We present an in-context image decomposition framework that leverages large diffusion foundation models for layered separation. Our method focuses on the challenging case of logo-object decomposition, where the goal is to disentangle a logo from the surface on which it appears while faithfully preserving both layers.

Our approach introduces:

• Cycle-Consistent Tuning: Joint training of decomposition and composition models with reconstruction consistency
• LoRA Adaptation: Lightweight fine-tuning of pretrained diffusion models
• Progressive Self-Improvement: Iterative training set augmentation with high-quality model-generated examples

The method achieves accurate and coherent decompositions and generalizes effectively across other decomposition types (intrinsic decomposition, foreground-background separation), suggesting its potential as a unified framework for layered image decomposition.

Code and pretrained models will be released soon.

If you find this work useful for your research, please cite:

@inproceedings{gu2026cycle,
  title={Cycle-Consistent Tuning for Layered Image Decomposition},
  author={Gu, Zheng and Lu, Min and Sun, Zhida and Lischinski, Dani and Cohen-Or, Daniel and Huang, Hui},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2026}
}