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English(EN) PathRelax: Parallel-Path Relaxed Speculative Jacobi Decoding for Accelerating Auto-Regressive Text-to-Image Generation

新的PathRelax方法加速文本到图像生成

研究人员开发了PathRelax,一个旨在显著加速自回归文本到图像生成的新框架。该方法采用并行路径投机式解码方法,通过扩展令牌搜索空间并利用序列间的语义相似性来提高令牌接受率。在多个数据集上进行评估,PathRelax实现了3.95倍到4.18倍的速度提升,优于现有方法,并为实时图像生成提供了高效的解决方案。 AI

影响 加速文本到图像生成,可能实现实时应用和加快创意工作流程的迭代速度。

排序理由 该集群包含一篇详细介绍加速AI模型推理新方法的论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Haodong Lei, Hongsong Wang, Bingxuan Dai, Pan Zhou ·

    PathRelax: Parallel-Path Relaxed Speculative Jacobi Decoding for Accelerating Auto-Regressive Text-to-Image Generation

    arXiv:2606.10492v1 Announce Type: new Abstract: The growing need for high-resolution image generation in autoregressive text-to-image models has resulted in extended token sequences, significantly increasing computational costs and inference times. However, existing state-of-the-…

  2. arXiv cs.CV TIER_1 English(EN) · Pan Zhou ·

    PathRelax: Parallel-Path Relaxed Speculative Jacobi Decoding for Accelerating Auto-Regressive Text-to-Image Generation

    The growing need for high-resolution image generation in autoregressive text-to-image models has resulted in extended token sequences, significantly increasing computational costs and inference times. However, existing state-of-the-art methods for accelerating autoregressive text…