LayerFusion: Harmonized Multi-Layer Text-to-Image Generation with Generative Priors (2412.04460v1)

Abstract: 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.

• The paper introduces a dynamic multi-layer generation pipeline using generative priors to create distinct yet visually coherent foreground and background layers.
• It employs an innovative attention-level blending scheme that leverages both cross-attention and self-attention masks for seamless image integration.
• Quantitative experiments and user studies show superior visual quality and spatial editing capabilities compared to existing baseline methods.

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

The paper "LayerFusion: Harmonized Multi-Layer Text-to-Image Generation with Generative Priors" addresses the challenge of generating layered content from textual prompts, a feature crucial for many creative domains like graphic design, animation, and digital art. The proposed framework successfully generates images with distinct foreground and background layers using Latent Diffusion Models (LDMs). This research fills a gap in the generative AI space by introducing a technique that allows for coherent layered content creation, compared to traditional sequential methods.

Key Contributions

• Harmonized Multi-Layer Generation: The paper introduces a novel generation pipeline capable of creating harmonized images consisting of separate RGBA foreground and RGB background layers. Unlike previous approaches that generate these layers sequentially, LayerFusion employs a dynamic interaction between layers, enhancing the visual coherence and quality of the output.
• Attention-Level Blending Scheme: A unique aspect of LayerFusion is its innovative blending mechanism at the attention level, leveraging masks derived from attention layers in the model to achieve seamless blending between layers. This mechanism ensures natural interactions between image components, which results in visually appealing and consistent compositions.
• Generative Attention Masks as Priors: The framework extracts and utilizes attention masks (structure priors from self-attention and content priors from cross-attention) from the foreground generator. These masks guide the generation process, ensuring that the layered outputs maintain a high degree of visual harmony.

Methodological Insights

The authors employ cross-attention and self-attention strategies to refine the interaction between image layers. Cross-attention masks help capture the content's focus from the input prompts, while self-attention masks are used to discern and maintain the structural integrity of the generated foreground. This dual approach aids in modulating the resulting imagery in response to varying background and foreground contents, facilitating fine-grained control over the generative process.

Experimental Evaluation

LayerFusion's effectiveness is demonstrated through a series of qualitative and quantitative experiments. The results exhibit substantial improvements in visual coherence, image quality, and consistency across layers when compared to existing baseline methods like LayerDiffuse. Notably, the design allows for enhanced spatial editing capabilities, which are crucial for creative professionals.

Quantitative tests, including metrics like CLIP score, KID, and FID, show that the proposed method aligns better with real imaging distributions than previous models, particularly in generating background images. The user paper further validates these findings, indicating a preference for LayerFusion's outputs in terms of blending and aesthetic appeal.

Implications and Future Directions

Practically, LayerFusion provides an efficient tool for the creative industry, enabling intricate image editing and composition tasks with a high degree of adaptability and minimal manual intervention. Theoretically, this approach suggests new avenues for leveraging attention mechanisms in generative models to achieve more nuanced control over image synthesis.

Looking forward, extending this method to generate multi-layer compositions with more than two layers could be beneficial. Further exploration into mitigating any biases inherited from the pre-trained models used and expanding the framework's applicability across different domains will contribute to the advancement of generative AI technologies.

In conclusion, "LayerFusion" represents a significant step forward in the field of text-to-image generation, providing a sophisticated yet practical method for producing layered digital content.