Instructions to use FlashStight/Mobile-VTON with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FlashStight/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("FlashStight/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
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Download README.md from FlashStight/Mobile-VTON: direct link, hf CLI and curl.
- Browser
- Download file 641 Bytes
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https://huggingface.co/FlashStight/Mobile-VTON/resolve/main/README.md
- Command line
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hf download hf://FlashStight/Mobile-VTON/README.md
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curl -L -o README.md https://huggingface.co/FlashStight/Mobile-VTON/resolve/main/README.md
641 Bytes
metadata
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
📄 Paper: https://arxiv.org/abs/2603.00947
🌐 Project Page: https://zhenchenwan.github.io/Mobile-VTON/
💻 Code: https://github.com/tmllab/2026_CVPR_Mobile-VTON
Inference
Please refer to the official repository for inference scripts.
