ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models (2403.02084v1)

Abstract: Recent advancement in text-to-image models (e.g., Stable Diffusion) and corresponding personalized technologies (e.g., DreamBooth and LoRA) enables individuals to generate high-quality and imaginative images. However, they often suffer from limitations when generating images with resolutions outside of their trained domain. To overcome this limitation, we present the Resolution Adapter (ResAdapter), a domain-consistent adapter designed for diffusion models to generate images with unrestricted resolutions and aspect ratios. Unlike other multi-resolution generation methods that process images of static resolution with complex post-process operations, ResAdapter directly generates images with the dynamical resolution. Especially, after learning a deep understanding of pure resolution priors, ResAdapter trained on the general dataset, generates resolution-free images with personalized diffusion models while preserving their original style domain. Comprehensive experiments demonstrate that ResAdapter with only 0.5M can process images with flexible resolutions for arbitrary diffusion models. More extended experiments demonstrate that ResAdapter is compatible with other modules (e.g., ControlNet, IP-Adapter and LCM-LoRA) for image generation across a broad range of resolutions, and can be integrated into other multi-resolution model (e.g., ElasticDiffusion) for efficiently generating higher-resolution images. Project link is https://res-adapter.github.io

• The paper introduces ResAdapter, achieving domain-consistent resolution adaptation in diffusion models through innovative interpolation and extrapolation techniques.
• It leverages Resolution Convolution LoRA to adjust convolution receptive fields and Resolution Extrapolation Normalization to bridge quality gaps.
• ResAdapter seamlessly integrates with modules like ControlNet and LCM-LoRA, enhancing inference efficiency with minimal computational overhead.

Unveiling ResAdapter: Domain-Consistent Resolution Adaptation in Diffusion Models

Introduction to ResAdapter's Capabilities

The era of generative models, especially diffusion models, has seen extraordinary advancements, enabling the generation of high-quality, imaginative images. Despite these capabilities, a barrier remains in extending the resolution beyond their training spectrum. Addressing this, the "Resolution Adapter" (ResAdapter) introduces a novel angle by providing a domain-consistent resolution adaptation for diffusion models across various resolutions and aspect ratios, without compromising the original style domain.

Technical Insights and Innovations

Innovations in Resolution Interpolation and Extrapolation

• Resolution Interpolation: ResAdapter leverages Resolution Convolution LoRA (ResCLoRA) tailored for convolution layers, focusing on dynamically adjusting the convolution layers' receptive field according to the image's resolution, thus preserving image fidelity at various resolutions.
• Resolution Extrapolation: The Resolution Extrapolation Normalization (ResENorm) strategy is proposed to bridge the quality gap in resolution extrapolation efforts, optimizing normalization layers to adapt better to statistical distributions of high-resolution feature maps.

Compatibility and Integration

A striking benefit of ResAdapter lies in its compatibility and interoperability with existing diffusion models and other auxiliary modules such as ControlNet, IP-Adapter, and LCM-LoRA. This inclusivity means it can be seamlessly integrated into a breadth of generative tasks, refining quality and resolution adaptability without additional complex post-processing or extensive training phases. Moreover, ResAdapter has shown promising results when combined with multi-resolution models like ElasticDiffusion, further optimizing inference efficiency for exceptionally high-resolution image generation.

Comparative Analysis and Performance

In comprehensive experiments, ResAdapter demonstrated its ability to closely rival, and in many instances surpass, pre-existing resolution generation methods. Its innovative approach allowed for maintaining resolution and style domain consistency across multiple resolutions - a considerable improvement over the existing methods that either compromise on image fidelity or require elaborate post-processing. Furthermore, it establishes a new benchmark in adapting generative models for any resolution with minimal computational overhead, achieving impressive fidelity in generated images.

Implications and Speculations on Future Developments

The advent of ResAdapter opens several avenues for exploration in generative AI. Its approach to resolution adaptation likely heralds a transformative period where the ability to generate high-resolution, domain-consistent images becomes a standard capability across diffusion models. Moreover, the compatibility of ResAdapter with different models and modules foreshadows a more modular and versatile approach to improving generative tasks, suggesting a future where AI-generated images can be effortlessly tailored to specific resolutions and styles without sacrificing quality.

Conclusion

ResAdapter emerges as a key innovation within the generative AI landscape, pushing the boundaries of how models perceive and adapt resolution. Through its strategic handling of resolution interpolation and extrapolation, alongside its domain-consistency preservation, it sets a new standard for future developments in diffusion models. As we edge closer to overcoming the resolution adaptation challenge, the potential applications of ResAdapter in enhancing image generation tasks become increasingly significant, making it a notable milestone in the ongoing evolution of generative AI technologies.