约7步内实现文本引导图像编辑的可逆一致性蒸馏
Invertible Consistency Distillation for Text-Guided Image Editing in Around 7 Steps
AI总结：
本文提出可逆一致性蒸馏框架iCD，结合动态引导，在3到4步内实现高质量生成、真实图像准确编码及零样本文本引导图像编辑。
摘要：
扩散蒸馏是实现少采样步骤下可信文本到图像生成的一个极具前景的方向。然而，尽管近期取得了成功，现有蒸馏模型仍无法提供完整的扩散能力谱系，例如真实图像反演，而该能力支撑着许多精确图像处理方法。本研究旨在丰富蒸馏文本到图像扩散模型，使其能够有效地将真实图像编码到自身潜空间中。为此，我们提出可逆一致性蒸馏（iCD），这是一种通用化一致性蒸馏框架，仅需3到4个推理步骤即可同时促进高质量图像合成与准确图像编码。尽管文本到图像扩散模型的反演问题会因较高的无分类器引导尺度而恶化，但我们注意到，动态引导可显著降低重建误差，且不会造成可察觉的生成性能下降。因此，我们证明，配备动态引导的iCD可作为零样本文本引导图像编辑的高效工具，与计算成本更高的前沿替代方案相竞争。
英文摘要：
Diffusion distillation represents a highly promising direction for achieving faithful text-to-image generation in a few sampling steps. However, despite recent successes, existing distilled models still do not provide the full spectrum of diffusion abilities, such as real image inversion, which enables many precise image manipulation methods. This work aims to enrich distilled text-to-image diffusion models with the ability to effectively encode real images into their latent space. To this end, we introduce invertible Consistency Distillation (iCD), a generalized consistency distillation framework that facilitates both high-quality image synthesis and accurate image encoding in only 3-4 inference steps. Though the inversion problem for text-to-image diffusion models gets exacerbated by high classifier-free guidance scales, we notice that dynamic guidance significantly reduces reconstruction errors without noticeable degradation in generation performance. As a result, we demonstrate that iCD equipped with dynamic guidance may serve as a highly effective tool for zero-shot text-guided image editing, competing with more expensive state-of-the-art alternatives.
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。
