Training-free Content Injection using h-space in Diffusion models
Training-free Style Transfer Emerges from h-space in Diffusion models
Jaeseok Jeong*, Mingi Kwon*, Youngjung Uh *denotes equal contribution
Arxiv preprint. Abstract:
Diffusion models (DMs) synthesize high-quality images in various domains. However, controlling their generative process is still hazy because the intermediate variables in the process are not rigorously studied. Recently, the bottleneck feature of the U-Net, namely $h$-space, is found to convey the semantics of the resulting image. It enables StyleCLIP-like latent editing within DMs. In this paper, we explore further usage of $h$-space beyond attribute editing, and introduce a method to inject the content of one image into another image by combining their features in the generative processes. Briefly, given the original generative process of the other image, 1) we gradually blend the bottleneck feature of the content with proper normalization, and 2) we calibrate the skip connections to match the injected content. Unlike custom-diffusion approaches, our method does not require time-consuming optimization or fine-tuning. Instead, our method manipulates intermediate features within a feed-forward generative process. Furthermore, our method does not require supervision from external networks.
Description
This repo includes the official Pytorch implementation of InjectFusion, Training-free Content Injection using h-space in Diffusion models.
- InjectFusion offers training-free style mixing and harmonization-like style transfer capabilities through content injection on h-space of diffusion models.
- InjectFusion allows (a) style mixing by content injection within the trained domain, (b) local style mixing by injecting masked content features, and (c) harmonization-like style transfer with out-of-domain style references. All results are pro- duced by frozen pretrained diffusion models. Furthermore, flexibility of InjectFusion enables content injection into any style.
Getting Started
We recommend running our code using NVIDIA GPU + CUDA, CuDNN.
Pretrained Models for InjectFusion
To manipulate soure images, the pretrained Diffuson models are required.
| Image Type to Edit | Size | Pretrained Model | Dataset | Reference Repo. |
|---|---|---|---|---|
| Human face | 256×256 | Diffusion (Auto) | CelebA-HQ | SDEdit |
| Human face | 256×256 | Diffusion | FFHQ | P2 weighting |
| Church | 256×256 | Diffusion (Auto) | LSUN-Bedroom | SDEdit |
| Bedroom | 256×256 | Diffusion (Auto) | LSUN-Church | SDEdit |
| Dog face | 256×256 | Diffusion | AFHQ-Dog | ILVR |
| Painting face | 256×256 | Diffusion | METFACES | P2 weighting |
| ImageNet | 256x256 | Diffusion | ImageNet | Guided Diffusion |
- The pretrained Diffuson models on 256x256 images in CelebA-HQ, LSUN-Church, and LSUN-Bedroom are automatically downloaded in the code. (codes from DiffusionCLIP)
- In contrast, you need to download the models pretrained on other datasets in the table and put it in
./pretraineddirectory. - You can manually revise the checkpoint paths and names in
./configs/paths_config.pyfile.
Datasets
To precompute latents and find the direction of h-space, you need about 100+ images in the dataset. You can use both sampled images from the pretrained models or real images from the pretraining dataset.
If you want to use real images, check the URLs :
You can simply modify ./configs/paths_config.py for dataset path.
InjectFusion
We provide some examples of inference script for InjectFusion. (script_InjectFusion.sh)
- Determine
content_dir,style_dir,save_dir.
#AFHQ
config="afhq.yml"
save_dir="./results/afhq" # output directory
content_dir="./test_images/afhq/contents"
style_dir="./test_images/afhq/styles"
h_gamma=0.3 # Slerp ratio
t_boost=200 # 0 for out-of-domain style transfer.
n_gen_step=1000
n_inv_step=50
omega=0.0
python main.py --diff_style \
--content_dir $content_dir \
--style_dir $style_dir \
--save_dir $save_dir \
--config $config \
--n_gen_step $n_gen_step \
--n_inv_step $n_inv_step \
--n_test_step 1000 \
--hs_coeff $h_gamma \
--t_noise $t_boost \
--sh_file_name $sh_file_name \
--omega $omega \
#CelebA_HQ style mixing with feature mask
config="celeba.yml"
save_dir="./results/masked_style_mixing" # output directory
content_dir="./test_images/celeba/contents"
style_dir="./test_images/celeba/styles"
h_gamma=0.3 # Slerp ratio
dt_lambda=0.9985 # 1.0 for out-of-domain style transfer.
t_boost=200 # 0 for out-of-domain style transfer.
n_gen_step=1000
n_inv_step=50
omega=0.0
python main.py --diff_style \
--content_dir $content_dir \
--style_dir $style_dir \
--save_dir $save_dir \
--config $config \
--n_gen_step $n_gen_step \
--n_inv_step $n_inv_step \
--n_test_step 1000 \
--dt_lambda $dt_lambda \
--hs_coeff $h_gamma \
--t_noise $t_boost \
--sh_file_name $sh_file_name \
--omega $omega \
--use_mask \
#Harmonization-like style mixing with artistic references
config="celeba.yml"
save_dir="./results/style_literature" # output directory
content_dir="./test_images/celeba/contents2"
style_dir="./test_images/style_literature"
h_gamma=0.4
n_gen_step=1000
n_inv_step=1000
CUDA_VISIBLE_DEVICES=$gpu python main.py --diff_style \
--content_dir $content_dir \
--style_dir $style_dir \
--save_dir $save_dir \
--config $config \
--n_gen_step $n_gen_step \
--n_inv_step $n_inv_step \
--n_test_step 1000 \
--hs_coeff $h_gamma \
--sh_file_name $sh_file_name \
Acknowledge
Codes are based on Asryp and DiffusionCLIP.