1. Bibliographic Information

1.1. Title

S$^2$Edit: Text-Guided Image Editing with Precise Semantic and Spatial Control

1.2. Authors

The authors are Xudong Liu, Zikun Chen, Ruowei Jiang, Ziyi Wu, Kejia Yin, Han Zhao, Parham Aarabi, and Igor Gilitschenski. Their affiliations include the University of Toronto, the Vector Institute, and the University of Illinois Urbana-Champaign. The authors have backgrounds in computer vision, machine learning, and generative models, contributing to a strong foundation for this research.

1.3. Journal/Conference

The paper was submitted to arXiv as a preprint. The link format (2507.04584v1) and the provided publication date suggest a submission intended for a future conference cycle, likely in 2025. arXiv is a common platform for researchers to share their work before or during the peer-review process for major conferences like CVPR, ICCV, or NeurIPS, which are top-tier venues in the field of computer vision and artificial intelligence.

1.4. Publication Year

The publication date listed on the preprint is July 7, 2025.

1.5. Abstract

The paper introduces $S^2Edit$, a novel method for text-guided image editing using pre-trained text-to-image diffusion models. The authors identify a key problem in existing methods: when performing fine-grained edits (like face editing), they often lose the subject's identity, destroy high-frequency details, or mistakenly alter irrelevant image regions. This is attributed to the entanglement of identity concepts with editable attributes. $S^2Edit$ addresses this through a two-stage process. First, it fine-tunes a diffusion model on a single image to embed the subject's identity into a special learnable text token. During this fine-tuning, it applies two novel controls: (1) Semantic Control, an orthogonality constraint in the text feature space to disentangle the identity token from editable attributes, and (2) Spatial Control, using object masks to force the identity token's attention to focus only on the region of interest. At inference, this controlled identity token allows for localized editing that faithfully preserves the original identity. The paper demonstrates through extensive experiments that $S^2Edit$ outperforms state-of-the-art methods both quantitatively and qualitatively, and showcases its application in compositional tasks like makeup transfer.

1.6. Original Source Link

• Original Source Link: https://arxiv.org/abs/2507.04584v1
• PDF Link: https://arxiv.org/pdf/2507.04584v1.pdf
• Publication Status: The paper is a preprint available on arXiv.

2. Executive Summary

2.1. Background & Motivation

The core problem this paper addresses is the difficulty of achieving precise and faithful text-guided image editing with modern diffusion models. While these models excel at generating high-quality images from text, editing existing real-world images presents a significant challenge. Specifically, for tasks requiring fine-grained control, such as editing human faces, current methods face a critical trade-off:

• Identity Preservation vs. Edit Accuracy: Methods often struggle to apply a desired edit (e.g., "add a smile") without altering the person's fundamental identity (e.g., changing their facial structure or skin tone).

• Concept Entanglement: In the learned representations of the model, concepts like a person's identity are often mixed up with their attributes (e.g., wearing glasses, having a beard). When a user tries to edit one attribute, the entangled identity is also affected, leading to undesirable changes.

• Lack of Spatial Control: Edits can "bleed" into irrelevant parts of the image. For instance, an attempt to change hair color might also affect the background or clothing.

  Existing methods like Prompt-to-Prompt or InstructPix2Pix often fail to navigate this trade-off effectively. Personalization techniques like DreamBooth can preserve identity but lack the fine-grained control needed for editing, as the learned identity token can become entangled with the attributes present in the training images. This paper's entry point is to create a "purified" representation of a subject's identity—one that is both semantically disentangled from editable attributes and spatially focused on the subject itself.

2.2. Main Contributions / Findings

The paper makes three primary contributions:

1. Proposal of $S^2Edit$: A novel two-stage method for text-guided image editing that achieves precise semantic and spatial control, enabling high-fidelity edits that preserve the subject's identity.
2. A Novel Controlled Fine-tuning Strategy: This is the core technical innovation. Instead of simple fine-tuning, $S^2Edit$ introduces two specific controls over a learnable identity token:
   • Semantic Control: An orthogonality constraint is enforced between the text embedding of the identity token and the text embedding of the source prompt. This forces the identity token to represent only the subject's unique features, not the general attributes described in the text (e.g., hair color, expression).
   • Spatial Control: The cross-attention maps associated with the identity token are masked during training, ensuring that the token only influences the pixels corresponding to the subject of interest and not the background or other objects.
3. Superior Performance and New Applications: The paper demonstrates through comprehensive experiments that $S^2Edit$ surpasses existing state-of-the-art methods in both quantitative metrics (FID, LPIPS, PSNR) and qualitative user studies. It also showcases the method's flexibility by extending it to compositional editing tasks, such as transferring makeup from a reference image to a source image.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To understand this paper, one must be familiar with the following concepts:

• Diffusion Models: These are a class of generative models that learn to create data, such as images, by reversing a noise-adding process.

  • Forward Process: This is a fixed process where a real image is gradually destroyed by adding a small amount of Gaussian noise at each step over a series of timesteps $T$. By the end, the image becomes pure noise.
  • Reverse Process (Denoising): The model, typically a U-Net architecture, is trained to predict the noise that was added at each step. By iteratively subtracting this predicted noise, the model can generate a clean image starting from pure noise.
  • Text-Conditioning: In models like Stable Diffusion, the denoising process is conditioned on a text prompt. This is achieved by feeding text embeddings into the U-Net, usually via a cross-attention mechanism, which guides the generation process to match the text description.

• Latent Diffusion Models (LDMs): To make diffusion models more efficient, LDMs (like the Stable Diffusion model used in this paper) operate in a compressed latent space instead of the high-dimensional pixel space. An encoder first compresses the image into a smaller latent representation, the diffusion process happens in this space, and a decoder then converts the final denoised latent back into a full-resolution image.

• Cross-Attention Mechanism: This is the key mechanism that allows text to control image generation in diffusion models. At certain layers of the denoising U-Net, the image representation (at a specific spatial location) acts as a Query (Q). The embeddings of the words in the text prompt act as Keys (K) and Values (V). The attention mechanism calculates how much each pixel (Query) should "attend to" each word (Key), and then creates an updated image representation by taking a weighted sum of the word Values. The formula is: $$\mathrm{Attention}(Q, K, V) = \mathrm{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$$ The output of $\mathrm{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)$ is the attention map, a matrix of weights indicating the spatial influence of each text token. $S^2Edit$ directly manipulates these maps for its spatial control.

• Personalized Generation (DreamBooth / Textual Inversion): These techniques adapt a pre-trained text-to-image model to a new, user-provided concept (e.g., a specific person or object) from just a few images. They typically introduce a new, unique placeholder token (e.g., [V]) into the model's vocabulary and fine-tune the model so that this token represents the new concept. Afterward, the user can generate images of this concept in new contexts (e.g., "a photo of [V] dog on the moon"). $S^2Edit$ adapts this idea by learning a unique identity token [I] from a single image.

3.2. Previous Works

The paper positions itself relative to several lines of prior research:

• Diffusion-based Editing:

  • Prompt-to-Prompt (Hertz et al., 2022): A pioneering method that enables editing by changing the prompt and carefully manipulating the cross-attention maps. It preserves the structure of the original image by injecting attention maps from the source generation into the target generation. However, it can struggle to balance identity preservation with the desired edit, especially for significant changes.
  • Null-text Inversion (Mokady et al., 2023): A technique for "inverting" a real image into an initial noise vector that can be perfectly reconstructed by the diffusion model. This is a crucial first step for editing real images, as it provides a starting point for the generation process. $S^2Edit$ uses this technique.
  • InstructPix2Pix (Brooks et al., 2023): This model is fine-tuned on a large dataset of synthetic triplets (source image, editing instruction, edited image). It learns to follow natural language instructions like "make her smile." While powerful, it can fail on fine-grained details and may not always preserve the subject's identity perfectly.

• Personalized Models:

  • DreamBooth (Ruiz et al., 2023): Fine-tunes the entire diffusion model on a few images of a subject to embed its identity into a unique token. It excels at re-contextualization but is not designed for subtle editing. The learned token can suffer from concept entanglement, as noted by the $S^2Edit$ authors.
  • SINE (Zhang et al., 2023): Focuses on editing from a single image and tackles the overfitting problem during fine-tuning. However, the paper's comparison shows that SINE can produce unrealistic results with artifacts.

• GAN-based Editing:

  • DeltaEdit (Lyu et al., 2023): Operates by finding semantic directions in the latent space of a pre-trained GAN (Generative Adversarial Network) that correspond to edits. While effective, GANs are trained on smaller datasets than large diffusion models, limiting their latent space's capacity to represent diverse real-world images faithfully. This often leads to failures in preserving identity.

3.3. Technological Evolution

The field of image editing has evolved rapidly:

1. Early GAN-based Methods: Required paired image-text data or involved complex latent space manipulation. They were often limited by the representational power of GANs.
2. Rise of Diffusion Models: Large-scale pre-trained models like Imagen and Stable Diffusion demonstrated unprecedented text-to-image generation quality, opening new avenues for editing.
3. Zero-Shot Editing: Methods like Prompt-to-Prompt showed it was possible to edit images by just manipulating the text prompt and internal model representations (like attention maps), without any further training.
4. Personalization: DreamBooth and others introduced fine-tuning to teach models about specific user-provided subjects, greatly improving fidelity for known objects/people.
5. The Current Frontier (addressed by $S^2Edit$): The challenge has now shifted to combining the fidelity of personalization with the precision of zero-shot editing. The key problem is ensuring that the learned "identity" is pure and does not interfere with the editing process. $S^2Edit$ sits at this frontier, proposing a solution to disentangle identity from attributes.

3.4. Differentiation Analysis

The core innovation of $S^2Edit$ compared to prior works is how it controls the information encoded in the learned identity token.

• vs. DreamBooth: DreamBooth learns an identity token via simple reconstruction loss. $S^2Edit$ argues this is insufficient, as the token also learns editable attributes from the source image. $S^2Edit$ adds semantic and spatial control during this learning process to create a disentangled token specifically for high-fidelity editing.

• vs. Prompt-to-Prompt: P2P relies solely on attention manipulation at inference time and does not learn a persistent representation of the subject's identity. This makes it vulnerable to identity drift. $S^2Edit$ learns a robust identity token first, making the subsequent editing process more stable.

• vs. InstructPix2Pix: InstructPix2Pix is a general-purpose instruction-following model. It does not have a mechanism to explicitly preserve the identity of a specific subject in an input image. $S^2Edit$ is designed specifically for this personalized, identity-preserving editing task.

  In essence, $S^2Edit$ combines the strengths of personalization (learning a high-fidelity identity token) and attention-based editing, while introducing novel constraints to overcome their respective weaknesses (concept entanglement and identity drift).

4. Methodology

4.1. Principles

The central idea of $S^2Edit$ is that for precise and faithful image editing, the model needs a representation of the subject's identity that is "pure"—it should contain only the essential, unique features of the subject, without being contaminated by editable attributes (like expression, accessories, or lighting) or irrelevant spatial context (like the background).

To achieve this, $S^2Edit$ employs a two-stage approach:

1. Identity Fine-tuning Stage: A pre-trained diffusion model is fine-tuned on a single source image. The goal is to embed the subject's identity into a special learnable token, [I]. The key is that this fine-tuning is not naive; it is guided by two specific control mechanisms to ensure the purity of the learned identity token.
2. Inference Stage: Using the fine-tuned model and the learned identity token, the image is edited according to a new target prompt. This stage leverages existing techniques like null-text inversion and cross-attention control, but is made more effective by the high-quality identity token.

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

The entire process is built upon a pre-trained text-to-image latent diffusion model, such as Stable Diffusion.

4.2.1. Stage 1: Identity Fine-tuning

Given a source image $\mathcal{T}$ and a corresponding text prompt $\mathcal{P}$ (e.g., "A photo of a lady with glasses"), the goal is to learn a special token [I] that captures the lady's identity.

1. Prompt Enhancement and Base Objective: The source prompt $\mathcal{P}$ is modified to include the unique identity token [I], creating an enhanced prompt $\widetilde{\mathcal{P}}$ (e.g., "A photo of a [I] lady with glasses"). The model (both the U-Net and the text encoder) is then fine-tuned to reconstruct the source image $\mathcal{T}$ from this prompt. The basic loss function for this is the standard diffusion reconstruction loss, $\mathcal{L}_{recons}$. This step alone is similar to DreamBooth but on a single image. However, the authors note this is insufficient.

2. Semantic Control: The main issue with the base objective is that the token [I] can learn to represent everything about the subject in the image, including attributes like "glasses". This makes it difficult to later remove the glasses, as the concept is baked into the identity token. To solve this, $S^2Edit$ introduces a semantic loss to force the identity token to be semantically different from the attributes described in the prompt.

   • Integrated Explanation: This is achieved by enforcing an orthogonality constraint in the text embedding space. The loss is defined as: $$\mathcal{L}_{semantic} = \| \mathrm{Proj}_{e_{\mathcal{P}}}(e_{[I]}) \| = \| \cos(e_{\mathcal{P}}, e_{[I]}) \|$$ Here's a breakdown of the formula:
     • $e_{[I]}$: This is the vector embedding of the learnable identity token [I] produced by the text encoder.
     • $e_{\mathcal{P}}$: This is the vector embedding of the special [CLS] token from the source prompt $\mathcal{P}$. The [CLS] token's embedding is commonly used in natural language processing to represent the aggregate semantic meaning of an entire sequence.
     • $\cos(e_{\mathcal{P}}, e_{[I]})$: This is the cosine similarity between the two vectors. It measures the angle between them, with a value of 1 meaning they point in the same direction (semantically similar) and 0 meaning they are orthogonal (semantically unrelated).
     • $\| \dots \|$: The norm is used to ensure the loss is a positive value to be minimized. Minimizing this loss pushes the cosine similarity towards 0, making the vectors $e_{[I]}$ and $e_{\mathcal{P}}$ orthogonal.
   • Intuition: By forcing the identity token's embedding to be orthogonal to the prompt's overall embedding, the model is discouraged from encoding information already present in the prompt (like "glasses") into [I]. [I] is thus trained to represent the residual information—the unique identity of the person not captured by the text.

3. Final Fine-tuning Objective: The semantic loss is added to the reconstruction loss, creating the final objective for the fine-tuning stage: $$\mathcal{L} = \mathcal{L}_{recons} + \lambda \cdot \mathcal{L}_{semantic}$$

   • $\lambda$: A hyperparameter that balances the importance of reconstruction fidelity and semantic disentanglement. The authors set it to 0.1.

4. Spatial Control: Another problem is that the identity token [I] might learn to influence irrelevant parts of the image, like the background. To prevent this, $S^2Edit$ introduces spatial control by manipulating the cross-attention maps during both fine-tuning and inference.

   • Integrated Explanation: The cross-attention map for the token [I], denoted $\mathcal{A}_{[I]}$, determines the spatial region where [I] has an effect. $S^2Edit$ forces [I] to only attend to the object of interest. This is done by element-wise multiplying its attention map with a binary object mask $M_{obj}$: $$\mathcal{A}_{[I]}^{\ast} = \mathcal{A}_{[I]} \odot M_{obj}$$

     • $\mathcal{A}_{[I]}$: The original cross-attention map for the token [I]. This is a 2D map where each value corresponds to a spatial location in the image's feature map.
     • $M_{obj}$: A binary mask where pixels corresponding to the object of interest are 1 and all other pixels are 0.
     • $\odot$: The element-wise product.
     • $\mathcal{A}_{[I]}^{\ast}$: The resulting masked attention map, which is used in the subsequent steps of the diffusion model. This ensures that the identity token [I] can only influence the generation within the masked region.

   • Automatic Mask Generation: A key practical advantage is that $M_{obj}$ does not require manual annotation. It is generated automatically by taking the cross-attention map of the noun in the prompt that describes the object (e.g., "lady" or "cat") and binarizing it with a threshold. This makes the method user-friendly.

     The overview of this fine-tuning stage is shown in Figure 2 from the paper.

     该图像是示意图，展示了 S$^2$Edit 方法的身份微调和推理过程。左侧部分中，通过文本编码器将身份令牌插入文本提示，并使用语义损失 $L_{semantic}$ 强制生成编辑结果。右侧部分则展示了在推理阶段，如何利用对象掩码和反向文本技术进行局部编辑和身份保留。

4.2.2. Stage 2: Inference for Editing

After fine-tuning, the model now has a specialized identity token [I] and tuned weights. To edit the original image $\mathcal{T}$:

1. Image Inversion: Null-text Inversion is used to find the initial noise map $z_T$ that, when denoised with the source prompt $\widetilde{\mathcal{P}}$, perfectly reconstructs the source image $\mathcal{T}$.
2. Prompt Modification: The user provides a target prompt $\mathcal{P}^*$ which includes the learned token [I] (e.g., "A [I] lady with a smile").
3. Denoising with Edited Attention: The model denoises from the inverted noise map $z_T$ using the target prompt $\mathcal{P}^*$. During this process, a Prompt-to-Prompt-style cross-attention injection is used to preserve the original image's structure. Crucially, the spatial control from the fine-tuning stage is also applied here: the attention map for the token [I] is masked at every denoising step to ensure the identity remains localized to the subject.

4.2.3. Extension to Compositional Image Editing

$S^2Edit$ can be extended to transfer an attribute from a reference image $\mathcal{I}_{ref}$ to a source image $\mathcal{I}_{src}$.

1. Dual Token Learning: Two special tokens are introduced: an identity token [I] for the source image and an attribute token [A] for the reference image (e.g., for makeup).
2. Joint Fine-tuning: The model is fine-tuned on both image-prompt pairs simultaneously: $(\mathcal{T}_{src}, \mathcal{P}_{src}^*)$ where $\mathcal{P}_{src}^*$ is "a [I] lady", and $(\mathcal{T}_{ref}, \mathcal{P}_{ref}^*)$ where $\mathcal{P}_{ref}^*$ is "a lady with [A] makeup". Both semantic and spatial controls are applied to their respective tokens.
3. Inference with Mixed Prompt: To generate the final image, a mixed prompt is used, such as "A [I] lady with [A] makeup". The generation starts from the inverted noise map of the source image $\mathcal{I}_{src}$. This allows the model to apply the learned attribute [A] to the subject with identity [I].

5. Experimental Setup

5.1. Datasets

The authors used a variety of datasets to demonstrate the versatility and robustness of $S^2Edit$.

• Human Faces:
  • FFHQ (Flickr-Faces-HQ): A high-quality dataset of 70,000 human faces. It is a standard benchmark for face generation and editing because it contains diverse appearances, ages, and ethnicities.
  • CelebA-HQ: A high-quality version of the CelebA dataset, containing about 30,000 celebrity faces.
  • Justification: Human faces are an excellent test case for fine-grained editing because humans are highly sensitive to even minor distortions in identity or unrealistic features.
• Non-Face Domains:
  • AFHQ (Animal Faces-HQ): A dataset of high-quality animal faces, including cats, dogs, and wildlife.
  • LSUN (Large-scale Scene Understanding): A large dataset of various scenes and objects. The authors specifically use images from the cat and church categories.
  • Justification: Using these datasets demonstrates that $S^2Edit$ is not limited to human faces and can be generalized to other object domains.

5.2. Evaluation Metrics

The paper uses several standard metrics to quantitatively evaluate the performance of the editing methods.

• FID (Fréchet Inception Distance)

  1. Conceptual Definition: FID measures the quality and diversity of generated images. It calculates the distance between the feature distributions of a set of real images and a set of generated images. The features are extracted from a pre-trained InceptionV3 network. A lower FID score indicates that the distribution of generated images is closer to the distribution of real images, implying higher quality and realism.
  2. Mathematical Formula: $$\text{FID} = \left\| \mu_r - \mu_g \right\|_2^2 + \mathrm{Tr}\left(\Sigma_r + \Sigma_g - 2(\Sigma_r \Sigma_g)^{1/2}\right)$$
  3. Symbol Explanation:
     • $\mu_r, \mu_g$: The mean of the feature vectors for real and generated images, respectively.
     • $\Sigma_r, \Sigma_g$: The covariance matrices of the feature vectors for real and generated images.
     • $\mathrm{Tr}(\cdot)$: The trace of a matrix (the sum of its diagonal elements).

• LPIPS (Learned Perceptual Image Patch Similarity)

  1. Conceptual Definition: LPIPS measures the perceptual similarity between two images. Unlike pixel-wise metrics like PSNR, it is designed to align better with human judgment. It computes the distance between deep features extracted from two images using a pre-trained network (e.g., VGG). It is particularly useful for evaluating identity preservation, where a lower LPIPS score between the source and edited image indicates that the identity is better preserved.
  2. Mathematical Formula: $$d(x, x_0) = \sum_l \frac{1}{H_l W_l} \sum_{h,w} \left\| w_l \odot (\hat{y}_{hw}^l - \hat{y}_{0hw}^l) \right\|_2^2$$
  3. Symbol Explanation:
     • $x, x_0$: The two images being compared.
     • $l$: Index of the convolutional layer.
     • $\hat{y}^l, \hat{y}_0^l$: Feature activations from layer $l$ for images $x$ and $x_0$.
     • $H_l, W_l$: Height and width of the feature maps at layer $l$.
     • $w_l$: A learned channel-wise weight to scale the importance of different channels.

• PSNR (Peak Signal-to-Noise Ratio)

  1. Conceptual Definition: PSNR is a traditional metric that measures the reconstruction quality of an image by comparing it pixel-by-pixel to a reference image. It is based on the mean squared error (MSE). A higher PSNR value indicates better reconstruction quality (less error).
  2. Mathematical Formula: $$\text{PSNR} = 20 \cdot \log_{10}\left(\frac{\text{MAX}_I}{\sqrt{\text{MSE}}}\right)$$ where $\text{MSE} = \frac{1}{mn} \sum_{i=0}^{m-1} \sum_{j=0}^{n-1} [I(i,j) - K(i,j)]^2$.
  3. Symbol Explanation:
     • $\text{MAX}_I$: The maximum possible pixel value of the image (e.g., 255 for 8-bit images).
     • MSE: The mean squared error between the source image $I$ and the edited image $K$.
     • m, n: The dimensions of the images.

• CLIP Score

  1. Conceptual Definition: The CLIP Score measures the semantic similarity between an image and a text description. It uses the pre-trained CLIP model, which is designed to embed images and text into a shared space. A higher CLIP score between the edited image and the target prompt indicates better prompt alignment.
  2. Mathematical Formula: $$\text{CLIP Score} = 100 \times \cos(I_e, T_e)$$
  3. Symbol Explanation:
     • $I_e$: The CLIP embedding of the image.
     • $T_e$: The CLIP embedding of the text.
     • $\cos(\cdot, \cdot)$: The cosine similarity between the two embeddings.

5.3. Baselines

$S^2Edit$ was compared against four representative state-of-the-art methods:

• Null-text Inversion + Prompt-to-Prompt: A strong diffusion-based baseline that combines a high-fidelity inversion technique with an attention-based editing method.

• InstructPix2Pix: A leading method for instruction-based image editing.

• SINE: A recent method specifically designed for single-image editing with diffusion models.

• DeltaEdit: A state-of-the-art GAN-based editing method, included to show the comparison between diffusion and GAN approaches.

  These baselines were chosen because they represent the main competing paradigms in text-guided image editing: zero-shot attention manipulation, instruction-following fine-tuning, single-image diffusion fine-tuning, and GAN-based latent space editing.

6. Results & Analysis

6.1. Core Results Analysis

The experimental results strongly support the effectiveness of $S^2Edit$.

Qualitative Analysis:

• Face Editing (Figure 3): This figure provides a compelling visual comparison.

  • $S^2Edit$ successfully performs edits like adding a beard, removing glasses, or changing expression while keeping the person's identity (facial structure, skin tone, hair) remarkably consistent.

  • DeltaEdit (GAN-based) often changes the subject's identity.

  • InstructPix2Pix makes localized errors, like only partially removing glasses.

  • Null-text Inversion either fails to perform the edit or causes identity drift.

  • SINE produces results with noticeable artifacts and distortions.

    The qualitative results on face editing are displayed in the following figure:

    该图像是插图，展示了使用 S$^2$Edit 方法进行人脸编辑的效果对比。图中包含了输入图像及多个不同编辑结果，显示了在真实与合成图像中应用该方法的能力，包括面部表情、化妆等变化。

• Versatility and Disentanglement (Figure 4): This figure shows multiple different edits applied to the same source image. The model can add a beard, age the person, and add glasses, all while preserving the core identity. This demonstrates that the learned identity token [I] is well-disentangled from the editable attributes.

  The following figure showcases these fine-grained editing results:

  该图像是展示$ext{S}^2 ext{Edit}$方法进行细粒度编辑的效果图，呈现了同一图像在不同属性下的变化，包括添加胡须、变老、添加眼镜等。每一列代表一种编辑效果，展示了该方法在保持人物身份的同时，实现针对性的图像编辑。

• Generalizability (Figure 5): The results on non-face domains like cats and churches show that the method is not limited to faces. It can perform edits ranging from local changes (making a cat robotic) to global style changes (making a church gothic or snowy).

  Examples of edits on non-face images are shown here:

  该图像是展示了不同猫咪和建筑风格的对比图。上排显示了四种不同特征的猫咪，分别是橙色猫、白眼猫、项圈猫和机械猫；下排展示了四种建筑风格，包括雪天、灰色、哥特式和白色建筑。图像通过细致的视觉呈现出各种主题的特点。

• Compositional Editing (Figure 6): The makeup transfer application shows that $S^2Edit$ can successfully learn an attribute from a reference image and apply it naturally to a source image, outperforming a baseline that uses plain identity fine-tuning.

  The compositional editing results are presented below:

  

  Quantitative Analysis:

• Trade-off between Prompt Alignment and Identity Preservation (Figure 7): This plot is crucial as it visualizes the core challenge of image editing.

  • The x-axis represents identity preservation (LPIPS, lower is better), and the y-axis represents prompt alignment (CLIP score, higher is better). The ideal method would be in the top-left corner.

  • $S^2Edit$'s curve is consistently above and to the left of all other methods. This means for any given level of identity preservation, $S^2Edit$ achieves better prompt alignment, and for any given level of prompt alignment, it better preserves identity. It strikes the best balance between the two competing goals.

    The trade-off curve is shown in the figure below:

    该图像是一个图表，展示了不同图像编辑方法的性能比较。横轴为 LPIPS 值，纵轴为 CLIP 分数，各种方法（如 Ours, DeltaEdit, Null-text Inversion, SINE）的表现通过不同颜色的曲线呈现，表明 S$^2$Edit 方法在性能评估中的优势。

6.2. Data Presentation (Tables)

The quantitative results from the paper's tables further confirm the superiority of $S^2Edit$.

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

• Analysis of Table 1: $S^2Edit$ achieves the best scores across all three metrics.

  • The lowest FID score (52.31) indicates the highest overall image quality and realism.

  • The lowest LPIPS score (0.13) confirms superior identity preservation.

  • The highest PSNR score (30.75) shows better reconstruction fidelity.

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

    Method	ID. Preservation	Prompt Alignment
    Null-text Inversion	27.75%	26.00%
    InstructPix2Pix	33.13%	35.00%
    SINE	0.50%	10.75%
    DeltaEdit	30.00%	49.75%
    Ours	71.38%	72.38%

• Analysis of Table 2: The user study results are particularly compelling.

  • Participants overwhelmingly preferred $S^2Edit$'s results, with over 71% selecting it for identity preservation and over 72% for prompt alignment.
  • This subjective evaluation confirms that the quantitative improvements translate into a significantly better user experience.

6.3. Ablation Studies / Parameter Analysis

The paper includes a thorough ablation study to validate the contribution of each component of $S^2Edit$.

• Effect of Each Component (Figure 8): This is the most important analysis, as it deconstructs the method's effectiveness.

  • Baseline (Null-text Inversion): Without any of the proposed components, the edit fails completely, and the person's identity is lost.

  • + Identity Fine-tuning (IFT): Adding naive fine-tuning (like DreamBooth) preserves the identity but fails to perform the edit ("add bangs"). The attention map for the identity token [I] shows it has learned to represent the hair, preventing it from being edited. This confirms the concept entanglement problem.

  • + IFT + Semantic Control (SeC): Adding the orthogonality loss disentangles [I] from the prompt attributes. The edit is now successful (bangs are added). However, the result shows subtle distortions (skin tone change). The attention map reveals that the token's spatial focus has drifted to the background, as it is no longer constrained.

  • + IFT + SeC + Spatial Control (SpC) (Full $S^2Edit$): Adding the attention masking corrects the spatial drift. The attention map for [I] is now tightly focused on the face. The final result is both accurate (bangs added) and faithful (identity and details preserved). This step-by-step analysis clearly demonstrates that both semantic and spatial control are essential.

    The following figure illustrates this ablation analysis:

    该图像是图表，展示了不同组件对 S$^2$Edit 方法的影响。上行展示了输入图像与基线及应用不同组件（IFT、IFT + SeC、IFT + SpC）的编辑结果；下行是对应的交叉注意力图，显示了“lady”和[I]的注意力分布。

• Impact of Guidance Scale (Figure 9): The paper analyzes the effect of the classifier-free guidance scale $w$. As expected, a higher $w$ leads to stronger alignment with the target prompt ("An angry lady") but at the cost of losing some identity details. The authors find that a range of [3.5, 5.5] provides a good balance.

  This analysis is shown in the figure below:

  该图像是示意图，展示了在不同的分类器无关引导尺度 $w$ 下编辑的图像。可以看到，随着 $w$ 的增加，图像中的情感表达逐渐变化，从平静到愤怒，体现了文本引导图像编辑的效果。

7. Conclusion & Reflections

7.1. Conclusion Summary

The paper successfully presents $S^2Edit$, a novel and effective method for text-guided image editing that excels at tasks requiring fine-grained control. The core contribution is a principled approach to learning a "pure" identity representation by fine-tuning a diffusion model with two key constraints: a semantic orthogonality loss to disentangle identity from editable attributes, and a spatial attention mask to localize the identity's influence. By doing so, $S^2Edit$ elegantly resolves the critical trade-off between identity preservation and edit accuracy. Extensive experiments and ablation studies provide strong evidence that the method surpasses existing state-of-the-art techniques both quantitatively and qualitatively, setting a new standard for high-fidelity personalized image editing.

7.2. Limitations & Future Work

The authors acknowledge one primary limitation:

• Requirement of a Source Prompt: The method requires the user to provide an accurate text description of the source image to perform fine-tuning and editing. This is a common constraint for many text-guided editing methods built on text-to-image models.

  As a direction for future work, they suggest overcoming this limitation. This could be achieved by integrating automated image captioning models (like BLIP) or prompt inversion techniques that can estimate a descriptive prompt directly from the image, thereby making the editing process more seamless for the user.

7.3. Personal Insights & Critique

• Strengths:

  • Problem-Driven Innovation: The paper does an excellent job of identifying a specific, critical failure mode in existing methods (concept entanglement in the identity token) and proposing a direct and elegant solution. The semantic and spatial controls are well-motivated and intuitive.
  • Methodological Rigor: The ablation study is particularly strong. It clearly dissects the contribution of each component, making a convincing case for why the full $S^2Edit$ model is necessary.
  • Practicality: The automatic generation of the spatial mask from cross-attention maps is a clever design choice that avoids placing an extra burden on the user (e.g., requiring them to provide segmentation masks).
  • Strong Empirical Evidence: The combination of quantitative metrics, a user study, and extensive qualitative examples across different domains provides a very robust validation of the method's claims.

• Potential Issues and Areas for Improvement:

  • Efficiency: While the per-image fine-tuning time of ~95 seconds is reasonable for an offline process, it still presents a barrier for real-time, interactive applications. Future work could explore more efficient adaptation methods, perhaps inspired by parameter-efficient fine-tuning (PEFT) techniques that can achieve better performance than the ones the authors tried (as mentioned in the appendix).

  • Dependency on Prompt Quality: The effectiveness of the semantic disentanglement likely depends on the quality and specificity of the user-provided source prompt. If the prompt is vague or inaccurate, the orthogonality constraint may not work as intended.

  • Ethical Considerations: As the authors briefly note in their Impact Statement, tools that allow for high-fidelity, realistic image manipulation carry significant potential for misuse (e.g., creating convincing fake images or non-consensual content). While beyond the scope of this technical paper, the development of robust provenance and detection tools is a critical parallel research direction that must accompany advances in this area.

    Overall, $S^2Edit$ is a strong piece of research that makes a significant contribution to the field of generative image editing. Its core ideas of actively controlling what a learned concept represents are likely to be influential in future work on personalized and controllable generative models.