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
| license: mit | |
| datasets: | |
| - 3DAIGC/gobjaverse | |
| base_model: | |
| - stabilityai/stable-diffusion-3.5-medium | |
| - PixArt-alpha/PixArt-Sigma-XL-2-512-MS | |
| - stable-diffusion-v1-5/stable-diffusion-v1-5 | |
| library_name: diffusers | |
| # [[ICLR 2025] DiffSplat](https://chenguolin.github.io/projects/DiffSplat) | |
| This HuggingFace🤗 repo stores all pretrained model weights for the ICLR 2025 paper: "DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation". | |
| For more details about usage, please refer to the [official GitHub repo](https://github.com/chenguolin/DiffSplat). | |
| - Project page: https://chenguolin.github.io/projects/DiffSplat | |
| - Code: https://github.com/chenguolin/DiffSplat | |
| - Paper: https://arxiv.org/abs/2501.16764 |