Instructions to use sheldon-97/Dual-VTOFF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sheldon-97/Dual-VTOFF 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("sheldon-97/Dual-VTOFF", 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
DualVTOFF checkpoints
Official checkpoints for What Matters in Virtual Try-Off? Dual-UNet Diffusion Models for Garment Reconstruction. DualVTOFF reconstructs a canonical garment image from a person image and a binary garment-region mask.
| Checkpoint | Output height x width |
|---|---|
| VITON-HD-512-res | 512 x 384 |
| VITON-HD-1024-res | 1024 x 768 |
See the inference code and instructions. Each checkpoint includes its inference components, manifest, checksums, license, and attribution notices.
Licenses
The checkpoint weights in this directory are released under CreativeML Open RAIL++-M, including the use restrictions in Attachment A. The full license is included in LICENSE. Users and downstream distributors must comply with its use, attribution, and redistribution conditions.
The inference source code is released separately under Apache-2.0. Component copyright and license notices are preserved in NOTICE.md and licenses/. The code license does not replace this model license. VITON-HD data is not included and remains subject to its separate dataset terms.
- Downloads last month
- -