Create README.md

README.md

@@ -0,0 +1,31 @@
+---
+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
+
+<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 [CelebA-HQ Dataset](https://arxiv.org/abs/1710.10196), available on the Hugging Face Hub: https://huggingface.co/datasets/huggan/CelebA-HQ.
+
+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}
+}
+```