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LayerFusion: Harmonized Multi-Layer Text-to-Image Generation with Generative Priors

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arxiv 2412.04460 v1 pith:MPZG3A65 submitted 2024-12-05 cs.CV

Yusuf Dalva , Yijun Li , Qing Liu , Nanxuan Zhao , Jianming Zhang , Zhe Lin , Pinar Yanardag This is my paper
classification cs.CV
keywords generationlayersimageslayerbackgroundcontentdiffusionforeground
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Large-scale diffusion models have achieved remarkable success in generating high-quality images from textual descriptions, gaining popularity across various applications. However, the generation of layered content, such as transparent images with foreground and background layers, remains an under-explored area. Layered content generation is crucial for creative workflows in fields like graphic design, animation, and digital art, where layer-based approaches are fundamental for flexible editing and composition. In this paper, we propose a novel image generation pipeline based on Latent Diffusion Models (LDMs) that generates images with two layers: a foreground layer (RGBA) with transparency information and a background layer (RGB). Unlike existing methods that generate these layers sequentially, our approach introduces a harmonized generation mechanism that enables dynamic interactions between the layers for more coherent outputs. We demonstrate the effectiveness of our method through extensive qualitative and quantitative experiments, showing significant improvements in visual coherence, image quality, and layer consistency compared to baseline methods.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. BFS: Back-to-Front Layered Image Synthesis via Knowledge Transfer

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

    BFS is a dual-branch diffusion model with bidirectional knowledge transfer that synthesizes coherent foreground layers with visual effects using a two-stage training scheme on unlayered data.

  2. A Unified and Controllable Framework for Layered Image Generation with Visual Effects

    cs.CV 2026-01 unverdicted novelty 7.0 of 10

    LASAGNA produces layered images with integrated visual effects in a single pass, enabling drift-free edits via alpha compositing while releasing a 48K dataset and a 242-sample benchmark.

  3. LaRender: Training-Free Occlusion Control in Image Generation via Latent Rendering

    cs.CV 2025-08 conditional novelty 7.0 of 10

    LaRender replaces cross-attention layers in a pretrained diffusion model with a latent alpha-compositing operation that renders object features in occlusion order, giving training-free occlusion control.

  4. UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    UniVidX unifies diverse video generation tasks into one conditional diffusion model using stochastic condition masking, decoupled gated LoRAs, and cross-modal self-attention.

  5. Text-Conditioned Background Generation for Editable Multi-Layer Documents

    cs.CV 2025-12 conditional novelty 5.0 of 10

    A training-free system combines soft latent masking, WCAG-contrast-optimized semi-transparent text backings, and recursive LLM summaries to generate readable, style-consistent backgrounds for multi-page documents.

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