CSD: 内容感知的投机解码用于高效图像生成
CSD: Content-aware Speculative Decoding for Efficient Image Generation
AI总结：
提出内容感知投机解码算法CSD，通过熵驱动的概率松弛和最优重采样策略，在保持生成质量的同时加速自回归图像生成。
摘要：
投机解码（SD）已成为加速自回归模型推理的关键解决方案。然而，在图像生成领域，它面临接受率低的挑战，直接放宽标准会导致图像质量下降。本文提出了一种新颖的内容感知投机解码算法，称为CSD，该算法将基于熵的概率松弛机制与最优重采样策略相结合，以提高自回归图像生成的推理效率。通过利用图像不同区域固有的信息不确定性，CSD动态调整候选令牌的接受概率，增加低细节区域的接受率以加速生成。此外，引入分布对齐滤波器以确保输出分布与目标模型对齐，从而显著提高生成质量。在Lumina-mGPT和Janus-Pro上进行的实验证明了所提出的CSD的优越性。我们的源代码可在以下网址获取：https://this https URL。
英文摘要：
Speculative decoding (SD) has emerged as a key solution to accelerate the inference of autoregressive models. However, in the field of image generation, it faces the challenge of low acceptance rates, and directly relaxing its criteria leads to degradation in image quality. In this paper, we propose a novel content-aware speculative decoding algorithm, termed CSD, which integrates an entropy-based probability relaxation mechanism with an optimal resampling strategy to enhance the inference efficiency for autoregressive image generation. By leveraging the informational uncertainty inherent in different regions of an image, CSD dynamically adjusts the acceptance probability of candidate tokens, increasing the acceptance rate in low-detail areas to accelerate generation. Moreover, a distribution alignment filter is introduced to ensure the output distribution to be aligned with the target model, which significantly improves the generative quality. Experiments conducted on Lumina-mGPT and Janus-Pro demonstrate that the superiority of the proposed CSD. Our source code is available at https://github.com/aderfebr/CSD.
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。
