To further reduce VRAM usage, pass --gradient_checkpointing and --use_8bit_adam flag to use 8 bit adam optimizer from bitsandbytes.
Training takes around 11GB VRAM and 18-20 minutes on Tesla T4 in colab free tier.
Imagic is a method for Text-Based Real Image editing with models like stable diffusion with just one image of a subject.
The train_imagic.py script shows how to implement the training procedure and adapt it for stable diffusion.
Below are examples produced using the colab notebook.
| Target Text | Input Image | Edited Image |
|---|---|---|
| A photo of Barack Obama smiling with a big grin. |  |
 |
| A bird spreading wings |  |
 |
TODO: Update README, Please refer to the colab notebook for example usage until then.
