无需扩散模型，实现快速可控图像生成与编辑
Beyond and Free from Diffusion: Invertible Guided Consistency Training
- 提出可逆引导一致性训练框架，纯数据驱动
- 高引导系数下精度达0.8，远超扩散模型的0.47
- 适合追求高效可控生成的研究者和开发者
图像生成中的引导技术通常依赖于扩散模型（DMs）的无分类器引导（CFG），但近期的一致性模型（CMs）为实现更少函数评估而需从预训练的DM中蒸馏引导知识，成本高且不灵活。本文提出可逆引导一致性训练（iGCT），一种全新的引导式CM训练框架，完全基于数据驱动，无需训练或蒸馏扩散模型，大幅降低计算开销。iGCT解决了高引导系数下出现的饱和伪影问题。在CIFAR-10和ImageNet64上的大量实验表明，iGCT显著提升FID与精度：当引导值为13时，精度达到0.8，而扩散模型降至0.47。本工作首次实现不依赖扩散模型的引导与反演能力。
原文摘要 · Abstract (English)
Guidance in image generation steers models towards higher-quality or more targeted outputs, typically achieved in Diffusion Models (DMs) via Classifier-free Guidance (CFG). However, recent Consistency Models (CMs), which offer fewer function evaluations, rely on distilling CFG knowledge from pretrained DMs to achieve guidance, making them costly and inflexible. In this work, we propose invertible Guided Consistency Training (iGCT), a novel training framework for guided CMs that is entirely data-driven. iGCT, as a pioneering work, contributes to fast and guided image generation and editing without requiring the training and distillation of DMs, greatly reducing the overall compute requirements. iGCT addresses the saturation artifacts seen in CFG under high guidance scales. Our extensive experiments on CIFAR-10 and ImageNet64 show that iGCT significantly improves FID and precision compared to CFG. At a guidance of 13, iGCT improves precision to 0.8, while DM's drops to 0.47. Our work takes the first step toward enabling guidance and inversion for CMs without relying on DMs.
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