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Training-free Content Injection using h-space in Diffusion models

arXiv project_page

Training-free Style Transfer Emerges from h-space in Diffusion models<br> Jaeseok Jeong*, Mingi Kwon*, Youngjung Uh *denotes equal contribution <br> Arxiv preprint. Abstract: <br>

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.

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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 EditSizePretrained ModelDatasetReference Repo.
Human face256×256Diffusion (Auto)CelebA-HQSDEdit
Human face256×256DiffusionFFHQP2 weighting
Church256×256Diffusion (Auto)LSUN-BedroomSDEdit
Bedroom256×256Diffusion (Auto)LSUN-ChurchSDEdit
Dog face256×256DiffusionAFHQ-DogILVR
Painting face256×256DiffusionMETFACESP2 weighting
ImageNet256x256DiffusionImageNetGuided Diffusion

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)

#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.