EasyInv

This is the official implementation of our paper "EasyInv: Toward Fast and Better DDIM Inversion"

1. Before running the codes, copy three files in folder "replace_diffusers" to ".../site-packages/diffusers/models/" and replace the original files. Rember to make a backup of these replaced files incase you want to run other project in this enviroment.

2. Run "by_referenceSDV1-4.ipynb" for trying our method.

3. During runing the code, if you got problem for download the SD pre-train model by "StableDiffusionPipeline.from_pretrained", download it by yourself and place it under "CompVis" folder.

4. Images in "special_cases" folder in the original results of some pictures we presented in our paper

5. Since COCO 2017 is a open source dataset, we submit our image chosen script "choose_img.py" together with codes instead. Anyone who have download the COCO 2017 dataset can extract images we used by this script from the testing and validation set.

Implement our method on your own inversion framework

If you have already implement DDIM inversion, please check example.py for applying our methods to improve it. As we mentioned, this is really easy.

Reference

Part of our codes are based on following project:

1. prompt-to-prompt

   @article{hertz2022prompt,
   title = {Prompt-to-Prompt Image Editing with Cross Attention Control},
   author = {Hertz, Amir and Mokady, Ron and Tenenbaum, Jay and Aberman, Kfir and Pritch, Yael and Cohen-Or, Daniel},
   journal = {arXiv preprint arXiv:2208.01626},
   year = {2022},
   }