ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models

TL;DR

ResAdapter is a novel domain-consistent resolution adapter for diffusion models that enables high-quality image generation at arbitrary resolutions and aspect ratios, overcoming limitations of existing multi-resolution methods.

Contribution

It introduces ResAdapter, a deep understanding-based resolution adapter that allows diffusion models to generate resolution-free images while maintaining style and compatibility with other modules.

Findings

ResAdapter can process images with flexible resolutions using only 0.5M parameters.

It is compatible with modules like ControlNet, IP-Adapter, and LCM-LoRA.

ResAdapter can be integrated into other multi-resolution models for higher-resolution image generation.

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