FreeStyle : Free Lunch for Text-guided Style Transfer using Diffusion Models

The rapid development of generative diffusion models has significantly advanced the field of style transfer. However, most current style transfer methods based on diffusion models typically involve a slow iterative optimization process, e.g., model fine-tuning and textual inversion of style concept. In this paper, we introduce FreeStyle, an innovative style transfer method built upon a pre-trained large diffusion model, requiring no further optimization. Besides, our method enables style transfer only through a text description of the desired style, eliminating the necessity of style images. Specifically, we propose a dual-stream encoder and single-stream decoder architecture, replacing the conventional U-Net in diffusion models. In the dual-stream encoder, two distinct branches take the content image and style text prompt as inputs, achieving content and style decoupling. In the decoder, we further modulate features from the dual streams based on a given content image and the corresponding style text prompt for precise style transfer.

For details see the project page and paper

Getting Started

Prerequisites

conda create -n stylefree python==3.8.18
conda activate stylefree

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Installation

Installation dependencies

 cd diffusers
 pip install -e .
 pip install torchsde -i https://pypi.tuna.tsinghua.edu.cn/simple
 cd ../diffusers_test
 pip install transformers
 pip install accelerate

###Download model and weight files download the SDXL and put it into: ./diffusers_test/stable-diffusion-xl-base-1.0

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Demo

####Oil painting Style

  cd ./diffusers_test
  python stable_diffusion_xl_test.py --refimgpath ./ContentImages/imgs0 --model_name "./stable-diffusion-xl-base-1.0" --unet_name ./stable-diffusion-xl-base-1.0/unet/ --prompt_json ./style_prompt0.json --num_images_per_prompt 4 --output_dir ./output0 --sampler "DDIM" --step 30 --cfg 5 --height 1024 --width 1024 --seed 123456789 --n 160 --b 1.8 --s 1

####Origami Art Style

  cd ./diffusers_test
  python stable_diffusion_xl_test.py --refimgpath ./ContentImages/imgs1 --model_name "./stable-diffusion-xl-base-1.0" --unet_name ./stable-diffusion-xl-base-1.0/unet/ --prompt_json ./style_prompt1.json --num_images_per_prompt 4 --output_dir ./output1 --sampler "DDIM" --step 30 --cfg 5 --height 1024 --width 1024 --seed 123456789 --n 160 --b 2.5 --s 1

####Gogh Starry Sky Style

  cd ./diffusers_test
  python stable_diffusion_xl_test.py --refimgpath ./ContentImages/imgs2 --model_name "./stable-diffusion-xl-base-1.0" --unet_name ./stable-diffusion-xl-base-1.0/unet/ --prompt_json ./style_prompt2.json --num_images_per_prompt 4 --output_dir ./output2 --sampler "DDIM" --step 30 --cfg 5 --height 1024 --width 1024 --seed 123456789 --n 160 --b 2.5 --s 1

####Studio Ghibli Style

  cd ./diffusers_test
  python stable_diffusion_xl_test.py --refimgpath ./ContentImages/imgs3 --model_name "./stable-diffusion-xl-base-1.0" --unet_name ./stable-diffusion-xl-base-1.0/unet/ --prompt_json ./style_prompt3.json --num_images_per_prompt 4 --output_dir ./output3 --sampler "DDIM" --step 30 --cfg 5 --height 1024 --width 1024 --seed 123456789 --n 160 --b 2.8 --s 1

####Cyberpunk Style

  cd ./diffusers_test
  python stable_diffusion_xl_test.py --refimgpath ./ContentImages/imgs4 --model_name "./stable-diffusion-xl-base-1.0" --unet_name ./stable-diffusion-xl-base-1.0/unet/ --prompt_json ./style_prompt4.json --num_images_per_prompt 4 --output_dir ./output4 --sampler "DDIM" --step 30 --cfg 5 --height 1024 --width 1024 --seed 123456789 --n 160 --b 2.8 --s 1

####Children Crayon Drawing Style

  cd ./diffusers_test
  python stable_diffusion_xl_test.py --refimgpath ./ContentImages/imgs5 --model_name "./stable-diffusion-xl-base-1.0" --unet_name ./stable-diffusion-xl-base-1.0/unet/ --prompt_json ./style_prompt5.json --num_images_per_prompt 4 --output_dir ./output5 --sampler "DDIM" --step 30 --cfg 5 --height 1024 --width 1024 --seed 123456789 --n 160 --b 1.8 --s 1

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Start Inference

Perform style transfer inference according to the following method.

Inference Command

  cd ./diffusers_test
  python stable_diffusion_xl_test.py --refimgpath ./ContentImages/imgs0 --model_name "./stable-diffusion-xl-base-1.0" --unet_name ./stable-diffusion-xl-base-1.0/unet/ --prompt_json ./style_prompt0.json --num_images_per_prompt 4 --output_dir ./output1 --sampler "DDIM" --step 30 --cfg 5 --height 1024 --width 1024 --seed 123456789 --n 640 --b 1.5 --s 2

Parameter Interpretation

--refimgpath            Path to Content Images
--model_name            SDXL saved path
--unet_name             Path to unet folder in SDXL
--prompt_json           JSON file for style prompts
--num_images_per_prompt how many images are generated for each image and style
--output_dir            Path to save stylized images
--n                     hyperparameter n∈[160,320,640,1280] 
--b                     hyperparameter b∈(1,3) 
--s                     hyperparameter s∈（1,2）

Parameter Recommendation

Content images with higher quality can achieve better stylization results.

We recommend setting the parameters as: n=160, b=2.5, s=1.

When the expression of style information is ambiguous, please reduce b (increase s).

When the expression of content information is unclear, please increase b (decrease s).

Adjust the parameter n appropriately when there is noise in the stylized image.

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Citation

@misc{he2024freestyle,
   title={FreeStyle: Free Lunch for Text-guided Style Transfer using Diffusion Models},
   author={Feihong He and Gang Li and Mengyuan Zhang and Leilei Yan and Lingyu Si and Fanzhang Li},
   year={2024},
   eprint={2401.15636},
   archivePrefix={arXiv},
   primaryClass={cs.CV}
   }

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Contact

Please feel free to open an issue or contact us personally if you have questions, need help, or need explanations. Write to one of the following email addresses:

[email protected]

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