Does Diffusion Beat GAN in Image Super Resolution?

• URL: http://arxiv.org/abs/2405.17261v2
• Date: Fri, 25 Oct 2024 18:04:01 GMT
• Title: Does Diffusion Beat GAN in Image Super Resolution?
• Authors: Denis Kuznedelev, Valerii Startsev, Daniil Shlenskii, Sergey Kastryulin,
• Abstract summary: In most studies, diffusion-based ISR models employ larger networks and are trained longer than the GAN baselines. We show that a GAN-based model can achieve results comparable or superior to a diffusion-based model. We will release the inference code and weights of our scaled GAN.
• Score: 4.196273175812082
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is a prevalent opinion that diffusion-based models outperform GAN-based counterparts in the Image Super Resolution (ISR) problem. However, in most studies, diffusion-based ISR models employ larger networks and are trained longer than the GAN baselines. This raises the question of whether the high performance stems from the superiority of the diffusion paradigm or if it is a consequence of the increased scale and the greater computational resources of the contemporary studies. In our work, we thoroughly compare diffusion-based and GAN-based Super Resolution models under controlled settings, with both approaches having matched architecture, model and dataset sizes, and computational budget. We show that a GAN-based model can achieve results comparable or superior to a diffusion-based model. Additionally, we explore the impact of popular design choices, such as text conditioning and augmentation on the performance of ISR models, showcasing their effect in several downstream tasks. We will release the inference code and weights of our scaled GAN.

Related papers

• SupResDiffGAN a new approach for the Super-Resolution task [0.0]
  SupResDiffGAN is a novel hybrid architecture that combines the strengths of Generative Adversarial Networks (GANs) and diffusion models for super-resolution tasks. By leveraging latent space representations and reducing the number of diffusion steps, SupResDiffGAN achieves significantly faster inference times than other diffusion-based super-resolution models.
  arXiv  Detail & Related papers  (2025-04-18T10:55:24Z)
• DeltaDiff: A Residual-Guided Diffusion Model for Enhanced Image Super-Resolution [9.948203187433196]
  We propose a new diffusion model called Deltadiff, which uses only residuals between images for diffusion. Our method surpasses state-of-the-art models and generates results with better fidelity.
  arXiv  Detail & Related papers  (2025-02-18T06:07:14Z)
• Inverse design with conditional cascaded diffusion models [0.0]
  Adjoint-based design optimizations are usually computationally expensive and those costs scale with resolution. We extend the use of diffusion models over traditional generative models by proposing the conditional cascaded diffusion model (cCDM) Our study compares cCDM against a cGAN model with transfer learning. While both models show decreased performance with reduced high-resolution training data, the cCDM loses its superiority to the cGAN model with transfer learning when training data is limited.
  arXiv  Detail & Related papers  (2024-08-16T04:54:09Z)
• DiffSG: A Generative Solver for Network Optimization with Diffusion Model [75.27274046562806]
  Diffusion generative models can consider a broader range of solutions and exhibit stronger generalization by learning parameters. We propose a new framework, which leverages intrinsic distribution learning of diffusion generative models to learn high-quality solutions.
  arXiv  Detail & Related papers  (2024-08-13T07:56:21Z)
• Bigger is not Always Better: Scaling Properties of Latent Diffusion Models [46.52780730073693]
  We study the scaling properties of latent diffusion models (LDMs) with an emphasis on their sampling efficiency. We conduct an in-depth investigation into how model size influences sampling efficiency across varying sampling steps. Our findings unveil a surprising trend: when operating under a given inference budget, smaller models frequently outperform their larger equivalents in generating high-quality results.
  arXiv  Detail & Related papers  (2024-04-01T17:59:48Z)
• Enhancing Hyperspectral Images via Diffusion Model and Group-Autoencoder Super-resolution Network [29.6360974619655]
  Group-Autoencoder (GAE) framework encodes high-dimensional hyperspectral data into low-dimensional latent space. DMGASR construct highly effective HSI SR model (DMGASR) Experimental results on both natural and remote sensing hyperspectral datasets demonstrate that the proposed method is superior to other state-of-the-art methods both visually and metrically.
  arXiv  Detail & Related papers  (2024-02-27T07:57:28Z)
• Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation [59.184980778643464]
  Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI) In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion) Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment.
  arXiv  Detail & Related papers  (2024-02-15T18:59:18Z)
• Guided Diffusion from Self-Supervised Diffusion Features [49.78673164423208]
  Guidance serves as a key concept in diffusion models, yet its effectiveness is often limited by the need for extra data annotation or pretraining. We propose a framework to extract guidance from, and specifically for, diffusion models.
  arXiv  Detail & Related papers  (2023-12-14T11:19:11Z)
• DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution [88.13972071356422]
  We propose a diffusion-style data augmentation scheme for GAN-based image super-resolution (SR) methods, known as DifAugGAN. It involves adapting the diffusion process in generative diffusion models for improving the calibration of the discriminator during training. Our DifAugGAN can be a Plug-and-Play strategy for current GAN-based SISR methods to improve the calibration of the discriminator and thus improve SR performance.
  arXiv  Detail & Related papers  (2023-11-30T12:37:53Z)
• ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution [84.73658185158222]
  We propose a diffusion model-based super-resolution method called ACDMSR. Our method adapts the standard diffusion model to perform super-resolution through a deterministic iterative denoising process. Our approach generates more visually realistic counterparts for low-resolution images, emphasizing its effectiveness in practical scenarios.
  arXiv  Detail & Related papers  (2023-07-03T06:49:04Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• Implicit Diffusion Models for Continuous Super-Resolution [65.45848137914592]
  This paper introduces an Implicit Diffusion Model (IDM) for high-fidelity continuous image super-resolution. IDM integrates an implicit neural representation and a denoising diffusion model in a unified end-to-end framework. The scaling factor regulates the resolution and accordingly modulates the proportion of the LR information and generated features in the final output.
  arXiv  Detail & Related papers  (2023-03-29T07:02:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.