A Noise is Worth Diffusion Guidance
Donghoon Ahn, Jiwon Kang, Sanghyun Lee, Jaewon Min, Minjae Kim, Wooseok Jang, Hyoungwon Cho, Sayak Paul, SeonHwa Kim, Eunju Cha, Kyong Hwan Jin, Seungryong Kim
2024-12-06
Summary
This paper talks about a new method called 'Noise Refinement' that improves how diffusion models generate images by focusing on refining the initial noise, allowing for high-quality image creation without needing extra guidance.
What's the problem?
Diffusion models are great at creating realistic images, but they often rely on guidance methods to help them produce good results. This reliance can slow down the image generation process and require more memory, making it less efficient.
What's the solution?
The authors discovered that by refining the initial noise used in the diffusion process, they could generate high-quality images without needing guidance methods. They introduced a new technique that enhances this noise, which leads to better image quality and faster processing times. Their model learns effectively from just 50,000 pairs of text and images, proving that it can achieve strong performance without the extra steps usually required for guidance.
Why it matters?
This research is important because it makes the process of generating images more efficient and accessible. By eliminating the need for guidance, it allows for quicker image production while maintaining high quality. This could benefit various applications in art, design, and AI by enabling faster and more flexible image generation.
Abstract
Diffusion models excel in generating high-quality images. However, current diffusion models struggle to produce reliable images without guidance methods, such as classifier-free guidance (CFG). Are guidance methods truly necessary? Observing that noise obtained via diffusion inversion can reconstruct high-quality images without guidance, we focus on the initial noise of the denoising pipeline. By mapping Gaussian noise to `guidance-free noise', we uncover that small low-magnitude low-frequency components significantly enhance the denoising process, removing the need for guidance and thus improving both inference throughput and memory. Expanding on this, we propose \ours, a novel method that replaces guidance methods with a single refinement of the initial noise. This refined noise enables high-quality image generation without guidance, within the same diffusion pipeline. Our noise-refining model leverages efficient noise-space learning, achieving rapid convergence and strong performance with just 50K text-image pairs. We validate its effectiveness across diverse metrics and analyze how refined noise can eliminate the need for guidance. See our project page: https://cvlab-kaist.github.io/NoiseRefine/.