Instructions to use prodevroger/tryait with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use prodevroger/tryait with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image # switch to "mps" for apple devices pipe = AutoPipelineForInpainting.from_pretrained("prodevroger/tryait", dtype=torch.float16, device_map="cuda") img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" image = load_image(img_url).resize((1024, 1024)) mask_image = load_image(mask_url).resize((1024, 1024)) prompt = "a tiger sitting on a park bench" generator = torch.Generator(device="cuda").manual_seed(0) image = pipe( prompt=prompt, image=image, mask_image=mask_image, guidance_scale=8.0, num_inference_steps=20, # steps between 15 and 30 work well for us strength=0.99, # make sure to use `strength` below 1.0 generator=generator, ).images[0] - Notebooks
- Google Colab
- Kaggle
Delete README.md
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README.md
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---
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license: cc-by-nc-sa-4.0
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language:
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pipeline_tag: any-to-any
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---
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# Check out more codes on our [GitHub repository](https://github.com/hdevtech/tryait)!
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# TryAIt: Enhancing AI Models for Virtual Try-on in the Wild
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This is the official implementation of the TryAIt project.
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- [GitHub Repository](https://github.com/hdevtech/tryait)
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- [Hugging Face Model](https://huggingface.co/prodevroger/tryait)
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🤗 Try our Hugging Face [Demo](https://huggingface.co/prodevroger/tryait)
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## Acknowledgements
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For the demo, GPUs are supported from AWS and Google Cloud, and auto masking generation codes are based on [OOTDiffusion](https://github.com/levihsu/OOTDiffusion) and [DCI-VTON](https://github.com/bcmi/DCI-VTON-Virtual-Try-On).
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Parts of the code are based on [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter).
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## License
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The codes and checkpoints in this repository are under the [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
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