Image-to-Image
Diffusers
Safetensors
virtual-try-on
image-editing
multi-view
human
vton360
cvpr-2025
Instructions to use nielsr/VTON360-MVHumanNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nielsr/VTON360-MVHumanNet 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("nielsr/VTON360-MVHumanNet", 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: creativeml-openrail-m | |
| library_name: diffusers | |
| pipeline_tag: image-to-image | |
| base_model: stable-diffusion-v1-5/stable-diffusion-v1-5 | |
| tags: | |
| - virtual-try-on | |
| - image-editing | |
| - multi-view | |
| - human | |
| - vton360 | |
| - cvpr-2025 | |
| - arxiv:2503.12165 | |
| # VTON360 checkpoint for MVHumanNet | |
| This repository contains the `checkpoint-40000` model bundle for the | |
| MVHumanNet experiment released with **VTON 360: High-Fidelity Virtual Try-On | |
| from Any Viewing Direction** (CVPR 2025). | |
| Use it with the custom implementation in the | |
| [official VTON360 repository](https://github.com/scnuhealthy/VTON360). The | |
| repository README instructs users to place this checkpoint under | |
| `src/multiview_consist_edit/checkpoints` and run | |
| `src/multiview_consist_edit/infer_tryon_multi.py`. | |
| ```python | |
| from huggingface_hub import snapshot_download | |
| snapshot_download( | |
| repo_id="nielsr/VTON360-MVHumanNet", | |
| local_dir="src/multiview_consist_edit/checkpoints/mvhumannet_tryon_mvattn_multi", | |
| ) | |
| ``` | |
| The released configuration also expects: | |
| - [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) | |
| - [`stabilityai/sd-vae-ft-mse`](https://huggingface.co/stabilityai/sd-vae-ft-mse) | |
| - [`openai/clip-vit-base-patch32`](https://huggingface.co/openai/clip-vit-base-patch32) | |
| ## Provenance and attribution | |
| - Official code: https://github.com/scnuhealthy/VTON360 | |
| - Project page: https://scnuhealthy.github.io/VTON360/ | |
| - Paper: https://huggingface.co/papers/2503.12165 | |
| - arXiv: https://arxiv.org/abs/2503.12165 | |
| - Original Baidu release: https://pan.baidu.com/s/1Onu7BIFzOppRSzO97ZmlmQ (code `mahx`) | |
| - Community Hub source used for this mirror: https://huggingface.co/NTUST-DDRC/mvhumannet_tryon_mvattn_multi | |
| The checkpoint is a derivative of Stable Diffusion v1.5 and retains the | |
| CreativeML Open RAIL-M license. Users must also follow the licenses and terms | |
| of VTON360, its code dependencies, and the underlying MVHumanNet data. | |