Pytorch 1.9.0, Python 3.9

$ conda create --name DiffuseIT python=3.9
$ conda activate DiffuseIT
$ pip install ftfy regex matplotlib lpips kornia opencv-python torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
$ pip install color-matcher
$ pip install git+https://github.com/openai/CLIP.git

To generate images, please download the pre-trained diffusion model

imagenet 256x256 LINK

FFHQ 256x256 LINK

download the model into ./checkpoints folder

For face identity loss when using FFHQ pre-trained model, download pre-trained ArcFace model LINK

save the model into ./id_model

We provide Colab Demo for Text-guided Image translation

python main.py -p "Black Leopard" -s "Lion" -i "input_example/lion1.jpg" --output_path "./outputs/output_leopard" 
--use_range_restart --use_noise_aug_all --regularize_content

To to further regularize content when CLIP loss is extremely low, activate --regularize_content

To use noise augmented images for our ViT losses, activate --use_noise_aug_all

To use progressively increasing our contrastive loss, activate --use_prog_contrast

To restart the whole process with high rgb regularize loss, activate --use_range_restart

To use FFHQ pre-trained model, activate --use_ffhq

For memory saving, we can use single CLIP model with --clip_models 'ViT-B/32'

We provide Colab Demo for Image-guided Image translation

python main.py -i "input_example/reptile1.jpg"  --output_path "./outputs/output_reptile" 
-tg "input_example/reptile2.jpg" --use_range_restart --diff_iter 100 --timestep_respacing 200 --skip_timesteps 80 
--use_colormatch --use_noise_aug_all

To remove the color matching, deactivate --use_colormatch

Our source code rely on Blended-diffusion, guided-diffusion, flexit, splicing vit