DeltaEdit: Exploring Text-free Training for Text-driven Image Manipulation

Overview

This repository contains the offical PyTorch implementation of paper:

DeltaEdit: Exploring Text-free Training for Text-driven Image Manipulation, CVPR 2023

News

[2023-03-11] Upload the training and inference code for the facial domain (◍•ڡ•◍).

To be continued...

We will release the training and inference code for the LSUN cat, church, horse later : )

Dependences

Install CLIP:

conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=<CUDA_VERSION>
pip install ftfy regex tqdm gdown
pip install git+https://github.com/openai/CLIP.git

Download pre-trained models :
- The code relies on the Rosinality pytorch implementation of StyleGAN2.
- Download the pre-trained StyleGAN2 generator model for the faical domain from here, and then place it into the folder ./models/pretrained_models.
- Download the pre-trained StyleGAN2 generator model for the LSUN cat, church, horse domains from here and then place them into the folder ./models/pretrained_models/stylegan2-{cat/church/horse}.

Training

Data preparing

DeltaEdit is trained on latent vectors.
For the facial domain, 58,000 real images from FFHQ dataset are randomly selected and 200,000 fake images from the z space in StyleGAN are sampled for training. Note that all real images are inverted by e4e encoder.
Download the provided FFHQ latent vectors from here and then place all numpy files into the folder ./latent_code/ffhq.

Generate the 200,000 sampled latent vectors by running the following commands for each specific domain:

CUDA_VISIBLE_DEVICES=0 python generate_codes.py --classname ffhq --samples 200000
CUDA_VISIBLE_DEVICES=0 python generate_codes.py --classname cat --samples 200000
CUDA_VISIBLE_DEVICES=0 python generate_codes.py --classname church --samples 200000
CUDA_VISIBLE_DEVICES=0 python generate_codes.py --classname horse --samples 200000

Usage

The main training script is placed in ./scripts/train.py.
Training arguments can be found at ./options/train_options.py.

For training please run the following commands:

CUDA_VISIBLE_DEVICES=0 python scripts/train.py

Inference

The main inferece script is placed in ./scripts/inference.py.
Inference arguments can be found at ./options/test_options.py.
Download the pretrained DeltaMapper model for editing human face from here, and then place it into the folder ./checkpoints .
Some inference data are provided in ./examples.

To produce editing results please run the following commands :

CUDA_VISIBLE_DEVICES=1 python scripts/inference.py --target "chubby face","face with eyeglasses","face with smile","face with pale skin","face with tanned skin","face with big eyes","face with black clothes","face with blue suit","happy face","face with bangs","face with red hair","face with black hair","face with blond hair","face with curly hair","face with receding hairline","face with bowlcut hairstyle"

The produced results are showed in the following.

You can also specify your desired target attributes to the flag of --target.

Inference for real images

The main inferece script is placed in ./scripts/inference_real.py.
Inference arguments can be found at ./options/test_options.py.
Download the pretrained DeltaMapper model for editing human face from here, and then place it into the folder ./checkpoints .
Download the pretrained e4e encoder e4e_ffhq_encode.pt from e4e.
One test image is provided in ./test_imgs.

To produce editing results please run the following commands :

CUDA_VISIBLE_DEVICES=1 python scripts/inference_real.py --target "chubby face","face with eyeglasses","face with smile","face with pale skin","face with tanned skin","face with big eyes","face with black clothes","face with blue suit","happy face","face with bangs","face with red hair","face with black hair","face with blond hair","face with curly hair","face with receding hairline","face with bowlcut hairstyle"

Results

Acknowledgements

This code is developed based on the code of orpatashnik/StyleCLIP by Or Patashnik et al.

Citation

If you use this code for your research, please cite our paper:

@InProceedings{lyu2023deltaedit,
    author    = {Lyu, Yueming and Lin, Tianwei and Li, Fu and He, Dongliang and Dong, Jing and Tan, Tieniu},
    title     = {DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulation},
    booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year      = {2023},
}

Yueming6568/DeltaEdit