Instructions to use VAST-AI/HoloPart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use VAST-AI/HoloPart with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("VAST-AI/HoloPart", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Add pipeline tag and paper link
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by nielsr HF Staff - opened
README.md
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license: mit
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---
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HoloPart: Generative 3D Part Amodal Segmentation
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-- Decompose a 3D shape into complete, semantically meaningful parts.
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Project Page: [https://vast-ai-research.github.io/HoloPart](https://vast-ai-research.github.io/HoloPart)
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Github Code: [https://github.com/VAST-AI-Research/HoloPart](https://github.com/VAST-AI-Research/HoloPart)
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license: mit
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pipeline_tag: image-to-3d
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library_name: holopart
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---
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HoloPart: Generative 3D Part Amodal Segmentation
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-- Decompose a 3D shape into complete, semantically meaningful parts.
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Paper: [https://huggingface.co/papers/2504.07943](https://huggingface.co/papers/2504.07943)
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Project Page: [https://vast-ai-research.github.io/HoloPart](https://vast-ai-research.github.io/HoloPart)
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Github Code: [https://github.com/VAST-AI-Research/HoloPart](https://github.com/VAST-AI-Research/HoloPart)
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