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--- |
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library_name: pytorch |
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tags: |
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- diffusion |
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- image-to-image |
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--- |
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# DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation - Bedrooms |
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Creators: Gwanghyun Kim, Taesung Kwon, Jong Chul Ye |
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Paper: https://arxiv.org/abs/2110.02711 |
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<img src="https://github.com/submission10095/DiffusionCLIP_temp/raw/master/imgs/main1.png" alt="Excerpt from DiffusionCLIP paper showcasing comparison of DiffusionCLIP versus other methods for image reconstruction, manipulation, and style transfer." style="height: 300px;"/> |
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DiffusionCLIP is a diffusion model which is well suited for image manipulation thanks to its nearly perfect inversion capability, which is an important advantage over GAN-based models. This checkpoint was trained on the ["Bedrooms" category of the LSUN Dataset](https://www.yf.io/p/lsun). |
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This checkpoint is most appropriate for manipulation, reconstruction, and style transfer on images of indoor locations, such as bedrooms. The weights should be loaded into the [DiffusionCLIP model](https://github.com/gwang-kim/DiffusionCLIP). |
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### Credits |
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- Code repository available at: https://github.com/gwang-kim/DiffusionCLIP |
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### Citation |
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``` |
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@article{kim2021diffusionclip, |
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title={Diffusionclip: Text-guided image manipulation using diffusion models}, |
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author={Kim, Gwanghyun and Ye, Jong Chul}, |
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journal={arXiv preprint arXiv:2110.02711}, |
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year={2021} |
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} |
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``` |
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