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