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[CVPR 2024] Official implementation of "DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations"
| Date | Stars |
|---|---|
| 2026-07-31 | 280 |
| 2026-08-03 | 280 |
| 2026-08-06 | 280 |
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# DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations (CVPR 2024) <div align="center"> <a href='https://arxiv.org/abs/2403.06951'><img src='https://img.shields.io/badge/arXiv-2403.06951-b31b1b.svg'></a> <a href='https://tianhao-qi.github.io/DEADiff/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> _**[Tianhao Qi*](https://github.com/Tianhao-Qi/), [Shancheng Fang](https://tothebeginning.github.io/), [Yanze Wu✝](https://tothebeginning.github.io/), [Hongtao Xie✉](https://imcc.ustc.edu.cn/_upload/tpl/0d/13/3347/template3347/xiehongtao.html), [Jiawei Liu](https://scholar.google.com/citations?user=X21Fz-EAAAAJ&hl=en&authuser=1), <br>[Lang Chen](https://scholar.google.com/citations?user=h5xex20AAAAJ&hl=zh-CN), [Qian He](https://scholar.google.com/citations?view_op=list_works&hl=zh-CN&authuser=1&user=9rWWCgUAAAAJ), [Yongdong Zhang](https://scholar.google.com.hk/citations?user=hxGs4ukAAAAJ&hl=zh-CN)**_ <br><br> (*Works done during the internship at ByteDance, ✝Project Lead, ✉Corresponding author) From University of Science and Technology of China and ByteDance. </div> ## 🔆 Introduction **TL;DR:** We propose DEADiff, a generic method facilitating the synthesis of novel images that embody the style of a given reference image and adhere to text prompts. <br> ### ⭐⭐ Stylized Text-to-Image Generation. <div align="center"> <img src=docs/showcase_img.png> <p>Stylized text-to-image results. Resolution: 512 x 512. (Compressed)</p> </div> ### ⭐⭐ Style Transfer. <div align="center"> <img src=docs/showcase_controlnet.png> <p>Style transfer results with <a href="https://github.com/lllyasviel/ControlNet.git" target="_blank">ControlNet</a>. </p> </div> ## 📝 Changelog - __[2024.4.3]__: 🔥🔥 Release the inference code and pretrained checkpoint. - __[2024.3.5]__: 🔥🔥 Release the project page. ## ⏳ TODO - [x] Release the inference code. - [ ] Release training data. ## ⚙️ Setup ```bash conda create -n deadiff python=3.9.2 conda activate deadiff conda install pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.8 -c pytorch -c nvidia pip install git+https://github.com/salesforce/LAVIS.git@20230801-blip-diffusion-edit pip install -r requirements.txt pip install -e . ``` ## 💫 Inference 1) Download the pretrained model from [Hugging Face](https://huggingface.co/qth/DEADiff/tree/main) and put it under ./pretrained/. 2) Run the commands in terminal. ```python3 python3 scripts/app.py ``` The Gradio app allows you to transfer style from the reference image. Just try it for more details. Prompt: "A curly-haired boy"  Prompt: "A robot"  Prompt: "A motorcycle"  ### ➕ Style Transfer with ControlNet We support **style transfer with structural control** by combining DEADiff with [ControlNet](https://github.com/lllyasviel/ControlNet). This enables users to guide the spatial layout (e.g., edges or depth maps) of the generated images, while transferring the visual style from a reference image. To perform style transfer with ControlNet, please download the following pretrained models: - `control_sd15_canny.pth`: [Download](https://huggingface.co/lllyasviel/ControlNet/resolve/main/models/control_sd15_canny.pth) → place it under `./pretrained/` - `control_sd15_depth.pth`: [Download](https://huggingface.co/lllyasviel/ControlNet/resolve/main/models/control_sd15_depth.pth) → place it under `./pretrained/` - `dpt_hybrid-midas-501f0c75.pt` (for depth estimation): [Download](https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/dpt_hybrid-midas-501f0c75.pt) → place it under `ldm
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matched fp:7d3cd51bb8dc53c7, desc:diffusion model