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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Download README.md from sheldon-97/Dual-VTOFF: direct link, hf CLI and curl.
- Browser
- Download file 1.37 kB
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https://huggingface.co/sheldon-97/Dual-VTOFF/resolve/main/README.md
- Command line
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hf download hf://sheldon-97/Dual-VTOFF/README.md
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curl -L -o README.md https://huggingface.co/sheldon-97/Dual-VTOFF/resolve/main/README.md
1.37 kB
| 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. | |