Instructions to use AndrewChoyCS/Mobile-VTON with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AndrewChoyCS/Mobile-VTON 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("AndrewChoyCS/Mobile-VTON", 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
| license: cc-by-nc-sa-4.0 | |
| pipeline_tag: image-to-image | |
| tags: | |
| - virtual-try-on | |
| - diffusion | |
| - computer-vision | |
| - image-generation | |
| - vton | |
| datasets: | |
| - viton-hd | |
| - dresscode | |
| library_name: diffusers | |
| # Mobile-VTON: High-Fidelity On-Device Virtual Try-On | |
| This is the official implementation of the paper Mobile-VTON: High-Fidelity On-Device Virtual Try-On | |
| ๐ Paper: https://arxiv.org/abs/2603.00947 | |
| ๐ Project Page: https://zhenchenwan.github.io/Mobile-VTON/ | |
| ๐ป Code: https://github.com/tmllab/2026_CVPR_Mobile-VTON | |
|  | |
| ## Inference | |
| Please refer to the official repository for inference scripts. |