研究人员开发了一种新方法，通过改编大型语言模型的推测解码技术来加速扩散模型。该方法在 arXiv 的一篇论文中有所详述，引入了一种新颖的方案，可以有效地在连续空间中采样残差分布，而这在以前是限制改编的挑战。该方法实现了块验证，可证明地提高了草稿的接受率，并正式化了一种不需要训练的“自由起草者”启发式方法，与现有的推测方法相比，速度提高了 6.3%。 AI
影响 这项研究可能通过扩散模型实现更快、更高效的图像和媒体生成。
排序理由 该集群描述了一篇详细介绍加速扩散模型新颖方法的新研究论文。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
Speculative decoding speeds up LLM inference by using a draft model to generate tokens, with an acceptance-rejection scheme that ensures that the output matches the target distribution. Adapting this to continuous diffusions is difficult because speculative sampling requires draw…
arXiv:2606.13426v1 Announce Type: cross Abstract: Speculative decoding speeds up LLM inference by using a draft model to generate tokens, with an acceptance-rejection scheme that ensures that the output matches the target distribution. Adapting this to continuous diffusions is di…
Speculative decoding speeds up LLM inference by using a draft model to generate tokens, with an acceptance-rejection scheme that ensures that the output matches the target distribution. Adapting this to continuous diffusions is difficult because speculative sampling requires draw…