title: Invertible Consistency Distillation for Text-Guided Image Editing in
    Around 7 Steps
summary: 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.
paper_authors._id: 66753d0314e2aebef8bb1c98
paper_authors.name: Nikita Starodubcev
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-24T08:56:25.755Z
paper_authors._id: 6410d3a4cfbe9c4400233d1e
paper_authors.avatarUrl: /avatars/9e1c4e0ea5f6964c90013f6e19c41db1.svg
paper_authors.fullname: nikita
paper_authors.type: user
paper_authors.user: quickjkee
paper_authors._id: 66753d0314e2aebef8bb1c99
paper_authors.name: Mikhail Khoroshikh
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-21T11:15:06.540Z
paper_authors._id: 628b8e83ac304a69264a8c9c
paper_authors.avatarUrl: /avatars/a51b630e8d01224d5d8d5edff0a69d30.svg
paper_authors.fullname: Mikhail Khoroshikh
paper_authors.type: user
paper_authors.user: michellemoorre
paper_authors._id: 66753d0314e2aebef8bb1c9a
paper_authors.name: Artem Babenko
paper_authors._id: 66753d0314e2aebef8bb1c9b
paper_authors.name: Dmitry Baranchuk
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-21T11:14:56.342Z
paper_authors._id: 62b6cc49752323892323bc04
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62b6cc49752323892323bc04/gGBld1KJIP9AIpd81L3PC.jpeg
paper_authors.fullname: Dmitry Baranchuk
paper_authors.type: user
paper_authors.user: dbaranchuk
paper_title: Invertible Consistency Distillation for Text-Guided Image Editing in
    Around 7 Steps
paper_summary: 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.
paper_ai_summary: iCD, a consistency distillation framework, enhances text-to-image diffusion models for both high-quality synthesis and image inversion, enabling zero-shot text-guided image editing.
paper_ai_keywords: diffusion distillation text-to-image generation real image inversion latent space invertible Consistency Distillation (iCD) consistency distillation high-quality image synthesis image encoding classifier-free guidance dynamic guidance zero-shot text-guided image editing