DIFFNAT: Improving Diffusion Image Quality Using Natural Image Statistics

TL;DR

DIFFNAT introduces a kurtosis concentration loss that leverages natural image statistics to enhance diffusion model-generated image quality without extra guidance, validated across multiple tasks.

Contribution

Proposes a novel KC loss based on natural image kurtosis properties, applicable to any diffusion model to improve image quality.

Findings

Improved perceptual quality across tasks

Enhanced FID and MUSIQ scores

Positive user evaluations

Abstract

Diffusion models have advanced generative AI significantly in terms of editing and creating naturalistic images. However, efficiently improving generated image quality is still of paramount interest. In this context, we propose a generic "naturalness" preserving loss function, viz., kurtosis concentration (KC) loss, which can be readily applied to any standard diffusion model pipeline to elevate the image quality. Our motivation stems from the projected kurtosis concentration property of natural images, which states that natural images have nearly constant kurtosis values across different band-pass versions of the image. To retain the "naturalness" of the generated images, we enforce reducing the gap between the highest and lowest kurtosis values across the band-pass versions (e.g., Discrete Wavelet Transform (DWT)) of images. Note that our approach does not require any additional guidance like classifier or classifier-free guidance to improve the image quality. We validate the proposed approach for three diverse tasks, viz., (1) personalized few-shot finetuning using text guidance, (2) unconditional image generation, and (3) image super-resolution. Integrating the proposed KC loss has improved the perceptual quality across all these tasks in terms of both FID, MUSIQ score, and user evaluation.