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今天的主题是：
Simplifying, stabilizing, and scaling continuous-time consistency models
Summary
This research paper introduces simplified, stable, and scalable continuous-time consistency models (sCMs) for image generation. The authors propose TrigFlow, a new framework unifying existing diffusion model formulations, and implement key improvements to stabilize training. These improvements include refined time conditioning, adaptive normalization, and adaptive weighting. The resulting sCMs achieve state-of-the-art results on various datasets, even surpassing some competing methods with significantly less computational cost. Furthermore, the study compares sCMs to variational score distillation (VSD), highlighting sCMs' superior sample diversity and guidance compatibility.
原文链接：https://arxiv.org/abs/2410.11081
解读链接：https://openai.com/index/simplifying-stabilizing-and-scaling-continuous-time-consistency-models/
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