Official repository of "Diffusion-based Image Translation using Disentangled Style and Content Representation" (ICLR 2023)
LINK : https://arxiv.org/abs/2209.15264
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
