b6685c9560
- Add batch_generate.py: two-phase pipeline (shape→texture) that loads models sequentially to avoid OOM on RTX 3080 - Fix mesh_utils.py: make bpy import lazy so load_mesh/save_mesh work without Blender installed - Phase 1: shape generation for all images, then unload - Phase 2: texture generation for all meshes, then unload - Skip already-generated outputs for resumability - Tested: 9/9 images successfully generated textured GLB models Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
55 lines
2.2 KiB
Python
55 lines
2.2 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 os
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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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# CUDA include path: prefer conda env CUDA headers to match torch's CUDA version
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_cuda_home = os.environ.get("CUDA_HOME", os.environ.get("CUDA_PATH", "/usr/local/cuda"))
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_cuda_include = os.path.join(_cuda_home, "targets", "x86_64-linux", "include")
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if not os.path.isdir(_cuda_include):
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_cuda_include = os.path.join(_cuda_home, "include")
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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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include_dirs=[_cuda_include],
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# -D__GLIBC_USE_IEC_60559_FUNCS_EXT_C23=0 prevents glibc 2.38+ from declaring
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# sinpi/cospi/etc that conflict with CUDA 12.8 crt/math_functions.h on modern glibc.
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extra_compile_args={
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"nvcc": [],
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"cxx": [],
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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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