52 lines
2.4 KiB
Python
52 lines
2.4 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 PIL import Image
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from hy3dshape.rembg import BackgroundRemover
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from hy3dshape.pipelines import Hunyuan3DDiTFlowMatchingPipeline
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model_path = 'tencent/Hunyuan3D-2.1'
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pipeline_shapegen = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained(model_path)
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import torch
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import yaml
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from hy3dshape.utils import instantiate_from_config
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# For example, you can convert deepspeed weights to a single file
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# cd output_folder/dit/overfitting_depth_16_token_4096_lr1e4/ckpt/ckpt-step=00004000.ckpt
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# python3 zero_to_fp32.py ./ ./out --max_shard_size 30GB
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# then you can get output_folder/dit/overfitting_depth_16_token_4096_lr1e4/ckpt/ckpt-step=00004000.ckpt/out/pytorch_model.bin
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ckpt_cfg_path = 'output_folder/dit/overfitting_depth_16_token_4096_lr1e4/hunyuandit-mini-overfitting-flowmatching-dinol518-bf16-lr1e4-4096.yaml'
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ckpt_path = 'output_folder/dit/overfitting_depth_16_token_4096_lr1e4/ckpt/ckpt-step=00004000.ckpt/out/pytorch_model.bin'
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config = yaml.safe_load(open(ckpt_cfg_path, 'r'))
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model = instantiate_from_config(config['model']['params']['denoiser_cfg'])
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sd = torch.load(ckpt_path)
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sd = {k.replace('_forward_module.model.', ''):v for k,v in sd.items()}
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msg = model.load_state_dict(sd)
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print(msg)
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model = model.cuda().half()
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pipeline_shapegen.model = model
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image = 'tools/mini_testset/images/015.png'
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# image = Image.open(image_path).convert("RGBA")
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# if image.mode == 'RGB':
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# rembg = BackgroundRemover()
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# image = rembg(image)
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# mesh = pipeline_shapegen(image=image, guidance_scale=1.0)[0]
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mesh = pipeline_shapegen(image=image)[0]
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mesh.export('demo.glb')
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