e4cfa9d1e9
Fork of dgrauet/Hunyuan3D-2.1-mlx + our generate_e2e.py CLI, env-tunable remesh (HY3D_REMESH_FACES), and HARDWARE.md. Upstream training data (mini_trainset) and demo images stripped — inference needs none of it. Full upstream history: github.com/dgrauet/Hunyuan3D-2.1-mlx
41 lines
1.6 KiB
Python
41 lines
1.6 KiB
Python
# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
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# except for the third-party components listed below.
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# Hunyuan 3D does not impose any additional limitations beyond what is outlined
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# in the repsective licenses of these third-party components.
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# Users must comply with all terms and conditions of original licenses of these third-party
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# components and must ensure that the usage of the third party components adheres to
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# all relevant laws and regulations.
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# For avoidance of doubts, Hunyuan 3D means the large language models and
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# their software and algorithms, including trained model weights, parameters (including
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# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
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# fine-tuning enabling code and other elements of the foregoing made publicly available
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# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
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from setuptools import setup, find_packages
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import torch
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from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CppExtension
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# build custom rasterizer
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custom_rasterizer_module = CUDAExtension(
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"custom_rasterizer_kernel",
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[
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"lib/custom_rasterizer_kernel/rasterizer.cpp",
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"lib/custom_rasterizer_kernel/grid_neighbor.cpp",
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"lib/custom_rasterizer_kernel/rasterizer_gpu.cu",
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],
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)
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setup(
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packages=find_packages(),
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version="0.1",
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name="custom_rasterizer",
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include_package_data=True,
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package_dir={"": "."},
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ext_modules=[
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custom_rasterizer_module,
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],
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cmdclass={"build_ext": BuildExtension},
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)
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