English(EN) SpeeDiff: Scalable Pixel-Anchored End-to-End Latent Diffusion Model

SpeeDiff模型实现更快的端到端潜在扩散训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了SpeeDiff，一种新颖的潜在扩散模型，它从头开始联合训练变分自编码器（VAE）和扩散模型。这种端到端的方法旨在创建一个对生成更友好的潜在空间，从而实现更快的收敛。一项关键创新是Tweedie像素重建（TPR）损失，它通过提供像素级反馈来防止潜在空间崩溃。SpeeDiff-XL在ImageNet上实现了具有竞争力的FID分数，与现有方法相比显示出显著的速度提升。 AI

影响 SpeeDiff的端到端训练和TPR损失有望加速扩散模型开发并提高效率。

排序理由该集群描述了一篇详细介绍训练潜在扩散模型新方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Bingliang Zhang, Wenda Chu, Yizhuo Li, Linjie Yang, Yisong Yue, Katherine L. Bouman, Yang Song, Qiushan Guo · 2026-10-28 04:00

SpeeDiff：可扩展的像素锚定端到端潜在扩散模型

arXiv:2610.36419v1 Announce Type: new Abstract: We present Scalable Pixel-anchored End-to-end Diffusion (SpeeDiff), a latent diffusion method that jointly trains the VAE and the diffusion model from scratch. In principle, joint training allows the diffusion loss gradient to direc…

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报道来源 [1]

SpeeDiff：可扩展的像素锚定端到端潜在扩散模型

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