readme: ---
  library_name: pytorch
  tags:
  - diffusion
  - image-to-image
  ---
  
  # DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation - Bedrooms
  
  Creators: Gwanghyun Kim, Taesung Kwon, Jong Chul Ye
  Paper: https://arxiv.org/abs/2110.02711
  
  <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;"/>
  
  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).
  
  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).
  
  ### Credits
  
  - Code repository available at: https://github.com/gwang-kim/DiffusionCLIP
  
  ### Citation
  
  ```
  @article{kim2021diffusionclip,
    title={Diffusionclip: Text-guided image manipulation using diffusion models},
    author={Kim, Gwanghyun and Ye, Jong Chul},
    journal={arXiv preprint arXiv:2110.02711},
    year={2021}
  }
  ```
  