evolutionary-diffusion/setup.py at main · malthee/evolutionary-diffusion · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
from setuptools import setup, find_packages

torch_diffusers_requirements = [
    'torch~=2.11.0',
    'diffusers~=0.37.1',
    'transformers~=5.4.0',
    'accelerate~=1.13.0',
    'Pillow~=12.1',
    'certifi>=2024.0.0',
]

imaging_requirements = [
    'numpy>=2.1,<3.0',
    'graphviz~=0.21',
    # Used in the Aesthetics model for evaluation
    'clip @ git+https://github.com/openai/CLIP.git@ded190a052fdf4585bd685cee5bc96e0310d2c93',
    'torchvision~=0.26.0',
    'torchmetrics[multimodal]~=1.9.0',
    'pytorch-lightning~=2.6.1',
    'imageio>=2.37.3',
    'aesthetic-predictor-v2-5~=2024.12.18.1',
]

sound_requirements = [
    'audiobox_aesthetics~=0.0.4',
    'soundfile~=0.13.1',
]

extras_require = {
    'imaging': torch_diffusers_requirements + imaging_requirements,
    'model_helpers': torch_diffusers_requirements,
    'prompt_embedding': torch_diffusers_requirements + ['tensorflow~=2.21.0', 'tensorboard>=2.20,<2.21'],
    'prompt_embedding_utils': [
        'datasets~=4.8.4',
    ] + torch_diffusers_requirements,
    'sound': sound_requirements,
}

all_deps = set(dep for deps in extras_require.values() for dep in deps)
extras_require['all'] = sorted(all_deps)

setup(
    name='evolutionary',
    version='0.10.1',
    author='malthee',
    url='https://github.com/malthee/evolutionary-diffusion',
    description='''Base package defining a framework for evolutionary algorithms to be used with generative networks.
                   Splits up the Solution-Representation into arguments and results.''',
    long_description_content_type='text/markdown',
    long_description='''This package includes generic classes for evolutionary computation in a generational environment.
                     Crossover and Mutation happens on the argument (A) level, whilst the fitness is evaluated on the result (R) level.
                     SolutionCandidates are created by a SolutionCreator, their representation is split into arguments (A) and result (R).

                     Additional installation variants (pip install evolutionary[...]):
                     * all: Install all dependencies, full functionality across all subpackages.
                     * imaging (evolutionary_imaging): Contains base dependencies for evolutionary image generation,
                     evaluation and visualization.
                     * model_helpers (evolutionary_model_helpers): Auto-loading different model types on devices.
                     With additional utility functions. Variation for tensors.
                     * prompt_embedding (evolutionary_prompt_embedding): Using evolutionary_prompt_embeddings to
                     generate images and sound and perform evolutionary variation using prompt embeddings. Visualizing the
                     embeddings using Tensorboard. To be used with `imaging` or `sound` subpackages.
                     * prompt_embedding_utils (evolutionary_prompt_embedding.utils): Additional utilities for evaluating
                     the prompt embedding range.
                     * sound (evolutionary_sound): Adding evaluators for sound solutions.
                     ''',
    packages=find_packages(),
    install_requires=[
        'tqdm>=4.67.3',  # For visualizing progress of algorithms
        'matplotlib>=3.10.8',
        'pymoo>=0.6.1.6',  # For NSGA-III implementation
    ],
    python_requires='>=3.10',
    classifiers=[
        'Programming Language :: Python :: 3',
        'Programming Language :: Python :: 3.10',
        'Programming Language :: Python :: 3.11',
        'Programming Language :: Python :: 3.12',
        'Programming Language :: Python :: 3.13',
    ],
    package_data={
        'evolutionary_prompt_embedding': ['tensors/*'],
    },
    include_package_data=True,
    extras_require=extras_require,
)