Controllable Layered Image Generation for Real-World Editing

• URL: http://arxiv.org/abs/2601.15507v1
• Date: Wed, 21 Jan 2026 22:29:33 GMT
• Title: Controllable Layered Image Generation for Real-World Editing
• Authors: Jinrui Yang, Qing Liu, Yijun Li, Mengwei Ren, Letian Zhang, Zhe Lin, Cihang Xie, Yuyin Zhou,
• Abstract summary: LASAGNA is a novel, unified framework that generates an image jointly with its composing layers.<n>We introduce LASAGNA-48K, a new dataset composed of clean backgrounds and RGBA foregrounds with physically grounded visual effects.<n>We demonstrate that LASAGNA excels in generating highly consistent and coherent results across multiple image layers simultaneously.
• Score: 49.81321254149423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent image generation models have shown impressive progress, yet they often struggle to yield controllable and consistent results when users attempt to edit specific elements within an existing image. Layered representations enable flexible, user-driven content creation, but existing approaches often fail to produce layers with coherent compositing relationships, and their object layers typically lack realistic visual effects such as shadows and reflections. To overcome these limitations, we propose LASAGNA, a novel, unified framework that generates an image jointly with its composing layers--a photorealistic background and a high-quality transparent foreground with compelling visual effects. Unlike prior work, LASAGNA efficiently learns correct image composition from a wide range of conditioning inputs--text prompts, foreground, background, and location masks--offering greater controllability for real-world applications. To enable this, we introduce LASAGNA-48K, a new dataset composed of clean backgrounds and RGBA foregrounds with physically grounded visual effects. We also propose LASAGNABENCH, the first benchmark for layer editing. We demonstrate that LASAGNA excels in generating highly consistent and coherent results across multiple image layers simultaneously, enabling diverse post-editing applications that accurately preserve identity and visual effects. LASAGNA-48K and LASAGNABENCH will be publicly released to foster open research in the community. The project page is https://rayjryang.github.io/LASAGNA-Page/.

Related papers

• Referring Layer Decomposition [25.128453386102887]
  We introduce the Referring Layer Decomposition (RLD) task, which predicts complete RGBA layers from a single RGB image.<n>At the core is the RefLade, a large-scale dataset comprising 1.11M image-layer-prompt triplets produced by our scalable data engine.<n>We present RefLayer, a simple baseline designed for prompt-conditioned layer decomposition, achieving high visual fidelity and semantic alignment.
  arXiv  Detail & Related papers  (2026-02-22T22:05:17Z)
• Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition [73.43121650616804]
  We propose textbfQwen-Image-Layered, an end-to-end diffusion model that decomposes a single RGB image into multiple semantically disentangled RGBA layers.<n>Our method significantly surpasses existing approaches in decomposition quality and establishes a new paradigm for consistent image editing.
  arXiv  Detail & Related papers  (2025-12-17T17:12:42Z)
• MagicQuillV2: Precise and Interactive Image Editing with Layered Visual Cues [106.02577891104079]
  We propose MagicQuill V2, a novel system that introduces a textbflayered composition paradigm to generative image editing.<n>Our method deconstructs creative intent into a stack of controllable visual cues.
  arXiv  Detail & Related papers  (2025-12-02T18:59:58Z)
• PSDiffusion: Harmonized Multi-Layer Image Generation via Layout and Appearance Alignment [23.67447416568964]
  Transparent image layer generation plays a significant role in digital art and design.<n>Existing methods typically decompose transparent layers from a single RGB image using a set of tools or generate multiple transparent layers sequentially.<n>We propose PSDiffusion, a unified diffusion framework that leverages image composition priors from pre-trained image diffusion model for simultaneous multi-layer text-to-image generation.
  arXiv  Detail & Related papers  (2025-05-16T17:23:35Z)
• ART: Anonymous Region Transformer for Variable Multi-Layer Transparent Image Generation [108.69315278353932]
  We introduce the Anonymous Region Transformer (ART), which facilitates the direct generation of variable multi-layer transparent images.<n>By enabling precise control and scalable layer generation, ART establishes a new paradigm for interactive content creation.
  arXiv  Detail & Related papers  (2025-02-25T16:57:04Z)
• Materialist: Physically Based Editing Using Single-Image Inverse Rendering [47.85234717907478]
  Materialist is a method combining a learning-based approach with physically based progressive differentiable rendering.<n>Our approach enables a range of applications, including material editing, object insertion, and relighting.<n> Experiments demonstrate strong performance across synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2025-01-07T11:52:01Z)
• BrushEdit: All-In-One Image Inpainting and Editing [76.93556996538398]
  BrushEdit is a novel inpainting-based instruction-guided image editing paradigm.<n>We devise a system enabling free-form instruction editing by integrating MLLMs and a dual-branch image inpainting model.<n>Our framework effectively combines MLLMs and inpainting models, achieving superior performance across seven metrics.
  arXiv  Detail & Related papers  (2024-12-13T17:58:06Z)
• Generative Image Layer Decomposition with Visual Effects [49.75021036203426]
  LayerDecomp is a generative framework for image layer decomposition.<n>It produces clean backgrounds and high-quality transparent foregrounds with faithfully preserved visual effects.<n>Our method achieves superior quality in layer decomposition, outperforming existing approaches in object removal and spatial editing tasks.
  arXiv  Detail & Related papers  (2024-11-26T20:26:49Z)
• DesignEdit: Multi-Layered Latent Decomposition and Fusion for Unified & Accurate Image Editing [22.855660721387167]
  We transform the spatial-aware image editing task into a combination of two sub-tasks: multi-layered latent decomposition and multi-layered latent fusion. We show that our approach consistently surpasses the latest spatial editing methods, including Self-Guidance and DiffEditor.
  arXiv  Detail & Related papers  (2024-03-21T15:35:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.