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LayeringDiff: Layered Image Synthesis via Generation, then Disassembly with Generative Knowledge

Kyoungkook Kang, Gyujin Sim, Geonung Kim, Donguk Kim, Seungho Nam, Sunghyun Cho

arXiv:2501.01197v1cs.CV

TL;DR

Layered-image methods based on network fine-tuning require large-scale training and depend on training-data quality and diversity. LayeringDiff generates a composite image and decomposes it into foreground and background layers; experiments report that it outperforms existing methods and produces more diverse, natural layers.

  • Problem

    Network fine-tuning methods for layered-image synthesis are constrained by training-data quality and diversity and require large-scale training to achieve high-quality models.

    Source: §2.1 · p. 2

  • Method

    LayeringDiff generates a composite with an off-the-shelf image model, then decomposes it into foreground and background layers using an adapted generative prior and high-frequency alignment.

    Source: §1 · pp. 1–2, §5 · p. 8

  • Results

    Experiments report that LayeringDiff outperforms existing layered-image synthesis methods and produces more diverse, natural foreground and background layers.

    Source: §1 · p. 2

  • Takeaways & Limitations

    LayeringDiff is reported as practical for a wide range of applications.

    Source: §1 · p. 2

    LayeringDiff assumes accurate alpha predictions, and shadows in initial composite images may remain in the background after decomposition.

    Source: §5 · p. 9

Abstract

from arXiv · show

Layers have become indispensable tools for professional artists, allowing them to build a hierarchical structure that enables independent control over individual visual elements. In this paper, we propose LayeringDiff, a novel pipeline for the synthesis of layered images, which begins by generating a composite image using an off-the-shelf image generative model, followed by disassembling the image into its constituent foreground and background layers. By extracting layers from a composite image, rather than generating them from scratch, LayeringDiff bypasses the need for large-scale training to develop generative capabilities for individual layers. Furthermore, by utilizing a pretrained off-the-shelf generative model, our method can produce diverse contents and object scales in synthesized layers. For effective layer decomposition, we adapt a large-scale pretrained generative prior to estimate foreground and background layers. We also propose high-frequency alignment modules to refine the fine-details of the estimated layers. Our comprehensive experiments demonstrate that our approach effectively synthesizes layered images and supports various practical applications.

1. Introduction

LayeringDiff synthesizes layered images by first generating a composite with an off-the-shelf model, then decomposing it into foreground and background layers. The approach avoids large-scale training for layered-image generation while preserving access to diverse content and object scales.

1. Introduction

  • Prior layered-image training pipelines require costly data synthesis, filtering, and fine-tuning, and can introduce biases such as low-quality outputs or oversized foreground objects [37] [35].The paper attributes these examples to naïve saliency-based training data [37] and object-centric RGBA training data [35].Source: §1 · p. 1
  • Generating a composite image before decomposing it avoids large-scale layered-image training and enables diverse content, object scales, and conditioning through off-the-shelf models such as ControlNet.The initial image generator needs no fine-tuning, and layer decomposition can be learned with a small amount of training data.Source: §1 · pp. 1–2
  • The pipeline generates a composite, identifies its foreground from the text prompt, then uses FBDD and HFA to separate layers and refine their high-frequency details.The separated foreground and background are recombined into the final composite image.Source: §1 · p. 2
  • LayeringDiff is reported to outperform existing layered-image synthesis methods, producing more diverse and natural layers and supporting practical applications.The paper says its experiments include a user study.Source: §1 · p. 2

2. Related Work

Prior work synthesizes layers by fine-tuning text-to-image models, while matting and inpainting methods struggle with layer pixels and large occluded regions. LayeringDiff instead decomposes a pretrained model’s composite image using generative priors and high-frequency alignment.

2.1. Text-based Layered Image Synthesis

  • Text-guided methods learn joint layer representations, coordinate foreground and background generation, or jointly denoise distinct layers from user prompts [37] [35] [12] [14].Zhang et al. [37] use a unified latent representation; Zhang and Agrawala [35] use two LoRAs with shared attention; Huang et al. [14] use a 3D diffusion UNet.Source: §2.1 · p. 2
  • These fine-tuning approaches depend on training-data quality and diversity and require large-scale training, so LayeringDiff decomposes layers from a composite made by a pretrained generative model.Source: §2.1 · p. 2

2.2. Image Matting and Inpainting

  • Matting work emphasizes alpha mattes [4] [6] [11] [13] [18] [22] [33] [34]; simple-prior optimization and regression-based joint prediction [6, 11, 32] struggle with large occluded backgrounds.The direct-estimation methods lack the ability to synthesize new content.Source: §2.2 · p. 2
  • Inpainting methods [25] [31] use binary masks and cannot properly handle regions occluded by semi-transparent foregrounds; LayeringDiff addresses these limits with generative priors and HFA.Source: §2.2 · p. 2

3. LayeringDiff

LayeringDiff generates a composite image from a prompt, identifies its foreground, then decomposes it into foreground and background layers with an alpha mask. A pretrained diffusion prior estimates the layers, while high-frequency alignment refines their details.

3. LayeringDiff

  • The pipeline takes a text prompt and indices identifying its foreground words, then generates a composite image, determines the foreground, and separates foreground and background layers.The foreground words define the foreground prompt, such as “A bird flying” in “A bird flying at sunset over the mountains.”Source: §3 · pp. 2–3, Figure 1 · p. 3

3.1. Initial Image Generation Stage

  • The initial image generation stage uses an off-the-shelf text-conditioned model to synthesize a composite image; experiments use Stable Diffusion XL [23] by default.Layout-conditioned models such as ControlNet [36] can also support additional inputs including edge and depth maps.Source: §3.1 · p. 3

3.2. Foreground Determination Stage

  • Given a foreground prompt, the foreground determination stage detects a bounding box, segments a semantic mask, forms and refines a trimap, and estimates an alpha mask.It uses Grounding DINO [20], SAM, and ViTMatte [34] within the MatteAnything [18] pipeline.Source: §3.2 · p. 3

3.3. Layering Stage

  • The layering stage addresses ill-posed decomposition with FBDD diffusion models for foreground and background layers, then HFA alignment to refine their details.FBDD conditions on the composite image and resized alpha mask; HFA uses separate foreground and background alignment networks, then copies pixels from fully visible regions of the composite.Source: §3.3 · pp. 3–4

3.4. Training of LayeringDiff

  • LayeringDiff trains only FBDD and HFA on synthetic composites, using 150K RGBA foregrounds from MAGICK and 20K backgrounds from BG-20k.BAN’s loss balances pixel-level agreement with ground truth and high-frequency agreement with FBDD output, helping align occluded-region textures without color seams.Source: §3.4 · pp. 4–5, §3.3 · p. 4

4. Experiments

LayeringDiff is evaluated against layered-image synthesis and decomposition baselines, with results highlighting its layer quality and diversity. The experiments also demonstrate sequential multi-layer synthesis and decomposition of real-world images.

4.1. Comparative Evaluation

  • Compared with LayerDiffuse, Text2Layer, and LayerDiff, LayeringDiff produces well-proportioned, prompt-fitting layers that blend naturally into composites.The passages attribute the baselines' issues to oversized foregrounds, lost action knowledge, low-quality textures and masks, or layer-generation approaches that hinder natural blending.Source: §4.1 · pp. 5, 7, Figure 4 · p. 6
  • LayeringDiff achieves superior overall quantitative scores to existing synthesis methods, although its foreground FID and KID are slightly less favorable.The authors attribute the foreground exception to MAGICK's similarity to LayerDiffuse's foreground-focused training data.Source: Table 1 · p. 6, §4.1 · p. 7
  • In the user study, LayeringDiff achieves the highest text-alignment and image-quality scores for all layers.Twenty-four participants rated foreground, background, and composite images on both criteria using a 1–5 scale.Source: Table 3 · p. 6, §4.1 · p. 7
  • LayeringDiff produces foreground layers with greater scale and position diversity than LayerDiffuse, whose average longest span exceeds 90%.The comparison uses occupancy ratio, longest span ratio, and vertical and horizontal centers.Source: Table 2 · p. 6, §4.1 · p. 7

4.2. Evaluation of Layering Stage

  • LayeringDiff produces accurate, contextually coherent background decompositions where matting baselines leave blur and artifacts, and inpainting cannot fix artifacts in partially transparent regions.The layering stage leverages visible background areas; Fig. 6 compares the resulting background layers with baseline outputs.Source: §4.2 · p. 8, Figure 6 · p. 8
  • LayeringDiff achieves the most favorable foreground scores across all reported decomposition metrics, but background results vary by metric.It leads in background LPIPS, while Germer et al. and FBAMatting [6] outperform it in MAD, MSE, and SAD; compared with inpainting baselines, it scores better by using visible background information.Source: Table 4 · p. 7, §4.2 · p. 8

4.3. Applications

  • LayeringDiff supports sequential decomposition of a composite into multiple foreground layers and decomposition of real-world images.The paper presents both as applications of the layering stage.Source: §4.3 · p. 8, Figure 7 · p. 8, Figure 8 · p. 8

5. Conclusion

LayeringDiff synthesizes layered images by decomposing a generated composite, enabling high-quality results without large-scale training. Its quality depends on accurate alpha prediction, and residual shadows can make backgrounds look unnatural.

Conclusion

  • LayeringDiff synthesizes layered images from user prompts by decomposing an initial composite, achieving high-quality generation without large-scale training.The pipeline adapts a generative prior for layer decomposition and uses high-frequency alignment.Source: §5 · p. 8
  • Extensive experiments demonstrate the method’s effectiveness and diverse applications.The proposed decomposition uses an adapted generative prior and a high-frequency alignment strategy.Source: §5 · p. 8

Limitations

  • Inaccurate alpha predictions can compromise layer quality, while shadows in the initial composite may remain in the background and create an unnatural effect.Residual shadows may require further shadow removal.Source: §5 · p. 9

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