MagicTailor: Component-Controllable Personalization in Text-to-Image Diffusion Models

## Abstract

Abstract Recent advancements in text-to-image (T2I) diffusion models have enabled the creation of high-quality images from text prompts, but they still struggle to generate images with precise control over specific visual concepts. Existing approaches can replicate a given concept by learning from reference images, yet they lack the flexibility for fine-grained customization of the individual component within the concept.
In this paper, we introduce component-controllable personalization , a novel task that pushes the boundaries of T2I models by allowing users to reconfigure specific components when personalizing visual concepts. This task is particularly challenging due to two primary obstacles: semantic pollution , where unwanted visual elements corrupt the personalized concept, and semantic imbalance , which causes disproportionate learning of the concept and component.
To overcome these challenges, we design MagicTailor , an innovative framework that leverages Dynamic Masked Degradation (DM-Deg) to dynamically perturb undesired visual semantics and Dual-Stream Balancing (DS-Bal) to establish a balanced learning paradigm for desired visual semantics. Extensive comparisons, ablations, and analyses demonstrate that MagicTailor not only excels in this challenging task but also holds significant promise for practical applications, paving the way for more nuanced and creative image generation.

## 1 Introduction

Text-to-image (T2I) diffusion models (Rombach et al., ; Saharia et al., ; Ramesh et al., ; Chen et al., ) demonstrate remarkable capabilities in generating high-quality visual content from textual descriptions.
These models are able to create images that closely match the provided prompts.
But when certain visual concepts are difficult to articulate through natural language, they may face difficulties in accurately incorporating such elements into the generated images.
To address this limitation, some approaches (Gal et al., ; Ruiz et al., ) enable T2I models to learn specific concepts from a few reference images, thus allowing for more faithful integration of these concepts into the generated images.
This process, illustrated in Figure 1(a), is referred as personalization.
However, existing personalization methods are limited to replicating predefined concepts and lack the capability to offer flexible, fine-grained control over these elements.
This constraint significantly limits their practical applicability in real-world scenarios.
A concept often comprises multiple components, such as a house consisting of walls, windows, and doors. Therefore, a more sophisticated challenge in personalization is determining how to effectively control and manipulate these individual components during the personalization process.

This paper introduces a new task, component-controllable personalization, which aims to reconfigure specific components of personalized concepts using additional visual references (see Figure 1(b)).
In this task, a T2I model is fine-tuned with reference images and corresponding category labels, allowing it to learn and generate the desired concept along with the given component.
Achieving this capability would not only enable users to refine and customize concepts with precise control but also foster innovation and creativity, paving the way for novel ideas, inventions, and artworks across various creative domains.

A straightforward approach to this task is to treat each component as a separate concept and use existing personalization methods to combine multiple concepts with suitable text prompts. However, this naive strategy falls short in component-controllable personalization due to the inherent complexity of handling visual semantics during learning.
One of the key challenges in this task is semantic pollution (see Figure 2(a)), where undesired visual semantics inadvertently appear in generated images, thereby “polluting” the personalized concept. This occurs because the T2I model tends to blend visual semantics from different regions during the learning process. Simply masking out unwanted visual elements in reference images is not a viable solution, as it disrupts the overall visual context and leads to unintended compositions.
Another significant challenge is semantic imbalance (see Figure 2(b)), which causes the T2I model to focus disproportionately on certain aspects, resulting in unfaithful personalization. This issue arises from the semantic disparity between the concept and component, highlighting the need for an effective learning paradigm to better manage concept-level (*e.g.*, person) and component-level (*e.g.*, hair) visual semantics.

To address these challenges, we present MagicTailor, a novel framework that enables component-controllable personalization for T2I models (see Figure 1(c)).
As shown in Figure 3, we first employ a text-guided image segmenter to generate segmentation masks for both the concept and component. Then, we introduce a technique called Dynamic Masked Degradation (DM-Deg), which transforms the original reference images into randomly degraded versions, dynamically perturbing undesired visual semantics. This approach helps suppress the model’s sensitivity to irrelevant visual details while preserving the overall visual context, thereby effectively mitigating semantic pollution.
Next, we initiate a warm-up phase for the T2I model by jointly training it on these degraded images, using a masked diffusion loss to focus on the desired visual semantics and an attention loss to strengthen the correlation between these semantics and pseudo-words.
To tackle the issue of semantic imbalance, we employ Dual-Stream Balancing (DS-Bal), a dual-stream learning paradigm designed for balancing the learning of visual semantics, to launch the second phase. In this paradigm, the online denoising U-Net performs sample-wise min-max optimization, while the momentum denoising U-Net applies selective preserving regularization. This balanced approach ensures more faithful and accurate personalization of the target concept and component.

We validate the effectiveness of MagicTailor through comprehensive qualitative and quantitative experiments, demonstrating that it can achieve state-of-the-art (SOTA) performance in component-controllable personalization. Detailed ablation studies further confirm the impact of the key techniques integrated into MagicTailor. Additionally, we showcase its potential to enable a variety of further applications.
In summary, the main contributions of this work are as follows:

- •
We introduce a new task called component-controllable personalization for T2I models, enabling precise control over the individual components of concepts during personalization. Moreover, semantic pollution and semantic imbalance are identified as key challenges in this task.
- •
We propose MagicTailor, a novel framework specifically designed for component-controllable personalization. This framework incorporates Dynamic Masked Degradation (DM-Deg) to dynamically perturb undesired visual semantics, and Dual-Stream Balancing (DS-Bal) to ensure balanced learning of visual semantics.
- •
Comprehensive comparisons demonstrate that MagicTailor achieves superior performance in this task. Additionally, ablation studies and further applications highlight the effectiveness and versatility of the proposed method.

## 2 Related Works

Figure: Figure 2: Major challenges in component-controllable personalization. (a) Semantic pollution: (i) Undesired visual elements may inadvertently disturb the personalized concept. (ii) A simple mask-out strategy is ineffective and causes unintended compositions, whereas (iii) our DM-Deg effectively suppresses unwanted visual semantics, preventing such pollution. (b) Semantic imbalance: (i) Simultaneously learning the concept and component can lead to imbalance, resulting in concept or component distortion (here we present a case for the former). (ii) Our DS-Bal ensures balanced learning, enhancing personalization performance.
Refer to caption: x2.png

Text-to-Image Generation.
Text-to-image (T2I) generation has made remarkable advancements in recent years, enabling the synthesis of vivid and diverse imagery based on textual descriptions.
Early methods employed Generative Adversarial Networks (GANs) (Reed et al., ; Xu et al., ; Qiao et al., ; Zhu et al., ), and transformers (Ding et al., ; Ramesh et al., ; Ding et al., ; Yu et al., ; Bai et al., ) began to show the potential in conditioned image generation.
More recently, the advent of diffusion models has ushered in a new era in T2I generation (Li et al., ; Saharia et al., ; Ramesh et al., ; Chen et al., ; Xue et al., ). Leveraging these models, a range of related applications has rapidly emerged, including image editing (Li et al., ; Mou et al., ; Huang et al., ; Feng et al., ; Bai et al., ), image completion and translation (Xie et al., ; ; Lin et al., ), and controllable generation (Zhang et al., ; Wang et al., ; Zheng et al., ).
Despite advancements in T2I diffusion models, generating images that accurately capture specific, user-defined concepts remains challenging. This study explores component-controllable personalization, enabling precise adjustment of concept’s components through visual references.

Personalization.
Personalization seeks to adapt T2I models to generate specific concepts using reference images. Initial approaches such as textual inversion (Gal et al., ) and DreamBooth (Ruiz et al., ) addressed this task by either optimizing a text embedding or fine-tuning the entire T2I model. Additionally, low-rank adaptation (LoRA) (Hu et al., ) has been widely adopted by the research community for personalization (Ryu, ), offering an efficient and lightweight solution.
The scope of personalization has further expanded to accommodate multiple concepts (Kumari et al., ; Avrahami et al., ; Liu et al., ; Gu et al., ; Han et al., ; Gu et al., ).
Besides, a growing body of works has explored tuning-free approaches to personalization (Xiao et al., ; Li et al., ; Shi et al., ; Wei et al., ; Wang et al., ).
However, these methods often rely on training an encoder with extensive domain-specific image datasets.
There is also a category of works studying training-free schemes (Jeong et al., ; Zhang et al., ), but they generally suffer from inferior performance and tortuous inference processes.
In light of that, Our MagicTailor goes with a widely-adopted paradigm of test-time optimization to achieve stable performance and precise control over the concept during personalization.

## 3 Methodology

Let $\mathcal{I}=\{(\{I_{nk}\}_{k=1}^{K},c_{n})\}_{n=1}^{N}$ represents a concept-component pair with $N$ samples of concepts and components, where each sample contains $K$ reference images $\{I_{nk}\}_{k=1}^{K}$ with a category label $c_{n}$.
In this work, we focus on handling one concept and one component to make the task setting more practical.
Specifically, we set $N=2$ and define the first sample as a concept (*e.g.*, dog) while the second one as a component (*e.g.*, ear).
In addition, these samples are associated with the pseudo-words $\mathcal{P}=\{p_{n}\}_{n=1}^{N}$ serving as their text identifiers.
The objective of component-controllable personalization is to fine-tune a text-to-image (T2I) model to accurately learn both the concept and component from $\mathcal{I}$.
Using text prompts with $\mathcal{P}$, the fine-tuned model is expected to generate images that contain the personalized concept integrated with specified components.

In this section, we begin by providing an overview of the MagicTailor pipeline (refer to Section 3.1). Following this, we delve into its two core techniques: Dynamic Masked Degradation (DM-Deg, see Section 3.2) and Dual-Stream Balancing (DS-Bal, see Section 3.3).

Figure: Figure 3: Pipeline overview of MagicTailor. Using reference images as the inputs, MagicTailor fine-tunes a T2I diffusion model to learn both the target concept and component, enabling the generation of images that seamlessly integrate the component into the concept. Two key techniques, Dynamic Masked Degradation (DM-Deg, see Section 3.2) and Dual-Stream Balancing (DS-Bal, see Section 3.3), address the challenges of semantic pollution and semantic imbalance, respectively. For clarity, only one image per concept/component is presented and the warm-up stage is not depicted.
Refer to caption: x3.png

### 3.1 Overall Pipeline

The overall pipeline of MagicTailor is illustrated in Figure 3. The process begins with identifying the desired concept or component within each reference image $I_{nk}$, employing an off-the-shelf text-guided image segmenter to generate a segmentation mask $M_{nk}$ based on $I_{nk}$ and its associated category label $c_{n}$.
Conditioned on $M_{nk}$, we introduce Dynamic Masked Degradation (DM-Deg) to perturb undesired visual semantics within $I_{nk}$, addressing semantic pollution.
At each training step, DM-Deg transforms $I_{nk}$ into a randomly degraded image $\hat{I}_{nk}$, with the degradation intensity being dynamically regulated.
Subsequently, these degraded images, along with structured text prompts, are used to fine-tune a T2I diffusion model to facilitate concept and component learning.
The model is formally expressed as $\{\epsilon_{\theta},\tau_{\theta},\mathcal{E},\mathcal{D}\}$, where $\epsilon_{\theta}$ represents the denoising U-Net, $\tau_{\theta}$ is the text encoder, and $\mathcal{E}$ and $\mathcal{D}$ denote the image encoder and decoder, respectively.
To promote the learning of the desired visual semantics, we employ the masked diffusion loss, which is defined as

$$ $\mathcal{L}_{\text{diff}}=\mathbb{E}_{n,k,\epsilon,t}\Big{[}\big{\|}\epsilon \odot M_{nk}^{\prime}-\epsilon_{\theta}(z_{nk}^{(t)},t,e_{n})\odot M_{nk}^{ \prime}\big{\|}_{2}^{2}\Big{]},$ (1) $$

where $\epsilon\sim\mathcal{N}(0,1)$ is the unscaled noise, $z_{nk}^{(t)}$ is the noisy latent image of $\hat{I}_{nk}$ with a random time step $t$,
$e_{n}$ is the text embedding of the corresponding text prompt,
and $M_{nk}^{\prime}$ is downsampled from $M_{nk}$ to match the shape of $\epsilon$ and $z_{nk}$.
Additionally, we also incorporate the cross-attention loss to strengthen the correlation between desired visual semantics and their corresponding pseudo-words, formulated as

$$ $\mathcal{L}_{\text{attn}}=\mathbb{E}_{n,k,t}\Big{[}\big{\|}A_{\theta}(p_{n},z_ {nk}^{(t)})-M_{nk}^{\prime\prime}\big{\|}_{2}^{2}\Big{]},$ (2) $$

when $A_{\theta}(p_{n},z_{nk}^{(t)})$ is the cross-attention maps between the pseudo-word $p_{n}$ and the noisy latent image $z_{nk}^{(t)}$ and $M_{nk}^{\prime\prime}$ is downsampled from $M_{nk}$ to match the shape of $A_{\theta}(p_{n},z_{nk}^{(t)})$.
Using $\mathcal{L}_{\text{diff}}$ and $\mathcal{L}_{\text{attn}}$, we first warm up the T2I model by jointly learning all samples, aiming to preliminarily inject the knowledge of visual semantics.
The loss of the warm-up stage is defined as

$$ $\mathcal{L}_{\text{warm-up}}=\mathcal{L}_{\text{diff}}+\lambda_{\text{attn}} \mathcal{L}_{\text{attn}},$ (3) $$

where $\lambda_{\text{attn}}=0.01$ is the loss weight for $\mathcal{L}_{\text{attn}}$.
For efficient fine-tuning, we only train the denoising U-Net $\epsilon_{\theta}$ in a low-rank adaptation (LoRA) (Hu et al., ) manner and the text embedding of the pseudo-words $\mathcal{P}$, keeping the others frozen.
Thereafter, we employ Dual-Stream Balancing (DS-Bal) to establish a dual-stream learning paradigm to address the challenge called semantic imbalance.
In this paradigm, the online denoising U-Net $\epsilon_{\theta}$ conducts sample-wise min-max optimization for the hardest-to-learn sample, and meanwhile the momentum denoising U-Net $\tilde{\epsilon}_{\theta}$ applies selective preserving regularization for the other sample.

### 3.2 Dynamic Masked Degradation

Figure: Figure 4: Motivation of dynamic intensity. (a) Fixed intensity ($\alpha_{d}=0.5$ here) could cause noisy generated images. (b) Our dynamic intensity helps to mitigate noise memorization.
Refer to caption: x4.png

In this task, one of the major challenges is semantic pollution, where undesired visual semantics could be perceived by the T2I model and thus “pollute” the personalized concept.
As shown in Figure 2(a.i), the target concept (*i.e.*, person) could be severely disturbed by the owner of the target component (*i.e.*, eye), resulting in a hybrid person.
Unfortunately, directly masking out the regions other than the target concept and component would damage the overall visual context, thus leading to overfitting and weird compositions in Figure 2(a.ii).
In light of that, the undesired visual semantics of reference images should be processed properly.
Hence, we propose Dynamic Masked Degradation (DM-Deg) to dynamically perturb undesired visual semantics (see Figure 3), aiming at suppressing the T2I model’s perception for them while maintaining overall visual contexts (see Figure 2(a.iii)).

Degradation Imposition.
In each training step, DM-Deg imposes degradation in the regions outside segmentation masks for each reference image.
There are various types of degradation that can be adopted to perturb the visual semantics of an image, such as noise, blur, and geometric distortions,
but not all of them are easy to use and compatible with mask operations.
In DM-Deg, we choose to employ Gaussian noise due to its simplicity.
For a reference image $I_{nk}$, we randomly sample a Gaussian noise matrix $G_{nk}\sim\mathcal{N}(0,1)$ with the same shape as $I_{nk}$.
Note that here the pixel values of $I_{nk}$ range from $-1$ to $1$.
Then, the imposition of degradation is conducted as

$$ $\hat{I}_{nk}=\alpha_{d}G_{nk}\odot(1-M_{nk})+I_{nk},$ (4) $$

where $\odot$ indicates element-wise multiplication and $\alpha_{d}\in[0,1]$ is a dynamic weight used to regulate the degradation intensity for $I_{nk}$.
In this way, we can obtain a randomly degraded image $\hat{I}_{nk}$ where the original visual contexts are generally retained.
Encountering with $\hat{I}_{nk}$, it is more difficult for the T2I model to perceive undesired visual semantics in out-of-mask regions,
since these semantics would be randomly perturbed with Gaussian noise at each training step.

Dynamic Intensity.
Unfortunately, the T2I model may gradually memorize the introduced noise when learning meaningful visual semantics, thus making noise appear in generated images (see Figure 4(a)).
Such behavior also aligns with previous observations on deep networks (Arpit et al., ).
Hence, we design a descending scheme to dynamically regulate the intensity of the imposed noise during training.
This scheme adopts an exponential curve that maintains a relatively large intensity in the early steps and decreases dramatically in the later ones.
Let $d$ denote the current training step and $D$ denote the total training step. The curve of dynamic intensity is defined as

$$ $\alpha_{d}=\alpha_{\text{init}}(1-(\frac{d}{D})^{\gamma}),$ (5) $$

where $\alpha_{\text{init}}$ is the initial value of $\alpha_{d}$ and $\gamma$ is a factor to regulate the descent rate.
We empirically set $\alpha_{\text{init}}=0.5$ and $\gamma=32$ tuned within the powers of $2$.
Using such a scheme of dynamic intensity, we can effectively prevent semantic pollution and meanwhile alleviate the memorization of the introduced noise, achieving better generation performance (see Figure 4(b)).

Figure: Figure 5: Visualization of the learning process. (a) The vanilla learning paradigm lapses into overemphasizing the easier one. (b) DS-Bal effectively balances the learning of the concept and component.
Refer to caption: x5.png

### 3.3 Dual-Stream Balancing

Another primary challenge in this task is semantic imbalance, which arises from the inherent visual semantic disparity between the target concept and component.
A concept generally has richer visual semantics than a component (*e.g.* person vs. hair).
while the semantic richness of a component might be greater than a concept in some cases (*e.g.*, simple tower vs. intricate roof).
This imbalance complicates the joint learning process which could overemphasize on either concept or component, leading to incoherent generation of the concept or component (see Figure 5(a)).
To address this challenge, we design Dual-Stream Balancing (DS-Bal), which establishes a dual-stream learning paradigm with online and momentum denoising U-Nets (see Figure 3) to balance visual semantic learning of the concept and component, improving personalization fidelity (see Figure 5(b)).

Sample-wise Min-Max Optimization.
From a loss perspective, the visual semantics of the concept and component are learned by optimizing the masked diffusion loss $\mathcal{L}_{\text{diff}}$ for all the samples.
Unfortunately, this indiscriminate optimization does not allocate adequate learning efforts to the sample that is more challenging to learn, gradually leading to an imbalanced learning process.
To overcome this issue, DS-Bal utilizes the online denoising U-Net to learn only the visual semantics of the hardest-to-learn sample at each training step.
Inheriting the weights of the original denoising U-Net warmed up by joint learning, here the online denoising U-Net $\epsilon_{\theta}$ only optimizes the maximum masked diffusion loss among $N$ samples, which is defined as

$$ $\mathcal{L}_{\text{diff-max}}=\max_{n}\mathbb{E}_{k,\epsilon,t}\Big{[}\big{\|} \epsilon\odot M_{nk}^{\prime}-\epsilon_{\theta}(z_{nk}^{(t)},t,e_{n})\odot M_{ nk}^{\prime}\big{\|}_{2}^{2}\Big{]},$ (6) $$

where minimizing $\mathcal{L}_{\text{diff-max}}$ can be considered as a form of min-max optimization (Razaviyayn et al., ).
The learning objective of $\epsilon_{\theta}$ may switch across different training steps and is not consistently dominated by the concept or component.
Such an optimization scheme can effectively modulate the learning dynamics of different samples and avoid the overemphasis on any particular one.

Selective Preserving Regularization.
At a training step, the sample neglected in $\mathcal{L}_{\text{diff-max}}$ may suffer from knowledge forgetting.
This is because the optimization of $\mathcal{L}_{\text{diff-max}}$, which aims to enhance the knowledge of a specific sample, could inadvertently overshadow the knowledge of the others.
In light of this, DS-Bal meanwhile exploits the momentum denoising U-Net to preserve the learned visual semantics of the other sample in each training step.
Specifically, we first select the sample that is excluded in $\mathcal{L}_{\text{diff-max}}$, which can be expressed as
$S=\{n|n=1,...,N\}-\{n_{\text{max}}\},$
where $n_{\text{max}}$ is the index of the target sample in $\mathcal{L}_{\text{diff-max}}$ and $S$ is the selected index set.
Then, we use the momentum denoising U-Net $\tilde{\epsilon}_{\theta}$ to apply regularization for $S$,
with the masked preserving loss as

$$ $\mathcal{L}_{\text{pres}}=\mathbb{E}_{n\in S,k,t}\Big{[}\big{\|}\tilde{ \epsilon}_{\theta}(z_{nk}^{(t)},t,e_{n})\odot M_{nk}^{\prime}-\epsilon_{\theta }(z_{nk}^{(t)},t,e_{n})\odot M_{nk}^{\prime}\big{\|}_{2}^{2}\Big{]},$ (7) $$

where $\tilde{\epsilon}_{\theta}$ is updated from $\epsilon_{\theta}$ using EMA (Tarvainen & Valpola, ) with the smoothing coefficient $\beta=0.99$, thereby sustaining the prior accumulated knowledge of $\epsilon_{\theta}$ in each training step.
By encouraging the consistency between the output of $\epsilon_{\theta}$ and $\tilde{\epsilon}_{\theta}$ in $\mathcal{L}_{\text{pres}}$, we can facilitate the knowledge maintenance of the other sample while learning a specific sample in $\mathcal{L}_{\text{diff-max}}$.
Finally, using a loss weight $\lambda_{\text{pres}}=0.2$, the total loss of the DS-Bal stage is formulated as

$$ $\mathcal{L}_{\text{DS-Bal}}=\mathcal{L}_{\text{diff-max}}+\lambda_{\text{pres} }\mathcal{L}_{\text{pres}}+\lambda_{\text{attn}}\mathcal{L}_{\text{attn}}.$ (8) $$

## 4 Experiments

Figure: Figure 6: Qualitative comparisons. We present images generated by MagicTailor and the compared methods for various domains. MagicTailor generally achieves promising text alignment, strong identity fidelity, and high generation quality. More results are provided in Appendix D.
Refer to caption: x6.png

### 4.1 Experimental Setup

Dataset, Implementation, and Evaluation.
For a systemic investigation, we collect a dataset from various domains, including characters, animation, buildings, objects, and animals.
We employ Stable Diffusion 2.1 (Rombach et al., ) as the pretrained T2I diffusion model.
Reference images are resized to 512 $\times$ 512, and the LoRA rank and alpha are set to 32.
For the warm-up and DS-Bal stage, we set the training steps to 200 and 300 and the learning rate to 1e-4 and 1e-5, using AdamW (Loshchilov & Hutter, ) as the optimizer.
To generate evaluation images, we carefully design 20 text prompts covering extensive situations.
For each method, we generate 14,720 images to conduct a comprehensive evaluation.
To ensure fairness, all the seeds are fixed during training and inference.

Compared Methods.
We compare MagicTailor with SOTA methods of personalization, including Textual Inversion (TI) (Gal et al., ), DreamBooth (DB) (Ruiz et al., ), Custom Diffusion (CD) (Kumari et al., ), Break-A-Scene (BAS) (Avrahami et al., ), and CLiC (Safaee et al., ).
For a fair and meaningful comparison, they are adapted to our task with minimal modification, *i.e.*, incorporating the masked diffusion loss (Equation 1) into them.
Except for method-specific configurations, all methods use the same implementation above to ensure fairness.
Due to the space limit, more details of the experimental setup are provided in Appendix A.

### 4.2 Qualitative Comparisons

The qualitative results are presented in Figure 6.
It shows that TI, CD, and CLiC mainly suffer from semantic pollution, where undesired visual semantics severely influence the personalized concept.
Besides, we can observe that DB and BAS also underperform in this challenging task.
These methods exhibit an overemphasis on the concept or component due to semantic imbalance, which even leads to an absence of the target component.
Moreover, there is also an interesting observation that imbalanced learning could aggravate the effect of semantic pollution, making the color and texture of the target concept or component mistakenly transferred into an unexpected part of generated images.
Compared with these methods, MagicTailor can achieve superior performance in generating text-aligned images that also faithfully reflect the target concept and component, demonstrating its remarkable performance in this newly formulated task.

**Table 1: Quantitative comparisons. We compare our MagicTailor with SOTA methods of personalization based on automatic metrics and user study. The best results are marked in bold.**
| Methods | Automatic Metrics | User Study |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
| CLIP-T $\uparrow$ | CLIP-I $\uparrow$ | DINO $\uparrow$ | DreamSim $\downarrow$ | Text Align. $\uparrow$ | Id. Fidelity $\uparrow$ | Gen. Quality $\uparrow$ |  |
| Textual Inversion (Gal et al., ) | 0.236 | 0.742 | 0.620 | 0.558 | 5.8% | 2.5% | 5.2% |
| DreamBooth (Ruiz et al., ) | 0.266 | 0.841 | 0.798 | 0.323 | 15.3% | 14.7% | 12.5% |
| Custom Diffusion (Kumari et al., ) | 0.251 | 0.797 | 0.750 | 0.407 | 7.1% | 7.7% | 9.8% |
| Break-A-Scene (Avrahami et al., ) | 0.259 | 0.840 | 0.780 | 0.338 | 10.8% | 12.1% | 22.8% |
| CLiC (Safaee et al., ) | 0.263 | 0.764 | 0.663 | 0.499 | 4.5% | 5.1% | 6.2% |
| MagicTailor (Ours) | 0.270 | 0.854 | 0.813 | 0.279 | 56.5% | 57.9% | 43.4% |

Figure: Figure 7: Ablation of loss weights. We report CLIP-T for text alignment, and DreamSim for identity fidelity as it is most similar to human judgments (Fu et al., ). For reference, we also present the results of the second-best method in Table 1, highlighting our robustness on loss weights.
Refer to caption: x7.png

### 4.3 Quantitative Comparisons

Automatic Metrics.
We utilize four automatic metrics in the aspects of text alignment (CLIP-T (Gal et al., )) and identity fidelity (CLIP-I (Radford et al., ), DINO (Oquab et al., ), DreamSim (Fu et al., )).
To precisely measure identity fidelity, we segment out the concept and component in each reference and evaluation image, and then eliminate the target component from the segmented concept (see the detailed setup in Appendix A).
As we can see, component-controllable personalization remains a tough task even for SOTA methods of personalization.
By comparison, MagicTailor can achieve the best results in both identity fidelity and text alignment.
It should be credited to the utilization of an effective framework tailored to this special task.

User Study.
We further evaluate the methods with a user study.
Specifically, a detailed questionnaire is designed to display 20 groups of evaluation images with the corresponding text prompt and reference images.
Users are asked to select the best result in each group for three aspects, including text alignment, identity fidelity, and generation quality (see the detailed setup in Appendix A).
Finally, we collect a total of 3,180 valid answers and report the selected rates in Table 1.
It can be observed that MagicTailor can also achieve superior performance in human preferences, showcasing its effectiveness in component-controllable personalization.

**Table 2: Ablation of key techniques. Our DM-Deg and DS-Bal effectively contribute to a superior performance trade-off.**
| DM-Deg | DS-Bal | CLIP-T $\uparrow$ | CLIP-I $\uparrow$ | DINO $\uparrow$ | DreamSim $\downarrow$ |
| --- | --- | --- | --- | --- | --- |
|  |  | 0.275 | 0.837 | 0.798 | 0.317 |
| $\checkmark$ |  | 0.276 | 0.848 | 0.809 | 0.294 |
|  | $\checkmark$ | 0.270 | 0.845 | 0.802 | 0.304 |
| $\checkmark$ | $\checkmark$ | 0.270 | 0.854 | 0.813 | 0.279 |

### 4.4 Ablation Studies

We conduct comprehensive ablation studies of MagicTailor, aiming to verify the capability of the overall pipeline.
For more ablation studies on other aspects, please refer to Appendix C.

Figure: Figure 8: (a) Decoupled generation. MagicTailor can also separately generate the target concept and component, enriching prospective combinations. (b) Controlling multiple components. MagicTailor shows the potential to handle more than one component, highlighting its effectiveness.
Refer to caption: x8.png

Effectiveness of Key Techniques.
In Table 4, we investigate two key techniques of MagicTailor by starting from a baseline framework described in Section 3.1.
Even without DM-Deg and DS-Bal, such a baseline framework can still have competitive performance, showing its reliability.
On top of that, we introduce DM-Deg and DS-Bal, where the superior performance trade-off indicates the significance of these two key techniques.
Qualitative results can also refer to Figure 2.

Dynamic Intensity Matters.
In Table 4, we explore DM-Deg by comparing it with
1) the mask-out strategy;
2) the fixed intensity;
3) the linear intensity ($\alpha$ goes from 1 to 0, or from 0 to 1); and
4) the dynamic intensity with different $\gamma$.
First, the terrible performance of the mask-out strategy verifies that it is not a valid solution for semantic pollution.
Moreover, the dynamic intensity generally shows better results, and it can achieve better overall performance with a proper $\gamma$.

Momentum Denoising U-Net as a Good Regularizer.
In Table 4, we study DS-Bal by modifying the U-Net for regularization as
1) the fixed U-Net with $\beta=0$ (*i.e.*, the one just after warm-up);
2) the fixed U-Net with $\beta=1$ (*i.e.*, the one from the last step); and
3) the momentum U-Net with other $\beta$.
The results demonstrate that employing the U-Net with a high momentum rate can yield better regularization to tackle semantic imbalance, thus leading to excellent performance.

Sensitivity Analysis of Loss Weights.
In Figure 7, we analyze the sensitivity of loss weights in Equation 8 (*i.e.*, $\lambda_{\text{pres}}$ and $\lambda_{\text{attn}}$), since loss weights are often critical for model training.
As we can see, when $\lambda_{\text{pres}}$ and $\lambda_{\text{attn}}$ vary within a reasonable range, our MagicTailor can consistently attain SOTA performance, revealing the robustness of MagicTailor on loss weights.

### 4.5 Further Applications

Decoupled Generation.
After learning from a concept-component pair, MagicTailor can also enable decoupled generation.
As shown in Figure 8(a), MagicTailor can generate the target concept and component separately in various and even cross-domain contexts.
This should be credited to its remarkable ability to accurately capture different-level visual semantics.
Such an ability extends the flexibility of the possible combination between the concept and component.

Controlling Multiple Components.
In this paper, we focus on personalizing one concept and one component, because such a setting is enough to cover extensive scenarios in the real world, and can be further extended to reconfigure multiple components with an iterative procedure.
However, as shown in Figure 8(b), our MagicTailor also exhibits the potential to handle one concept and multiple components simultaneously.
These results reflect a prospective direction of exploring better control over diverse components for a single concept.

Enhancing Other Generative Tools.
In Figure 9, we provide some interesting method combinations to show that our MagicTailor can enhance other generative tools.
The combined tools include ContorlNet (Zhang et al., ), CSGO (Xing et al., ), and InstantMesh (Xu et al., ).
As we can see, MagicTailor can be seamlessly integrated into these tools, furnishing them with an additional ability to control the concept’s component in their pipelines.
For instance, working with MagicTailor, InstantMesh can conveniently achieve
fine-grained design of 3D mesh, demonstrating the practicability of MagicTailor in collaborative applications.

Figure: Figure 9: Enhancing other generative tools. MagicTailor can conveniently collaborate with a variety of generative tools that focus on other tasks, equipping them with an additional ability to control the concept’s component in their pipelines.
Refer to caption: x9.png

## 5 Conclusion

In this paper, we introduce the novel task of component-controllable personalization, which allows for precise customization of individual components within a personalized concept. We tackle two major challenges that make this task particularly difficult: semantic pollution, where unwanted visual elements disrupt the integrity of the concept, and semantic imbalance, which skews the learning process of visual semantics.
To address these challenges, we present MagicTailor, an innovative framework featuring Dynamic Masked Degradation (DM-Deg) to mitigate unwanted visual semantics and Dual-Stream Balancing (DS-Bal) to ensure balanced learning of visual components. Our comprehensive experiments demonstrate that MagicTailor not only sets a new benchmark in this challenging task but also opens up exciting possibilities for a wide range of creative applications.
Looking ahead, we envision extending our approach to other areas of image and video generation, exploring how multi-level visual semantics can be recognized, controlled, and manipulated to unlock even more sophisticated and imaginative generative capabilities.

## Appendix A More Details of Experimental Setup

### A.1 Dataset

As there is no existing dataset specifically for component-controllable personalization, we curate a dataset from the internet to conduct experiments.
Unlike previous works (Ruiz et al., ; Kumari et al., ) that focus on very few categories of concepts, the dataset contains various domains of concepts and components, such as characters, animation, buildings, objects, and animals.
Overall, the dataset consists of 23 concept-component pairs totally with 138 reference images, where each concept/component contains 3 reference images and a corresponding category label.
The scale of this dataset is aligned with the scale of those datasets used in compared methods (Gal et al., ; Ruiz et al., ; Kumari et al., ; Avrahami et al., ; Safaee et al., ).

### A.2 Implementation

We utilize Stable Diffusion 2.1 (Rombach et al., ) as the pretrained T2I diffusion model.
As commonly done, the resolution of reference images is set to 512 $\times$ 512.
Besides, the rank and alpha of the LoRA module are set to 32.
To simplify concept learning, we exclude the region of the target component from the segmentation masks of the target concept, *e.g.*, remove the hair from the person in a “<person> + <hair>” pair.
For the warm-up and DS-Bal stage, we set the learning rate to 1e-4 and 1e-5 and the training steps to 200 and 300.
Moreover, the learning rate is further scaled by the batch size, which is set to completely contain a concept-component pair.
For the cross-attention loss, we follow (Avrahami et al., ) to average the corresponding cross-attention maps at resolution 16 $\times$ 16 and normalized them to [0, 1], yielding $A_{\theta}(p_{n},z_{nk}^{(t)})$ in Equation 2.
MagicTailor is trained with an AdamW (Loshchilov & Hutter, ) optimizer and a DDPM (Ho et al., ) sampler on an NVIDIA A100 GPU.
For one concept-component pair, it runs for about 5 minutes.
All experiments are accomplished with Python 3.10.11 and PyTorch 1.13.1, based on CUDA 11.6.
As done in (Avrahami et al., ), the tensor precision is set to float16 to accelerate training.
For a fair comparison, all random seeds are fixed at 0 in each experiment, and all compared methods use the same implementation above except for method-specific configurations.

### A.3 Text Prompts For Evaluation

To generate images for evaluation, we carefully design 20 text prompts covering extensive situations, which are listed in Table 5.
These text prompts can be divided into four aspects, including recontextualization, restylization, interaction, and property modification, where each aspect is composed of 5 text prompts.
In recontextualization, we change the contexts to different locations and periods.
In restylization, we transfer concepts into various artistic styles.
In interaction, we explore the spatial interaction with other concepts.
In property modification, we modify the properties of concepts in rendering, views, and materials.
Such a group of diverse text prompts allows us to systemically evaluate the generalization capability of a method.

### A.4 Scheme of Generating Evaluation Images

We generate 32 images per text prompt for each pair, using a DDIM (Song et al., ) sampler with 50 steps and a classifier-free guidance scale of 7.5.
To ensure fairness, we fix the random seed within the range of [0, 31] across all methods.
This process results in a total of 14,720 images for each method to be evaluated, ensuring a robust and thorough comparison.

### A.5 Automatic Metrics

We utilize four automatic metrics in the aspects of text alignment (CLIP-T (Gal et al., )) and identity fidelity (CLIP-I (Radford et al., ), DINO (Oquab et al., ), DreamSim (Fu et al., )).
To precisely measure identity fidelity, we improve the traditional measurement approach for personalization.
This is because a reference image of the target concept/component could contain an undesired component/concept that is not expected to appear in evaluation images.
Specifically, we use Grounded-SAM (Ren et al., ) to segment out the concept and component in each reference and evaluation image.
Then, we further eliminate the target component from the segmented concept as we have done during training.
Such a process is similar to the one adopted in (Avrahami et al., ).
As a result, using the segmented version of evaluation images and reference images, we can accurately calculate the metrics of identity fidelity.

### A.6 User Study

In addition to using automatic metrics, we further evaluate the methods with a user study.
Specifically, we design a questionnaire to display 20 groups of evaluation images generated by our method and other methods.
Besides, each group also contains the corresponding text prompt and the reference images of the concept and component, where we adopt the same text prompts that are used to calculate CLIP-T.
The results of our method and all the compared methods are presented on the same page.
Clear rules are established for users to evaluate in three aspects, including text alignment, identity fidelity, and generation quality.
Users are requested to select the best result in each group by answering the corresponding questions of these three aspects.
We hide all the method names and randomize the order of methods to ensure fairness.
Finally, 3,180 valid answers are collected for a sufficient quantitative evaluation of human preferences.

**Table 5: Text prompts used to generate evaluation images. These text prompts can be divided into four aspects: recontextualization, restylization, interaction, and property modification, covering extensive situations to systemically evaluate the generalization capability of a method. Note that “<placeholder>” will be replaced by the combination of pseudo-words (*e.g.*, “<tower> with <roof>”) when generating evaluation images, and will be replaced by the combination of category labels (*e.g.*, “tower with roof”) when calculating the metric of text alignment.**
| Recontextualization | Restylization |
| --- | --- |
| “<placeholder>, on the beach” | “<placeholder>, watercolor painting” |
| “<placeholder>, in the jungle” | “<placeholder>, Ukiyo-e painting” |
| “<placeholder>, in the snow” | “<placeholder>, in Pixel Art style” |
| “<placeholder>, at night” | “<placeholder>, in Von Gogh style” |
| “<placeholder>, in autumn” | “<placeholder>, in a comic book” |

### A.7 Compared Methods

In our experiments, we compare MagicTailor with SOTA methods in the domain of personalization, including Textual Inversion (TI) (Gal et al., ), DreamBooth-LoRA (DB) (Ruiz et al., ), Custom Diffusion (CD) (Kumari et al., ), Break-A-Scene (BAS) (Avrahami et al., ), and CLiC (Safaee et al., ).
For TI, DB, and CD, we use the third-party implementation in Diffusers(^1^11[https://huggingface.co/docs/diffusers/index](https://huggingface.co/docs/diffusers/index)).
For BAS, we use the official implementation.
For CLiC, we reproduce it following the resource paper as the official code is not released.
Unless otherwise specified, method-specific configurations are set up by following their resource papers or Diffusers.
We empirically adjust the learning rate of CD and CLiC to 1e-4 and 5e-5 respectively, because they perform very poorly with the original learning rates.
For a fair and meaningful comparison, these methods should be adapted to our task setting with minimal modification.
Therefore, for those methods adopting a vanilla diffusion loss, we integrate the masked diffusion loss (Equation 1) into them while using the same segmentation masks from MagicTailor.

## Appendix B Additional Comparisons

### B.1 Comparing with Detailed-Text-Guided Generation

One might be curious about whether component-controllable personalization can be accomplished by providing detailed textual descriptions to the T2I model.
To investigate this, we separately feed the reference images of the concept and component into GPT-4o(^2^22[https://platform.openai.com/docs/models/gpt-4o](https://platform.openai.com/docs/models/gpt-4o)) to obtain detailed textual descriptions for them.
The text prompt we used is “Please detailedly describe the <concept/component> of the upload images in a parapraph”, where “<concept/component>” is replaced with the category label of the concept or component.
Then, we ask GPT-4o to merge these textual descriptions using natural language, and input them into the Stable Diffusion 2.1 (Rombach et al., ) to generate the corresponding images.
Some examples for a qualitative comparison are shown in Figure 10.
As we can see, such an approach cannot achieve satisfactory results, because it is hard to guarantee that visual semantics can be completely expressed by using the combination of text tokens.
In contrast, our MagicTailor is able to accurately learn the desired visual semantics of the concept and component from reference images, and thus lead to consistent and excellent generation in this tough task.

Figure: Figure 10: Comparing with detailed-text-guided generation. We use GPT-4o to generate and merge detailed textual descriptions for the target concept and component, which are fed into Stable Diffusion 2.1 to conduct text-to-image generation. This paradigm cannot perform well and produce inconsistent images, while MagicTailor can achieve faithful and consistent generation.
Refer to caption: x10.png

### B.2 Comparing with Commercial Models

It is also worth exploring whether existing commercial models, which can understand and somehow generate both text and images by themselves or other integrated tools, are capable of handling component-controllable personalization.
We choose two widely recognized commercial models, GPT-4o and Gemini 1.5 Flash(^3^33[https://gemini.google.com/](https://gemini.google.com/)), for a qualitative comparison.
First, we separately feed the reference images of the concept and component into them, along with the text prompt of “The uploaded images contain a special instance of the <concept/component>, please mark it as #<concept/component>”, where “<concept/component>” is replaced with the category label of the concept or component.
Then, we instruct them to perform image generation, using the text prompt of “Please generate images containing #<concept> with #<component>”, where “<concept>” and “<component>” are replaced with the category label of the concept and component, respectively.
As shown in Figure 11, these models struggle to reproduce the given concept, let alone reconfigure the concept’s component.
Whereas, our MagicTailor achieves superior results in component-controllable personalization, using a dedicated framework designed for this task.
It demonstrates that, even though large commercial models are able to tackle multiple general tasks, there is also plenty of room for the community to explore specialized tasks for real-world applications.

Figure: Figure 11: Comparing with commercial models. We input the reference images of the target concept and component to GPT-4o and Gemini, along with structured text prompts, for conducting image generation. Even though capable of handling multiple general tasks, these models still fall short in this task. In contrast, our MagicTailor performs well using a dedicated framework.
Refer to caption: x11.png

**Table 6: Ablations of warm-up. We compare MagicTailor with the variant that removes warm-up. The results exhibit the significance of the warm-up stage for the framework of MagicTailor.**
| Warm-up Variants | CLIP-T $\uparrow$ | CLIP-I $\uparrow$ | DINO $\uparrow$ | DreamSim $\downarrow$ |
| --- | --- | --- | --- | --- |
| w/o Warm-up | 0.272 | 0.844 | 0.793 | 0.320 |
| w/ Warm-up (Ours) | 0.270 | 0.854 | 0.813 | 0.279 |

## Appendix C Additional Ablation Studies

### C.1 Necessity of Warm-up Training

In MagicTailor, we start with a warm-up phase for the T2I model to preliminarily inject the knowledge for the subsequent phase of DS-Bal.
Here we investigate the necessity of such a warm-up phase for generation performance.
In Table 6, when removing the warm-up phase, even though MagicTailor could obtain slight improvement in text alignment, it severely suffers from the huge drop in identity fidelity.
This is because such a scheme makes it difficult to construct a decent momentum denoising U-Net for DS-Bal.
Whereas integrated with a warm-up phase, MagicTailor can achieve superior overall performance, which is attributed to the knowledge reserved from warm-up.

Figure: Figure 12: Ablation of the number of reference images. We present qualitative results to show that MagicTailor can still achieve satisfactory performance when provided only 1 or 2 reference image(s) per concept and component.
Refer to caption: x12.png

### C.2 Performance on Different Numbers of Reference Images

As described in Appendix A, each concept/component contains 3 reference images in a concept-component pair of the dataset.
Here we change the number of reference images to analyze the performance variation of MagicTailor.
The qualitative results are presented in Figure 12.
When the number of reference images is reduced, MagicTailor can still show satisfactory performance.
This also demonstrates that, while more reference images could lead to better generalization ability, one reference image per concept/component is enough to obtain a decent result with our MagicTailor.

### C.3 Robustness on Linking Words

Generally, we use “with” to link the pseudo-words of the concept and component in a text prompt, *e.g.*, “<person> with <beard>, in Von Gogh style”.
Here we evaluate the robustness of our method on different linking words.
We choose several words, which are commonly used to indicate ownership or association, to construct text prompts and then feed them into the same fine-tuned T2I model.
As shown in Figure 13, the generation performance of our MagicTailor remains robust regardless of the linking word used, exhibiting its flexibility to textual descriptions.

Figure: Figure 13: Ablation of the linking word. We present qualitative results generated with different linking words, showing the robustness of MagicTailor.
Refer to caption: x13.png

## Appendix D More Qualitative Results

In Figure 14, we provide more evaluation images for a substantial qualitative comparison.
It can be clearly observed that semantic pollution remains an intractable problem for these compared methods.
While employing the masked diffusion loss, they still fall short in suppressing the appearance of undesired visual semantics.
This is due to the leak of an effective mechanism to alleviate the T2I model’s perception for these semantics.
To address this, our MagicTailor utilizes Dynamic Masked Degradation (DM-Deg) to dynamically perturb undesired visual semantics during the learning phase, and thus achieve better performance.
On the other hand, the compared methods are also severely influenced by semantic imbalance, resulting in overemphasis or even overfitting on the concept or component.
This is because the inherent imbalance of visual semantics complicates the learning process.
In response to this issue, our MagicTailor applies Dual-Stream Balancing (DS-Bal) to balance the learning of visual semantics, effectively showcasing its prowess in this tough task.

Figure: Figure 14: More qualitative comparisons. We present images generated by our MagicTailor and SOTA methods of personalization for various domains including characters, animation, buildings, objects, and animals. MagicTailor generally achieves promising text alignment, strong identity fidelity, and high generation quality.
Refer to caption: x14.png

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 5
presentation: 2
questions: **1. Generalizability:**

Is it possible to use concept/composition pairs that exhibit large geometric deformations?
For example, <person> from upper body portrait images and <eyebrow> from face images.
Another example could be generating an image such as "A photo of <dog> with <human ears>."
Can the model generate more dynamic images such as "A photo of <person> with <beard> riding a bicycle in front of the Eiffel Tower"?

**2. The effectiveness of DM-Deg:** Is DM-Deg more effective than using detailed prompts during training [1]? For instance, if detailed captions describing the image are provided (e.g., "A photo of a <sks> dog in a jungle background..."), the model could use textual prior knowledge to determine where to attend and effectively learn the <sks>.

**3. Complexity Analysis:** It would be helpful to provide an analysis of the time and memory complexity compared to other methods.

**4. SDXL:** The image quality seems limited because of the diffusion backbone. Is the model compatible with other better backbone?


[1] Kim, J., Park, J., & Rhee, W. (2024). Selectively Informative Description Can Reduce Undesired Embedding Entanglements in Text-to-Image Personalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8312-8322).
soundness: 2
strengths: **1. Task Definition:** Unlike prior works that usually focus on multi-concept personalization, this paper uniquely targets component-controllable personalization, which separates the concept and the component and allows control over specific components (e.g., a person's eyebrow) within a larger concept (e.g., the person).

**2. Problem Statement:** The authors clearly depict the challenges they aim to solve (semantic pollution and imbalance), contributing to the community by highlighting a new problem to address.

**3. Comprehensive Evaluation:** The paper includes thorough quantitative and ablation results, showcasing the effectiveness of the proposed method.
summary: This paper proposes a new architecture for component-controllable personalization. It introduces two main design choices: Dynamic Masked Degradation (DM-Deg) to prevent semantic pollution by introducing noise to masked-out image regions, and Dual-Stream Balancing (DS-Bal) to avoid semantic imbalance through Sample-wise Min-Max Optimization and Selective Preserving Regularization.
weaknesses: **1. Complexity of Implementation:** The proposed methods, DS-Bal and DM-Deg, seem complex to implement and computationally demanding, requiring substantial resources.

**2. Limited Generalizability:** The paper primarily shows results in controlled or geometrically similar scenarios (e.g., where the layout in concept and composition are consistent), it raises questions about how the method would perform under more complex or varied conditions.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: Please refer to the weakness.
soundness: 3
strengths: 1. The writing of the paper is clear and easy to follow.
2. This work defines a task named component-controllable personalization, which aims at more precise and fine-grained customization for T2I models,  and this may be of interest to the community in a broader context.
3. The overall structure of the article is clear. Two challenges of component-controllable personalization are discussed, and visual effects are illustrated for better comprehension. Further, DM-Deg and DS-Bal are proposed respectively to handle the above semantic issues.
4. The visual results presented in this paper provide compelling evidence that MagicTailor method surpasses current competing techniques in fine-grained component-controllbale generation for diffusion models.
5. This work contributes a collection of paired data for the proposed novel task, which makes the work easier to be followed by future works.
summary: This paper presents MagicTailor, which achieves fine-grained concept customization for text-to-image diffusion models. Novel task component-controllable personalization is introduced, which allows user to reconfigure and personalize specific components of concepts. Different from existing personalization methods, MagicTailor enable model to capture local appearance from reference images, and generative more creative images with mixture of fine-grained concepts. The paper primarily addresses two challenging issues in component-controllable personalization: semantic pollution and semantic imbalance. The authors clearly explained the above two critical effects in the manuscript, and accordingly present different approaches to alleviate the semantic issues. The authors compare with several existing personalization baselines both quantitatively and qualitatively, and demonstrates the effectiveness of proposed MagicTailor.
weaknesses: 1. The designed method is a little bit complicated. It seems that undesired conflicts may occur during the learning processes of different concepts. Hence the authors present multiple carefully designed constraints for different 'unfortunate' occasions. I wonder whether the proposed methods generalize well when applied to different model architectures, will the hyper-params be carefully chosen again? As a personalization method, is it compatible with some trained LoRAs (e.g. cartoon, realistic) from the community? More experiments should be conducted to test the compatibility of MagicTailor with existing pretrained LoRAs and other model architectures. This would help address the concerns about generalizability and provide valuable information for potential users of the method.
2. Visual results of multiple concepts concepts are limited, I don't find comprehensive discussion when the number of reference fine-grained concepts increase to 4,5 or even more. Will the model suffer from fusion problems under such circumstance? More visual results should be provided (in Fig. 6 (b)) to test the boundary of MagicTailor on multiple concepts generation.
3. It seems that MagicTailor mainly combines concepts from areas which share same semantics (e.g., face from A and eyes from B), I wonder how MagicTailor performs in some more complicated scenarios, for example, I want to generate a beautiful girl wearing a T-shirt from person A, and she has eyes from boy B, and is holding a small dog shown in Picture C. I suggest the authors to study some more complex cases, where different semantic parts from different objects are specified by users, and provide some visual results or quantitative analysis. This would provide valuable insight into the method's limitations and potential applications.
4. Missing important related work, PartCraft [1], which also allows users to select semantic parts for creative personalization. With PartCraft as the pioneer work, The novelty of the task proposed in this paper needs to be rejudged. The authors should present a detailed discussion about the difference between MagicTailor and PartCraft to clearify their contribution. There are also many visual results in PartCraft by composing more than 3 parts from different reference images, the authors is supposed to present more visual comparisons in Figure. 6, so as to show how MagicTailor performs when the number of semantic parts increases. There are many meaningful visual cases from PartCraft (Fig. 7), the authors can refer to them and test some similar cases for more intuitive comprehension.

[1] Ng, Kam Woh, et al. "PartCraft: Crafting Creative Objects by Parts." European Conference on Computer Vision. Springer, Cham, 2025.

---

confidence: 4
contribution: 2
initial_rating: 3
presentation: 3
questions: Address to the weaknesses mentioned above, please.
soundness: 2
strengths: 1. The authors composed a diverse quantitative and qualitative comparisons. They addressed the problem holistically by created ablation studies, user studies, sensitivity studies for hyper parameters, etc.
2. The idea of customizing components and combine them together in a new generated image is relatively new.
summary: The authors of the paper propose a new way of customization of text-to-image synthesis. Instead of customizing by concepts, they propose to customize the components of concepts and synthesis new images with a combination of components and concepts. They also highlight 2 main problems they meet in customization process and propose solutions for them.
weaknesses: 1. The semantic pollution and semantic imbalance issues are too similar and not clearly disentangled. I am not convinced why the authors addressed them as different problems.
2. Lack of evidence to claim the 2 aforementioned problems occur. Only a couple of examples are shown in support.
3. The Dynamic Masked Degradation and Dual-Stream Balancing solutions also seem very similar. They use similar masking approaches. The separate approach of Sample-wise Min-Max Optimization and Selective Preserving Regularization is not clearly justified and seem overcomplication.
4. Lack of novelty in proposed solutions. The masking and pixel degradations methods are not new. They often are used to enhance diversity, avoid overfitting, and localize contributions in text-to-image generation.
5. Not fair comparisons. The authors only used competitors (Text Inversion, DreamBooth, Custom Diffusion, etc..) which are baseline methods in customization and personalization tasks. There are many new methods (such as [1,2,3,4,5, ...]) with new state of the art results
6. Bad quality of results. The generated images in comparisons have poor quality and poor fidelity.
7. In L259-L260, the authors claim that the model may memorize the introduced noise, and the memorization strength is correlated with the training steps. However, in the ablation study in Tab 4, the Linear (Ascent) and Linear (Descent), which are ascending and descending w.r.t training steps, have almost similar results in all metrics. This evidence works agains their claim.



[1] "Customization Assistant for Text-to-image Generation" Yufan Zhou, et al.

[2] "Orthogonal Adaptation for Modular Customization of Diffusion Models" Ryan Po, et al.

[3] "Attention Calibration for Disentangled Text-to-Image Personalization" Yanbing Zhang, et al.

[4] "JeDi: Joint-Image Diffusion Models for Finetuning-Free Personalized Text-to-Image Generation" Yu Zeng, et al.

[5] "A Neural Space-Time Representation for Text-to-Image Personalization" Yuval Alaluf, et al.