ParallelEdits: Efficient Multi-Aspect Text-Driven Image Editing with Attention Grouping

Authors: mingzhen  huang
jialing  cai
shan  jia
vishnu  suresh lokhande
siwei  lyu
Venue: NeurIPS_cc_2024_Conference
Published: 2024-04-25 20:57:34

Abstract: Text-driven image synthesis has made significant advancements with the development of diffusion models, transforming how visual content is generated from text prompts. Despite these advances, text-driven image editing, a key area in computer graphics, faces unique challenges. A major challenge is making simultaneous edits across multiple objects or attributes. Applying these methods sequentially for multi-attribute edits increases computational demands and efficiency losses. 
    In this paper, we address these challenges with significant contributions. Our main contribution is the development of ParallelEdits, a method that seamlessly manages simultaneous edits across multiple attributes. In contrast to previous approaches, ParallelEdits not only preserves the quality of single attribute edits but also significantly improves the performance of multitasking edits. This is achieved through innovative attention distribution mechanism and multi-branch design that operates across several processing heads. 
    Additionally, we introduce the PIE-Bench++ dataset, an expansion of the original PIE-Bench dataset, to better support evaluating image-editing tasks involving multiple objects and attributes simultaneously. This dataset is a benchmark for evaluating text-driven image editing methods in multifaceted scenarios.

Main paper: 
 introduction: 
 Recently, text-driven image editing has experienced remarkable growth, driven by advances in diffusion-based image generative models. This technique involves modifying existing images based on textual prompts to alter objects, their attributes, and the relationships among various objects. The latest methods [3, 1, 4] can produce edited images that closely match the semantic content described in the prompts while keeping the rest of the image unchanged. Unlike early image editing approaches that required image matting to precisely extract foreground objects using alpha mattes [5] , text-driven editing offers a less labor-intensive alternative. User-provided textual prompts guide the edits, with auxiliary inputs like masks facilitating localized modifications [6] .While these methods have showcased promising results, existing methods typically focus on editing a single aspect in the source image. An "aspect" refers to a specific attribute or entity within the textual prompt that describes the image and can be modified, such as object type, color, material, pose, or relationship. However, the ability to edit multiple aspects through text prompts is rarely explored. We introduce the concept of multi-aspect text-driven image editing to address this gap. Multi-aspect image editing is essential due to the rich and diverse content and structure of digital images, as well as the varied requirements of users. For example, it always occurs that users wish to modify multiple attributes or regions in an image, such as adding a necktie to a cat and changing the background wall to a beach (Fig. 1 , Left), or removing a man and replacing a mountain with a castle in the right example. Unlike traditional editing methods (e.g., [1, 2] ) that focus on a single aspect, multi-aspect editing allows users to manipulate various aspects simultaneously. Different from full text-to-image synthesis [7, 8] , which involves creating content from scratch, multi-aspect editing works with the source image to ensure essential content preservation. It bridges the gap between single-aspect editing and full synthesis, catering to a wide range of editing scenarios.However, we observe that directly applying the single-aspect text-driven image editing methods in cases where multiple image aspects must be modified often does not yield satisfactory results. A straightforward solution to this problem is to apply the single aspect editing method sequentiallywe can order the aspects to be modified and use a single-aspect editing method to change the aspects one by one. Although sequential applications of single-aspect text-driven image editing methods can modify multiple aspects of an image, they may introduce significantly higher computational overhead. More importantly, the order of the aspects modified may affect the quality -changes to later aspects may undo the early ones or accumulate the errors and artifacts, thus reducing the effectiveness of the final editing results, as the last two rows of Fig. 5 and Table 1 show.In this work, we introduce ParallelEdits as an efficient and effective solution to the problem of multi-aspect text-driven image editing. This method is based on a crucial insight that the editing step can occur in parallel with the image's diffusion steps. Therefore, in ParallelEdits, we build image aspect editing into the diffusion steps to accelerate the editing process. ParrallelEdits is based on an architecture with a fixed number of additional branches dedicated to handling rigid, non-rigid, and style changes. This design ensures scalability independent of the number of prompt aspects altered. In addition, we employ an attention aggregator to accurately assess editing difficulty and route aspects to appropriate branches within the ParallelEdits framework, ensuring precise and efficient editing. To enable subsequent research and evaluation of multi-aspect text-driven image editing methods, we also build the PIE-Bench++ dataset, an extension of the PIE-Bench [1] that has 700 images with detailed text prompts and tailored to facilitate simultaneous edits across multiple image aspects. We propose evaluation metrics and benchmark different text-driven image editing methods on PIE-Bench++. The ParallelEdits outperforms the state-of-the-art image editing methods on PIE-Bench++. 
related works: 
 Diffusion Models for Text-Driven Image Editing. Text-driven image editing aims to manipulate local regions of an image based on textual prompts. The editing has two main goals: ensuring the edits align with provided instructions and preserving essential content. Diffusion models [9] have gained popularity as a preferred image editing model for their capacity for generating high-quality samples by incorporating diverse conditions, especially using text [10, 11, 2, 12, 13, 14, 1] . This involves transforming the images into the latent space and generating regions using diffusion models conditioned by the text prompt while ensuring accurate reconstruction of unmodified regions during editing. To avoid the edited image deviating from original image, early text-driven image editing typically requires user-specified masks as additional condition [15, 16, 17] or training [18, 19, 20] to guided the editing process, which constrain their potential zero-shot application. To address this limitation, recent editing models, such as InfEdit [2] , PnP [21] , Direct Inversion [1] follow the work Prompt-to-Prompt (P2P) [3] , which proposed to obtain an attention map from the cross attention process and either swap or refine the attention map from text prompt for image editing. This design automatically obtains the editing mask and only allows image editing using a text prompt. Another method, MasaCtrl [4] , converts existing self-attention in diffusion models into mutual self-attention for non-rigid consistent image synthesis and editing, enabling to query correlated local contents and textures from source images for consistency.Multi-Aspect Image Editing. While current image editing models have shown promising results in their text-driven image editing benchmarks, we have observed that they work well on single-attribute editing while struggling to edit multiple aspects, especially when editing multiple objects (as shown in Fig. 1 ). We attribute this limitation to the following reasons. First, existing methods use the attention mask to direct where edits should be made. With multiple attributes, the editing area may expand significantly, incorporating extensive semantic information or scattered regions that are challenging to edit using a single mask. Second, employing a fixed mask from cross-attention maps struggles with edits involving changes in region size (such as pose adjustments), while using an adaptive mask faces challenges in maintaining edit fidelity. Therefore, integrating various attention masks for accurate multi-attribute editing presents a challenging technical problem. Early studies [22, 23] have employed GAN models such as StyleGAN2 [24] to edit multiple attributes in faces. The multiple-attribute editing is realized by training the GAN model with supervised multi-class training and a training dataset of image and attribute vector pairs. This solution heavily relies on the training sets and has limitations in generalizing to new editing types. Few recent works achieve multi-aspect editing with additional inputs: [25] leverages rich text to edit multiple objects and [26] pre-processes the image with grounding to localize multiple edited regions for multi-aspect editing. However, the editing performance highly relies on additional input beyond plain text, either from user input or other off-the-shelf models. A recent work [27] proposes an iterative multi-granular image editor, where a diffusion model can faithfully follow a series of image editing instructions from a user. However, this interactive editing pipeline will result in significant computational overhead.Image Editing with Multiple Branches. In the literature [4, 3] , image editing processes have been conducted by implementing a dual-branch approach. This methodology involves segregating source and target branches throughout the editing process. Specifically, the source branch is reverted to z 0 , while the trajectory of the target branch is iteratively adjusted. By computing the distance from the source branch, the calibration of the target branch occurs at each time-step. Our observation underscores the disparity between the effectiveness of a dual branch in enhancing the editing process and its failure in multi-aspect editing. A singular target branch proves inadequate in calibrating fully from the source branch, leading to imperfect incorporation of all aspects into the image. Hence, our primary proposition advocates for multi-aspect editing by utilizing multiple target branches. Each target branch's trajectory is meticulously calibrated, with simpler concepts addressed in the initial branches and more complex aspects deferred to subsequent ones. In the following section, we will delve deeper into this concept. 
diffusion-based image generation and editing: 
 We are provided with an image sample x 0 which transforms the latent space via an encoder/decoder pair E/D, such that z 0 = E(x 0 ). Here, z 0 represents the latent representation of the image x 0 . With a slight abuse of notation, we approximate the reconstructed image x0 as D(z 0 ), where z0 denotes the reconstructed version of z 0 . These operations are integral to the latent diffusion model [9] . The diffusion process constitutes two steps: the forward step incrementally adds zero-mean white Gaussian noise with time-varying variance to the latent vector z according to discrete-time t * ,EQUATIONα 1:T represents a variance schedule for t drawn from the interval [1, T ]. The variance schedule can be different, such as linear or cosine quadratic [28] . The backward step is an iterative process to remove the noise from the data progressively. Using the same variance schedule α 1:T as in the forward step, a noise schedule σ 1:T and a parameterized noise prediction network ϵ θ with coefficients c pred = √ α t-1 ,c dir = 1 -α t-1 -σ 2 t, and c noise = σ t , the backward step corresponds to the following process:EQUATIONThe noise schedule σ 1:T comprises hyperparameters requiring careful selection based on factors like image dimensions or desired performance [29][30] . In the framework of Denoising Diffusion Implicit Models (DDIM) [31] , the function f θ is employed for the prediction and reconstruction of z0 , based on the input z t . Specifically, we havez0 = f θ (z t , t) = 1 √ αt z t - √ 1-αt √ αt ϵ θ (z t , t).Consistency Models for Inversion-free Image Editing. Consistency models [32, 33] have been introduced to expedite the generation process through a consistent distillation approach. These models exhibit a self-consistency property, ensuring that samples along the same trajectory map to the same initial point. Specifically, the function f θ is rendered self-consistent by satisfying f θ (z t , t) = z 0 for a given sample z t at timestep t. As a result, the self-consistency property yields a closed-form solution for the noise predictor ϵ θ . We denote this particular ϵ θ as ϵ cons , which is derived asϵ cons = zt- √ αtz0 √ 1-αt .Since ϵ cons is not parameterized and contains the ground-truth z 0 , Xu et al. [2] propose starting directly with random noise, i.e., z T ∼ N (0, I), at the last time-step T , which is particularly advantageous for image-editing tasks as it eliminates the need for inversion from z 0 to z T . Therefore, starting with z τ = z T ∼ N (0, I), the sampling process proceeds as follows:1 z = zτ - √ 1-ατ ϵ cons τ √ ατ. Where, ϵ cons τ is given byzτ - √ αtz0 √ 1-αt 2 Noise is added to z τ , i.e, z τ = √ α τ z + √ 1 -α τ ϵ where ϵ ∼ N (0, I)After many iterations, the final output is z. Furthermore, [2] demonstrates that the dual-branch paradigm (involving a source and a target branch) used in image editing tasks can be executed in an inversion-free manner. We will delve into this, along with our method description, in Section 4.2.2.4 Multi-Aspect Image Editing 
problem definition: 
 The input to the multi-aspect image editing task includes a source image (I src ), the source prompt, and a set of edits to be applied to the source image, indicating the changes from the source prompt to target prompt. A text prompt (whether source or target) comprises several independent tokens, of which only a subset is editable. We refer to these editable tokens as Aspects. Definition 4.1 (Aspect). We define an i th aspect A i src in the source prompt (or the j th aspect A j edt in the target prompt) as any entity that can be substituted, deleted, or inserted into the text prompt, resulting in a meaningful sentence structure.Several examples of tokens corresponding to aspects or not are given in Fig. 3 . In other words, aspects correspond to single or multiple tokens representing object color, pose, material, content, background, image style, etc. An editing operation E i→j between the editing pair (A i src , A j edt ) as E i→j ∈ {⊗, ⊕, ⊖, ⊘}. Here, ⊗ denotes a swap action, ⊕ denotes an object addition action, ⊖ denotes object deletion, and ⊘ indicates no change in the aspect. Such an editing operation can be inferred directly by appropriately mapping the source and target prompts, or it can be provided as metadata [3, 34] . The editing task is considered successful if the edited source image, I edt , reflects the required edits while preserving the unaffected aspects of the original image.Figure 2 outlines the overall pipeline of our method, which has three steps. In the first step (Sec. 4.2.1), we perform aspect grouping using attention maps generated by running a few iterations of the diffusion process. The aspects in the source image are put into up to N groups, each processed by a distinct branch. The second step (Sec. 4.2.2) demonstrates how each branch, which receives a specific group of aspects, performs inversion-free editing. In the last step (Sec. 4.2.3), we perform the necessary adjustments for enabling cross-branch interaction and elucidate the significance of such interaction. Source prompt: a man sitting in a boat is silhouetted against the sunset with mountain in the background Target prompt: a man sitting in a sailboat is silhouetted against the evening glow with ducks on the water and bridge in the background Source BranchGlobal Editing Q K V K Q V Q K V K Q Q K V Q K V Edited image Q K VAttention maps Branch 3Self AttentionQ K V Q K V Cross Attention bridge man man Branch 4 K Q Q K V V V Q K V Q K saiboat K Q Rigid Editing K V bridge Branch 1 Branch 2 Branch 5Figure 2 : Pipeline. Our method, ParallelEdits, takes a source image, source prompt, and target prompt as input and produces an edited image. The target prompt specifies the edits needed in the source image. Attention maps for all edited aspects are first collected. Aspect Grouping (see Section 4.2.1) categorizes each aspect into one of N groups (in the above figure, N = 5). Each group is then assigned a branch and the branch-level updates are detailed in Section 4.2.2. Each branch can be viewed either as a rigid editing branch, non-rigid editing branch, or global editing branch. Finally, adjustments to query/key/value at the self-attention and cross-attention layers are made, as illustrated in the figure and described in Section 4.2.3. In a text prompt, there are multiple independent tokens, with only some being editable, known as aspects and are underlined in the above example. These aspects can be added, deleted, or swapped between the source and target prompts. Pairs of source and target aspects are grouped into branches, and the methodology for aspect grouping is explained in Section 4.2.1.We would like to group aspects in a prompt into N distinct groups using the cross-attention maps of the diffusion UNet [35] to characterize the spatial layouts as in previous studies [36] . Given an editing operation E i→j between the source aspect A i src and the target aspect A j edt , we obtain the corresponding attention maps from both the source and target prompts as Mi src and Mj edt , respectively. The attention map M is defined by the query feature Qand key feature K from the cross-attention asM = softmax Q KT √ d. The binarized attention map M is obtained by normalizing M and thresholding its values. Our aspect grouping proceeds in two steps,Step 1. Assign a type for every editing operation (E i→j ). We consider three possible types of edits, in line with previous works [4] , EQUATIONHere, ϕ represent mIoU [37] , while γ indicates the alpha mattes of attention maps. λ and β are tunable hyperparameters. For further details, please refer to the supplementary Sec. D.Step 2. Categorize every editing operation (E i→j ) into N groups. For each editing operation (E i→j ) of a specific type, we assess whether ϕ( Mj edt , Mk edt ) ≥ λ to determine if there exists substantial overlap between any pair of attention maps of that type. If significant overlap is detected, the attention maps are grouped together. On the other hand, if attention maps are isolated like the "boat" and "mountain" in Fig. 3 are categorized into separate groups due to small overall. Therefore, we have a total of N groups. Each group has a dedicated branch, resulting in a total of N > 2 branches.We use a set of N branches indexed by n. The source branch undergoes a DDCM sampling process [2] , but the n th target branch is calibrated to its (n -1) th target branch. The N -way target branch calibration can occur simultaneously, saving significant compute time. For the DDCM sampling process of the n th branch, it has the form of Section 3, Step 1 :EQUATIONedited latent noisy latent parameterized noise consistency noiseLet us break down Eq. 4 step by step. n = 1 representing the source branch, we have z(1) edt = z src and ϵ( 1)edt τ = ϵ src τ . Also, z(1) edt τ = z src τ , which at time step τ = τ 1 , is random noise drawn from N (0, I). Similarly, when n = N , z(N )edt represents the final calibrated/edited image containing all the required aspect edits after repeating for τ ∈ {τ 1 , τ 2 , . . . τ T } timesteps. The noise addition on any target branch remains the same as Step 2 , i.e., z(n) edt τ = √ α τ z(n) edt + √ 1 -α τ ϵ where ϵ ∼ N (0, I). For 1 < n < N , we have ϵ(n) edt τ = ϵ θ (z(n) edt τ , τ ), where ϵ θ represents a parameterized noise predictor network (details in the Appendix Sec. D). A key observation is that the difference in the parameterized noise at the n th branch and (n -1) th branch is utilized to calculate z(n) edt in (4). Finally, ϵ(n) cons τ is defined by ϵ(n) cons τ = (z(n) edt τ - √ α τ ẑ(n -1) edt )/ √ 1 -α τ .Unlike the dual-branch setup in [2] , the reference initial input is the estimated latent from the previous branch at a previous diffusion denoising iteration as indicated by ẑ(n -1) edt .For rigid local branches, the cross-attention map M i n from the previous branch is either switched or injected into the current branch, akin to the method used in P2P [3] . This approach facilitates local edits while preserving structural consistency. For non-rigid local branches, we observe that the query features in the shallow layers of UNet [35] can effectively query correlated local contents and textures from the prior branch's latent features, ensuring consistency. Consequently, the key and value features from the prior branch are retained in the current branch to maintain consistent editing. We use a non-rigid editing branch to manage non-rigid local edits. In the current branch n, textures from the previous branch (n -1) are preserved by replacing the K n-1 and V n-1 features from the last branch with the K n and V n features in the current branch. Only the query features are preserved to maintain layout semantic correspondence. Additionally, the attention mask M n-1 from the previous branch's cross-attention layer is used to guide the editing process by adding it to M n , thereby converting the object layout from M n-1 to M n . This step is crucial for object removal or shape modification edits, where the object mask is derived from the previous branch. For all global branches, there is no replacement of attention features or masks, and the attention mask is not used to guide the editing process, as the entire image is intended to be altered. 
experiments: 
 PIE-Bench++ Dataset. We introduce a new dataset, PIE-Bench++, derived from PIE-Bench [1] and dedicated to evaluate the performance of multi-aspect image editing. The PIE-Bench dataset contains 700 images and prompts with single-aspect editing including object-level manipulations (addition, deletion, or alteration), attribute-level manipulations (changes in content, pose, color, and material), and image-level manipulations that modify background and overall style. Our PIE-Bench++ extends PIE-Bench by enabling multi-aspect edits: 57% of our dataset have two aspect edits per prompt, 19% have more than two edits, and the remaining 24% have a signle aspect edit. For additional details and examples of the PIE-Bench++ dataset, please refer to the supplementary material.Evaluation Metrics. We introduce two new metrics designed for evaluating multi-aspect text-driven image editing, alongside standard evaluation metrics.(a) Aspect Accuracy-LLaVA. Drawing inspiration from the remarkable capability of large vision language models in comprehending intricate semantics within images, we propose to innovatively leverage them as an "omniscient" agent equipped with extensive knowledge to understand various attributes of images. We use the LLaVA [38] model, trained on visual grounding tasks, to evaluate the accuracy of multi-aspect image editing. Given a text prompt with multiple aspects, such as "A on top? Return a list of numbers where 1 is matched and 0 is unmatched." We then parse the returned list and compute its average to determine the aspect accuracy. We name this new evaluation metric as AspAcc-LLaVA. Examples and detailed explanations of this evaluation metric are available in the supplementary material.(b) Aspect Accuracy-CLIP. We also use the similarity of the CLIP [39] to evaluate if an attribute has been successfully edited. Given an edited image I edt and the target prompt P edt with k edited aspects A edt , every time we remove an aspect A j edt from P edt and revert it back to A i src as Pedt . We then extract the CLIP [39] similarity between the edited image I edt and two prompts, i.e., s 1 = CLIP (I edt , P edt ) and s 2 = CLIP (I edt , Pedt ). We expect s 1 > s 2 if the aspect A j edt has been successfully edited. Thus, the aspect accuracy is ks k when a total of k s aspects have been successfully edited among k target edits. Note that in the case of an edited or added object that also involves changes in attributes (such as color or material), we consider it a successful edit only if both the object and its attributes have been successfully modified. We name this metric as AspAcc-CLIP.(c) Standard Metrics. Several standard metrics widely used for evaluating text-image similarity and image quality are considered, including PSNR, LPIPS [40] , MSE, and SSIM [41] . We also use the CLIP [39] score to measure the image-text alignment performance. Additionally, the bi-directional CLIP (D-CLIP) score [42] is reported, which is formulated as follows:cos⟨CLIP img (I edt ) -CLIP img (I src ), CLIP text (P edt ) -CLIP text (P src )⟩We first conduct experiment on the PIE-Bench++ dataset to compare our method with the state-of-theart text-driven image editing methods combining their corresponding inversion method leads to best performance, including DDIM+MasaCtrl [4] , DDIM+Prompt-to-Prompt (P2P) [3] , DDIM+Plug-and-Play (PnP) [21] , StyleDiffusion (StyleD) [43] +P2P, Null-text Inversion (NTI) [34] +P2P, DirectInveri- son (DI) [1] +PnP, and InfEdit [2] . An intuitive way to improve off-the-shelf image editing methods is to apply the single-aspect editing method sequentially. We follow [27] to adapt existing image editing methods into sequential editing processes, where these methods are applied multiple times to achieve multi-aspect editing. Each time, only one aspect is edited. Table 1 presents the metrics in terms of text-image similarity (i.e., CILP and D-CLIP scores), computational efficiency, and aspect accuracy. Our ParallelEdits model outperforms all baselines in editing effectiveness, with a slightly longer runtime than the InfEdit model. Even though sequential editing better aligns the target prompt than their vanilla methods, it significantly increases computational overhead and may propagate editing errors over time. Moreover, although the sequential editing is conducted in the latent space, it would introduce more noise and artifacts to the edited image. Hence, their performance in all editing quality metrics was inferior to our method.Fig. 4 presents several examples of our method's multi-aspect editing on the PIE-Bench++ dataset.The results demonstrate the effectiveness of our method in handling multiple and varied types of edits across diverse image content. Fig. 5 further compares our method with several state-of-the-art models and one popular multi-modal large language model, GPT-4V [44] , by providing the source image, source prompt, and target prompt to guide the image editing. The Rich-text [25] model differs from other models, which uses rich-text prompt to edit the image generated from the plain (source) text prompt. The results show that current image editing models even with sequential editing fail to edit multiple aspects, while multi-modal large language models fail to preserve the content of source image. Our method achieves visually convincing results by successfully editing different attributes with good content preservation.(a) Impact of Editing Aspect Number. We first examine the performance of our ParallelEdits and baseline methods on various editing aspect numbers by comparing CLIP and LLaVA-based aspect accuracies on the original PIE-Bench [1] and our PIE-Bench++ datasets. The bar charts in Fig. 6 show the outstanding performance of our method across all settings, including single-aspect editing on two datasets and multi-aspect editing. Takeaway: the proposed ParallelEdits demonstrates robustness across varying numbers of editing aspects.(b) Evaluation on Perservation. We follow [1] to evaluate the background preservation. We first use the PSNR, LPIPS [40] , MSE and SSIM [41] to evaluate the background preservation. We measure that metric on a subset of images of our proposed PIE-Bench++ dataset where the background can be well defined in that image, e.g., no image style or background editing, and the background is visible after aspect editing. The results are shown in Table 2 , where we compare our method with the top performance methods in Table 1 . Moreover, we adopt the similar way as calculating the AspAcc-LLaVA to prompt LLaVA [38] for evaluating how the unchanged aspect preserves in the edited image. We also calculate the CLIP [39] score between the target image and the text prompt after removing all edited aspects. The results are reported in Table 2 noted as CLIP and LLaVA, respectively. Takeaway: preservation is even maintained in ParallelEdits.(c) Branches numbers and aspect grouping. To demonstrate the effectiveness of our multi-branch design and early aspect grouping, we design additional ablation studies for our method in threefold. (1) We only use one single non-rigid branch to conduct all edits; (2) we remove the aspect categorization process from the pipeline and use the same non-rigid branch for each edit; (3) we adopt one single branch for different type of edits without using any auxillary branches which results a total of three branches (also see Section B for more details). Takeaway: As shown in Table 3 , the multi-branch design and aspect grouping play a significant role in enhancing the performance of our proposed ParallelEdits. (d) Performance comparison on each category. Recall that our dataset includes nine different categories for editing. We compare the performance of baseline models and our approach across the nine categories, as presented in Table 4 . Takeaway: Our proposed ParallelEdits achieves state-of-theart performance across most categories.  
conclusion: 
 In this work, we propose a new research task, multi-aspect text-driven image editing, to modify multiple object types, attributes, and relationships. We introduce a dedicated method, ParallelEdits, to multi-aspect text-driven image editing as an effective and efficient solution to this problem. Due to the lack of evaluation benchmark, we introduce PIE-Bench++, an improved version of PIE-Bench [1] tailored for simultaneous multiple-aspect edits within images. ParallelEdits achieves better quality and performance than existing methods on proposed PIE-Bench++. Our work introduces ParallelEdits, a novel approach that adeptly handles multiple attribute edits simultaneously, preserving the quality of edits across single and multiple attributes through a unique attention grouping mechanism without adding computational complexity. There are several future works we would like to explore. First, different aspects of an image have a specific semantic order. Editing these aspects according to their intrinsic order will simplify the editing process. Secondly, the current ParallelEdits still has limitations, as shown in Fig. 4 . It will be of interest to study approaches to improve these aspects.Ethics Statement. In anticipation of contributing to the academic community, we plan to make the dataset and associated code publicly available for research. Nonetheless, we acknowledge the potential for misuse, particularly by those aiming to generate misinformation using our methodology. We will release our code under an open-source license with explicit stipulations to mitigate this risk. These conditions will prohibit the distribution of harmful, offensive, or dehumanizing content or negatively representing individuals, their environments, cultures, religions, and so forth through the use of our model weights. 
safeguards: 
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 Appendix: In this section we provide Algorithm 1: Early Aspect Grouping and Algorithm 2: ParallelEdits on a particular branch. These algorithms describe the overall idea behind ParallelEdits. They are also pictorially illustrated in Figures 2 and 3 of the main paper. Let us denote an arbitrary branch and the timestep in the diffusion process by n and t respectively. Firstly, in Algorithm 1, we demonstrate how Early Aspect Grouping is conducted over the attention maps. Recall that we refer to this as "early" aspect grouping because only a few steps (maximum of 5) are sufficient to perform the grouping. This phase of ParallelEdits takes as an input, the edit action set {E i→j } and the corresponding cross-attention maps for every token A j src , and outputs the grouped edit actions set Āc edt . Recall from Section 4 of the paper that E i→j ∈ {⊗, ⊕, ⊖, ⊘}, with ⊗ denoting a swap action, ⊕ denoting an add action, ⊖ denoting aspect deletion, and ⊘ indicating no change in the aspect. Once grouped edit actions set is computed, it is fed into Algorithm 1 to conduct multi-aspect editing and obtain the edited latent features. In Algorithm 2, we implement several operations on the attention masks, similar to the P2P method [3] , and describe them as follows.Replace: Swapping token attention mask M n-1 in the prompt from previous branch, overriding M n ;Refine: Injecting only the attention mask that corresponds to the unchanged part of the prompt from M n-1 to M n ;Retain: Keeping the attention mask M n unchanged. In the literature [4, 3] , image editing processes have been conducted through the implementation of a dual-branch approach. This method involves utilizing a source and target branches for editing. {Q n , K n , V n } ← {Q n , K n-1 , V n-1 } 8: refine(M n-1 , M n ) 9: end if 10: Mn ← binarize( m≤n m=0 M m ) 11: z t n ← Mn ⊙ z t n + (1 -Mn ) ⊙ z t n-1Output: Latent feature z t n Specifically, the source branch is reverted to z 0 , while the trajectory of the target branch is iteratively adjusted. By computing the distance from the source branch and ϵ cons with Latent Consistency Model [32] , the target branch is calibrated at each time step. Our experiments, as seen in Section 5 of the main paper, show the ineffectiveness of a dual-branch procedure for multi-aspect editing tasks. Specifically, a single target branch is inadequate, leading to imperfection in the target image. Thereby we advocate multi-aspect editing through the use of multiple target branches. Each target branch handles a group of aspects, with simpler aspects such as non-rigid local edits directed to initial branches, and more complex aspects such as rigid local edits deferred to subsequent ones. Note that however, all the branches operate simultaneously.Auxiliary Rigid / Non-Rigid Branches. In the main paper, it was noted that there was one dedicated branch for each type of edit: non-rigid, rigid, and global edit. The Early Aspect Grouping algorithm 1 classifies aspects into these three categories. Our experiments revealed that sometimes, due to low overlap between attention maps, aspects may not always be grouped into dedicated rigid or non-rigid branches. In such cases, it becomes necessary to include an auxiliary branch to handle the ungrouped aspects. Therefore, ParallelEdits may involve a single rigid branch and additional auxiliary branches to manage ungrouped aspects, and similarly, a single non-rigid branch and supplementary auxiliary branches to address ungrouped aspects. An ablation study on auxiliary branches is provided in Table 3 .In this section, we describe more details of our evaluation metrics.LLaVA aspect accuracy. We show how we leverage LLaVA [38] to evaluate the multi-aspect editing accuracy in Fig. 7 and Fig. 8 , we also prompt LLaVA [38] for explanation to human readers. LLaVA [38] could provide detailed summary for the image and also explanations for the mismatched between edited aspects and image.Other evaluation metrics. Moreover, even though the Structure Distance [46] has been used in PIE-Bench [1] to evaluate the structure between source and target image while ignoring appearance information, it could not serve as a good evaluation metric for multi-aspect editing. This is due to the fact that the structure of multi-aspect edited target image may necessitate substantial modifications, particularly when it involves adding or removing multiple objects.Does this image match the elements in []: "[money tree plant] and [word 'Hello, World!'] is written on the notebook on [black table]" Exactly follow the return format as a list where 1 is matched and 0 is unmatched, return list only.The list length is equal to 3.[1,0,0]Please provide some explanation.Certainly! Here's an explanation for the elements in the image and their match with the elements in the provided format:1, "[money tree plant]" -The image shows a plant that is commonly known as a money tree. It is a type of bonsai tree with a twisted trunk and glossy green leaves. So, this element is matched.2, "[word 'Hello, World!'] is written on the notebook" -The notebook in the image has the word "Hello, World!" written on it. However, the format specifies that the word should be written on the notebook, which is not the case here. The word is actually written on a whiteboard or a similar surface that is placed in front of the notebook. Therefore, this element is unmatched.]" -The table in the image is not black; it is a light color, possibly gray or beige. So, this element is unmatched.Therefore, the list of matches would be [1, 0, 0].this image match the elements in []: "a logo of [X] [with pink color] in a [light background]" Exactly follow the return format as a list where 1 is matched and 0 is unmatched, return list only. The list length is equal to 3.[1,1,0] Please provide some explanation.Certainly! The image you've provided features a logo that is composed of an X shape, which matches the first element in the description. The X is colored in a shade of pink, which matches the second element in the description. However, the background of the image is not light; it is dark, which does not match the third element in the description. Therefore, the list of matches would be: 1, Logo is an X -Matched 2, X is pink -Matched 3,Our proposed ParallelEdits is based on the Latent Consistency Model [32] , with the publicly available LCM † which is finetuned from Stable Diffusion v1.5. We then follow [2] to leverage their proposed inversion-free technique in ParallelEdits for image editing. During sampling, we perform LCM sampling [32] with 15 denoising steps, and the classifier-free guidance (CFG) is set to 4.0. Paral-lelEdits can control the editing strength by adjusting the CFG . There's a trade-off between achieving satisfactory inversion and robust editing ability. A higher CFG tends to produce stronger editing effects but may lower inversion results and identity preservation. We also set the hyper-parameter θ as 0.9 and β as 0.8 in our experiments, where θ, β are used to determine the edit type of a given edit action.In the inversion-free multi-branch editing approach, for 1 < n < N , the noise estimation is also conditioned on a text conditioning c n in branch n. This can be expressed as ϵ(n) Here, c 1 corresponds to the source prompt, c N corresponds to the target prompt, and c n represents the prompt that includes all aspect edits up to branch n.edt τ = ϵ θ (z(n) edt τ , τ, c n ).Unlike existing benchmarks that primarily focus on single-aspect edits, PIE-Bench++ is tailored to multiple aspect edits, reflecting the complexities inherent in real-world editing tasks. Our enhanced dataset, PIE-Bench++, builds upon the PIE-Bench [1] by incorporating 700 images across nine diverse categories, covering both natural and artificial scenes, with a significant focus on multi-aspect editing scenarios. Specifically, the Change Object category involves swapping objects in the scene with different yet reasonable alternatives. Add Object adds new elements to the scene. Delete Object focuses on removing objects, testing the model's ability to erase elements seamlessly. Change Object Content alters the content of specific objects, such as changing the design on a shirt or the pattern on a wall. Change Object Pose includes changes in the shape of objects, humans, or animals. Change Object Color assesses the model's ability to apply accurate color changes. Change Object Material evaluates the rendering of different textures and materials. Change Background involves editing scenarios where there is a distinct foreground object and a main background. This type of edit focuses on seamlessly integrating new background elements while preserving the integrity of the foreground object. Change Image Style involves the application of style transfer techniques to the entire image while ensuring the original content remains intact. For example, this could involve transforming a photograph to adopt a cartoon style. Each category is carefully curated to provide a comprehensive evaluation of the dataset's multi-aspect editing capabilities, the summary of the dataset is shown in Table 5 .The annotation process involves a primary annotator who labels the source prompt, describing the original image, and the target prompt, which outlines the desired modifications to generate the target image. The target prompt is carefully annotated to include all editing pairs expected to be reflected in the target image. Subsequently, a second annotator reviews the annotations for accuracy and consistency, ensuring the reliability of the dataset. The majority of target prompts in PIE-Bench++ feature at least two edited aspects. Nevertheless, within the categories that solely changing background and image styles, the number of edits is usually constrained to one or two aspects. This limitation is due to the intrinsic characteristics of these attributes, such as each image having only one background or style.Annotation format details. Each image in the dataset annotation is associated with key elements as shown in Fig. 9 : a source prompt, a target prompt, an edit action, and a mapping of aspects. The edit action specifies the position index in the source prompt where changes are to be made, the type of edit to be applied, and the operation required to achieve the desired outcome. The aspect mapping connects objects undergoing editing to their respective modified attributes, enabling the identification of which objects are subject to editing.We also provide more qualitative results in Fig. 10 , showing the effectiveness of our proposed method in handling multi-aspect editing tasks. These examples showcase the model's proficiency in executing intricate edits. For instance, as depicted in Fig. 10 (b), our method successfully removes a cup while accurately reconstructing the obscured parts of the lamp behind it. In Fig. 10 (a), the model demonstrates its ability to swap and add aspects, while preserving the composition of the scene.The results underscore the model's adeptness in interpreting and executing sophisticated editing instructions, leading to visually consistent and contextually fitting edited images. Additional, we also provide the results for sequential editing methods with different editing order in Answer: [Yes] Justification: The code and data will be open-sourced for academic use. Guidelines:• The answer NA means that paper does not include experiments requiring code. Guidelines:• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact. • Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations. • The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).† https://huggingface.co/SimianLuo/LCMDreamshaperv7 


Decision: This submission received two weak accept and one borderline accept. This paper aims to make multiple objects or attributes editing, Reviewer 8ae7 and Reviewer J7sk agreed that simultaneously editing multiple objects or attributes is interesting and practical. Reviewer 6th7 recognized the novelty of the method. The editing results are impressive. 
The reviewers raised some concerns like unclear method details and gave some useful suggestions. The authors well addressed reviewers' concerns. Given the strong recommendation from Reviewer 8ae7 & 6th7 and borderline accept recommendation from Reviewer J7sk, the AC recommends this paper for acceptance.

Reviews: review: paper_summary: paper_summary: In this paper, the authors present a novel multi-aspect image editing method ParallelEdits, by incorporating the attention distribution mechanism and multi-branch editing. Besides, this paper introduces a new dataset PIE-Bench++ for evaluating multi-aspect image editing. Extensive experiments demonstrate the effectiveness of this method.
strength_weakness: strength_weakness: 1. The pairing process of $E^{i \rightarrow j}$ imposes an additional burden on users, especially when multiple objects are present in the source image.
2.  It is not clear how to choose the hyperparameter $\lambda$ for assigning a type for rigid and non-rigid edit actions, and how it affects the cross-branch interaction and editing results.
3. This paper lacks a discussion on controlling the editing strength of multiple editing actions, which is crucial for harmonic and flexible editing.
questions: questions: See the weakness part above.
limitations: limitations: The author has discussed the limitations and potential negative societal impacts.
paper_summary: paper_summary: The paper introduces ParallelEdits, a method that manages simultaneous edits efficiently without compromising quality. This is achieved through a novel attention distribution mechanism and multi-branch design. Additionally, the authors present the PIE-Bench++ dataset, an expanded benchmark for evaluating multi-object and multi-attribute image-editing tasks.
strength_weakness: strength_weakness: 1. The authors claim the proposed method is based on the DDCM process [1]. However, when I checked the source paper of [1], I found something strange, especially its Algorithm 1. The dozens of iterations come to a simple conclusion that the output $z$ actually equals $z_0$, which means there is no need for such Virtual Inversion. Also, this process cannot be interpreted by Consistency Models. Hence, I wish the authors could revise their theoretical bases.

2. I think it is better to present the intermediate editing results for sequential editing, which can help readers identify what type of editing the previous methods are not good at.

3. I noticed that there was a benchmark [2] proposed for image editing. I advise the authors to use it for comprehensive evaluation.

[1] Inversion-Free Image Editing with Natural Language (https://arxiv.org/abs/2312.04965)

[2] Diffusion-Model Based Image Editing: A Survey (https://arxiv.org/abs/2402.17525)
questions: questions: I noticed that the man has changed after editing in Fig. 2. Could you please explain and address it?
limitations: limitations: See above.
paper_summary: paper_summary: This paper aims to make multiple objects or attributes editing, while preserving the quality, named ParallelEdits. The paper also introduces a new dataset PIE-Bench++. Experimental results on both PIE-Bench and PIE-Bench++ demonstrate that this method outperforms many existing editing techniques.
strength_weakness: strength_weakness: 1.	Direction Inversion is solely a method for inverting real images, not for editing them. Figure 1 is confusing in this context. Direction Inversion can be combined with various editing methods like P2P, PnP, P2P-zero, and MasaCtrl. Which editing method is Direction Inversion combined with in the third row of results? Why is this particular editing method shown?
2.	Same problem in the description in the introduction (Line 22, latest methods [3, 1, 4]; Line36, Unlike traditional editing methods (e.g., [1, 2])). [1] (Direction Inversion) is not a editing method.
3.	The paper claims that ParalleEdits can edit multiple objects or attributes simultaneously. Figure 1. illustrates the swapping of multiple objects within the same image, but it only demonstrates the addition of a single object. Can ParalleEdits add multiple objects within a single image, such as both a necktie and sunglasses to a cat?
4.	In aspect grouping, the paper does not explain how to get the attention map M for a real image.
5.	In Figure 2, why does the attention map from the original image combine with the target prompt.
6.	This paper divides aspect grouping based on cross-attention. What if cross-attention is inaccurate? For example, in Figure 2, will each image clearly show a "ducks" cross-attention map when "ducks" are added to the image?
7.	In Table 1, P2P is an editing method for generated images. When applying it to real images (PIE-Bench++ dataset), it needs to be combined with an inversion technique (i.e., NTI, DI). Need to explain.
8.	The finding that the order in which aspects are modified when applying single-aspect text-driven image editing methods can affect the quality is interesting. However, the paper does not conduct qualitative and quantitative experiments to support this claim.

Further comments:
+ Line 119, should z be z_0 ?
+ Line 135, Xu et al. [3] seems not to use \cite{} to link the reference?
+ Line148~Line149, the definition of tokens is not clear for me. Do the tokens represent all nouns and adjectives in a text prompt?
questions: questions: Does StyleDiffusion (StyleD) refer to “Controllable disentangled style transfer via diffusion models” or “Prompt-Embedding Inversion for Text-Based Editing” ? If it is the former, please explain why this paper is compared with style transfer methods. If it is the latter, please correct it.

Simultaneously editing multiple objects or attributes is interesting. But some aspects of the method are not adequately explained (e.g., how to get the attention-map M), and some references within the paper are quite casual (e.g., DirectInversion and StyleDiffusion). 

The weaknesses low my initial rate.

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**Update after author responses** 
Thanks to the authors for their efforts. I'm happy to see the extended discussion and additional comparisons that have resolved my concerns.
limitations: limitations: Yes

Comments: title: Thanks for the feedback
Thanks for the reply!
Additional clarifications
Additional results and clarifications
Response
comment: We thank Reviewer J7sk for the positive and constructive feedback. As the rebuttal period is ending, we wanted to check if there are any remaining questions. We have provided additional intermediate results on the baseline method (sequential editing) as requested and evaluated our approach on a different benchmark, where our method still outperforms the state-of-the-art. We appreciate the suggestion and will release PIE-Bench++ as a benchmark for multi-aspect editing. We are glad to address any further questions or suggestions from the reviewer that could help enhance our manuscript.
We appreciate the reviewer’s constructive and positive feedback on our paper and responses and happy to see the reviewer find our responses clear. If satisfied, we respectfully ask if they would consider raising the final rating of our paper.
Thanks for authors' reply. It is more clear for me.
We thank the reviewer for additional questions and helpful feedback.

***Q1: How to collect the pairing information for PIE-Bench++?***

***A1:*** All editing pairs have been manually annotated to establish editing correspondence like PIE-Bench. To build a robust dataset for community use, annotators manually labeled 700 image prompts. 

***Q2: Need further explain which edits are rigid edit and which are non-rigid in Fig.2 of the PDF appended and how they change the outputs in Fig.2?***

***A2:*** In Fig. 2 of the rebuttal PDF, the "harness" is always non-rigid, whereas the "horse" and "field" are rigid edits. According to line 194 of the main paper, edits with an overlap greater than $\lambda$ should be put into the same branch if they have the same editing type. In Fig. 2, decreasing $\lambda$ to 0.8 groups "horse" and "field" into a single rigid edit branch, yielding slightly different results.


***Q3: How to control the editing strength for ParallelEdits?***

***A3:*** ParallelEdits can control the editing strength by adjusting the CFG (Classifier-Free Guidance). As Reviewer 8ae7 also noted, there's a trade-off between achieving satisfactory inversion and robust editing ability. A higher CFG tends to produce stronger editing effects but may lower inversion results and identity preservation. Currently, we have set a fixed CFG of 4.0 for our evaluations, as detailed in Section E, Implementation Details, in the Appendix.
We are glad that the reviewer finds our rebuttal helpful. The reviewer's suggestions have been valuable, and we welcome any further input. 

***Q1: The authors should specify which method was used, DDIM inversion or naive DDPM inversion with mask.***

***A1:*** Our paper employs DDIM for inversion and P2P for editing in the P2P baseline, a combination that does NOT require masks, aligning with findings from previous research [1,2]. Following the reviewer's suggestions, we will include this discussion about mask-guided inversion into the paper. Note that, mask guidance uses a naive DDPM inversion by simply adding noise then denoising. However, mask-guided inversion P2P+DDPM cannot be used for non-rigid edits (pose/layout change) and global edits (style changes), and does not ensure identity preservation, often resulting in significant changes to the image content (please refer to the Introduction and Related Work Sections of [4] and Related work Section of [3]).


***Q2: Comparing the inversion method in P2P with methods specifically designed for real image inversion is not fair, as P2P typically combines other inversion methods when editing real images.***

***A2:*** Comparing the inversion method in P2P with others is only a part of our overall analysis. We have compared with P2P with other real image inversion method like StyleD and NTI, also other editing techniques such as PnP, MasaCtrl, and InfEdit. Regardless of the inversion or editing method of choice, we observe that ParallelEdits consistently outperforms benchmarks in aspect accuracy, aspect preservation, and CLIP/D-CLIP scores. Although we could explore more detailed comparisons across various baselines, it is important to note that current prevailing techniques in the research community primarily focus on single-aspect editing. Our research highlights the necessity to transition towards multi-aspect editing.


***Q3: In ParallelEdits, which CFG is used in DDIM inversion? Using a CFG of 1 typically results in satisfactory inversion of a real image but may reduce editing ability. Conversely, a CFG of 7.5 often leads to reconstruction failure.***

***A3:*** Great question! Adjusting the CFG (Classifier-Free Guidance) can influence editing quality and image/identity preservation. In our experiments, we used a moderate CFG setting of 4.0 to balance aspect accuracy, preservation metrics, and editing quality. For more information on hyper-parameters, please refer to Section E, Implementation Details, in the Appendix.

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[1] Inversion-Free Image Editing with Natural Language, Xu et al, CVPR24'

[2] Boosting Diffusion-based Editing with 3 Lines of Code, Ju et al, ICLR24'

[3] An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion, Gal et al, ICLR23'

[4] MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing, Cao et al, ICCV23'
Thanks for authors' rebuttal. There remain some questions regarding the paper and rebuttal.

- How did you collect the pairing information for PIE-Bench++?
- The hyperparameter $\lambda$ is the threshold for determining whether an edit is classified as a rigid edit or a non-rigid edit. Could you further explain which edits are rigid edit and which are non-rigid in Fig.2 of the PDF appended and how they change the outputs in Fig.2?
- Can you control editing strength for ParallelEdits? If yes, how do you control it? If no, does it mean the editing result for a given pair prompt is fixed?
I would like to thank the authors for the extensive and detailed rebuttal, it has been very helpful. There remain some questions that would be helpful of this paper.

**Q2. Applying DDIM Inversion for real image inversion.**

In the P2P paper, the authors found that the inversion is not sufficiently accurate in many cases, or that using a mask yields a more accurate inversion. The authors should specify which method was used and provide detailed explanations in the paper. Real image inversion and editing represent only a small portion of the P2P paper, and thus, are not the primary focus of P2P. Comparing the inversion method in P2P with methods specifically designed for real image inversion is not fair, as P2P typically combines other inversion methods when editing real images.

In ParallelEdits, which CFG is used in DDIM inversion? Using a CFG of 1 typically results in satisfactory inversion of a real image but may reduce editing ability. Conversely, a CFG of 7.5 often leads to reconstruction failure.

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I would like to thank the authors for their response. With the additional results and clarifications provided, I am pleased to increase my rating.