ConsistEdit: Highly Consistent and Precise Training-free Visual Editing
Recent advances in training-free attention control methods have enabled
flexible and efficient text-guided editing capabilities for existing generation
models. However, current approaches struggle to simultaneously deliver strong
editing strength while preserving consistency with the source. This limitation
becomes particularly critical in multi-round and video editing, where visual
errors can accumulate over time. Moreover, most existing methods enforce global
consistency, which limits their ability to modify individual attributes such as
texture while preserving others, thereby hindering fine-grained editing.
Recently, the architectural shift from U-Net to MM-DiT has brought significant
improvements in generative performance and introduced a novel mechanism for
integrating text and vision modalities. These advancements pave the way for
overcoming challenges that previous methods failed to resolve. Through an
in-depth analysis of MM-DiT, we identify three key insights into its attention
mechanisms. Building on these, we propose ConsistEdit, a novel attention
control method specifically tailored for MM-DiT. ConsistEdit incorporates
vision-only attention control, mask-guided pre-attention fusion, and
differentiated manipulation of the query, key, and value tokens to produce
consistent, prompt-aligned edits. Extensive experiments demonstrate that
ConsistEdit achieves state-of-the-art performance across a wide range of image
and video editing tasks, including both structure-consistent and
structure-inconsistent scenarios. Unlike prior methods, it is the first
approach to perform editing across all inference steps and attention layers
without handcraft, significantly enhancing reliability and consistency, which
enables robust multi-round and multi-region editing. Furthermore, it supports
progressive adjustment of structural consistency, enabling finer control.
68f6e6e924c44893631117db
Zixin Yin
claimed_verified
2025-10-21T14:04:19.320Z
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Zixin Yin
user
zachary-yin
68f6e6e924c44893631117dc
Ling-Hao Chen
68f6e6e924c44893631117dd
Lionel Ni
68f6e6e924c44893631117de
Xili Dai
ConsistEdit: Highly Consistent and Precise Training-free Visual Editing
Recent advances in training-free attention control methods have enabled
flexible and efficient text-guided editing capabilities for existing generation
models. However, current approaches struggle to simultaneously deliver strong
editing strength while preserving consistency with the source. This limitation
becomes particularly critical in multi-round and video editing, where visual
errors can accumulate over time. Moreover, most existing methods enforce global
consistency, which limits their ability to modify individual attributes such as
texture while preserving others, thereby hindering fine-grained editing.
Recently, the architectural shift from U-Net to MM-DiT has brought significant
improvements in generative performance and introduced a novel mechanism for
integrating text and vision modalities. These advancements pave the way for
overcoming challenges that previous methods failed to resolve. Through an
in-depth analysis of MM-DiT, we identify three key insights into its attention
mechanisms. Building on these, we propose ConsistEdit, a novel attention
control method specifically tailored for MM-DiT. ConsistEdit incorporates
vision-only attention control, mask-guided pre-attention fusion, and
differentiated manipulation of the query, key, and value tokens to produce
consistent, prompt-aligned edits. Extensive experiments demonstrate that
ConsistEdit achieves state-of-the-art performance across a wide range of image
and video editing tasks, including both structure-consistent and
structure-inconsistent scenarios. Unlike prior methods, it is the first
approach to perform editing across all inference steps and attention layers
without handcraft, significantly enhancing reliability and consistency, which
enables robust multi-round and multi-region editing. Furthermore, it supports
progressive adjustment of structural consistency, enabling finer control.
ConsistEdit, a novel attention control method for MM-DiT, enhances image and video editing by ensuring consistency and fine-grained control across all inference steps and attention layers.
attention control MM-DiT vision-only attention control mask-guided pre-attention fusion query key value tokens structure-consistent structure-inconsistent multi-round editing multi-region editing progressive adjustment