LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generation (2303.17189v2)

Abstract: Recently, diffusion models have achieved great success in image synthesis. However, when it comes to the layout-to-image generation where an image often has a complex scene of multiple objects, how to make strong control over both the global layout map and each detailed object remains a challenging task. In this paper, we propose a diffusion model named LayoutDiffusion that can obtain higher generation quality and greater controllability than the previous works. To overcome the difficult multimodal fusion of image and layout, we propose to construct a structural image patch with region information and transform the patched image into a special layout to fuse with the normal layout in a unified form. Moreover, Layout Fusion Module (LFM) and Object-aware Cross Attention (OaCA) are proposed to model the relationship among multiple objects and designed to be object-aware and position-sensitive, allowing for precisely controlling the spatial related information. Extensive experiments show that our LayoutDiffusion outperforms the previous SOTA methods on FID, CAS by relatively 46.35%, 26.70% on COCO-stuff and 44.29%, 41.82% on VG. Code is available at https://github.com/ZGCTroy/LayoutDiffusion.

Analysis of "LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generation"

The paper introduces LayoutDiffusion, a diffusion-based model designed to enhance controllability in layout-to-image generation tasks. Its primary objective is to synthesize images from structured layout information, overcoming limitations in multimodal fusion. This work marks a significant departure from previous generative adversarial network (GAN)-based approaches by shifting to a diffusion model framework.

Key Contributions and Methodological Advances

1. Unified Image-Layout Fusion: One of the main innovations is the transformation of layouts and images into a unified form through the construction of structural image patches. This transformation includes region information, effectively treating each image patch as a specialized object, which allows for a smoother fusion of image and layout.
2. Layout Fusion Module (LFM): LFM enhances interactions between multiple objects within a layout, better capturing the relationships and positioning among them. It does so by using a transformer encoder that leverages self-attention mechanisms, which assist in generating a latent representation of the entire layout.
3. Object-aware Cross Attention (OaCA): The implementation of OaCA is a notable improvement over standard cross attention approaches by incorporating sensitivity to object positions and regions. This allows for precise spatial control within the generated images, optimizing the incorporation of the layout's structural details.
4. Classifier-free Guidance: This method, also used for layout condition support, avoids the need for training additional classifiers by interpolating model predictions with and without conditioning. This technique contributes to the seamless integration of layout information into the image generation process.

Experimental Results

The experimental validation on the COCO-stuff and Visual Genome datasets demonstrates significant performance improvements over state-of-the-art methods. Specifically, LayoutDiffusion achieves enhancements in both quality and controllability:

• Quality Metrics: The model displays superior performance in FID and IS scores, indicating enhanced image generation quality over traditional GAN-based methods.
• Control and Diversity: The framework's sophisticated handling of spatial information is substantiated by improvements in the CAS and YOLOScore metrics, revealing stronger controlled generation capabilities with minimal compromise on diversity, as measured by the DS metric.

Implications and Future Directions

The introduction of diffusion models into the layout-to-image generation field is compelling, showcasing their potential beyond the prevalent text-to-image generation benchmarks. LayoutDiffusion's ability to maintain high-quality output while offering finer control over image attributes provides a robust foundation for practical applications, such as in video game design, architectural visualization, and complex scene generation in film production.

Future developments could explore the combination of LayoutDiffusion with pre-trained text-guided diffusion models, potentially overcoming its current limitation of requiring specific dataset annotations with bounding boxes. Moreover, integrating textual descriptions could enhance semantic richness and further refine control over generated content.

Conclusion

LayoutDiffusion represents a pivotal advance in controllable image synthesis, leveraging the inherent strengths of diffusion processes to improve image quality and controllability. Its pioneering approach in unifying image patches with layout information sets a precedent for future explorations in diffusion-based image generation systems. This shift from GAN-centric models to diffusion frameworks opens up substantial research avenues, encouraging further integration of multimodal inputs to expand the versatility and application scope of AI-driven generation technologies.