Instructions to use chenguolin/DiffSplat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use chenguolin/DiffSplat with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("chenguolin/DiffSplat", 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
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by nielsr HF Staff - opened
README.md
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- PixArt-alpha/PixArt-Sigma-XL-2-512-MS
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library_name: diffusers
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# [[ICLR 2025] DiffSplat](https://chenguolin.github.io/projects/DiffSplat)
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This HuggingFace🤗 repo stores all pretrained model weights for the ICLR 2025 paper: "DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation".
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- PixArt-alpha/PixArt-Sigma-XL-2-512-MS
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- stable-diffusion-v1-5/stable-diffusion-v1-5
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library_name: diffusers
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pipeline_tag: image-to-3d
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# [[ICLR 2025] DiffSplat](https://chenguolin.github.io/projects/DiffSplat)
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This HuggingFace🤗 repo stores all pretrained model weights for the ICLR 2025 paper: "DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation".
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