# Controllable Layer Decomposition for Reversible Multi-Layer Image Generation

arXiv: https://arxiv.org/abs/2511.16249 · HTML: https://mlsanity.com/paper/2511.16249/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2025.

## Built on by (most cited first, 9 of 9)

- [MIND-V: Hierarchical World Model for Long-Horizon Robotic Manipulation with RL-based Physical Alignment](https://mlsanity.com/paper/2512.06628/) (2025)
- [Embedding-perturbed Exploration Preference Optimization for Flow Models](https://mlsanity.com/paper/2605.15803/) (2026)
- [Workflow-Aware Structured Layer Decomposition for Illustration Production](https://mlsanity.com/paper/2603.14925/) (2026)
- [RevealLayer: Disentangling Hidden and Visible Layers via Occlusion-Aware Image Decomposition](https://mlsanity.com/paper/2605.11818/) (2026)
- [Does Synthetic Layered Design Data Benefit Layered Design Decomposition?](https://mlsanity.com/paper/2605.15167/) (2026)
- [Stable-Layers: Fine-Tuning Image Layer Decomposition Models with VLM-Scored Reinforcement Learning](https://mlsanity.com/paper/2605.30257/) (2026)
- [LimeCross: Context-Conditioned Layered Image Editing with Structural Consistency](https://mlsanity.com/paper/2605.10319/) (2026)
- [PixelART: Image-to-Layer Decomposition without Latents or Text-to-Image Pretraining](https://mlsanity.com/paper/2609.22789/) (2026)
- [TexTailor: Texture-Preserving Video Virtual Try-On via Adaptive Garment Conditioning](https://mlsanity.com/paper/2609.39335/) (2026)

## Abstract (arXiv)

This work presents Controllable Layer Decomposition (CLD), a method for achieving fine-grained and controllable multi-layer separation of raster images. In practical workflows, designers typically generate and edit each RGBA layer independently before compositing them into a final raster image. However, this process is irreversible: once composited, layer-level editing is no longer possible. Existing methods commonly rely on image matting and inpainting, but remain limited in controllability and segmentation precision. To address these challenges, we propose two key modules: LayerDecompose-DiT (LD-DiT), which decouples image elements into distinct layers and enables fine-grained control; and Multi-Layer Conditional Adapter (MLCA), which injects target image information into multi-layer tokens to achieve precise conditional generation. To enable a comprehensive evaluation, we build a new benchmark and introduce tailored evaluation metrics. Experimental results show that CLD consistently outperforms existing methods in both decomposition quality and controllability. Furthermore, the separated layers produced by CLD can be directly manipulated in commonly used design tools such as PowerPoint, highlighting its practical value and applicability in real-world creative workflows. Our project is available at https://monkek123king.github.io/CLD_page/.