Instructions to use gradient-spaces/ReStyle3D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use gradient-spaces/ReStyle3D with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("gradient-spaces/ReStyle3D", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 557 Bytes
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license: creativeml-openrail-m
language:
- en
base_model:
- stabilityai/stable-diffusion-xl-base-1.0
pipeline_tag: image-to-image
tags:
- SIGGRAPH
- 3D-Stylization
- ReStyle3D
- cvpr
---
This repo contains the model weights for our SIGGRAPH 2025 paper:
ReStyle3D: Scene-level Appearance Transfer with Semantic Correspondences
[Project](https://restyle3d.github.io/) | [Code](https://github.com/GradientSpaces/ReStyle3D)
<p align="center">
<a href="">
<img src="https://arxiv.org/html/2502.10377v1/x1.png" width="100%">
</a>
</p> |