https://github.com/compvis/scflow
[ICCV 2025] SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models
Science Score: 36.0%
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Repository
[ICCV 2025] SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models
Basic Info
- Host: GitHub
- Owner: CompVis
- License: mit
- Default Branch: main
- Size: 29.5 MB
Statistics
- Stars: 10
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models
Pingchuan Ma* · Xiaopei Yang* · Yusong Li
Ming Gui · Felix Krause · Johannes Schusterbauer · Björn Ommer
CompVis Group @ LMU Munich Munich Center for Machine Learning (MCML)
* equal contribution
📄 ICCV 2025
This repository contains the official implementation of the paper "SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models". We proposed a flow-matching framework that learns an invertible mapping between style-content mixtures and their separate representations, avoiding explicit disentanglement objectives. Together with the method, we have curated a 510k synthetic dataset consisting of 10k content instances and 51 distinct styles.
🛠️ Setup
Create the enviroment with conda:
bash
conda create -n scflow python=3.10
conda activate scflow
pip install -r requirements.txt
The enviroment was tested on Ubuntu 22.04.5 LTS with CUDA 12.1. You can optionally install jupyter-notebook to run the notebook provided in notebooks
Download the model checkpoints: ```bash mkdir ckpts cd ckpts
model checkpoint
wget -O scflowlast.ckpt https://huggingface.co/CompVis/SCFlow/resolve/main/scflowlast.ckpt?dowload=true
unclip checkpoint for visualization
wget -O sd21-unclip-l.ckpt https://huggingface.co/CompVis/SCFlow/resolve/main/sd21-unclip-l.ckpt?dowload=true ```
🔥 Usage
Inference forward (merge content and style)
bash
bash scripts/inference_forward.sh
Inference reverse (disentangle content and style from a given reference)
bash
bash scripts/inference_reverse.sh
Training (coming soon)
bash
bash ...
🗂️ Dataset
Coming soon
🎓 Citation
If you use this codebase or otherwise found our work valuable, please cite our paper:
bibtex
@article{ma2025scflow,
title={SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models},
author={Ma, Pingchuan and Yang, Xiaopei and Li, Yusong and Gui, Ming and Krause, Felix and Schusterbauer, Johannes and Ommer, Bj{\"o}rn},
journal={arXiv preprint arXiv:2508.03402},
year={2025}
}
🔥 Updates and Backlogs
- [x] [06.08.2025] ArXiv paper avaiable.
- [x] [12.08.2025] Release Inference code and ckpt
- [ ] Host the dataset and training code
Owner
- Name: CompVis - Computer Vision and Learning LMU Munich
- Login: CompVis
- Kind: organization
- Email: assist.mvl@lrz.uni-muenchen.de
- Location: Germany
- Website: https://ommer-lab.com/
- Repositories: 33
- Profile: https://github.com/CompVis
Computer Vision and Learning research group at Ludwig Maximilian University of Munich (formerly Computer Vision Group at Heidelberg University)
GitHub Events
Total
- Watch event: 11
- Member event: 1
- Push event: 2
- Create event: 2
Last Year
- Watch event: 11
- Member event: 1
- Push event: 2
- Create event: 2
Committers
Last synced: 12 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| m990130 | p****a@l****e | 2 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 12 months ago