *connector.parameters()]: + torch.nn.init.normal_(parameter, std=0.2) + return adapter.eval(), connector.eval() + + +@torch.no_grad() +def test_connector_output_changes_with_the_timestep() -> None: + adapter, connector = _tiny_connector() + source, ids = torch.randn(1, 5, 32), torch.randint(0, 50, (1, 6)) + states = [torch.randn(1, 7, 24) for _ in range(4)] + mask = torch.ones(1, 7, dtype=torch.bool) + + early = connector(adapter, source, ids, states, mask, torch.tensor([0.9])) + late = connector(adapter, source, ids, states, mask, torch.tensor([0.1])) + + assert early.shape == (1, 6, 32) + assert not torch.allclose(early, late) + + +@torch.no_grad() +def test_an_empty_prompt_contributes_no_qwen3_5_signal_through_the_anchor() -> None: + """A fully masked source: finite output, and the anchor's states make no difference.""" + adapter, connector = _tiny_connector() + source, ids = torch.randn(1, 5, 32), torch.randint(0, 50, (1, 6)) + mask = torch.zeros(1, 1, dtype=torch.bool) + t = torch.tensor([0.5]) + + one = connector(adapter, source, ids, [torch.randn(1, 1, 24) for _ in range(4)], mask, t) + other = connector(adapter, source, ids, [torch.randn(1, 1, 24) for _ in range(4)], mask, t) + + assert torch.isfinite(one).all() + # The resampler's own state still evolves (self-attention, MLP) but reads nothing from Qwen3.5 either. + assert torch.allclose(one, other) + + +def test_a_transformer_with_the_connector_refuses_to_run_without_qwen3_5() -> None: + with accelerate.init_empty_weights(): + model = AnimaTransformer( + **{**ANIMA_TRANSFORMER_CONFIG, "num_blocks": 1}, semantic_connector=AnimaSemanticConnectorConfig() + ) + with pytest.raises(ValueError, match="needs Qwen3.5 conditioning"): + model.preprocess_text_embeds(torch.empty(1, 3, 1024), torch.zeros(1, 3, dtype=torch.long)) diff --git a/tests/backend/architectures/test_variants.py b/tests/backend/architectures/test_variants.py index df728397b59..2375f97b1d5 100644 --- a/tests/backend/architectures/test_variants.py +++ b/tests/backend/architectures/test_variants.py @@ -21,6 +21,7 @@ from invokeai.backend.model_manager.configs.base import Config_Base from invokeai.backend.model_manager.configs.factory import AnyModelConfig # noqa: F401 (registers every config class) from invokeai.backend.model_manager.taxonomy import ( + AnimaVariantType, AnyVariant, BaseModelType, ClipVariantType, @@ -28,14 +29,17 @@ ModelType, Qwen3VariantType, Qwen3VLVariantType, + Qwen35VariantType, variant_type_adapter, ) -BASE_AGNOSTIC_VARIANT_ENUMS = frozenset({ClipVariantType, Qwen3VariantType, Qwen3VLVariantType, MistralVariantType}) +BASE_AGNOSTIC_VARIANT_ENUMS = frozenset( + {ClipVariantType, Qwen3VariantType, Qwen3VLVariantType, Qwen35VariantType, MistralVariantType} +) """Variant enums that cannot be declared by any architecture. -All four sit on `base=Any` configs -- CLIP embedders and the Qwen3, Qwen3-VL and Mistral text -encoders are components shared across architectures, not architectures. `Any` is a sentinel the +All of them sit on `base=Any` configs -- CLIP embedders and the Qwen3, Qwen3-VL, Qwen3.5 and Mistral +text encoders are components shared across architectures, not architectures. `Any` is a sentinel the registry refuses to register, so these are named here to keep the completeness check against `AnyVariant` total rather than quietly partial. """ @@ -223,7 +227,14 @@ def test_the_cases_that_motivate_the_model_type_dimension( def test_an_architecture_without_variants_declares_nothing() -> None: - # CogView4, ERNIE-Image, Ideogram 4 and Anima model no variants at all. An empty facet would be + # CogView4, ERNIE-Image and Ideogram 4 model no variants at all. An empty facet would be # indistinguishable from an absent one, so they omit it. - for base in (BaseModelType.CogView4, BaseModelType.ErnieImage, BaseModelType.Ideogram4, BaseModelType.Anima): + for base in (BaseModelType.CogView4, BaseModelType.ErnieImage, BaseModelType.Ideogram4): assert get_variant_enum(base, ModelType.Main) is None + + +def test_anima_variants_label_mains_only() -> None: + # The variant says which encoders a main model needs. Anima's LLLite control adapters share the + # base and carry no variant. + assert get_variant_enum(BaseModelType.Anima, ModelType.Main) is AnimaVariantType + assert get_variant_enum(BaseModelType.Anima, ModelType.ControlNet) is None diff --git a/tests/backend/model_manager/configs/test_anima_finetune_identification.py b/tests/backend/model_manager/configs/test_anima_finetune_identification.py new file mode 100644 index 00000000000..fc70879eb41 --- /dev/null +++ b/tests/backend/model_manager/configs/test_anima_finetune_identification.py @@ -0,0 +1,124 @@ +"""Identification of the depth-expanded Anima finetunes and of the Qwen3.5 encoder Anima-3.8B reads. + +Written against small safetensors files carrying the real key names (and, where identification reads +it, the real header), because identification reads both through `ModelOnDisk`. +""" + +from pathlib import Path + +import pytest +import torch +from safetensors.torch import save_file + +from invokeai.backend.model_manager.configs.factory import AnyModelConfig, ModelConfigFactory +from invokeai.backend.model_manager.configs.identification_utils import InvalidMatchError +from invokeai.backend.model_manager.configs.main import Main_Checkpoint_Anima_Config, _get_anima_variant +from invokeai.backend.model_manager.configs.qwen3_5_encoder import Qwen35Encoder_Checkpoint_Config +from invokeai.backend.model_manager.configs.qwen3_encoder import Qwen3Encoder_Checkpoint_Config +from invokeai.backend.model_manager.model_on_disk import ModelOnDisk +from invokeai.backend.model_manager.taxonomy import AnimaVariantType, Qwen35VariantType +from tests.backend.model_manager.load.state_dicts.anima_3_8b_connector_keys import metadata as anima_3_8b_metadata + +_ANIMA_KEYS = [ + "net.llm_adapter.blocks.0.cross_attn.k_norm.weight", + "net.blocks.0.adaln_modulation_cross_attn.1.weight", + "net.t_embedder.1.linear_1.weight", + "net.x_embedder.proj.1.weight", + "net.final_layer.adaln_modulation.1.weight", +] +_CONNECTOR_KEY = "net.anima_v2_connector.quality_anchor.layer_mix_logits" + + +def _write(path: Path, keys: dict[str, list[int]], metadata: dict[str, str] | None = None) -> ModelOnDisk: + save_file({k: torch.zeros(shape) for k, shape in keys.items()}, path, metadata=metadata) + return ModelOnDisk(path) + + +def _identify(mod: ModelOnDisk, override_fields: dict | None = None) -> AnyModelConfig | None: + """Identify through the real factory: every config class gets its say, so a double match shows.""" + result = ModelConfigFactory.from_model_on_disk(mod, override_fields, allow_unknown=False) + matched = [type(v).__name__ for v in result.details.values() if not isinstance(v, Exception)] + assert len(matched) <= 1, f"more than one config matched: {matched}" + return result.config + + +def _anima(tmp_path: Path, *, connector: bool, metadata: dict[str, str] | None = None) -> ModelOnDisk: + keys = {k: [1] for k in _ANIMA_KEYS} + if connector: + keys[_CONNECTOR_KEY] = [6, 4] + return _write(tmp_path / "anima.safetensors", keys, metadata) + + +class TestAnimaVariant: + @pytest.mark.parametrize("prefix", ["", "net.", "model.diffusion_model."]) + def test_a_bundled_connector_needs_qwen3_5(self, prefix: str) -> None: + sd = {f"{prefix}blocks.0.mlp.layer1.weight": None, f"{prefix}anima_v2_connector.query_tokens": None} + assert _get_anima_variant(sd) is AnimaVariantType.Qwen35 + + def test_everything_else_is_qwen3_only(self) -> None: + # Any depth: the official 28 blocks and Anima-2.9B's 40 are the same variant. + assert _get_anima_variant({"net.blocks.39.mlp.layer1.weight": None}) is AnimaVariantType.Qwen3 + + def test_official_release_and_2_9b_identify_as_qwen3(self, tmp_path: Path) -> None: + config = _identify(_anima(tmp_path, connector=False)) + assert isinstance(config, Main_Checkpoint_Anima_Config) + assert config.variant is AnimaVariantType.Qwen3 + + def test_3_8b_v1_1_identifies_as_qwen35(self, tmp_path: Path) -> None: + mod = _anima(tmp_path, connector=True, metadata=anima_3_8b_metadata) + config = _identify(mod) + assert isinstance(config, Main_Checkpoint_Anima_Config) + assert config.variant is AnimaVariantType.Qwen35 + # Default settings follow the variant (the reference workflow's CFG 6 / 40 steps). + assert config.default_settings is not None + assert (config.default_settings.steps, config.default_settings.cfg_scale) == (40, 6.0) + + def test_3_8b_v1_0_without_its_adapter_is_refused_not_installed(self, tmp_path: Path) -> None: + # The v1.0 DiT is a plain 52-block Anima on the outside; only its header says it was trained + # jointly with a Qwen3.5 adapter it does not carry. + mod = _anima(tmp_path, connector=False, metadata={"qwen35_joint_dit_blocks": "[0, 1, 2]"}) + result = ModelConfigFactory.from_model_on_disk(mod, allow_unknown=True) + # Final, not "unknown": nothing is written to the database. + assert result.config is None + refusal = result.details[Main_Checkpoint_Anima_Config.__name__] + assert isinstance(refusal, InvalidMatchError) + assert "v1.0" in str(refusal) + + def test_an_explicit_variant_override_wins(self, tmp_path: Path) -> None: + mod = _anima(tmp_path, connector=False) + config = _identify(mod, {"variant": AnimaVariantType.Qwen35}) + assert isinstance(config, Main_Checkpoint_Anima_Config) + assert config.variant is AnimaVariantType.Qwen35 + + +# The keys of Anima-3.8B's `qwen35_4b.safetensors` that identification reads, at token extents except +# where the width is the signal. +_QWEN3_5_KEYS = { + "embed_tokens.weight": [4, 2560], + "layers.0.linear_attn.in_proj_qkv.weight": [4, 4], + "layers.3.self_attn.q_proj.weight": [4, 4], + "layers.3.self_attn.q_norm.weight": [4], +} + + +class TestQwen35Encoder: + @pytest.mark.parametrize("prefix", ["", "model.", "model.language_model."]) + def test_qwen3_5_4b_is_identified(self, tmp_path: Path, prefix: str) -> None: + mod = _write(tmp_path / "q35.safetensors", {f"{prefix}{k}": s for k, s in _QWEN3_5_KEYS.items()}) + config = _identify(mod) + assert isinstance(config, Qwen35Encoder_Checkpoint_Config) + assert config.variant is Qwen35VariantType.Qwen35_4B + + def test_an_unknown_width_is_not_a_match(self, tmp_path: Path) -> None: + keys = {**_QWEN3_5_KEYS, "embed_tokens.weight": [4, 1024]} + assert _identify(_write(tmp_path / "q35.safetensors", keys)) is None + + def test_a_qwen3_encoder_is_not_a_match(self, tmp_path: Path) -> None: + keys = {"model.embed_tokens.weight": [4, 2560], "model.layers.0.self_attn.q_norm.weight": [4]} + assert isinstance(_identify(_write(tmp_path / "q3.safetensors", keys)), Qwen3Encoder_Checkpoint_Config) + + def test_the_qwen3_config_refuses_qwen3_5_at_the_same_width(self, tmp_path: Path) -> None: + # Both 4Bs are 2560 wide and a `model.`-prefixed Qwen3.5 export satisfies every Qwen3 heuristic; + # only the linear-attention layers tell them apart. + mod = _write(tmp_path / "q35.safetensors", {f"model.{k}": s for k, s in _QWEN3_5_KEYS.items()}) + assert isinstance(_identify(mod), Qwen35Encoder_Checkpoint_Config) diff --git a/tests/backend/model_manager/load/state_dicts/anima_2_9b_keys.py b/tests/backend/model_manager/load/state_dicts/anima_2_9b_keys.py new file mode 100644 index 00000000000..19d45046275 --- /dev/null +++ b/tests/backend/model_manager/load/state_dicts/anima_2_9b_keys.py @@ -0,0 +1,181 @@ +"""Representative key layout of the Anima-2.9B depth-expanded single-file checkpoint. + +Captured from `Gazingstars123/Anima-2.9B` `Anima-2.9B-preview-v1.safetensors` (bf16). The full +checkpoint has 928 tensors under the `net.` prefix: 40 DiT blocks of 20 tensors each, against 28 in +the official release, grown by interleaved insertion with every other dimension unchanged. This +fixture keeps the first and the last DiT block and every non-DiT-block key -- among them the three +`pos_embedder.*` buffers this export serializes and the official one does not. +""" + +ANIMA_2_9B_NUM_BLOCKS = 40 + +state_dict_keys: dict[str, list[int]] = { + "net.blocks.0.adaln_modulation_cross_attn.1.weight": [256, 2048], + "net.blocks.0.adaln_modulation_cross_attn.2.weight": [6144, 256], + "net.blocks.0.adaln_modulation_mlp.1.weight": [256, 2048], + "net.blocks.0.adaln_modulation_mlp.2.weight": [6144, 256], + "net.blocks.0.adaln_modulation_self_attn.1.weight": [256, 2048], + "net.blocks.0.adaln_modulation_self_attn.2.weight": [6144, 256], + "net.blocks.0.cross_attn.k_norm.weight": [128], + "net.blocks.0.cross_attn.k_proj.weight": [2048, 1024], + "net.blocks.0.cross_attn.output_proj.weight": [2048, 2048], + "net.blocks.0.cross_attn.q_norm.weight": [128], + "net.blocks.0.cross_attn.q_proj.weight": [2048, 2048], + "net.blocks.0.cross_attn.v_proj.weight": [2048, 1024], + "net.blocks.0.mlp.layer1.weight": [8192, 2048], + "net.blocks.0.mlp.layer2.weight": [2048, 8192], + "net.blocks.0.self_attn.k_norm.weight": [128], + "net.blocks.0.self_attn.k_proj.weight": [2048, 2048], + "net.blocks.0.self_attn.output_proj.weight": [2048, 2048], + "net.blocks.0.self_attn.q_norm.weight": [128], + "net.blocks.0.self_attn.q_proj.weight": [2048, 2048], + "net.blocks.0.self_attn.v_proj.weight": [2048, 2048], + "net.blocks.39.adaln_modulation_cross_attn.1.weight": [256, 2048], + "net.blocks.39.adaln_modulation_cross_attn.2.weight": [6144, 256], + "net.blocks.39.adaln_modulation_mlp.1.weight": [256, 2048], + "net.blocks.39.adaln_modulation_mlp.2.weight": [6144, 256], + "net.blocks.39.adaln_modulation_self_attn.1.weight": [256, 2048], + "net.blocks.39.adaln_modulation_self_attn.2.weight": [6144, 256], + "net.blocks.39.cross_attn.k_norm.weight": [128], + "net.blocks.39.cross_attn.k_proj.weight": [2048, 1024], + "net.blocks.39.cross_attn.output_proj.weight": [2048, 2048], + 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"net.llm_adapter.embed.weight": [32128, 1024], + "net.llm_adapter.norm.weight": [1024], + "net.llm_adapter.out_proj.bias": [1024], + "net.llm_adapter.out_proj.weight": [1024, 1024], + "net.pos_embedder.dim_spatial_range": [21], + "net.pos_embedder.dim_temporal_range": [22], + "net.pos_embedder.seq": [256], + "net.t_embedder.1.linear_1.weight": [2048, 2048], + "net.t_embedder.1.linear_2.weight": [6144, 2048], + "net.t_embedding_norm.weight": [2048], + "net.x_embedder.proj.1.weight": [2048, 68], +} diff --git a/tests/backend/model_manager/load/state_dicts/anima_3_8b_connector_keys.py b/tests/backend/model_manager/load/state_dicts/anima_3_8b_connector_keys.py new file mode 100644 index 00000000000..65ade5f8b69 --- /dev/null +++ b/tests/backend/model_manager/load/state_dicts/anima_3_8b_connector_keys.py @@ -0,0 +1,218 @@ +"""The semantic connector Anima-3.8B v1.1 bundles, as its checkpoint records it. + +Captured from `lylogummy/Anima-3.8B` `difussion_models/Anima-3.8B-v1.1.safetensors` (bf16, 1358 tensors): +all 190 tensors under `net.anima_v2_connector.`, and the header entries the loader and identification +read. The DiT itself is 52 blocks with the per-block layout of the official release (see +`anima_2_9b_keys.py`); `qwen35_joint_dit_blocks` is the header key that marks a checkpoint trained jointly +with Qwen3.5 -- present here and in the unbundled v1.0 DiT. +""" + +ANIMA_3_8B_NUM_BLOCKS = 52 + +metadata: dict[str, str] = { + "anima_v2_adapter_architecture": "anima_qwen35_quality_anchored_semantic_connector_v2", + "anima_v2_adapter_layer_indices": "[7, 15, 23, 31]", + "anima_v2_adapter_semantic_query_tokens": "64", + "anima_v2_adapter_semantic_resampler_blocks": "6", + "anima_v2_adapter_semantic_resampler_dim": "2048", + "anima_v2_adapter_semantic_resampler_heads": "16", + "anima_v2_adapter_semantic_resampler_mlp_hidden_dim": "5632", + "architecture": "anima_3_8b_semantic_connector_v2_bundle", + "anima_v2_bundle_format": "1", + "anima_v2_connector_prefix": "net.anima_v2_connector.", + "new_block_count": "52", + "qwen35_joint_dit_blocks": "[0, 1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 14, 16, 17, 18, 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of the checkpoint it loads. + +Anima-2.9B (40 blocks) and Anima-3.8B (52 blocks) are depth-expanded finetunes of the official +28-block release. A transformer built at the official depth loads the first 28 blocks of such a +checkpoint and drops the rest as unexpected keys under `strict=False` -- logged at DEBUG only, so the +failure is a degraded image, not an error. These tests pin the depth detection to the real key +layouts and check that a model built from it takes every key. +""" + +import re + +import accelerate +import pytest +import torch + +from invokeai.backend.anima.anima_transformer import AnimaTransformer +from invokeai.backend.model_manager.load.model_loaders.anima import ( + ANIMA_TRANSFORMER_CONFIG, + _filter_non_model_keys, + _strip_anima_bundle_prefix, + anima_transformer_config, + count_anima_dit_blocks, +) +from tests.backend.model_manager.load.state_dicts.anima_2_9b_keys import ANIMA_2_9B_NUM_BLOCKS +from tests.backend.model_manager.load.state_dicts.anima_2_9b_keys import state_dict_keys as anima_2_9b_keys +from tests.backend.model_manager.load.state_dicts.anima_comfyui_keys import state_dict_keys as anima_keys + +_BLOCK_KEY = re.compile(r"^net\.blocks\.(\d+)\.(.+)$") + + +def _full_depth_meta_state_dict(fixture: dict[str, list[int]], num_blocks: int) -> dict[str, torch.Tensor]: + """Expand a fixture's block-0 keys to `num_blocks` blocks, as meta tensors. + + Meta, because the real extents of 40 blocks are billions of elements and nothing here reads a + value. + """ + block_suffixes = { + m.group(2): shape for key, shape in fixture.items() if (m := _BLOCK_KEY.match(key)) and m.group(1) == "0" + } + sd = {key: torch.empty(shape, device="meta") for key, shape in fixture.items() if not _BLOCK_KEY.match(key)} + for index in range(num_blocks): + for suffix, shape in block_suffixes.items(): + sd[f"net.blocks.{index}.{suffix}"] = torch.empty(shape, device="meta") + return sd + + +def _prepare(sd: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: + """The two key passes the loader runs before it builds the model.""" + return _filter_non_model_keys(_strip_anima_bundle_prefix(sd)) + + +class TestCountAnimaDitBlocks: + def test_official_release_has_28_blocks(self) -> None: + sd = _prepare(_full_depth_meta_state_dict(anima_keys, 28)) + assert count_anima_dit_blocks(sd) == 28 == ANIMA_TRANSFORMER_CONFIG["num_blocks"] + + def test_anima_2_9b_has_40_blocks(self) -> None: + sd = _prepare(_full_depth_meta_state_dict(anima_2_9b_keys, ANIMA_2_9B_NUM_BLOCKS)) + assert count_anima_dit_blocks(sd) == 40 + + def test_llm_adapter_blocks_are_not_counted(self) -> None: + # `llm_adapter.blocks.` shares the `blocks.` segment; only a key that starts with it counts. + sd = {"blocks.0.mlp.layer1.weight": torch.empty(0), "llm_adapter.blocks.5.mlp.0.weight": torch.empty(0)} + assert count_anima_dit_blocks(sd) == 1 + + def test_gap_in_block_indices_is_refused(self) -> None: + sd = {f"blocks.{i}.mlp.layer1.weight": torch.empty(0) for i in (0, 1, 3)} + with pytest.raises(ValueError, match=r"gaps .*\[2\]"): + count_anima_dit_blocks(sd) + + def test_state_dict_without_blocks_is_refused(self) -> None: + with pytest.raises(ValueError, match="no DiT blocks"): + count_anima_dit_blocks({"final_layer.linear.weight": torch.empty(0)}) + + +class TestRealAnima29BLayout: + """What the captured 2.9B header says, beyond the block count.""" + + def test_last_block_has_the_same_layout_as_the_first(self) -> None: + first = {m.group(2): s for k, s in anima_2_9b_keys.items() if (m := _BLOCK_KEY.match(k)) and m.group(1) == "0"} + last = { + m.group(2): s + for k, s in anima_2_9b_keys.items() + if (m := _BLOCK_KEY.match(k)) and m.group(1) == str(ANIMA_2_9B_NUM_BLOCKS - 1) + } + assert first and first == last + + def test_every_non_block_tensor_matches_the_official_release(self) -> None: + # Only the depth changed: the LLM adapter, embedders and final layer are the official shapes. + official = {k: s for k, s in anima_keys.items() if not _BLOCK_KEY.match(k)} + expanded = {k: s for k, s in anima_2_9b_keys.items() if not _BLOCK_KEY.match(k)} + assert {k: s for k, s in expanded.items() if not k.startswith("net.pos_embedder.")} == official + + +@pytest.mark.parametrize( + ("fixture", "num_blocks"), + [(anima_keys, 28), (anima_2_9b_keys, ANIMA_2_9B_NUM_BLOCKS)], + ids=["official-28", "anima-2.9b-40"], +) +def test_model_built_at_detected_depth_takes_every_key(fixture: dict[str, list[int]], num_blocks: int) -> None: + sd = _prepare(_full_depth_meta_state_dict(fixture, num_blocks)) + + with accelerate.init_empty_weights(): + model = AnimaTransformer(**anima_transformer_config(sd)) + result = model.load_state_dict(sd, strict=False, assign=True) + + assert len(model.blocks) == num_blocks + assert result.unexpected_keys == [] + assert result.missing_keys == [] diff --git a/tests/backend/model_manager/load/test_anima_fp8_wiring.py b/tests/backend/model_manager/load/test_anima_fp8_wiring.py index 485e96dbb64..b9b66bed8ca 100644 --- a/tests/backend/model_manager/load/test_anima_fp8_wiring.py +++ b/tests/backend/model_manager/load/test_anima_fp8_wiring.py @@ -121,6 +121,10 @@ def test_single_file_loader_applies_fp8_layerwise_casting(monkeypatch, tmp_path) monkeypatch.setattr(anima_transformer_module, "AnimaTransformer", _TinyAnimaTransformer) monkeypatch.setattr(safetensors.torch, "load_file", lambda _path: {"weight": torch.ones(2, 2)}) + # The stand-in state dict has no DiT blocks to read a depth from, and the tiny model takes no config. + monkeypatch.setattr( + "invokeai.backend.model_manager.load.model_loaders.anima.anima_transformer_config", lambda _sd: {} + ) monkeypatch.setattr( "invokeai.backend.model_manager.load.model_loaders.anima.TorchDevice.choose_torch_device", lambda: torch.device("cpu"), diff --git a/tests/backend/qwen3_5/test_qwen3_5_encoder.py b/tests/backend/qwen3_5/test_qwen3_5_encoder.py new file mode 100644 index 00000000000..e3013084ebf --- /dev/null +++ b/tests/backend/qwen3_5/test_qwen3_5_encoder.py @@ -0,0 +1,130 @@ +"""`Qwen35Encoder` runs the `transformers` Qwen3.5 decoder layers in its own loop. + +The loop exists so the encoder can stop at the deepest requested layer and return intermediate +outputs without the final norm. These tests pin that it computes exactly what `Qwen3_5TextModel` +computes for the same weights, and what the attention-only last layer means. (Against the reference +implementation and the real Anima-3.8B encoder it was checked separately: fp32 relative error ~1e-6.) +""" + +import pytest +import torch +from transformers.models.qwen3_5.configuration_qwen3_5 import Qwen3_5TextConfig +from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5TextModel + +from invokeai.backend.qwen3_5.qwen3_5_encoder import ( + Qwen35Encoder, + load_bundled_qwen3_5_tokenizer, + qwen3_5_4b_text_config, +) + + +def _tiny_config() -> Qwen3_5TextConfig: + config = Qwen3_5TextConfig( + vocab_size=64, + hidden_size=32, + intermediate_size=48, + num_hidden_layers=8, + num_attention_heads=4, + num_key_value_heads=2, + head_dim=16, + linear_num_key_heads=2, + linear_num_value_heads=4, + linear_key_head_dim=8, + linear_value_head_dim=8, + linear_conv_kernel_dim=4, + full_attention_interval=4, + rope_parameters={ + "rope_type": "default", + "rope_theta": 10000.0, + "partial_rotary_factor": 0.25, + "mrope_section": [1, 1, 0], + "mrope_interleaved": True, + }, + ) + config._attn_implementation = "sdpa" + return config + + +@pytest.fixture +def models() -> tuple[Qwen3_5TextModel, Qwen35Encoder]: + torch.manual_seed(0) + config = _tiny_config() + reference = Qwen3_5TextModel(config).eval() + for parameter in reference.parameters(): + torch.nn.init.normal_(parameter, std=0.1) + encoder = Qwen35Encoder(config).eval() + encoder.load_state_dict({k: v for k, v in reference.state_dict().items() if not k.startswith("norm.")}, strict=True) + return reference, encoder + + +def _layer_outputs(reference: Qwen3_5TextModel, input_ids: torch.Tensor) -> list[torch.Tensor]: + """Each decoder layer's output, captured by hook -- not through `output_hidden_states`, whose last entry is normed.""" + captured: list[torch.Tensor] = [] + hooks = [layer.register_forward_hook(lambda _m, _i, out: captured.append(out)) for layer in reference.layers] + try: + with torch.no_grad(): + reference(input_ids=input_ids) + finally: + for hook in hooks: + hook.remove() + return captured + + +def test_layer_outputs_match_the_transformers_model(models) -> None: + reference, encoder = models + input_ids = torch.randint(0, 64, (1, 70)) # longer than one 64-token delta-rule chunk + expected = _layer_outputs(reference, input_ids) + + outputs = encoder(input_ids, (1, 3, 7)) + + for index, output in zip((1, 3, 7), outputs, strict=True): + torch.testing.assert_close(output, expected[index]) + + +def test_attention_only_last_layer_skips_exactly_its_mlp(models) -> None: + reference, encoder = models + input_ids = torch.randint(0, 64, (1, 9)) + expected = _layer_outputs(reference, input_ids) + last = encoder.layers[7] + with torch.no_grad(): + residual = expected[6] + mlp_term = last.mlp(last.post_attention_layernorm(_post_attention(last, residual))) + + (output,) = encoder(input_ids, (7,), last_layer_attention_only=True) + + torch.testing.assert_close(output + mlp_term, expected[7]) + + +def _post_attention(layer, hidden: torch.Tensor) -> torch.Tensor: + rotary = Qwen35Encoder(_tiny_config()).rotary_emb + position_ids = torch.arange(hidden.shape[1]).unsqueeze(0).unsqueeze(0).expand(3, -1, -1) + return hidden + Qwen35Encoder._token_mixer(layer, hidden, rotary(hidden, position_ids)) + + +def test_layers_past_the_deepest_requested_one_are_not_run(models) -> None: + _, encoder = models + calls: list[int] = [] + for index, layer in enumerate(encoder.layers): + layer.register_forward_hook(lambda *_a, i=index: calls.append(i)) + + encoder(torch.randint(0, 64, (1, 5)), (2,)) + + assert calls == [0, 1, 2] + + +def test_the_vendored_4b_config_is_the_published_one() -> None: + config = qwen3_5_4b_text_config() + assert (config.hidden_size, config.num_hidden_layers, config.vocab_size) == (2560, 32, 248320) + assert [i for i, t in enumerate(config.layer_types) if t == "full_attention"] == [3, 7, 11, 15, 19, 23, 27, 31] + assert config.rope_parameters["partial_rotary_factor"] == 0.25 + assert config._attn_implementation == "sdpa" + + +def test_the_vendored_tokenizer_encodes_offline() -> None: + tokenizer = load_bundled_qwen3_5_tokenizer() + # Token ids pinned from the tokenizer the reference extension ships (Qwen/Qwen3.5-4B @ 851bf6e). + assert tokenizer.encode("1girl, Miku from Vocaloid", add_special_tokens=False) == [ + 16, 27620, 11, 380, 36974, 494, 93948, 573, + ] # fmt: skip + assert tokenizer.encode("", add_special_tokens=False) == [] + assert tokenizer.pad_token == "<|endoftext|>" diff --git a/tests/test_package_data.py b/tests/test_package_data.py new file mode 100644 index 00000000000..eab4bbbbf41 --- /dev/null +++ b/tests/test_package_data.py @@ -0,0 +1,47 @@ +"""Every data file a backend module reads at runtime ships in the wheel. + +Vendored tokenizers and configs are read from the installed package, so a file that +`[tool.setuptools.package-data]` does not match is absent from a wheel install: the loader raises +`FileNotFoundError`, or -- for a tokenizer missing only its vocabulary -- encodes every prompt to +nothing. A source checkout has every file, so nothing but this test notices. +""" + +import tomllib +from fnmatch import fnmatch +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +BACKEND = REPO_ROOT / "invokeai" / "backend" +DATA_SUFFIXES = (".json", ".json.gz") + + +def _package_data() -> dict[str, list[str]]: + with open(REPO_ROOT / "pyproject.toml", "rb") as f: + return tomllib.load(f)["tool"]["setuptools"]["package-data"] + + +def _is_shipped(path: Path, package_data: dict[str, list[str]]) -> bool: + """True if a package-data glob of `path`'s package, or of any package above it, matches it.""" + relative = path.relative_to(REPO_ROOT) + parts = relative.parts + for depth in range(len(parts) - 1, 0, -1): + package = ".".join(parts[:depth]) + member = "/".join(parts[depth:]) + for pattern in package_data.get(package, []): + # setuptools globs: `**` spans directories, `*` stays within one. + if fnmatch(member, pattern) and (pattern.count("/") == member.count("/") or "**" in pattern): + return True + return False + + +def test_every_vendored_backend_data_file_is_package_data() -> None: + package_data = _package_data() + vendored = [ + p + for p in BACKEND.rglob("*") + if p.is_file() and p.name.endswith(DATA_SUFFIXES) and "__pycache__" not in p.parts + ] + assert vendored, "found no vendored data files -- the scan is broken" + + missing = sorted(str(p.relative_to(REPO_ROOT)) for p in vendored if not _is_shipped(p, package_data)) + assert not missing, "not matched by [tool.setuptools.package-data] in pyproject.toml:\n " + "\n ".join(missing) From 347f948e450fe69835cf8b47f706245680e624c1 Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Fri, 2 Oct 2026 01:01:29 +0200 Subject: [PATCH 2/5] style: ruff format test_package_data.py --- tests/test_package_data.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/tests/test_package_data.py b/tests/test_package_data.py index eab4bbbbf41..5d4d287b607 100644 --- a/tests/test_package_data.py +++ b/tests/test_package_data.py @@ -37,9 +37,7 @@ def _is_shipped(path: Path, package_data: dict[str, list[str]]) -> bool: def test_every_vendored_backend_data_file_is_package_data() -> None: package_data = _package_data() vendored = [ - p - for p in BACKEND.rglob("*") - if p.is_file() and p.name.endswith(DATA_SUFFIXES) and "__pycache__" not in p.parts + p for p in BACKEND.rglob("*") if p.is_file() and p.name.endswith(DATA_SUFFIXES) and "__pycache__" not in p.parts ] assert vendored, "found no vendored data files -- the scan is broken" From 4c83925d6b66ecdeaaa7cc230aaf0a2226c22811 Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Fri, 2 Oct 2026 04:31:16 +0200 Subject: [PATCH 3/5] perf(anima): import the Qwen3.5 encoder only when a prompt uses it The prompt node imported invokeai.backend.qwen3_5 at module level, which pulls in transformers' qwen3_5 modeling module (~0.25 s) on every app start for a node input only Anima-3.8B uses. It is now imported where the encoder runs, as the loader already did. A subprocess test pins that importing the node leaves transformers.models.qwen3_5 unloaded. --- .../text_encoder/anima_text_encoder.py | 5 ++++- .../test_anima_qwen3_5_conditioning.py | 20 ++++++++++++++++++- 2 files changed, 23 insertions(+), 2 deletions(-) diff --git a/invokeai/app/invocations/text_encoder/anima_text_encoder.py b/invokeai/app/invocations/text_encoder/anima_text_encoder.py index 3774bb5a248..3647a766c7d 100644 --- a/invokeai/app/invocations/text_encoder/anima_text_encoder.py +++ b/invokeai/app/invocations/text_encoder/anima_text_encoder.py @@ -40,7 +40,6 @@ from invokeai.backend.patches.lora_conversions.anima_lora_constants import ANIMA_LORA_QWEN3_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.qwen3_5.qwen3_5_encoder import Qwen35Encoder from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ( AnimaConditioningInfo, ConditioningFieldData, @@ -268,6 +267,10 @@ def _encode_qwen3_5(self, context: InvocationContext) -> tuple[torch.Tensor, tor token_ids = [pad_token_id] mask = torch.full((len(token_ids),), not is_empty, dtype=torch.bool) + # Imported here: transformers' qwen3_5 modeling module costs ~0.25 s, which every app start would + # otherwise pay for a node that only Anima-3.8B uses. + from invokeai.backend.qwen3_5.qwen3_5_encoder import Qwen35Encoder + encoder_info = context.models.load(self.qwen3_5_encoder.text_encoder) with encoder_info.model_on_device() as (_, encoder): if not isinstance(encoder, Qwen35Encoder): diff --git a/tests/app/invocations/test_anima_qwen3_5_conditioning.py b/tests/app/invocations/test_anima_qwen3_5_conditioning.py index 6844b236269..34fb231a0c6 100644 --- a/tests/app/invocations/test_anima_qwen3_5_conditioning.py +++ b/tests/app/invocations/test_anima_qwen3_5_conditioning.py @@ -5,6 +5,8 @@ it -- while every other Anima model keeps computing its context once. """ +import subprocess +import sys from contextlib import contextmanager, nullcontext from types import SimpleNamespace from unittest.mock import MagicMock @@ -53,7 +55,8 @@ def forward(self, input_ids, layer_indices, last_layer_attention_only=False): def _encode_qwen3_5(monkeypatch, prompt: str) -> tuple[torch.Tensor, torch.Tensor, _FakeQwen35Encoder]: module = "invokeai.app.invocations.text_encoder.anima_text_encoder" monkeypatch.setattr(f"{module}.PreTrainedTokenizerBase", _FakeQwen35Tokenizer) - monkeypatch.setattr(f"{module}.Qwen35Encoder", _FakeQwen35Encoder) + # The node imports Qwen35Encoder when it runs, from its own module. + monkeypatch.setattr("invokeai.backend.qwen3_5.qwen3_5_encoder.Qwen35Encoder", _FakeQwen35Encoder) encoder = _FakeQwen35Encoder() context = MagicMock() context.models.load.side_effect = [_Loaded(_FakeQwen35Tokenizer()), _Loaded(encoder)] @@ -72,6 +75,20 @@ def test_qwen3_5_is_read_at_the_trained_layers_with_an_attention_only_last_layer assert mask.tolist() == [True, True, True] +def test_the_prompt_node_does_not_import_transformers_qwen3_5_at_startup() -> None: + """Every app start imports every node module; transformers' qwen3_5 modeling costs ~0.25 s of it.""" + program = ( + "import sys\n" + "import invokeai.app.invocations.text_encoder.anima_text_encoder\n" + "loaded = [m for m in sys.modules if m.startswith('transformers.models.qwen3_5')]\n" + "assert not loaded, loaded\n" + "print('OK')\n" + ) + result = subprocess.run([sys.executable, "-c", program], capture_output=True, text=True, timeout=300) + assert result.returncode == 0, f"stderr:\n{result.stderr[-3000:]}" + assert "OK" in result.stdout + + def test_an_empty_prompt_is_one_masked_padding_token(monkeypatch) -> None: states, mask, encoder = _encode_qwen3_5(monkeypatch, "") @@ -91,6 +108,7 @@ def __init__(self, connector: bool) -> None: self.has_semantic_connector = connector self.adapter_calls: list[tuple[torch.Tensor | None, bool]] = [] self.patch_spatial = 2 + self.blocks = torch.nn.ModuleList([torch.nn.Identity() for _ in range(28)]) def preprocess_text_embeds(self, text_embeds, text_ids, t5xxl_weights=None, *, semantic_states=None, semantic_mask=None, timesteps=None): # fmt: skip From bc85648e7c82ca2e485ace9095423f8ac8983897 Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Fri, 2 Oct 2026 04:31:26 +0200 Subject: [PATCH 4/5] feat(anima): apply Anima LoRAs and LLLite adapters to the blocks the finetunes kept Anima-2.9B and Anima-3.8B insert new DiT blocks between the existing ones, so an adapter trained on Anima patched different blocks from the third one on. Measured from the checkpoints: - Anima-2.9B holds all 28 blocks of Anima base-v1.0 bit for bit; its 12 new blocks sit at 2, 5, 8, ..., 36. - Anima-3.8B holds the 40 blocks of 2.9B nearly unchanged (cosine similarity >= 0.9997, 11 bit-identical), with 12 new blocks at 3, 7, ..., 47. Ten Anima base blocks are still bit-identical there, all where the composed table puts them. invokeai/backend/anima/block_layout.py records the tables. A LoRA's DiT block keys and an LLLite adapter's bindings move to the blocks of the model they were trained on, whose depth is read off the highest block they address: Anima adapters on 2.9B and 3.8B, 2.9B adapters on 3.8B. The cached LoRA is not modified; the denoise node applies a re-keyed copy that shares its layers. --- .../03.Models/Local Models/anima.mdx | 11 ++-- .../contributing/new-model-integration.mdx | 2 +- .../app/invocations/anima/anima_denoise.py | 20 ++++-- invokeai/backend/anima/block_layout.py | 47 ++++++++++++++ invokeai/backend/anima/control_net_lllite.py | 27 +++++++- .../anima_lora_conversion_utils.py | 31 ++++++++- tests/backend/anima/test_block_layout.py | 63 ++++++++++++++++++ .../backend/anima/test_control_net_lllite.py | 45 +++++++++++++ .../test_anima_lora_conversion_utils.py | 64 +++++++++++++++++++ 9 files changed, 296 insertions(+), 14 deletions(-) create mode 100644 invokeai/backend/anima/block_layout.py create mode 100644 tests/backend/anima/test_block_layout.py diff --git a/docs/src/content/docs/Users Guide/03.Models/Local Models/anima.mdx b/docs/src/content/docs/Users Guide/03.Models/Local Models/anima.mdx index 2260a444125..706c57b5a19 100644 --- a/docs/src/content/docs/Users Guide/03.Models/Local Models/anima.mdx +++ b/docs/src/content/docs/Users Guide/03.Models/Local Models/anima.mdx @@ -75,11 +75,12 @@ file and is refused at install with a message that says so. Both finetunes are derivatives of Anima and fall under the same non-commercial license. -:::caution[LoRAs and LLLite adapters] -LoRAs and ControlNet-LLLite adapters trained on Anima address its transformer blocks by position. The -finetunes insert their extra blocks between the original ones, so from the third block on, such an -adapter patches different blocks than it was trained on. It loads and runs, but expect weaker or -different results than on Anima itself. +:::note[LoRAs and LLLite adapters] +LoRAs and ControlNet-LLLite adapters address the transformer's blocks by position, and the finetunes +insert their extra blocks between the original ones. Invoke moves an adapter trained on Anima to the +blocks the finetune kept from Anima, and one trained on Anima-2.9B to its blocks in Anima-3.8B. +Anima-2.9B keeps Anima's blocks unchanged, so Anima adapters work there as on Anima itself. +Anima-3.8B trained most of them further, so expect weaker or different results there. ::: ## Generation settings diff --git a/docs/src/content/docs/contributing/new-model-integration.mdx b/docs/src/content/docs/contributing/new-model-integration.mdx index be2fe41cf81..f53f0e34d76 100644 --- a/docs/src/content/docs/contributing/new-model-integration.mdx +++ b/docs/src/content/docs/contributing/new-model-integration.mdx @@ -971,7 +971,7 @@ Anima-2.9B (40 blocks instead of 28) needed only the second row. Anima-3.8B (52 6. **Unsupported releases.** Refuse a recognized checkpoint you do not support with `InvalidMatchError` and a pointer to the one you do. -7. **Existing adapters.** LoRAs and control adapters address blocks by name, and names carry the block index. A depth expansion that inserts blocks between the original ones shifts every index after the first insertion, so an adapter trained on the original loads and runs against the wrong blocks. Say so in the user guide; a remap needs the insertion positions, which only some checkpoints record. +7. **Existing adapters.** LoRAs and control adapters address blocks by name, and names carry the block index. A depth expansion that inserts blocks between the original ones shifts every index after the first insertion, so an adapter trained on the original loads and runs against the wrong blocks. A remap needs the insertion positions, and model cards rarely list them, but the weights do. Compare each block of the expanded checkpoint with the original's: a block that was not trained further is bit-identical to its source, and one trained further still has a cosine similarity near 1 to it, while an inserted copy stays far lower. Record the measured table next to the architecture (Anima: `invokeai/backend/anima/block_layout.py`), apply it where the adapter binds, and say in the user guide which results to expect. :::tip[Checklist: Extending an architecture]{icon="approve-check"} diff --git a/invokeai/app/invocations/anima/anima_denoise.py b/invokeai/app/invocations/anima/anima_denoise.py index df3a13cec46..adc67402b0a 100644 --- a/invokeai/app/invocations/anima/anima_denoise.py +++ b/invokeai/app/invocations/anima/anima_denoise.py @@ -60,6 +60,7 @@ from invokeai.backend.model_manager.taxonomy import BaseModelType from invokeai.backend.patches.layer_patcher import LayerPatcher, PatchSpec from invokeai.backend.patches.lora_conversions.anima_lora_constants import ANIMA_LORA_TRANSFORMER_PREFIX +from invokeai.backend.patches.lora_conversions.anima_lora_conversion_utils import anima_lora_for_depth from invokeai.backend.patches.model_patch_raw import ModelPatchRaw from invokeai.backend.rectified_flow.rectified_flow_inpaint_extension import ( RectifiedFlowInpaintExtension, @@ -761,7 +762,7 @@ def _run_diffusion(self, context: InvocationContext) -> torch.Tensor: exit_stack.enter_context( LayerPatcher.apply_smart_model_patches( model=transformer, - patches=self._lora_iterator(context), + patches=self._lora_iterator(context, len(transformer.blocks)), prefix=ANIMA_LORA_TRANSFORMER_PREFIX, dtype=inference_dtype, cached_weights=cached_weights, @@ -990,8 +991,12 @@ def step_callback(state: PipelineIntermediateState) -> None: return step_callback - def _lora_iterator(self, context: InvocationContext) -> Iterator[PatchSpec]: - """Iterate over LoRA models to apply to the transformer.""" + def _lora_iterator(self, context: InvocationContext, transformer_depth: int) -> Iterator[PatchSpec]: + """Iterate over LoRA models to apply to the transformer. + + A LoRA trained on a shallower Anima has its blocks moved to where this depth-expanded model keeps + them (see `invokeai.backend.anima.block_layout`). + """ for lora in self.transformer.loras: lora_info = context.models.load(lora.lora) if not isinstance(lora_info.model, ModelPatchRaw): @@ -999,4 +1004,11 @@ def _lora_iterator(self, context: InvocationContext) -> Iterator[PatchSpec]: f"Expected ModelPatchRaw for LoRA '{lora.lora.key}', got {type(lora_info.model).__name__}. " "The LoRA model may be corrupted or incompatible." ) - yield (lora_info.model, lora.weight, lora_info.model_in_ram()) + patch, moved_from = anima_lora_for_depth(lora_info.model, transformer_depth) + if moved_from is not None: + name = lora_info.config.name if lora_info.config is not None else lora.lora.key + context.logger.info( + f"LoRA '{name}' was trained on a {moved_from}-block Anima; applying it to the " + f"matching blocks of this {transformer_depth}-block model." + ) + yield (patch, lora.weight, lora_info.model_in_ram()) diff --git a/invokeai/backend/anima/block_layout.py b/invokeai/backend/anima/block_layout.py new file mode 100644 index 00000000000..2319f09b355 --- /dev/null +++ b/invokeai/backend/anima/block_layout.py @@ -0,0 +1,47 @@ +"""Where the depth-expanded Anima finetunes keep the blocks of the model they grew from. + +Anima-2.9B and Anima-3.8B insert new DiT blocks between the existing ones (LLaMA-Pro style interleaved expansion), +so an adapter that addresses blocks by index -- a LoRA, a ControlNet-LLLite -- trained on the shallower model would, +from the third block on, patch different blocks than the ones it was trained on. These tables send it to the right +ones. Measured from the released checkpoints: + +- Anima-2.9B-preview-v1 (40 blocks) holds all 28 blocks of Anima base-v1.0 bit for bit: its card says only the new + layers were trained. The 12 new blocks sit at 2, 5, 8, 11, 14, 17, 21, 24, 27, 30, 33 and 36. +- Anima-3.8B v1.1 (52 blocks) holds the 40 blocks of Anima-2.9B nearly unchanged: each has a mean cosine similarity of + at least 0.9997 to its 2.9B block, and 11 are bit-identical. The 12 new blocks, at 3, 7, 11, ..., 47, are at about + 0.6 to the neighbor they were copied from. Ten blocks of Anima base-v1.0 are still bit-identical in it, all at the + positions below. + +Keyed by depth: these are the only depth-expanded Anima models. A future finetune with one of these depths but +another layout would need its own entry. +""" + +from typing import Optional + +ANIMA_BASE_DEPTH = 28 + +# (source depth, target depth) -> the target position of each source block. +_POSITIONS: dict[tuple[int, int], tuple[int, ...]] = { + (28, 40): (0, 1, 3, 4, 6, 7, 9, 10, 12, 13, 15, 16, 18, 19, 20, 22, 23, 25, 26, 28, 29, 31, 32, 34, 35, 37, 38, 39), + (40, 52): ( + 0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20, 21, 22, 24, 25, + 26, 28, 29, 30, 32, 33, 34, 36, 37, 38, 40, 41, 42, 44, 45, 46, 48, 49, 50, 51, + ), +} # fmt: skip +_POSITIONS[(28, 52)] = tuple(_POSITIONS[(40, 52)][i] for i in _POSITIONS[(28, 40)]) + +KNOWN_DEPTHS = (28, 40, 52) + + +def adapter_block_positions(max_block_index: int, target_depth: int) -> Optional[tuple[int, ...]]: + """Where, in a `target_depth`-block model, the blocks of the model an adapter was trained on sit. + + The adapter's own depth is read off the highest block it addresses: the smallest known depth that has that block. + An adapter trained on Anima base touches block 27; one trained on Anima-2.9B, a block past 27. Returns None when + the adapter addresses blocks by index as before -- same depth, a deeper adapter than the model, or no layout for + the pair. + """ + source_depth = next((depth for depth in KNOWN_DEPTHS if depth > max_block_index), None) + if source_depth is None or source_depth >= target_depth: + return None + return _POSITIONS.get((source_depth, target_depth)) diff --git a/invokeai/backend/anima/control_net_lllite.py b/invokeai/backend/anima/control_net_lllite.py index dd36f2e5b2f..df818222745 100644 --- a/invokeai/backend/anima/control_net_lllite.py +++ b/invokeai/backend/anima/control_net_lllite.py @@ -32,6 +32,9 @@ import torch.nn.functional as F from torch import nn +from invokeai.backend.anima.block_layout import adapter_block_positions +from invokeai.backend.util.logging import InvokeAILogger + ASPP_DEFAULT_DILATIONS: tuple[int, ...] = (1, 2, 4, 8) _SAVED_COND_PREFIX = "lllite_conditioning1." @@ -511,10 +514,21 @@ def set_multiplier(self, multiplier: float) -> None: m.multiplier = multiplier def apply_to(self, transformer: nn.Module) -> None: - """Swap the forward of each target Linear in ``transformer``. Idempotent.""" + """Swap the forward of each target Linear in ``transformer``. Idempotent. + + An adapter trained on a shallower Anima binds to the blocks where this depth-expanded model keeps + them (see ``invokeai.backend.anima.block_layout``). + """ self.restore() + depth = len(transformer.blocks) + positions = adapter_block_positions(self._max_block_index(), depth) + if positions is not None: + InvokeAILogger.get_logger(__name__).info( + f"ControlNet-LLLite addresses {self._max_block_index() + 1} blocks; binding it to the matching " + f"blocks of this {depth}-block model." + ) for m in self.lllite_modules: - target = self._resolve_target(transformer, m.lllite_name) + target = self._resolve_target(transformer, m.lllite_name, positions) if not isinstance(target, nn.Linear): raise TypeError(f"LLLite target for '{m.lllite_name}' is {type(target).__name__}, expected nn.Linear") if target.in_features != m.in_dim: @@ -536,12 +550,19 @@ def restore(self) -> None: for m in self.lllite_modules: m.unbind() + def _max_block_index(self) -> int: + return max( + int(match.group(1)) for m in self.lllite_modules if (match := MODULE_NAME_PATTERN.match(m.lllite_name)) + ) + @staticmethod - def _resolve_target(transformer: nn.Module, name: str) -> nn.Module: + def _resolve_target(transformer: nn.Module, name: str, positions: Sequence[int] | None = None) -> nn.Module: match = MODULE_NAME_PATTERN.match(name) if match is None: raise ValueError(f"Unrecognized LLLite module name: '{name}'") block_idx = int(match.group(1)) + if positions is not None: + block_idx = positions[block_idx] blocks = transformer.blocks if block_idx >= len(blocks): raise ValueError( diff --git a/invokeai/backend/patches/lora_conversions/anima_lora_conversion_utils.py b/invokeai/backend/patches/lora_conversions/anima_lora_conversion_utils.py index b55a96dca75..1707a2a2122 100644 --- a/invokeai/backend/patches/lora_conversions/anima_lora_conversion_utils.py +++ b/invokeai/backend/patches/lora_conversions/anima_lora_conversion_utils.py @@ -10,10 +10,11 @@ """ import re -from typing import Dict +from typing import Dict, Optional import torch +from invokeai.backend.anima.block_layout import KNOWN_DEPTHS, adapter_block_positions 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.anima_lora_constants import ( @@ -298,3 +299,31 @@ def lora_model_from_anima_state_dict(state_dict: Dict[str, torch.Tensor], alpha: layers[final_key] = layer return ModelPatchRaw(layers=layers) + + +_TRANSFORMER_BLOCK_KEY_RE = re.compile(rf"^{re.escape(ANIMA_LORA_TRANSFORMER_PREFIX)}blocks\.(\d+)\.") + + +def anima_lora_for_depth(patch: ModelPatchRaw, target_depth: int) -> tuple[ModelPatchRaw, Optional[int]]: + """The LoRA with its DiT block keys moved to where `target_depth`'s model keeps the blocks it was trained on. + + A LoRA trained on Anima base, applied to a depth-expanded finetune (see `invokeai.backend.anima.block_layout`), + would otherwise patch the wrong blocks from the third one on. Returns the patch -- a new one sharing the layers, + so the cached LoRA stays as it is -- and the depth it was moved from, or the patch itself and None when nothing + moves. Only `blocks.N.` keys of the transformer move; the LLM adapter's own `llm_adapter.blocks.N.` and the text + encoder's keys do not. + """ + indices = [int(match.group(1)) for key in patch.layers if (match := _TRANSFORMER_BLOCK_KEY_RE.match(key))] + if not indices: + return patch, None + positions = adapter_block_positions(max(indices), target_depth) + if positions is None: + return patch, None + + def moved(key: str) -> str: + return _TRANSFORMER_BLOCK_KEY_RE.sub( + lambda m: f"{ANIMA_LORA_TRANSFORMER_PREFIX}blocks.{positions[int(m.group(1))]}.", key, count=1 + ) + + source_depth = next(depth for depth in KNOWN_DEPTHS if depth > max(indices)) + return ModelPatchRaw(layers={moved(key): layer for key, layer in patch.layers.items()}), source_depth diff --git a/tests/backend/anima/test_block_layout.py b/tests/backend/anima/test_block_layout.py new file mode 100644 index 00000000000..2c86d5a20ab --- /dev/null +++ b/tests/backend/anima/test_block_layout.py @@ -0,0 +1,63 @@ +"""Where the depth-expanded Anima finetunes keep the blocks of the model they grew from.""" + +import pytest + +from invokeai.backend.anima import block_layout +from invokeai.backend.anima.block_layout import adapter_block_positions + +BASE_TO_2_9B = block_layout._POSITIONS[(28, 40)] +FROM_2_9B_TO_3_8B = block_layout._POSITIONS[(40, 52)] +BASE_TO_3_8B = block_layout._POSITIONS[(28, 52)] + + +@pytest.mark.parametrize(("source", "target"), [(28, 40), (40, 52), (28, 52)]) +def test_every_source_block_has_its_own_target_block_in_order(source: int, target: int) -> None: + positions = block_layout._POSITIONS[(source, target)] + assert len(positions) == source + assert list(positions) == sorted(set(positions)) + assert positions[0] == 0 and positions[-1] < target + + +def test_the_new_blocks_sit_where_the_checkpoints_have_them() -> None: + """Measured: in 2.9B, the blocks that are not bit-identical to an Anima base block; in 3.8B, the blocks at a cosine + similarity of about 0.6 to every 2.9B block.""" + assert sorted(set(range(40)) - set(BASE_TO_2_9B)) == [2, 5, 8, 11, 14, 17, 21, 24, 27, 30, 33, 36] + assert sorted(set(range(52)) - set(FROM_2_9B_TO_3_8B)) == list(range(3, 48, 4)) + + +def test_the_base_blocks_still_bit_identical_in_3_8b_are_where_the_composed_table_puts_them() -> None: + """Ten blocks of Anima base-v1.0 are bit-identical in Anima-3.8B v1.1 -- an independent check of the composition.""" + measured = {5: 9, 7: 13, 10: 20, 14: 26, 15: 29, 19: 37, 21: 41, 22: 42, 23: 45, 27: 51} + assert {base: BASE_TO_3_8B[base] for base in measured} == measured + + +@pytest.mark.parametrize( + ("max_block_index", "target_depth", "expected"), + [ + (27, 40, BASE_TO_2_9B), + (27, 52, BASE_TO_3_8B), + (39, 52, FROM_2_9B_TO_3_8B), + # A base LoRA that leaves out the last blocks still comes from a 28-block model. + (20, 40, BASE_TO_2_9B), + ], + ids=["base-on-2.9B", "base-on-3.8B", "2.9B-on-3.8B", "partial-base-on-2.9B"], +) +def test_a_shallower_adapter_moves_to_the_blocks_it_was_trained_on(max_block_index, target_depth, expected) -> None: + assert adapter_block_positions(max_block_index, target_depth) == expected + + +@pytest.mark.parametrize( + ("max_block_index", "target_depth"), + [(27, 28), (39, 40), (51, 52), (39, 28), (51, 40), (60, 52), (27, 30)], + ids=[ + "base-on-base", + "2.9B-on-2.9B", + "3.8B-on-3.8B", + "deeper-adapter", + "3.8B-on-2.9B", + "unknown-depth", + "no-layout", + ], +) +def test_everything_else_addresses_blocks_by_index(max_block_index, target_depth) -> None: + assert adapter_block_positions(max_block_index, target_depth) is None diff --git a/tests/backend/anima/test_control_net_lllite.py b/tests/backend/anima/test_control_net_lllite.py index f09b7f1a48f..f64dd88e98b 100644 --- a/tests/backend/anima/test_control_net_lllite.py +++ b/tests/backend/anima/test_control_net_lllite.py @@ -3,6 +3,7 @@ multi-adapter composition, and the conditioning image preprocessing helpers.""" import os +import re from pathlib import Path import pytest @@ -762,3 +763,47 @@ def test_from_state_dict_real_file() -> None: assert not torch.equal(y, plain_linear(linear, x)) finally: module.unbind() + + +# ---------------------------------------------------------------------------- +# Depth-expanded finetunes +# ---------------------------------------------------------------------------- + + +def _adapter_for_blocks(*indices: int) -> AnimaControlNetLLLite: + """The synthetic 2-block adapter, its blocks renamed to ``indices``.""" + renamed = {} + for key, value in make_synthetic_state_dict().items(): + match = re.match(r"^lllite_dit_blocks_(\d+)_", key) + if match: + key = f"lllite_dit_blocks_{indices[int(match.group(1))]}_{key[match.end() :]}" + renamed[key] = value + return AnimaControlNetLLLite.from_state_dict(renamed, None) + + +def _is_wrapped(model: AnimaControlNetLLLite, linear: nn.Linear) -> bool: + x = torch.randn(1, 4, IN_DIM, generator=torch.Generator().manual_seed(5)) + model.set_multiplier(1.0) + model.set_cond_image(matching_cond_image()) + return not torch.equal(linear(x), plain_linear(linear, x)) + + +def test_a_base_adapter_binds_to_the_base_blocks_of_anima_2_9b() -> None: + """Anima base's block 27 is Anima-2.9B's block 39 (see ``invokeai.backend.anima.block_layout``).""" + model = _adapter_for_blocks(0, 27) + transformer = FakeTransformer(IN_DIM, 40) + model.apply_to(transformer) + + assert _is_wrapped(model, transformer.blocks[39].self_attn.q_proj) + assert not _is_wrapped(model, transformer.blocks[27].self_attn.q_proj) + assert _is_wrapped(model, transformer.blocks[0].self_attn.q_proj) + model.restore() + + +def test_a_base_adapter_binds_by_index_on_anima_base() -> None: + model = _adapter_for_blocks(0, 27) + transformer = FakeTransformer(IN_DIM, 28) + model.apply_to(transformer) + + assert _is_wrapped(model, transformer.blocks[27].self_attn.q_proj) + model.restore() diff --git a/tests/backend/patches/lora_conversions/test_anima_lora_conversion_utils.py b/tests/backend/patches/lora_conversions/test_anima_lora_conversion_utils.py index 3d7972cda38..3bdfd6771d6 100644 --- a/tests/backend/patches/lora_conversions/test_anima_lora_conversion_utils.py +++ b/tests/backend/patches/lora_conversions/test_anima_lora_conversion_utils.py @@ -8,9 +8,11 @@ from invokeai.backend.patches.lora_conversions.anima_lora_conversion_utils import ( _convert_kohya_te_key, _convert_kohya_unet_key, + anima_lora_for_depth, is_state_dict_likely_anima_lora, lora_model_from_anima_state_dict, ) +from invokeai.backend.patches.model_patch_raw import ModelPatchRaw from tests.backend.patches.lora_conversions.lora_state_dicts.anima_lora_kohya_format import ( state_dict_keys as anima_kohya_keys, ) @@ -228,3 +230,65 @@ def test_empty_state_dict_returns_empty_model(): """An empty state dict should produce a ModelPatchRaw with no layers.""" lora_model = lora_model_from_anima_state_dict({}) assert len(lora_model.layers) == 0 + + +# --- depth-expanded finetunes ------------------------------------------------------------------------ + + +def _patch(*keys: str) -> ModelPatchRaw: + return ModelPatchRaw(layers={key: object() for key in keys}) # type: ignore[misc] + + +T = ANIMA_LORA_TRANSFORMER_PREFIX + + +def test_a_base_lora_moves_to_the_base_blocks_of_anima_2_9b() -> None: + original = _patch( + f"{T}blocks.0.self_attn.q_proj", + f"{T}blocks.2.cross_attn.k_proj", + f"{T}blocks.27.mlp.layer2", + f"{T}llm_adapter.blocks.3.self_attn.q_proj", + f"{ANIMA_LORA_QWEN3_PREFIX}layers.2.self_attn.q_proj", + ) + moved, moved_from = anima_lora_for_depth(original, 40) + + assert moved_from == 28 + assert sorted(moved.layers) == sorted( + [ + f"{T}blocks.0.self_attn.q_proj", + f"{T}blocks.3.cross_attn.k_proj", + f"{T}blocks.39.mlp.layer2", + # The LLM adapter's own blocks and the text encoder are not DiT blocks. + f"{T}llm_adapter.blocks.3.self_attn.q_proj", + f"{ANIMA_LORA_QWEN3_PREFIX}layers.2.self_attn.q_proj", + ] + ) + # The same layer objects, and the cached LoRA itself untouched. + assert set(map(id, moved.layers.values())) == set(map(id, original.layers.values())) + assert f"{T}blocks.27.mlp.layer2" in original.layers + + +def test_a_base_lora_moves_to_the_base_blocks_of_anima_3_8b() -> None: + moved, moved_from = anima_lora_for_depth(_patch(f"{T}blocks.2.mlp.layer1", f"{T}blocks.27.mlp.layer1"), 52) + assert moved_from == 28 + assert sorted(moved.layers) == [f"{T}blocks.4.mlp.layer1", f"{T}blocks.51.mlp.layer1"] + + +def test_an_anima_2_9b_lora_moves_to_its_blocks_in_anima_3_8b() -> None: + moved, moved_from = anima_lora_for_depth(_patch(f"{T}blocks.3.mlp.layer1", f"{T}blocks.39.mlp.layer1"), 52) + assert moved_from == 40 + assert sorted(moved.layers) == [f"{T}blocks.4.mlp.layer1", f"{T}blocks.51.mlp.layer1"] + + +@pytest.mark.parametrize( + ("keys", "depth"), + [ + ((f"{T}blocks.27.mlp.layer1",), 28), + ((f"{T}blocks.39.mlp.layer1",), 40), + ((f"{T}llm_adapter.blocks.0.self_attn.q_proj", f"{ANIMA_LORA_QWEN3_PREFIX}layers.0.mlp.up_proj"), 40), + ], + ids=["base-on-base", "2.9B-on-2.9B", "no-dit-blocks"], +) +def test_a_lora_for_this_depth_is_returned_as_it_is(keys, depth) -> None: + original = _patch(*keys) + assert anima_lora_for_depth(original, depth) == (original, None) From 8c6729540485a3802ce52ed09248f4193085b9dd Mon Sep 17 00:00:00 2001 From: Alexander Eichhorn Date: Fri, 2 Oct 2026 04:43:45 +0200 Subject: [PATCH 5/5] fix(anima): cast the whole semantic connector under FP8 Storage The connector's timestep path (159M parameters) was kept in bf16 by analogy with t_embedder, unmeasured. Measured on Anima-3.8B, 30 steps, three seeds, PSNR against bf16: - with that exception: 8.4/10.3/9.6 dB; - without it: 8.4/10.2/9.6 dB; - with the whole connector in bf16: 8.3/10.1/9.6 dB. The exception changes nothing and costs 152 MiB, so it goes. FP8 Storage moves Anima-3.8B's composition through its DiT blocks (Anima base stays at 14-16 dB); the user guide now says so. --- .../03.Models/Local Models/anima.mdx | 5 +++- invokeai/backend/anima/anima_transformer.py | 12 ++++---- .../backend/anima/test_semantic_connector.py | 28 ++++++++----------- 3 files changed, 21 insertions(+), 24 deletions(-) diff --git a/docs/src/content/docs/Users Guide/03.Models/Local Models/anima.mdx b/docs/src/content/docs/Users Guide/03.Models/Local Models/anima.mdx index 706c57b5a19..83275ec83ff 100644 --- a/docs/src/content/docs/Users Guide/03.Models/Local Models/anima.mdx +++ b/docs/src/content/docs/Users Guide/03.Models/Local Models/anima.mdx @@ -70,6 +70,9 @@ iterations per second at 832×1216 on an RTX 4090), and its Qwen3.5 encoder adds is encoded. **Reset all to model defaults** sets 40 steps at CFG 6, the settings of the author's reference workflow. +[FP8 Storage](/configuration/optimization/fp8-storage/) changes Anima-3.8B's images more than Anima's: at +the same seed the composition can change, where Anima keeps it and only details differ. + Only the v1.1 checkpoint of Anima-3.8B is supported. The earlier v1.0 transformer needs a separate adapter file and is refused at install with a message that says so. @@ -80,7 +83,7 @@ LoRAs and ControlNet-LLLite adapters address the transformer's blocks by positio insert their extra blocks between the original ones. Invoke moves an adapter trained on Anima to the blocks the finetune kept from Anima, and one trained on Anima-2.9B to its blocks in Anima-3.8B. Anima-2.9B keeps Anima's blocks unchanged, so Anima adapters work there as on Anima itself. -Anima-3.8B trained most of them further, so expect weaker or different results there. +Anima-3.8B changed them slightly and adds the semantic connector, so results there can differ more. ::: ## Generation settings diff --git a/invokeai/backend/anima/anima_transformer.py b/invokeai/backend/anima/anima_transformer.py index 231ecbc3153..f7cc928306a 100644 --- a/invokeai/backend/anima/anima_transformer.py +++ b/invokeai/backend/anima/anima_transformer.py @@ -1049,14 +1049,12 @@ def __init__(self, *args, semantic_connector: Optional["AnimaSemanticConnectorCo from invokeai.backend.anima.semantic_connector import AnimaSemanticConnector # Named as in the Anima-3.8B bundle (`net.anima_v2_connector.*`), so its keys load as-is. + # FP8 Storage casts all of it. Keeping its timestep path (`time_mlp`, `time_modulation`, 159M + # params) in bf16 by analogy with `t_embedder` was measured and changes nothing: 30 steps, three + # seeds, PSNR against bf16 8.4/10.3/9.6 dB with that exception and 8.4/10.2/9.6 dB without, and + # 8.3/10.1/9.6 dB with the whole connector in bf16. FP8 Storage moves Anima-3.8B's composition + # through the DiT blocks, not the connector; Anima base stays at 14-16 dB. self.anima_v2_connector = AnimaSemanticConnector(semantic_connector) - # The resampler's timestep path plays the role `t_embedder` plays for the DiT: its output - # modulates every resampler block. Kept out of FP8 Storage on that analogy -- unmeasured. - # Declared on the instance so a model without the connector does not carry dead patterns. - self._skip_layerwise_casting_patterns = [ - *type(self)._skip_layerwise_casting_patterns, - r"^anima_v2_connector\.semantic_resampler\.(time_mlp|blocks\.\d+\.time_modulation)", - ] @property def has_semantic_connector(self) -> bool: diff --git a/tests/backend/anima/test_semantic_connector.py b/tests/backend/anima/test_semantic_connector.py index 2b7b2277f1a..6c39cf5f4db 100644 --- a/tests/backend/anima/test_semantic_connector.py +++ b/tests/backend/anima/test_semantic_connector.py @@ -85,26 +85,22 @@ def test_transformer_built_from_the_bundle_takes_every_key() -> None: assert result.missing_keys == [] -def test_only_a_transformer_with_the_connector_declares_its_skip_pattern() -> None: +def test_fp8_storage_casts_the_whole_connector() -> None: + """No connector module is kept out of FP8 Storage: keeping its timestep path in bf16 was measured to change + nothing (see `AnimaTransformer.__init__`), so a model with the connector declares the same patterns as one + without.""" with accelerate.init_empty_weights(): - plain = AnimaTransformer(**ANIMA_TRANSFORMER_CONFIG) expanded = AnimaTransformer(**ANIMA_TRANSFORMER_CONFIG, semantic_connector=AnimaSemanticConnectorConfig()) - assert not plain.has_semantic_connector - assert plain._skip_layerwise_casting_patterns is AnimaTransformer._skip_layerwise_casting_patterns - skipped = { + assert expanded.has_semantic_connector + assert expanded._skip_layerwise_casting_patterns is AnimaTransformer._skip_layerwise_casting_patterns + kept = [ name for name, module in expanded.named_modules() - if isinstance(module, torch.nn.Linear) - and any( - re.search(p, name) - for p in expanded._skip_layerwise_casting_patterns[len(plain._skip_layerwise_casting_patterns) :] - ) - } - assert skipped == { - "anima_v2_connector.semantic_resampler.time_mlp.0", - "anima_v2_connector.semantic_resampler.time_mlp.2", - *(f"anima_v2_connector.semantic_resampler.blocks.{i}.time_modulation" for i in range(6)), - } + if name.startswith("anima_v2_connector.") + and isinstance(module, torch.nn.Linear) + and any(re.search(p, name) for p in expanded._skip_layerwise_casting_patterns) + ] + assert kept == [] class TestMaskedSdpa: