Invertible Consistency Distillation for Text-Guided Image Editing in
  Around 7 Steps
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.
66753d0314e2aebef8bb1c98
Nikita Starodubcev
claimed_verified
2025-03-24T08:56:25.755Z
6410d3a4cfbe9c4400233d1e
/avatars/9e1c4e0ea5f6964c90013f6e19c41db1.svg
nikita
user
quickjkee
66753d0314e2aebef8bb1c99
Mikhail Khoroshikh
admin_assigned
2024-06-21T11:15:06.540Z
628b8e83ac304a69264a8c9c
/avatars/a51b630e8d01224d5d8d5edff0a69d30.svg
Mikhail Khoroshikh
user
michellemoorre
66753d0314e2aebef8bb1c9a
Artem Babenko
66753d0314e2aebef8bb1c9b
Dmitry Baranchuk
admin_assigned
2024-06-21T11:14:56.342Z
62b6cc49752323892323bc04
https://cdn-avatars.huggingface.co/v1/production/uploads/62b6cc49752323892323bc04/gGBld1KJIP9AIpd81L3PC.jpeg
Dmitry Baranchuk
user
dbaranchuk
Invertible Consistency Distillation for Text-Guided Image Editing in
  Around 7 Steps
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.
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.
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