When StyleGAN Meets Stable Diffusion:
a ${\mathcal{W}_+}$ Adapter for Personalized Image Generation

S-Lab, Nanyang Technological University

We propose a $\mathcal{W}_+$ adapter, a method that aligns the face latent space $\mathcal{W}_+$ of StyleGAN with text-to-image diffusion models, achieving high fidelity in identity preservation and semantic editing.

Given a single reference image (thumbnail in the top left), our $\mathcal{W}_+$ adapter not only integrates the identity into the text-to-image generation accurately but also enables modifications of facial attributes along the $\Delta w$ trajectory derived from StyleGAN. The text prompt is ``a woman wearing a spacesuit in a forest''.

TODO

Release the source code and model.
Extend to more diffusion models.

Installation

conda create -n wplus python=3.8
conda activate wplus
pip install torch==2.0.1 torchvision==0.15.2 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
BASICSR_EXT=True pip install basicsr

Inference for in-the-wild Images (Stage 2)

Step 0: download the weights

If you encounter errors about StyleGAN that are not easy to solve, you can create a new environment and use a lower torch version, e.g., 1.12.1+cu113. You can refer to installation of our MARCONet

python script/download_weights.py

Step 1: get e4e vector from real-world face images

For in the wild face image:

CUDA_VISIBLE_DEVICES=0 python ./script/ProcessWildImage.py -i ./test_data/in_the_wild -o ./test_data/in_the_wild_Result -n

For aligned face image:

CUDA_VISIBLE_DEVICES=0 python ./script/ProcessWildImage.py -i ./test_data/aligned_face -o ./test_data/aligned_face_Result

# Parameters:
-i: input path
-o: save path
-n: need alignment like FFHQ. This is for in-the-wild images.
-s: blind super-resolution using PSFRGAN. This is for low-quality face images

Step 2: Stable Diffusion Generation Using Our $\mathcal{W}_+$ Adapter.

The base model supports many pre-trained stable diffusion models, like runwayml/stable-diffusion-v1-5, dreamlike-art/dreamlike-anime-1.0 and Controlnet, without any training. See the details in the test_demo.ipynb
You can control the parameter of residual_att_scale to balance the identity preservation and text alignment.

Attributes Editing Examples (Stage 2):

- Prompt: 'a woman wearing a red shirt in a garden'
- Seed: 23
- e4e_path: ./test_data/e4e/1.pth

Emotion Editing

Lipstick Editing

Roundness Editing

Eye Editing using Animate Model of dreamlike-anime-1.0

ControlNet using control_v11p_sd15_openpose

Inference for Face Images (Stage 1)

See test_demo_stage1.ipynb

Attributes Editing Examples (Stage 1):

Face Image Inversion and Editing

Training

Training Data for Stage 1:

face image
e4e vector

./train_face.sh

Training Data for Stage 2:

face image
e4e vector
background mask
in-the-wild image
in-the-wild face mask
in-the-wild caption

./train_wild.sh

For more details, please refer to the ./train_face.py and ./train_wild.py

Others

You can convert the pytorch_model.bin to wplus_adapter.bin by running:

python script/transfer_pytorchmodel_to_wplus.py

Failure Case Analyses

Since our $\mathcal{W}_+$ adapter affects the results by using the format of residual cross-attention, the final performance relies on the original results of stable diffusion. If the original result is not good, you can manually adjust the prompt or seed to get a better result.

License

This project is licensed under NTU S-Lab License 1.0. Redistribution and use should follow this license.

Acknowledgement

This project is built based on the excellent IP-Adapter. We also refer to StyleRes, FreeU and PSFRGAN.

Citation

@article{li2023w-plus-adapter,
author = {Li, Xiaoming and Hou, Xinyu and Loy, Chen Change},
title = {When StyleGAN Meets Stable Diffusion: a $\mathcal{W}_+$ Adapter for Personalized Image Generation},
journal = {arXiv preprint arXiv: 2311.17461},
year = {2023}
}

AyanKumarBhunia/w-plus-adapter

When StyleGAN Meets Stable Diffusion: a ${\mathcal{W}_+}$ Adapter for Personalized Image Generation