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
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license: openrail++
library_name: diffusers
pipeline_tag: image-to-image
tags:
- virtual-try-off
- garment-reconstruction
- viton-hd
---
# 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](VITON-HD-512-res/) | 512 x 384 |
| [VITON-HD-1024-res](VITON-HD-1024-res/) | 1024 x 768 |
See the [inference code and instructions](https://github.com/locphattruong/dual-vtoff).
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](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](NOTICE.md) and
[licenses/](licenses/). The code license does not replace this model license.
VITON-HD data is not included and remains subject to its separate dataset terms.
|