1. Bibliographic Information

1.1. Title

ProEdit: Inversion-based Editing From Prompts Done Right

1.2. Authors

The authors of the paper are:

• Zhi Ouyang

• Dian Zheng

• Xiao-Ming Wu

• Jian-Jian Jiang

• Kun-Yu Lin

• Jingke Meng

• Wei-Shi Zheng

  Their affiliations include:

• Sun Yat-sen University

• CUHK MMLab

• College of Computing and Data Science, Nanyang Technological University

• The University of Hong Kong

• Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China

1.3. Journal/Conference

The paper's abstract indicates it is published at a UTC timestamp. The Original Source Link points to $huggingface.co/papers/2512.22118$ and PDF Link points to arxiv.org/pdf/2512.22118.pdf. This suggests the paper is a preprint, likely on arXiv, which is a popular repository for pre-publication research. As such, it is not yet published in a peer-reviewed journal or conference.

1.4. Publication Year

The provided publication date is 2001-12-25T16:00:00.000Z. However, this date is highly anomalous given the content of the paper, which extensively cites recent works up to 2025 (e.g., [18] "UniEdit-flow: Unleashing inversion and editing in the era of flow models," arXiv preprint arXiv:2504.13109, 2025; [27] "Editthinker," arXiv preprint arXiv:2512.05965, 2025; [8] "Fireflow," In ICML, 2025). Based on these internal citations, the paper itself is evidently a very recent work, likely from late 2024 or 2025, and the provided 2001 date is an error.

1.5. Abstract

The paper addresses a critical issue in inversion-based visual editing: existing methods, which inject source image information during sampling to maintain consistency, often over-rely on this source information. This over-reliance negatively impacts the ability to make instructed edits, especially for subject attributes like pose, number, or color. To solve this, the authors propose ProEdit, a novel method that intervenes in both the attention and latent aspects of the editing process. In the attention aspect, ProEdit introduces KV-mix, which selectively mixes Key ($K$) and Value ($V$) features of the source and target in edited regions while preserving background consistency. In the latent aspect, ProEdit proposes Latents-Shift, which perturbs the edited region of the inverted source latent, thereby reducing its influence on sampling. Extensive experiments across image and video editing benchmarks demonstrate that ProEdit achieves state-of-the-art (SOTA) performance. Furthermore, its plug-and-play design allows seamless integration into existing inversion and editing methods such as RFSolver, FireFlow, and UniEdit.

1.6. Original Source Link

https://huggingface.co/papers/2512.22118 (likely a mirror or specific project page for the arXiv preprint) https://arxiv.org/pdf/2512.22118.pdf (PDF link to the arXiv preprint) The publication status is that of a preprint, meaning it has not yet undergone formal peer review and publication in a journal or conference.

2. Executive Summary

2.1. Background & Motivation

The core problem ProEdit aims to solve lies within inversion-based visual editing, a training-free paradigm for modifying images and videos based on user instructions. While effective, existing inversion-based methods typically start with inverted latents from a source image and re-sample using a target prompt. To maintain fidelity and consistency with the original source content (e.g., background, overall structure), these methods employ a source injection strategy, re-introducing source-specific information during the sampling process.

The critical challenge identified by the paper is that this source injection strategy often leads to an over-reliance on source information. This excessive injection negatively affects the quality and accuracy of the intended edits in the target image. Specifically, when users instruct changes to subject attributes such as color, pose, or even the number of objects, existing methods frequently fail to fully implement these changes because the strong prior from the source image resists modification. The paper identifies this issue stemming from two main aspects:

1. Attention Aspect: Global attention feature injection mechanisms (e.g., injecting Value features) introduce too much attribute-related information from the source, causing the model to prioritize source consistency over textual guidance for edits.

2. Latent Aspect: Starting the sampling process from inverted latents that are too close to the source image distribution creates an overly strong prior. This prior directs the sampling towards reconstructing the original source distribution, hindering significant attribute changes, especially when the gap between target and source prompts is too large.

   This problem is important because it limits the controllability and flexibility of text-driven editing, preventing users from achieving precise and drastic changes to specific subject attributes without sacrificing overall image quality or background consistency. The paper's innovative idea is to systematically investigate and address these two root causes of editing failure in inversion-based editing by proposing targeted modifications to both the attention and latent spaces.

2.2. Main Contributions / Findings

The paper's primary contributions and key findings are:

1. Identification of Core Issues: ProEdit provides an in-depth investigation into why inversion-based editing methods fail to properly modify target image contents. It identifies that excessive source image information injection from both latent initialization and global attention injection mechanisms are the root causes, leading to editing failures, especially for attribute editing.
2. Novel Training-Free Solution (ProEdit): The paper proposes ProEdit, a novel, training-free method designed to eliminate the negative impact of the source image while maintaining background consistency. It comprises two main modules:
   • KV-mix (Attention Aspect): This module mixes Key (K) and Value (V) features of the source and target prompts specifically within the edited regions, while fully injecting source KV features in non-edited areas. This mitigates source influence on edits without compromising background consistency. It is applied to all attention operations without requiring manual selection of heads, layers, or blocks, a novel aspect compared to previous attention-based methods.
   • Latents-Shift (Latent Aspect): Inspired by AdaIN, this module perturbs the inverted latent of the edited region by injecting random noise. This reduces the strong prior from the source image's latent distribution, allowing for more flexible and accurate attribute changes.
3. State-of-the-Art (SOTA) Performance: Extensive experiments on several image and video editing benchmarks demonstrate that ProEdit achieves SOTA performance, effectively eliminating negative source impacts on edited content while preserving non-edited content.
4. Plug-and-Play Design: ProEdit is designed to be plug-and-play, allowing it to be seamlessly integrated into a wide range of existing inversion and editing methods, including RF-Solver, FireFlow, and UniEdit. This enhances the capabilities of existing pipelines without requiring extensive retraining.
5. Unprecedented Attribute Correction: The method showcases unprecedented performance in attribute editing tasks, where existing methods often perform poorly, directly addressing one of the core limitations identified.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To understand ProEdit, a grasp of several foundational concepts in generative AI and image processing is essential:

• Diffusion Models: Diffusion models, such as Denoising Diffusion Probabilistic Models (DDPMs), are a class of generative models that learn to generate data by reversing a diffusion process. This process gradually adds Gaussian noise to data until it becomes pure noise. The model then learns to denoise this noisy data step-by-step, effectively learning to generate new data from random noise. They are particularly known for their high-quality image generation capabilities.

  • U-Net Architecture: Many diffusion models, especially earlier ones, utilize a U-Net architecture for their noise prediction network. A U-Net consists of an encoder that downsamples the input, extracting features, and a decoder that upsamples these features to reconstruct the output, with skip connections between corresponding encoder and decoder layers to preserve fine-grained details.
  • Latent Diffusion Models (LDMs): LDMs operate in a compressed latent space rather than directly on pixel space, making them more computationally efficient for high-resolution image synthesis. The diffusion process and denoising occur in this latent space.

• Flow-based Generative Models / Rectified Flow (RF): Flow-based generative models learn a continuous transformation (a "flow") that maps a simple prior distribution (like Gaussian noise) to a complex data distribution. Rectified Flow (RF) is a specific type of flow matching model that learns a velocity field to transform noise into data along a straight trajectory defined by a probability flow ordinary differential equation (ODE). This deterministic transformation allows for faster and more stable generation with fewer sampling steps compared to traditional diffusion models, especially when implemented with DiT (Diffusion Transformers) architectures like MMDiT (Multi-Modal Diffusion Transformer).

  • Ordinary Differential Equation (ODE) Solvers: These are numerical methods used to approximate the solution of ODEs. In flow-based models, they are used to discretize and solve the continuous velocity field to move from noise to data (generation) or from data to noise (inversion).

• Inversion-based Editing: This is a training-free paradigm for image editing. The general idea is to take an existing source image and "invert" it back into the latent space (or noise space) of a generative model (e.g., a diffusion or flow model). This inverted latent (or noise) represents the source image. Then, this inverted latent is used as a starting point for a new generation process, but this time guided by a target text prompt that describes the desired edit. The goal is to generate a new image that incorporates the edits from the target prompt while retaining fidelity to the non-edited parts of the source image. DDIM inversion is a common technique used in diffusion models for this purpose.

• Attention Mechanism: Central to transformer-based models, the attention mechanism allows the model to weigh the importance of different parts of the input sequence (or different modalities) when processing another part.

  • Self-Attention: Computes attention within a single sequence (e.g., how different patches in an image relate to each other).
  • Cross-Attention: Computes attention between two different sequences (e.g., how image patches relate to words in a text prompt). In text-to-image models, cross-attention layers typically take image features as Query (Q) and text embeddings as Key (K) and Value (V).
  • Query (Q), Key (K), Value (V): These are linear transformations of the input features. $Q$ represents what you're looking for, $K$ represents what's available, and $V$ contains the information to be retrieved. The attention score (or attention map) is computed by multiplying $Q$ with $K$ (often scaled), and then a softmax operation is applied to get weights. These weights are then multiplied by $V$ to get the weighted sum of information.
  • Mathematical Formula for Attention: $ \mathrm{Attention}(Q, K, V) = \mathrm{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V $
    • $Q$: Query matrix (from current input, e.g., image features)
    • $K$: Key matrix (from contextual input, e.g., text embeddings or source image features)
    • $V$: Value matrix (from contextual input, e.g., text embeddings or source image features)
    • $d_k$: Dimensionality of the key vectors, used for scaling to prevent vanishing gradients.
    • $\mathrm{softmax}$: Normalizes the attention scores into a probability distribution.
    • $Q K^T$: Dot product of Query and Key, measuring similarity.

• AdaIN (Adaptive Instance Normalization): AdaIN is a technique used in style transfer that aligns the mean and variance of the content feature maps to those of the style feature maps. This allows for transferring the style (e.g., color, texture) of one image to the content (e.g., structure) of another, effectively disentangling content and style. The formula for AdaIN is: $ \mathrm{AdaIN}(x, y) = \sigma(y) \left( \frac{x - \mu(x)}{\sigma(x)} \right) + \mu(y) $

  • $x$: Content input feature map.
  • $y$: Style input feature map.
  • $\mu(x)$, $\sigma(x)$: Mean and standard deviation of $x$.
  • $\mu(y)$, $\sigma(y)$: Mean and standard deviation of $y$. This operation effectively "re-colors" and "re-textures" the content image based on the style image's statistics while preserving the content image's spatial structure.

3.2. Previous Works

The paper contextualizes its contributions within two main areas: Text-to-Visual generation and Text-driven Editing.

3.2.1. Text-to-Visual Generation

• Diffusion Models (U-Net based): Early foundational models like DDPMs [19] and Latent Diffusion Models (LDMs) [41] achieved significant success, often relying on U-Net architectures [42]. These models generate images by progressively denoising a noisy latent representation.
• Flow-based Models (DiT/MMDiT based): More recently, the paradigm has shifted towards flow models based on the Diffusion Transformer (DiT) [37] architecture, such as MMDiT [10]. Models like FLUX [26] and HunyuanVideo [25] utilize MMDiT to simulate a straight path between noise and data distributions via probability flow ODEs. This enables faster and higher-quality generation with fewer sampling steps.

3.2.2. Text-driven Editing

• Training-based Methods: Earlier approaches [3, 20, 22, 23, 27, 29, 60] focused on training generative models to achieve controllable image editing. Examples include InstructPix2Pix [3] and CycleGAN [22].
• Training-free Inversion-based Methods: With the rise of advanced generative models, training-free methods have gained traction due to their flexibility. Inversion-based methods [49] are prominent, with DDIM inversion [44] being a representative technique. This involves inverting an image to its latent representation and then regenerating it with modifications. Recent works have focused on high-precision solvers [32, 50, 57] to minimize inversion errors and improve sampling efficiency.
  • Sampling-based Methods: Introduce controlled randomness for flexible editing [9, 21, 36, 53]. PnP-Inversion [21] is a recent example aiming to boost diffusion-based editing.
  • Attention-based Methods: Achieve controllable editing by manipulating attention tokens [5, 24, 28, 46, 48, 55]. Prompt-to-Prompt (P2P) [13] and MasaCtrl [5] are notable for using cross-attention control. These methods have also extended to video editing [4].
• Flow-based Inversion Methods: Following the trajectory of diffusion models, recent inversion methods based on flow models (e.g., RF-Solver [51], FireFlow [8], UniEdit [18]) have focused on improving inversion solvers and joint attention mechanisms in MM-DiT [10]. These methods aim for better generative abilities and efficiency.

3.3. Technological Evolution

The field has evolved from training-based generative models for editing to training-free inversion-based methods that leverage powerful pre-trained models. Initially dominated by U-Net-based diffusion models, the trend is shifting towards transformer-based flow models (like DiT/MMDiT) due to their efficiency and performance. Early inversion-based methods primarily focused on minimizing inversion errors or manipulating attention in diffusion models. More recent advancements have applied these principles to flow models, developing specialized solvers and attention mechanisms.

3.4. Differentiation Analysis

Compared to the main methods in related work, ProEdit distinguishes itself by:

• Holistic Problem Addressing: While existing flow-based inversion methods (e.g., RF-Solver, FireFlow, UniEdit) achieve good editing performance, the paper argues they overlook the negative impact of inversion strategies on the editing content itself. ProEdit is the first to systematically identify and address the excessive source image information injection problem from both the attention and latent distribution perspectives.

• Comprehensive Attention Control (KV-mix): Previous attention-based methods (e.g., P2P, MasaCtrl) often require selecting specific attention heads, layers, or block types to modify the attention mechanism. ProEdit's KV-mix achieves this without such manual selection, applying attention control to visual components across all blocks and enabling precise text control for consistent editing. By mixing $K$ and $V$ features in edited regions, it more effectively mitigates source influence compared to global Value injection.

• Latent Distribution Perturbation (Latents-Shift): ProEdit introduces Latents-Shift to specifically tackle the latent distribution injection problem. This is a novel approach for inversion-based editing, drawing inspiration from AdaIN to perturb the inverted noise distribution in edited regions. This directly addresses the issue of the inverted latent retaining too many source image attributes, which often causes editing failures, particularly for drastic attribute changes. Existing methods generally do not explicitly modify the inverted latent distribution in this targeted manner for editing.

  In essence, ProEdit offers a more refined and comprehensive approach to managing the delicate balance between source consistency and editability, particularly in the context of flow-based generative models.

4. Methodology

4.1. Principles

The core idea behind ProEdit is to mitigate the negative impact of excessive source image information during the inversion-based editing process. The authors identify that this issue stems from two main aspects: the attention mechanism and the latent distribution of the inverted noise.

1. Attention Aspect: Previous methods globally inject source visual attention features to maintain background consistency. However, this also injects source attributes into the edited region, hindering desired changes. ProEdit's KV-mix module addresses this by selectively mixing source and target visual attention features only in the edited regions, while preserving full source injection in non-edited areas to maintain background consistency.

2. Latent Aspect: The inverted latent from the source image inherently carries strong source attributes and acts as a rigid prior, making it difficult to achieve significant edits. ProEdit's Latents-Shift module perturbs the latent distribution of the edited region within the inverted noise by injecting random noise, thereby loosening the source prior and allowing for more flexible modifications.

   By addressing these two intertwined issues, ProEdit aims to achieve high-quality edits that are faithful to the target prompt while maintaining the consistency of non-edited content and background structure.

4.2. Core Methodology In-depth (Layer by Layer)

4.2.1. Preliminaries: Flow-based Generative Models and Inversion

The paper first establishes the groundwork by introducing flow-based generative models, which learn a velocity field ${ \pmb v } _ { \theta }$ to transform noise $Z_0$ (from a Gaussian distribution $\pi_0$) into data $Z_1$ (following a real data distribution $\pi_1$) along a straight trajectory.

The training objective for learning this velocity field is defined as: $ \begin{array} { r l } & { \underset { \theta } { \operatorname* { m i n } } \mathbb { E } _ { Z _ { 0 } , Z _ { 1 } , t } \left[ \left| ( Z _ { 1 } - Z _ { 0 } ) - { \pmb v } _ { \theta } ( Z _ { t } , t ) \right| ^ { 2 } \right] , } \ & { \quad \quad \quad \quad \quad Z _ { t } = t Z _ { 1 } + ( 1 - t ) Z _ { 0 } , t \in [ 0 , 1 ] , } \end{array} $ Here:

• $\theta$: Parameters of the neural network approximating the velocity field.
• $\mathbb{E}$: Expectation over different samples of $Z_0$, $Z_1$, and time $t$.
• $Z_0$: Initial noise sample from Gaussian distribution $\pi_0$.
• $Z_1$: Target data sample from real data distribution $\pi_1$.
• $Z_t$: A point on the straight trajectory between $Z_0$ and $Z_1$ at time $t$.
• $(Z_1 - Z_0)$: Represents the target velocity or the direction from noise to data.
• ${ \pmb v } _ { \theta } ( Z _ { t } , t )$: The velocity field learned by the model, which predicts the direction of flow at point $Z_t$ at time $t$. This objective trains the model to predict the straight line velocity between $Z_0$ and $Z_1$ at any point $Z_t$ on that line.

The learned velocity field ${ \pmb v } _ { \theta }$ allows for a deterministic transformation from noise to data via an Ordinary Differential Equation (ODE) over the continuous time interval $t \in [0, 1]$: $ d { \bf Z } _ { t } = v _ { \theta } ( { \bf Z } _ { t } , t ) d t , \quad t \in [ 0 , 1 ] $ Here:

• $d { \bf Z } _ { t }$: Infinitesimal change in the latent state at time $t$.

• $v _ { \theta } ( { \bf Z } _ { t } , t )$: The learned velocity field at latent state ${ \bf Z } _ { t }$ and time $t$.

• d t: Infinitesimal change in time.

  This ODE can be numerically solved by solvers using discretization: $ Z _ { t _ { i + 1 } } = Z _ { t _ { i } } + ( t _ { i + 1 } - t _ { i } ) v _ { \theta } ( Z _ { t _ { i } } , t _ { i } ) , $ Here:

• $Z_{t_i}$: Latent state at discrete time step $t_i$.

• $Z_{t_{i+1}}$: Latent state at the next discrete time step $t_{i+1}$.

• $i \in \{0, \ldots, N\}$: Index for discrete time steps.

• $t_0 = 0$: Starting time.

• $t_N = 1$: Ending time.

  For inversion, the reverse process is obtained by reversing the learned flow trajectory. Starting from data $Z_1 \sim \pi_1$, the reverse ODE is given by reversing the velocity field: $ d \pmb { Z } _ { t } = - \pmb { v } _ { \theta } ( \pmb { Z } _ { t } , t ) d t , \quad t \in [ 1 , 0 ] $ Here, the negative sign indicates moving backward along the flow.

Correspondingly, this reverse ODE is discretized and solved numerically: $ Z _ { t _ { i - 1 } } = Z _ { t _ { i } } - ( t _ { i - 1 } - t _ { i } ) v _ { \theta } ( Z _ { t _ { i } } , t _ { i } ) , $ Here:

• $Z_{t_i}$: Latent state at discrete time step $t_i$.
• $Z_{t_{i-1}}$: Latent state at the previous discrete time step $t_{i-1}$.
• $i \in \{N, \ldots, 0\}$: Index for discrete time steps, moving backward.
• $t_N = 1$: Starting time for inversion.
• $t_0 = 0$: Ending time for inversion (resulting in the inverted noise). This inverse process generates $Z_0 \sim \pi_0$ (the inverted noise corresponding to $Z_1$) by utilizing the symmetry of the velocity field $v$. This inversion method is then applied in visual reconstruction and editing.

4.2.2. Rethinking the Inversion-Sampling Paradigm

The paper investigates why existing inversion-sampling paradigms face challenges in removing source image influence on edited content. It concludes that previous works rely on sampling with inverted noise and a source attention injection mechanism to maintain consistency, which often injects excessive source image information, leading to editing failures.

• Attention Injection Problem: Current methods, to maintain structural consistency, globally inject value attention features ($V$) from the source prompt during sampling. This is typically described as: $ \begin{array} { r } { z _ { t g } ^ { t } ( l + 1 ) = \mathrm { A t t n } ( Q _ { t g } ^ { t } , K _ { t g } ^ { t } , V _ { s } ^ { t } ) , } \end{array} $ Here:

  • $z_{tg}^t(l+1)$: Output of the attention layer $l+1$ for the target generation at time $t$.
  • $\mathrm{Attn}(\cdot)$: The attention mechanism.
  • $Q_{tg}^t$: Query features corresponding to the target prompt at time $t$.
  • $K_{tg}^t$: Key features corresponding to the target prompt at time $t$.
  • $V_s^t$: Value features corresponding to the source prompt at time $t$. The problem is that this global injection mechanism forces source attributes into the target image, as seen in Figure 3. For example, if the source is an "orange cat" and the target is a "black cat," injecting the source's $V$ features might make the model focus more on "orange" even when guided by "black," making it hard to change the color.

• Latent Distribution Injection Problem: Even after inverting an image back to noise, the inverted latent still retains substantial source image attributes. Figure 3 shows that attention from the word "orange" to visual tokens is significantly higher than from "black" even in the inverted noise state. This strong prior from the source latent distribution makes editing difficult when the difference between target and source prompts is large, as the sampling process is biased towards reconstructing the original source distribution.

• Summary: The paper attributes editing failures to these two factors: global attention feature injection and the latent distribution injection.

4.2.3. KV-mix

Motivation: Previous methods' global injection of source attention features negatively impacts editing quality by forcing source attributes onto the target. KV-mix aims to mix source and target visual attention to better align with the target prompt while preserving non-edited content consistency.

Method: KV-mix applies attention control to the visual components across all blocks. Text attention consistently uses features from the target prompt. To differentiate edited and non-edited regions, a mask $M$ is obtained by processing the attention map (details in Supplementary File A).

• For non-editing regions, full injection of source visual attention features is applied to maintain background consistency.

• For editing regions, a mix of source and target visual attention features is used to improve editing quality.

  The KV-mix design is formally defined as: $ \begin{array} { r l r } & { } & { \hat { K } _ { t g } ^ { l } = \delta K _ { t g } ^ { l } + ( 1 - \delta ) K _ { s } ^ { l } , } \ & { } & { \hat { V } _ { t g } ^ { l } = \delta V _ { t g } ^ { l } + ( 1 - \delta ) V _ { s } ^ { l } , } \ & { } & { \tilde { K } _ { t g } ^ { l } = M \odot \hat { K } _ { t g } ^ { l } + ( 1 - M ) \odot K _ { s } ^ { l } , } \ & { } & { \tilde { V } _ { t g } ^ { l } = M \odot \hat { V } _ { t g } ^ { l } + ( 1 - M ) \odot V _ { s } ^ { l } , } \ & { } & { z ^ { t } ( l + 1 ) = \mathrm { A t t n } \left( Q _ { t g } ^ { l } , \tilde { K } _ { t g } ^ { l } , \tilde { V } _ { t g } ^ { l } \right) , } \end{array} $ Here:

• $K_{tg}^l, V_{tg}^l$: Key and Value features from the target prompt at layer $l$.

• $K_{s}^l, V_{s}^l$: Key and Value features from the source image (cached during inversion) at layer $l$.

• $\delta$: Mixing strength, a ratio (hyperparameter) that controls the balance between target and source features in the edited region. A higher $\delta$ means more target feature influence.

• $\hat{K}_{tg}^l, \hat{V}_{tg}^l$: Mixed Key and Value features, representing a weighted sum of target and source features.

• $M$: The edited region mask, extracted from the attention map. It is applied only to the visual branch.

• $\odot$: Element-wise multiplication (Hadamard product).

• (1-M): Mask for non-edited regions.

• $\tilde{K}_{tg}^l, \tilde{V}_{tg}^l$: Final Key and Value features used in the attention operation. These are a combination: the mixed features ($\hat{K}_{tg}^l, \hat{V}_{tg}^l$) for the edited region and source features ($K_{s}^l, V_{s}^l$) for the non-edited region.

• $Q_{tg}^l$: Query features from the target prompt at layer $l$.

• $z^t(l+1)$: Output of the attention layer $l+1$ at time $t$. This mechanism ensures precise text control in edited regions while preserving background consistency. Since KV-mix operates only within visual tokens, it's applied in both Double and Single Attention blocks of the underlying MMDiT architecture.

4.2.4. Latents-Shift

Motivation: This module aims to mitigate the latent distribution injection problem while preserving structural consistency. It is inspired by AdaIN from style transfer, which effectively transfers color and texture distributions while maintaining structural integrity.

Method: To eliminate the influence of source image information, Latents-Shift uses random noise as a "style image" to shift the distribution of the inverted noise. The formula for Latents-Shift is: $ \begin{array} { c } { { \tilde { z } _ { T } = \sigma ( z _ { T } ^ { r } ) \left( \frac { z _ { T } - \mu ( z _ { T } ) } { \sigma ( z _ { T } ) } \right) + \mu ( z _ { T } ^ { r } ) , } } \ { { \hat { z } _ { T } = M \odot ( \beta \tilde { z } _ { T } + ( 1 - \beta ) z _ { T } ) + ( 1 - M ) \odot z _ { T } , } } \end{array} $ Here:

• $z_T$: The inverted noise from the source image.
• $z_T^r$: Pure random noise (serving as the "style" for distribution shift).
• $\mu(z_T), \sigma(z_T)$: Mean and standard deviation of the inverted noise $z_T$.
• $\mu(z_T^r), \sigma(z_T^r)$: Mean and standard deviation of the random noise $z_T^r$.
• $\tilde{z}_T$: The shifted inverted noise after applying an AdaIN-like transformation, aligning the statistics of $z_T$ to those of $z_T^r$. This AdaIN operation is performed on the inverted noise itself, effectively injecting new "style" into its distribution.
• $\beta$: Fusion ratio between the shifted noise ($\tilde{z}_T$) and the original inverted noise ($z_T$). This parameter controls the level of shift in the inverted noise distribution. A higher $\beta$ means more influence from the shifted noise.
• $M$: The edited region mask, inherited from KV-mix.
• (1-M): Mask for non-edited regions.
• $\hat{z}_T$: The final initial latent for sampling. This is a spatially-masked combination: the fused noise ($\beta \tilde{z}_T + (1 - \beta) z_T$) is used for the edited region ($M$), while the original inverted noise ($z_T$) is preserved for the non-edited region ((1-M)).

4.2.5. Overall Pipeline

The complete ProEdit pipeline integrates these modules sequentially, as schematically described (and shown in an abstract Figure 4, which the VLM describes as a schematic for the overall process):

1. Inversion Stage:

   • The source image and source prompt are input into the model to perform the inversion process.
   • During inversion, the Key ($K_s^l$) and Value ($V_s^l$) features from the source attention are cached "on the fly".
   • The attention map is processed to obtain the mask $M$ of the editing region (as detailed in Supplementary A).
   • The inverted noise ($z_T$) corresponding to the source image is output, serving as the initial input for the sampling stage.

2. Sampling Stage:

   • The inverted noise ($z_T$) first passes through the Latents-Shift module to obtain the fusion noise ($\hat{z}_T$), which has its distribution perturbed in the edited region.

   • This fusion noise ($\hat{z}_T$) is then input into the model along with the target prompt for sampling.

   • During the multi-step sampling process, the cached source visual attention features ($K_s^l$ and $V_s^l$) are selectively injected through the KV-mix module (as defined in Eq. 7). This ensures mixing of $K$ and $V$ in edited regions and full source injection in non-edited regions.

   • The model finally outputs the target image after multiple sampling steps.

     The overall process ensures that source information is carefully managed: its influence is reduced in edited regions (both in latent distribution and attention) but preserved in non-edited regions (for background consistency).

     

     Supplementary A: Extracting Mask From Attention Map The mask $M$ for editing regions is extracted from the attention map. The paper notes that the attention map of the last Double block is effective for associating text and image regions, and this approach reduces memory consumption. The mask is extracted from either the first step of inversion or the last step of sampling, as images at these steps are least affected by noise and show the best text-to-image correlation. Due to downsampling in the feature space, the initial mask can be coarse. To ensure full coverage and smooth edges, a diffusion operation is applied to expand the mask outward by one step. The target object for mask extraction can be identified by the noun in the editing object or through an externally provided mask.

Supplementary B: Implementation Details

• Mixing strength $\delta$ (for KV-mix): Set to 0.9 to balance source content preservation and editing performance.
• Fusion ratio $\beta$ (for Latents-Shift): Set to 0.25 for best editing results.
• Feature fusion injection: Applied to all Double and Single blocks at each timestep.
• Base Models: FLUX.1-[dev] [26] for image editing, HunyuanVideo-720p [25] for video editing.
• Plug-and-play: ProEdit is integrated with RF-Solver, FireFlow, and UniEdit for image editing, and RF-Solver for video editing.
• UniEdit specific: Uses $\alpha$ for delay injection rate. Experiments conducted with $\alpha=0.6$ and $\alpha=0.8$. Default for comparison is $\alpha=0.8$.
• Sampling Steps: 15 for image editing, 25 for video editing.

5. Experimental Setup

5.1. Datasets

• Text-driven Image Editing:
  • PIE-Bench [21]: This dataset was used for evaluating text-driven image editing. It comprises 700 images across 10 different editing types.
  • Characteristics: These images likely cover a diverse range of subjects and scenarios, and the 10 editing types represent common visual modifications, allowing for comprehensive evaluation of editing capabilities.
• Text-driven Video Editing:
  • Custom Collected Dataset: The authors collected 55 text-video editing pairs.
  • Characteristics: The videos have resolutions of $480 \times 480$, $540 \times 960$, or $960 \times 540$, and consist of 40 to 120 frames. The dataset includes videos from the DAVIS dataset [38] and other online platforms. The prompts were derived from ChatGPT or contributed by the authors. This dataset aims to provide diverse scenarios for evaluating video editing performance, including temporal consistency.

5.2. Evaluation Metrics

The paper uses a comprehensive set of metrics to evaluate different aspects of image and video editing performance.

5.2.1. Text-driven Image Editing Metrics

• Edit-irrelevant Context Preservation (Background Consistency):
  • Structure Distance [47]:
    • Conceptual Definition: Quantifies the structural difference between the original unedited region and the corresponding region in the edited image. It is typically calculated in a feature space to capture perceptual differences that are more aligned with human perception than simple pixel-wise differences. A lower value indicates better preservation of the original structure.
    • Mathematical Formula: The paper refers to [47], "Splicing ViT Features for Semantic Appearance Transfer," which commonly uses feature-level distances. A generalized formula for structure distance using a feature extractor $\Phi$ would be: $ \mathrm{StructureDistance}(I_{orig}, I_{edit}, \text{mask}) = \left| \Phi(I_{orig} \odot (1-\text{mask})) - \Phi(I_{edit} \odot (1-\text{mask})) \right|_2 $
    • Symbol Explanation:
      • $I_{orig}$: Original source image.
      • $I_{edit}$: Edited target image.
      • $\text{mask}$: Binary mask identifying the edited regions.
      • $(1-\text{mask})$: Mask identifying the unedited background regions.
      • $\Phi(\cdot)$: A pre-trained feature extractor (e.g., from a Vision Transformer like ViT, or a perceptual loss network like VGG features in LPIPS).
      • $\odot$: Element-wise multiplication.
      • $\|\cdot\|_2$: L2 norm, calculating the Euclidean distance between feature vectors.
  • PSNR (Peak Signal-to-Noise Ratio) [17]:
    • Conceptual Definition: Measures the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. It is a common measure of quality for reconstructed lossy images. A higher PSNR indicates higher similarity between the preserved background of the edited image and the original background.
    • Mathematical Formula: $ \mathrm{PSNR} = 10 \cdot \log_{10} \left( \frac{MAX_I^2}{\mathrm{MSE}} \right) $
    • Symbol Explanation:
      • $MAX_I$: The maximum possible pixel value of the image (e.g., 255 for 8-bit grayscale images, or $2^B-1$ for B-bit images).
      • $\mathrm{MSE}$: Mean Squared Error between the compared images (or regions). For images $I_1$ and $I_2$ of size $H \times W$: $ \mathrm{MSE} = \frac{1}{H \cdot W} \sum_{i=1}^{H} \sum_{j=1}^{W} (I_1(i,j) - I_2(i,j))^2 $
  • SSIM (Structural Similarity Index Measure) [52]:
    • Conceptual Definition: Evaluates the similarity between two images based on luminance, contrast, and structural information. Unlike PSNR which measures absolute error, SSIM aims to quantify perceived changes. It outputs a value between -1 and 1, where 1 indicates perfect similarity. A higher SSIM for unedited regions suggests better visual preservation of the background.
    • Mathematical Formula: $ \mathrm{SSIM}(x,y) = [l(x,y)]^{\alpha} [c(x,y)]^{\beta} [s(x,y)]^{\gamma} $ where: $ l(x,y) = \frac{2\mu_x\mu_y + C_1}{\mu_x^2 + \mu_y^2 + C_1} $ $ c(x,y) = \frac{2\sigma_x\sigma_y + C_2}{\mu_x^2 + \mu_y^2 + C_2} $ $ s(x,y) = \frac{\sigma_{xy} + C_3}{\sigma_x\sigma_y + C_3} $
    • Symbol Explanation:
      • x, y: The two image windows being compared (e.g., original background and edited background).
      • $\mu_x, \mu_y$: The average (mean) pixel intensity of $x$ and $y$.
      • $\sigma_x, \sigma_y$: The standard deviation of pixel intensities of $x$ and $y$.
      • $\sigma_{xy}$: The covariance of $x$ and $y$.
      • $C_1, C_2, C_3$: Small constants used to stabilize the division with weak denominators (e.g., $C_1 = (K_1 L)^2, C_2 = (K_2 L)^2$, where $K_1, K_2 \ll 1$ and $L$ is the dynamic range of pixel values). $C_3 = C_2/2$.
      • $\alpha, \beta, \gamma$: Exponents (typically set to 1) to weight the importance of each component.
• Edit Quality:
  • CLIP Similarity [40]:
    • Conceptual Definition: CLIP (Contrastive Language-Image Pre-training) is a neural network trained on a wide variety of image-text pairs. It learns to embed images and text into a shared multi-modal latent space where semantically similar image-text pairs are closer together. CLIP Similarity measures the cosine similarity between the CLIP embedding of an image and the CLIP embedding of a text prompt. A higher value indicates that the image is better aligned with the given text prompt. It is used to assess how well the edited image (or its edited region) matches the target prompt.
    • Mathematical Formula: $ \mathrm{CLIP_Sim}(I, T) = \frac{E_I(I) \cdot E_T(T)}{|E_I(I)|_2 \cdot |E_T(T)|_2} $
    • Symbol Explanation:
      • $I$: The image (either the whole edited image or just the edited region).
      • $T$: The text prompt (e.g., the target editing instruction).
      • $E_I(\cdot)$: CLIP image encoder function, which maps an image to its CLIP embedding.
      • $E_T(\cdot)$: CLIP text encoder function, which maps a text prompt to its CLIP embedding.
      • $\cdot$: Dot product (cosine similarity is dot product of L2-normalized vectors).
      • $\|\cdot\|_2$: L2 norm (magnitude of the vector).

5.2.2. Text-driven Video Editing Metrics

The paper follows metrics proposed in VBench [15, 59], which are typically derived from specialized models or aggregations of lower-level metrics.

• Subject Consistency (SC):
  • Conceptual Definition: Measures how consistently the identity and attributes of the main subject are preserved across different frames of the video, and how well they align with the textual prompt. High SC means the subject remains recognizable and consistent throughout the video and matches the description.
  • Computational Basis: Often involves extracting CLIP embeddings of the subject across frames and comparing them, or using facial recognition models for human subjects.
• Motion Smoothness (MS):
  • Conceptual Definition: Quantifies the fluidity and naturalness of movement within the video. It assesses whether the motion appears continuous and realistic without jerky transitions or unnatural accelerations/decelerations.
  • Computational Basis: Typically calculated by analyzing optical flow fields between consecutive frames, or by measuring variations in motion vectors.
• Aesthetic Quality (AQ):
  • Conceptual Definition: Evaluates the overall visual appeal and artistic quality of the generated video. This is subjective but can be approximated by models trained on human aesthetic judgments.
  • Computational Basis: Often utilizes a pre-trained aesthetic predictor model that outputs a score based on learned aesthetic features.
• Imaging Quality (IQ):
  • Conceptual Definition: Refers to the general technical quality of the video frames, including aspects like sharpness, clarity, absence of artifacts (e.g., blur, noise, distortions), and overall visual fidelity.
  • Computational Basis: Can involve various metrics like FID (Fréchet Inception Distance), Inception Score (IS), or specialized models that detect visual defects.

5.3. Baselines

5.3.1. Text-driven Image Editing Baselines

The proposed ProEdit method is compared against a range of state-of-the-art training-free visual editing methods:

• Diffusion-based methods:
  • P2P [13] (Prompt-to-Prompt image editing with cross attention control)
  • PnP [48] (Plug-and-play diffusion features for text-driven image-to-image translation)
  • PnP-Inversion [21] (PnP inversion: Boosting diffusion-base editing with 3 lines of code)
  • EditFriendly [16] (An edit friendly DDPM noise space: Inversion and manipulations)
  • MasaCtrl [5] (MasaCtrl: Tuning-free mutual self-attention control for consistent image synthesis and editing)
  • InfEdit [55] (Inversion-free image editing with natural language)
• Flow-based methods:
  • RF-Inversion [43] (Semantic image inversion and editing using rectified stochastic differential equations)
  • RF-Solver [51] (Taming rectified flow for inversion and editing)
  • FireFlow [8] (FireFlow: Fast inversion of rectified flow for image semantic editing)
  • UniEdit [18] (UniEdit-flow: Unleashing inversion and editing in the era of flow models)

5.3.2. Text-driven Video Editing Baselines

For video editing, ProEdit is compared against:

• FateZero [39] (FateZero: Fusing attentions for zero-shot text-based video editing)

• Flatten [6] (Flatten: optical flow-guided attention for consistent text-to-video editing)

• Tokenflow [12] (Tokenflow: Consistent diffusion features for consistent video editing)

• RF-Solver [51] (Taming rectified flow for inversion and editing)

  These baselines are representative of the current state-of-the-art in both diffusion-based and flow-based image and video editing, covering various approaches to inversion, attention control, and sampling strategies.

6. Results & Analysis

6.1. Core Results Analysis

The experimental results demonstrate that ProEdit consistently improves the performance of existing flow-based inversion methods across both image and video editing tasks, achieving state-of-the-art (SOTA) results in many categories. The key findings reinforce the paper's motivation: addressing excessive source information injection leads to better editability without sacrificing background consistency.

6.1.1. Text-driven Image Editing

The quantitative results in Table 1 showcase the overall performance of ProEdit when integrated with various flow-based inversion methods.

The following are the results from Table 1 of the original paper:

• ProEdit consistently improves the background preservation metrics (PSNR and SSIM) and CLIP similarity for both whole and edited regions when integrated with RF-Solver, FireFlow, and UniEdit.
• Notably, UniEdit with ProEdit ($UniEdit(α=0.6)+Ours$ and $UniEdit(α=0.8)+Ours$) achieves the best or second-best performance across almost all metrics, including the lowest Structure Distance (9.22 for $\alpha=0.6$) and highest SSIM (90.87 for $\alpha=0.6$). This indicates ProEdit's ability to maintain excellent source content preservation while delivering high editing quality.
• The improvements are visible across various Number of Function Evaluations (NFE), showing that ProEdit's benefits are not tied to increased computational cost in terms of sampling steps.

6.1.2. Color Editing

Color editing tasks are particularly sensitive to the latent distribution injection problem. Table 2 specifically evaluates performance on color editing.

The following are the results from Table 2 of the original paper:

• ProEdit significantly boosts SSIM for background preservation (e.g., RF-Solver goes from 80.21 to 86.63, FireFlow from 80.14 to 86.53, UniEdit from 85.39 to 89.26).

• It also substantially increases CLIP Similarity for Whole and Edited regions, showing improved adherence to the target color prompt. This strongly validates the effectiveness of the Latents-Shift module, which helps the editing process overcome the constraints of the source image distribution in tasks like color change. Figure 6 visualizes this effect by showing how Latents-Shift improves attention map focus for color attributes.

  该图像是示意图，展示了在编辑过程中使用的反向噪声注意力和采样注意力的不同效果。左侧展示了原始的橙色和黑色猫咪图像，右侧则是相应的编辑结果，阐明了不同颜色提示对图像处理的影响。

6.1.3. Text-driven Video Editing

Table 3 presents the quantitative results for video editing, where ProEdit is integrated with RF-Solver.

The following are the results from Table 3 of the original paper:

• ProEdit improves all video editing metrics: Subject Consistency (SC), Motion Smoothness (MS), Aesthetic Quality (AQ), and Imaging Quality (IQ).
• For example, RF-Solver+Ours achieves the highest MS (0.9920), AQ (0.6518), and IQ (0.6936), and a very competitive SC (0.9712). This demonstrates ProEdit's versatility and ability to enhance temporal consistency and editing performance in video generation, beyond static images.

6.1.4. Qualitative Evaluation

• Image Editing (Figure 5, 9): ProEdit successfully performs high-quality editing while maintaining background consistency and non-editing content. Baseline methods often fail to preserve backgrounds or posture, or produce unsatisfactory edits (e.g., in color, pose, number changes). ProEdit's results show semantic consistency and effective preservation of human characteristics.

  该图像是一个示意图，展示了多种图像编辑方法的对比，包括源图像及其通过不同算法（如 PnP、RF-Solver、FireFlow 等）获得的编辑效果。每行代表不同的编辑任务，如猫、雨伞等，清晰地显示了各方法在目标图像转换中的效果。

  该图像是图表，展示了在PIE-Bench上的图像编辑效果对比。多个方法（如PnP、RF-Solver等）在不同场景中处理源图像，分别展示了编辑前后的变化，体现了不同技术在图像编辑上的效果差异。

• Video Editing (Figure 7, 10): ProEdit demonstrates impressive performance across a wide range of video editing tasks, notably maintaining temporal consistency and preserving original motion patterns, which are critical for video quality. Baseline methods often exhibit inconsistencies across frames.

  该图像是一个示意图，展示了视频编辑的定性比较。图中包含多张猫在草地上活动的帧，分别显示了五种不同的方法（Source、Flatten、TokenFlow、RF-Solver、Ours）及其在编辑后的效果。每种方法对应的结果在不同帧上有所变化，右侧标注了“+Crown”的增强效果。整体呈现了一种对比，展示不同编辑技术对相同场景的影响。

  该图像是图表，展示了视频编辑的多种结果，包括源视频和目标视频的对比。图中展示了不同场景下物体的替换，如将吉普车换成蓝色吉普车、将汽车换成卡车，以及将鹿替换为牛。同时显示了天气变化，例如将阳光转为阴雨。

• Editing by Instruction (Figure 11): By integrating with a large language model (Qwen3-8B), ProEdit can also perform edits directly guided by natural language instructions, further enhancing user-friendliness.

  该图像是一个示意图，展示了通过图像编辑实现各种变化的例子，包括将坐在木椅上的猫变成狗、将花的颜色从粉色变为红色等。这样的编辑展示了如何通过反转机制进行效果修改。

6.2. Ablation Studies / Parameter Analysis

6.2.1. The Synergistic Effect Analysis

Table 4 evaluates the individual and combined effectiveness of the proposed KV-mix and Latents-Shift modules.

The following are the results from Table 4 of the original paper:

• KV-mix (KV-m) Effect: Applying only KV-mix (replacing the original feature injection mechanism) consistently improves CLIP similarity (both Whole and Edited regions). For RF-Solver, CLIP Sim. Whole increases from 26.00 to 26.21, and Edited from 22.88 to 23.21. This indicates that reducing the influence of source features in attention by mixing them leads to better alignment with the target prompt.
• Latents-Shift (LS) Effect: Further incorporating the Latents-Shift module (after KV-mix) results in additional improvements in CLIP similarity. For RF-Solver, CLIP Sim. Whole goes from 26.21 to 26.28, and Edited from 23.21 to 23.25. This confirms that eliminating the influence of the source image on the inversion noise latent distribution further enhances editing quality.
• Synergy: The results clearly demonstrate that KV-mix and Latents-Shift work synergistically. Each module addresses a distinct aspect of source information injection, and their combined effect leads to a more robust and effective editing system.

6.2.2. The Attention Feature Combination Effect Analysis

The supplementary materials (Table 5) provide results for different attention feature combinations used in the fusion injection mechanism, with RF-Solver as the base. The study explores Q&V, Q&K&V, $V$ (only Value), and K&V (the proposed KV-mix).

The following are the results from Table 5 of the original paper:

• The KV combination (the proposed KV-mix) achieved the best performance across all metrics: BG Preservation PSNR (24.77), SSIM (84.78), CLIP Sim. Whole (26.28), and CLIP Sim. Edited (23.25).
• The $V$ (Value only) injection, while showing decent CLIP Sim. for Whole and Edited, performed worse on background preservation metrics (PSNR, SSIM).
• This validates the design choice of KV-mix, indicating that mixing both Key and Value features is crucial for simultaneously achieving high background consistency and superior editing quality. The Key features likely contribute to better structural alignment and context understanding, while Value features influence the content details.

6.3. Figures 2 and 3: Framework and Attention Map Comparisons

• Figure 2 (Framework Comparison): This figure visually contrasts previous methods' framework (a) with ProEdit's framework (b). Previous methods are shown with global attention injection and direct use of inverted noise. ProEdit introduces the Shift module for inverted noise (Latents-Shift) and the Mix module for attention injection (KV-mix), illustrating how these specifically target and alleviate issues caused by excessive source information injection.

  该图像是示意图，展示了(a) 以前的方法与(b) 我们的方法的框架对比。为了解决过多源图像信息注入带来的问题，我们引入了用于反转噪声的Shift模块和用于注意力注入的Mix模块，从而减轻了这些问题造成的编辑失败。

• Figure 3 (Attention Maps): This figure provides a crucial visualization of the attention injection and latent distribution injection problems. It shows attention maps from RF-Solver and a method without V injection for an "orange cat" source image being edited to a "black cat". The attention maps highlight that even after inversion, strong source attributes (like "orange") persist in the inverted noise and sampling attention, dominating over the target prompt ("black"). This visual evidence supports the paper's core hypothesis about why editing failures occur.

  该图像是示意图，展示了RF-Solver与无V注入方法在不同颜色（“orange”和“black”）下的注意力图。图中包含了反向噪声注意力和采样注意力的对比，显示了不同处理方法的可视化结果。

7. Conclusion & Reflections

7.1. Conclusion Summary

This paper effectively identifies and addresses a critical limitation in inversion-based editing methods: the excessive injection of source image information. This issue, stemming from both inverted latent initialization and global attention injection, often compromises editing quality in favor of background consistency. ProEdit offers a novel, training-free solution by introducing two core modules: KV-mix and Latents-Shift. KV-mix selectively blends source and target Key and Value features in edited regions to improve text guidance while fully preserving source background information. Latents-Shift perturbs the latent distribution of the inverted noise in edited regions to reduce the source prior's rigidity. Extensive experiments confirm that ProEdit achieves state-of-the-art performance across various image and video editing tasks, demonstrating superior editing quality, improved attribute correction, and robust background preservation. Its plug-and-play nature further enhances its value by seamlessly integrating with existing flow-based inversion methods.

7.2. Limitations & Future Work

The paper does not explicitly dedicate a section to Limitations or Future Work. However, some aspects can be inferred:

• Mask Extraction Dependency: The quality of the editing mask is crucial. While the paper discusses its extraction from attention maps and refinement via diffusion, complex or ambiguous editing scenarios might still pose challenges for precise mask generation. Incorrect masks could lead to unwanted edits or inconsistencies.

• Hyperparameter Sensitivity: The method introduces several hyperparameters such as mixing strength $\delta$ and fusion ratio $\beta$. Optimal performance depends on tuning these, which might require domain-specific adjustments for new tasks or base models.

• Generality of Flow Models: While ProEdit is plug-and-play for flow-based models, its direct applicability to diffusion-based models (which have different underlying ODEs and sampling mechanisms) is not explicitly discussed, though the general principles might be transferable.

• Computational Overhead: Although the method is training-free and does not increase NFE (Number of Function Evaluations), the additional steps of mask extraction, KV-mix operations, and Latents-Shift computations do add some inference time overhead.

• Complex Scene Understanding: While ProEdit excels at attribute changes, highly complex edits involving significant scene restructuring or object interactions might still be challenging without more sophisticated semantic understanding or 3D scene representations.

  Implicitly, future work could involve:

• Developing more robust and automated mask generation techniques, perhaps incorporating segmentation models or user interaction for greater precision.

• Exploring adaptive or learned strategies for hyperparameter tuning ($\delta$, $\beta$) to reduce manual effort.

• Extending ProEdit's principles and modules to other generative model architectures, including diffusion models or emerging paradigms.

• Investigating real-time editing capabilities or optimizing the computational efficiency of the added modules.

• Applying ProEdit to more challenging long-range video editing tasks or multi-object editing scenarios.

7.3. Personal Insights & Critique

• Elegant Problem Identification: The paper's strength lies in its clear and concise identification of the "excessive source image information injection" problem, breaking it down into distinct attention and latent aspects. This precise diagnosis makes the proposed solutions feel targeted and intuitive.

• Modular and Plug-and-Play Design: The modular nature of KV-mix and Latents-Shift, combined with their plug-and-play compatibility, is a significant advantage. This allows ProEdit to enhance existing SOTA inversion methods without requiring architectural changes or retraining, making it highly practical for researchers and developers.

• Attribute Correction Breakthrough: The demonstrated performance in attribute editing, especially color changes (validated by Table 2), is particularly impressive. This has been a stubborn challenge for many inversion-based methods that struggle to deviate significantly from the source's ingrained properties.

• Leveraging Existing Concepts: The inspiration from AdaIN for Latents-Shift is a clever adaptation of a style transfer technique to the latent distribution problem in editing. Similarly, refining attention control through KV-mix shows a deep understanding of how cross-attention operates.

• Clarity in Methodology: The detailed breakdown of the flow-based model preliminaries and the step-by-step explanation of KV-mix and Latents-Shift (including formulas) are commendable, making the technical aspects digestible.

• Critique on Publication Date: The erroneous publication date (2001) is a minor but confusing detail that should be rectified in a final version. While understandable for preprints, clear labeling of the actual submission/creation date would be beneficial.

• Lack of Explicit Limitations: While I inferred some limitations, the absence of a dedicated section for this in the paper itself is a missed opportunity for authors to guide future research and acknowledge potential drawbacks. For example, the trade-off between editing strength and background consistency (controlled by $\delta$ and $\beta$) is inherent, and explicitly discussing this balance and guidelines for choosing parameters would be valuable.

• Broader Applicability: The core ideas of controlling source influence in attention and latent space could potentially be adapted to other generative tasks beyond text-driven image/video editing, such as style transfer with stricter content preservation or domain adaptation.

  Overall, ProEdit presents a well-motivated and effective solution to a fundamental problem in inversion-based visual editing, offering a valuable contribution to the field of controllable generation.