Invertible Consistency Distillation for Text-Guided Image Editing in Around 7 Steps
This work addresses the need for efficient and precise text-guided image editing tools for users in creative and AI applications, representing an incremental improvement by enhancing existing distillation methods with inversion capabilities.
The paper tackled the problem of enabling real image inversion in distilled text-to-image diffusion models, which is crucial for precise image editing, and achieved high-quality synthesis and accurate encoding in only 3-4 inference steps, competing with more expensive state-of-the-art alternatives.
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.