NSARM: Next-Scale Autoregressive Modeling for Robust Real-World Image Super-Resolution

• URL: http://arxiv.org/abs/2510.00820v1
• Date: Wed, 01 Oct 2025 12:29:58 GMT
• Title: NSARM: Next-Scale Autoregressive Modeling for Robust Real-World Image Super-Resolution
• Authors: Xiangtao Kong, Rongyuan Wu, Shuaizheng Liu, Lingchen Sun, Lei Zhang,
• Abstract summary: We introduce a robust Real-ISR framework, namely Next-Scale Autoregressive Modeling (NSARM)<n>As a pure AR model, NSARM achieves superior visual results over existing Real-ISR methods while maintaining a fast inference speed.
• Score: 17.72407853450265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most recent real-world image super-resolution (Real-ISR) methods employ pre-trained text-to-image (T2I) diffusion models to synthesize the high-quality image either from random Gaussian noise, which yields realistic results but is slow due to iterative denoising, or directly from the input low-quality image, which is efficient but at the price of lower output quality. These approaches train ControlNet or LoRA modules while keeping the pre-trained model fixed, which often introduces over-enhanced artifacts and hallucinations, suffering from the robustness to inputs of varying degradations. Recent visual autoregressive (AR) models, such as pre-trained Infinity, can provide strong T2I generation capabilities while offering superior efficiency by using the bitwise next-scale prediction strategy. Building upon next-scale prediction, we introduce a robust Real-ISR framework, namely Next-Scale Autoregressive Modeling (NSARM). Specifically, we train NSARM in two stages: a transformation network is first trained to map the input low-quality image to preliminary scales, followed by an end-to-end full-model fine-tuning. Such a comprehensive fine-tuning enhances the robustness of NSARM in Real-ISR tasks without compromising its generative capability. Extensive quantitative and qualitative evaluations demonstrate that as a pure AR model, NSARM achieves superior visual results over existing Real-ISR methods while maintaining a fast inference speed. Most importantly, it demonstrates much higher robustness to the quality of input images, showing stronger generalization performance. Project page: https://github.com/Xiangtaokong/NSARM

Related papers

• Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders [74.72147962028265]
  Representation Autoencoders (RAEs) have shown distinct advantages in diffusion modeling on ImageNet.<n>We investigate whether this framework can scale to large-scale, freeform text-to-image (T2I) generation.
  arXiv  Detail & Related papers  (2026-01-22T18:58:16Z)
• Rethinking Training Dynamics in Scale-wise Autoregressive Generation [22.58390823803937]
  Next-scale prediction has emerged as a popular paradigm, where models generate images in a coarse-to-fine manner.<n>Scale-wise AR models suffer from exposure bias, which undermines generation quality.<n>We propose Self-Autoregressive Refinement to address these limitations.
  arXiv  Detail & Related papers  (2025-12-06T12:41:42Z)
• Dual-domain Adaptation Networks for Realistic Image Super-resolution [81.34345637776408]
  Realistic image super-resolution (SR) focuses on transforming real-world low-resolution (LR) images into high-resolution (HR) ones.<n>Current methods struggle with limited real-world LR-HR data, impacting the learning of basic image features.<n>We introduce a novel approach, which is able to efficiently adapt pre-trained image SR models from simulated to real-world datasets.
  arXiv  Detail & Related papers  (2025-11-21T12:57:23Z)
• Frequency-Aware Autoregressive Modeling for Efficient High-Resolution Image Synthesis [40.93077975823353]
  Visual autoregressive modeling, based on the next-scale prediction paradigm, exhibits notable advantages in image quality and model scalability.<n>However, the computational overhead in high-resolution stages remains a critical challenge due to the substantial number of tokens involved.<n>We introduce Sparsevar, a plug-and-play acceleration framework for next-scale prediction that dynamically excludes low-frequency tokens during inference without requiring additional training.
  arXiv  Detail & Related papers  (2025-07-28T01:13:24Z)
• Multi-scale Image Super Resolution with a Single Auto-Regressive Model [40.77470215283583]
  We tackle Image Super Resolution (ISR) using recent advances in Visual Auto-Regressive ( VAR) modeling.<n>To the best of our knowledge, this is the first time a quantizer is trained to force semantically consistent residuals at different scales.<n>Our model can denoise the LR image and super-resolve at half and full target upscale factors in a single forward pass.
  arXiv  Detail & Related papers  (2025-06-05T13:02:23Z)
• RestoreVAR: Visual Autoregressive Generation for All-in-One Image Restoration [51.77917733024544]
  latent diffusion models (LDMs) have improved the perceptual quality of All-in-One image Restoration (AiOR) methods.<n>LDMs suffer from slow inference due to their iterative denoising process, rendering them impractical for time-sensitive applications.<n>Visual autoregressive modeling ( VAR) performs scale-space autoregression and achieves comparable performance to that of state-of-the-art diffusion transformers.
  arXiv  Detail & Related papers  (2025-05-23T15:52:26Z)
• Visual Autoregressive Modeling for Image Super-Resolution [14.935662351654601]
  We propose a novel visual autoregressive modeling for ISR framework with the form of next-scale prediction.<n>We collect large-scale data and design a training process to obtain robust generative priors.
  arXiv  Detail & Related papers  (2025-01-31T09:53:47Z)
• Time Step Generating: A Universal Synthesized Deepfake Image Detector [0.4488895231267077]
  We propose a universal synthetic image detector Time Step Generating (TSG) TSG does not rely on pre-trained models' reconstructing ability, specific datasets, or sampling algorithms. We test the proposed TSG on the large-scale GenImage benchmark and it achieves significant improvements in both accuracy and generalizability.
  arXiv  Detail & Related papers  (2024-11-17T09:39:50Z)
• Randomized Autoregressive Visual Generation [26.195148077398223]
  This paper presents Randomized AutoRegressive modeling (RAR) for visual generation. RAR sets a new state-of-the-art performance on the image generation task while maintaining full compatibility with language modeling frameworks. On the ImageNet-256 benchmark, RAR achieves an FID score of 1.48, not only surpassing prior state-the-art autoregressive image generators but also outperforming leading diffusion-based and masked transformer-based methods.
  arXiv  Detail & Related papers  (2024-11-01T17:59:58Z)
• A-SDM: Accelerating Stable Diffusion through Redundancy Removal and Performance Optimization [54.113083217869516]
  In this work, we first explore the computational redundancy part of the network. We then prune the redundancy blocks of the model and maintain the network performance. Thirdly, we propose a global-regional interactive (GRI) attention to speed up the computationally intensive attention part.
  arXiv  Detail & Related papers  (2023-12-24T15:37:47Z)
• Investigating Tradeoffs in Real-World Video Super-Resolution [90.81396836308085]
  Real-world video super-resolution (VSR) models are often trained with diverse degradations to improve generalizability. To alleviate the first tradeoff, we propose a degradation scheme that reduces up to 40% of training time without sacrificing performance. To facilitate fair comparisons, we propose the new VideoLQ dataset, which contains a large variety of real-world low-quality video sequences.
  arXiv  Detail & Related papers  (2021-11-24T18:58:21Z)
• Unsupervised Single Image Super-resolution Under Complex Noise [60.566471567837574]
  This paper proposes a model-based unsupervised SISR method to deal with the general SISR task with unknown degradations. The proposed method can evidently surpass the current state of the art (SotA) method (about 1dB PSNR) not only with a slighter model (0.34M vs. 2.40M) but also faster speed.
  arXiv  Detail & Related papers  (2021-07-02T11:55:40Z)
• TSNAT: Two-Step Non-Autoregressvie Transformer Models for Speech Recognition [69.68154370877615]
  The non-autoregressive (NAR) models can get rid of the temporal dependency between the output tokens and predict the entire output tokens in at least one step. To address these two problems, we propose a new model named the two-step non-autoregressive transformer(TSNAT) The results show that the TSNAT can achieve a competitive performance with the AR model and outperform many complicated NAR models.
  arXiv  Detail & Related papers  (2021-04-04T02:34:55Z)

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.