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--- |
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license: mit |
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--- |
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# Unique3d-MVImage-Diffuser Model Card |
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[🌟GitHub](https://github.com/TingtingLiao/unique3d_diffuser) | [🦸 Project Page](https://wukailu.github.io/Unique3D/) | [🔋Normal Diffuser](https://huggingface.co/Luffuly/unique3d-normal-diffuser)</a> |
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## Example |
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Note the input image is required to be **white background**. |
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 |
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```bash |
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import torch |
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import numpy as np |
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from PIL import Image |
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from pipeline import StableDiffusionImage2MVCustomPipeline |
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pipe = Unique3dDiffusionPipeline.from_pretrained( |
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"Luffuly/unique3d-mvimage-diffuser", |
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torch_dtype=torch.float16, |
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trust_remote_code=True, |
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class_labels=torch.tensor(range(4)), |
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).to("cuda") |
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seed = -1 |
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generator = torch.Generator(device='cuda').manual_seed(-1) |
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image = Image.open('data/boy.png') |
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forward_args = dict( |
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width=256, |
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height=256, |
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num_images_per_prompt=4, |
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num_inference_steps=50, |
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width_cond=256, |
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height_cond=256, |
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generator=generator, |
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guidance_scale=1.5, |
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) |
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out = pipe(image, **forward_args).images |
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rgb_np = np.hstack([np.array(img) for img in out]) |
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Image.fromarray(rgb_np).save(f"mv-boy.png") |
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``` |
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## Citation |
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```bash |
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@misc{wu2024unique3d, |
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title={Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image}, |
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author={Kailu Wu and Fangfu Liu and Zhihan Cai and Runjie Yan and Hanyang Wang and Yating Hu and Yueqi Duan and Kaisheng Ma}, |
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year={2024}, |
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eprint={2405.20343}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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