Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis (2208.13753v2)

Abstract: Diffusion models (DMs) have shown great potential for high-quality image synthesis. However, when it comes to producing images with complex scenes, how to properly describe both image global structures and object details remains a challenging task. In this paper, we present Frido, a Feature Pyramid Diffusion model performing a multi-scale coarse-to-fine denoising process for image synthesis. Our model decomposes an input image into scale-dependent vector quantized features, followed by a coarse-to-fine gating for producing image output. During the above multi-scale representation learning stage, additional input conditions like text, scene graph, or image layout can be further exploited. Thus, Frido can be also applied for conditional or cross-modality image synthesis. We conduct extensive experiments over various unconditioned and conditional image generation tasks, ranging from text-to-image synthesis, layout-to-image, scene-graph-to-image, to label-to-image. More specifically, we achieved state-of-the-art FID scores on five benchmarks, namely layout-to-image on COCO and OpenImages, scene-graph-to-image on COCO and Visual Genome, and label-to-image on COCO. Code is available at https://github.com/davidhalladay/Frido.

• The paper introduces Frido, a feature pyramid diffusion model that employs coarse-to-fine denoising to capture both global structure and intricate details.
• It utilizes multi-scale vector quantized features and a PyU-Net architecture to efficiently process and refine images at different resolution levels.
• Empirical results demonstrate state-of-the-art performance with improved FID and CLIP scores across various image synthesis tasks.

An Analytical Overview of "Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis"

The paper "Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis" introduces a novel approach to enhancing the effectiveness of diffusion models (DMs) in generating high-quality images with complex scenes. The authors propose Frido, a Feature Pyramid Diffusion model that utilizes a multi-scale coarse-to-fine denoising approach, which allows for a more detailed and structurally aware image synthesis process. This model is particularly tailored for tasks that require capturing both the global structure and intricate details of multiple objects within a scene.

Key Contributions

The core contributions of the work lie in the development of Frido and its key components:

1. Multi-Scale Vector Quantized Features: Frido employs a new MS-VQGAN (Multi-Scale Vector Quantized Generative Adversarial Network) to encode images into multi-resolution latent spaces. This design enables the model to learn and leverage coarse and fine details more effectively.
2. Feature Pyramid U-Net (PyU-Net): The model introduces PyU-Net, a shared neural architecture for efficient denoising across multiple feature scales. PyU-Net facilitates the sequential processing of low-to-high resolution features, leveraging high-level information to guide the generation of finer details.
3. Coarse-to-Fine Modulation: The model includes a coarse-to-fine modulation mechanism to incorporate high-level feature conditions into the denoising framework, allowing the model to better capture intricate relationships within an image, such as those found in scene graphs or text descriptions.

Empirical Evaluation

Frido's performance is empirically validated across multiple image synthesis tasks, including text-to-image, scene-graph-to-image, label-to-image, and layout-to-image generation. On these tasks, Frido achieves state-of-the-art results, improving upon existing benchmarks, particularly in terms of Fréchet Inception Distance (FID) and CLIP scores. This performance underscores the model's ability to generate photo-realistic images that align well with complex, abstract input conditions.

Numerical Results and Insights

The authors provide compelling numerical results that highlight Frido's superiority over existing models. For instance, Frido achieves state-of-the-art FID scores across five benchmarks and demonstrates improved alignment between generated images and input conditions, as measured by CLIP scores. These improvements are achieved without incurring excessive computational costs, as Frido maintains efficiency through its design of sharing neural network parameters across feature scales and employing a fast inference process.

Practical and Theoretical Implications

Practically, Frido extends the capability of diffusion models to generate images that require understanding high-level scene compositions and maintaining coherence across diverse conditions such as text descriptions or scene graphs. Theoretically, the introduction of multiscale representation learning combined with a coarse-to-fine denoising strategy provides new insights into improving the fidelity and semantic correctness of generated images.

Future Developments

Future directions for enhancing Frido could involve regularizing the latent feature distributions across different scale levels for more uniform training of the diffusion process. Additionally, extending the framework to be compatible with pre-trained models like CLIP could further improve semantic alignment and potentially reduce the computational burden of training on large, diverse datasets.

In summary, "Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis" presents a robust framework that significantly pushes the boundaries of what diffusion models can achieve in complex scene image synthesis, providing both scalability and adaptability across various input conditions, a promising advancement for diverse applications in AI-driven image generation.