VARestorer: 一种一步式VAR蒸馏用于现实世界图像超分辨率
VARestorer: One-Step VAR Distillation for Real-World Image Super-Resolution
AI总结：
本文提出VARestorer，通过蒸馏预训练的文本到图像VAR模型，实现一步式图像超分辨率，解决迭代预测中的误差累积问题，提升效率与质量。
摘要：
近期视觉自回归模型（VAR）在图像生成中的进展展示了其在现实世界图像超分辨率（Real-ISR）中的潜力。然而，将VAR应用于ISR面临关键挑战。下一步预测机制受限于因果注意力，无法充分利用全局低质量（LQ）上下文，导致输出模糊且不一致。此外，迭代预测中的误差累积严重损害ISR任务的连贯性。为了解决这些问题，我们提出VARestorer，一种简单而有效的蒸馏框架，将预训练的文本到图像VAR模型转换为一步式ISR模型。通过利用分布匹配，我们的方法消除了迭代细化的需要，显著减少了误差传播和推理时间。此外，我们引入了金字塔图像条件与跨尺度注意力，这使得能够实现双向尺度交互，并在适应自回归机制的同时充分利用输入图像信息。这防止了后续LQ标记在Transformer中被忽视。通过仅微调1.2%的模型参数通过参数高效适配器，我们的方法在保持原始VAR模型的表达能力的同时显著提高了效率。大量实验表明，VARestorer在DIV2K数据集上实现了72.32 MUSIQ和0.7669 CLIPIQA的最先进性能，同时将推理速度提升10倍相比传统VAR推理。
英文摘要：
Recent advancements in visual autoregressive models (VAR) have demonstrated their effectiveness in image generation, highlighting their potential for real-world image super-resolution (Real-ISR). However, adapting VAR for ISR presents critical challenges. The next-scale prediction mechanism, constrained by causal attention, fails to fully exploit global low-quality (LQ) context, resulting in blurry and inconsistent high-quality (HQ) outputs. Additionally, error accumulation in the iterative prediction severely degrades coherence in ISR task. To address these issues, we propose VARestorer, a simple yet effective distillation framework that transforms a pre-trained text-to-image VAR model into a one-step ISR model. By leveraging distribution matching, our method eliminates the need for iterative refinement, significantly reducing error propagation and inference time. Furthermore, we introduce pyramid image conditioning with cross-scale attention, which enables bidirectional scale-wise interactions and fully utilizes the input image information while adapting to the autoregressive mechanism. This prevents later LQ tokens from being overlooked in the transformer. By fine-tuning only 1.2\% of the model parameters through parameter-efficient adapters, our method maintains the expressive power of the original VAR model while significantly enhancing efficiency. Extensive experiments show that VARestorer achieves state-of-the-art performance with 72.32 MUSIQ and 0.7669 CLIPIQA on DIV2K dataset, while accelerating inference by 10 times compared to conventional VAR inference.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
