Value Proposition: A plug-and-play, parameter-efficient module for diffusion-based image generation that measurably improves perceptual quality and naturalness across diverse tasks without requiring full model retraining.
Unmet Need: As diffusion models proliferate across consumer and enterprise applications, including text-to-image synthesis, photo editing, super-resolution, and face restoration, users and developers still grapple with images that look perceptually “unnatural,” exhibit artifacts, or lose fine detail, especially under mobile and edge compute constraints. Existing quality-improvement approaches often require heavy retraining, model-specific engineering, or computationally expensive guidance that can degrade fidelity or increase latency. There is a large and growing need for a broadly compatible, lightweight method that reliably elevates visual realism and user satisfaction while preserving faithfulness to prompts and conditions, and that can be dropped into existing pipelines with minimal disruption. Such a solution should improve objective quality metrics and human evaluations across tasks, run efficiently on edge devices, and be easily adopted by companies already invested in image generation stacks.
Technology Description: The technology (DiffNat) combines a kurtosis-concentration loss grounded in natural image statistics in the wavelet domain with a perceptual guidance strategy at inference to steer diffusion trajectories toward more natural, high-quality outputs in text-to-image, super-resolution, and blind face-restoration pipelines.
Stage of Development: Fully developed, working prototype with demonstrated improvements in FID, MUSIQ, and user studies across multiple tasks and datasets, and ready for plug-and-play integration into commercial image-generation pipelines and edge deployments.
Publication(s): Roy, A., Suin, M., Shah, A., Shah, K., Liu, J., & Chellappa, R. (2023). DIFFNAT: Improving Diffusion Image Quality Using Natural Image Statistics. arXiv. arXiv:2311.09753; Roy, A., Suin, M., Shah, A., Shah, K., Liu, J., & Chellappa, R. (2025). DiffNat: Exploiting the Kurtosis Concentration Property for Image quality improvement. TMLR (or Pre-print). DiffNat : Exploiting the Kurtosis Concentration Property for Image quality improvement | OpenReview
