DisEnvisioner: Disentangled and Enriched Visual Prompt for Customized Image Generation

## Abstract

Abstract In the realm of image generation, creating customized images from visual prompt with additional textual instruction emerges as a promising endeavor. However, existing methods, both tuning-based and tuning-free, struggle with interpreting the subject-essential attributes from the visual prompt. This leads to subject-irrelevant attributes infiltrating the generation process, ultimately compromising the personalization quality in both editability and ID preservation. In this paper, we present DisEnvisioner , a novel approach for effectively extracting and enriching the subject-essential features while filtering out -irrelevant information, enabling exceptional customization performance, in a tuning-free manner and using only a single image . Specifically, the feature of the subject and other irrelevant components are effectively separated into distinctive visual tokens, enabling a much more accurate customization. Aiming to further improving the ID consistency, we enrich the disentangled features, sculpting them into a more granular representation. Experiments demonstrate the superiority of our approach over existing methods in instruction response (editability), ID consistency, inference speed, and the overall image quality, highlighting the effectiveness and efficiency of DisEnvisioner.
Project page: disenvisioner.github.io .

## 1 Introduction

By training with billions of image-text pairs, state-of-the-art text-to-image generation models, e.g., DALL$\cdot$E (Ramesh et al., ), Imagen (Saharia et al., ), UnCLIP (Ramesh et al., ), Stable Diffusion (SD) (Rombach et al., ), and PixArt-$\alpha$ (Chen et al., ), have demonstrated remarkable proficiency in generating contextually aligned images from textual descriptions.
Despite unprecedented creative capabilities of these text-to-image models, customized image generation poses a new and more intricate challenge. This task aims to synthesize life-like imagery that not only accurately responds to natural language instructions (editability) but also preserves the subject’s identity based on reference images (ID consistency), is garnering great attention from both academia and industry (Chen et al., ; Dong et al., ; Shi et al., ; Ma et al., ; Gal et al., ; Kumari et al., ; Ruiz et al., ; Li et al., ; Wei et al., ; Ye et al., ; Li et al., ; Wang et al., ; Chen et al., ; Arar et al., ; Voynov et al., ).

Figure: Figure 2: Comparisons between DisEnvisioner and existing methods (Ruiz et al., ; Chen et al., ; Li et al., ; Wei et al., ; Ye et al., ) under single-image setting. We evaluate these methods on the same subject with different poses and environments. It can be observed that irrelevant factors, such as the subject’s posture () and background (), can affect the customization quality and result in poor editability () or poor ID consistency (). For instance, BLIP-Diffusion falls in both two factors, leading to poor editability and ID consistency. We denotes its performance as “” (the symbols without color filling, such as “”, indicates that the customization is not affected by subject’s posture, and “” indicates good editability). We also try to use masks to filter out irrelevant information for these methods. However, the harmful influence of subject’s posture still exists. And solid background colors (*e.g.*, white or black) also can harmfully impact the customization quality, leading to textureless backgrounds.
Refer to caption: x2.png

For high-quality image customization, accurate interpretation of the visual prompt (i.e., the input image) is crucial. This involves effectively extracting subject-essential attributes from the reference image while minimizing the influence of subject-irrelevant attributes.
Failure to do so may result in
1) overemphasis on irrelevant details: generated images may prioritize irrelevant information, sidelining the textual instructions and compromising the overall editability;
2) diminished subject identity: the feature of subject-essential attributes becomes entangled with irrelevant information, degrading the subject representation.

Existing methods, both tuning-based (Ruiz et al., ; Gal et al., ; Kumari et al., ; Chen et al., ; Dong et al., ) and tuning-free (Wei et al., ; Li et al., ; Ye et al., ; Ma et al., ; Li et al., ), struggle to accurately interpret subject-essential attributes, particularly given only a single reference image.
Specifically, prevailing tuning-based methods
like DreamBooth (Ruiz et al., ) and DisenBooth (Chen et al., ), heavily rely on multiple reference images to capture the common subject concept into the model.
Despite their impressive results, it is difficult to interpret the subject-essential attributes in single-image scenarios, leading to compromised customization quality (Fig. 2).
Additionally, each subject requires individual fine-tuning, which is time-consuming and hinders practical application.
Recent tuning-free methods (Wei et al., ; Ma et al., ; Li et al., ; Shi et al., ; Ye et al., ; Li et al., ; Chen et al., ), aim to offer a significant boost in inference speed, and can generate customized images with a single reference image.
However, these approaches still fail to accurately disentangle the subject-essential features from the reference image.
Among those methods, IP-adapter (Ye et al., ) and PhotoVerse (Chen et al., ) directly treat the global feature of the given image as subject-essential, inevitably introducing the subject-irrelevant information that diminishes the personalizing quality.
Moreover, ELITE (Wei et al., ) and BLIP-Diffusion (Li et al., ) attempt to learn subject representations, but their implicit extraction of subject features proves ineffective (please see Sec. 2.2 for detailed discussion), also leading to unsatisfactory customization performance hindered by irrelevant information.
As illustrated in Fig. 2, the customization quality of these methods are notably compromised by irrelevant factors (we conclude the primary irrelevant factors in this case as: subject’s posture, background and image tone).
A direct solution of these methods to prevent the irrelevant information is to use a segmentation mask to remove the background, but it is inadequate. As shown in Fig. 2: ① factors like the subject’s pose still introduce irrelevant details; ② replacing the background with solid colors (*e.g.*, white or black) also influence the customization, potential leading to texture-less backgrounds in the generated customized images.
Thus, an effective disentanglement of the subject-essential features is indeed necessary.

Figure: Figure 3: Overview of DisEnvisioner, which consists of two key components: DisVisioner and EnVisioner. During inference, subject-irrelevant features are discarded to avoid harmful disturbances.
Refer to caption: x17.png

Motivated by the above analysis, we propose DisEnvisioner, a novel framework meticulously designed to addresses the core issues via feature disentanglement and enrichment.
As illustrated in Fig. 3, the image is tokenized into compact disentangled features, *i.e.*, subject-essential and subject-irrelevant tokens, through the DisVisioner.
Thus, the subject-irrelevant features can be filtered, making
sure the model only focus on essential attributes of the subject, facilitating more accurate editability during customization.
The disentangled subject-essential features are further enriched by EnVisioner before feeding into the pre-trained SD model (Rombach et al., ) for customized generation, significantly boosting the ID-consistency and the overall customization quality. Together with above innovations, we achieve both accurate and high-quality image customization.

In summary, our key contributions are as follows.

- •
We emphasize the critical role of subject-essential attribute in customized image generation, which is the foundation of faithful subject concept reconstruction and reliable editability, thereby ensuring more accurate customization under more diverse textual instructions.
- •
We present DisEnvisioner, a simple yet effective framework designed for single-image, tuning-free image customization, utilizing visual disentanglement and enrichment.
- •
Comprehensive experiments validate DisEnvisioner’s superiority in adhering to instructions, maintaining ID consistency, and inference speed, demonstrating its superior personalization capabilities and efficiency.

## 2 Related Works

### 2.1 Text-to-Image Generation

In the field of text-to-image generation, the evolution of methodologies has transitioned from Generative Adversarial Networks (GANs) (Goodfellow et al., ; Zhang et al., ; ; ; He et al., ; Karras et al., ; ; ) to advanced Diffusion Models (Ho et al., ; Ramesh et al., ; Saharia et al., ; Ramesh et al., ; Nichol et al., ; Chen et al., ; He et al., ; Rombach et al., ).
Early GAN-based models like StackGAN (Zhang et al., ; ), AttnGAN (Xu et al., ), and XMC-GAN (Zhang et al., ) introduced multi-stage generation and attention mechanisms to improve image fidelity and alignment with textual descriptions.
A significant breakthrough, DALL$\cdot$E (Ramesh et al., ), utilizes a transformer and auto-regressive model to merge text and image data, trained on 250M pairs for high-quality, intuitive image synthesis.
Following this, a series of diffusion-based methods such as GLIDE (Nichol et al., ), DALL$\cdot$E2 (Ramesh et al., ), and Imagen (Saharia et al., ) have been introduced, offering enhanced image quality and textual coherence.
The Latent Diffusion Model (LDM) (Rombach et al., ),
trained on 5 billion pairs, further enhanced training efficiency without compromising performance, becoming a community standard.
Despite remarkable strides in generating images from textual descriptions, current methodologies fall short in rendering customized visual concepts from reference images. In our paper, we dive into customized image generation, extending the capabilities of existing text-to-image techniques to craft personalized visual concepts.

### 2.2 Customized Image Generation

Existing methods in the field of customized image generation primarily fall into two categories: tuning-based and tuning-free.
Tuning-based methods (Gal et al., ; Ruiz et al., ; Dong et al., ; Han et al., ; Kumari et al., ; Chen et al., ; Hua et al., ) involve fine-tuning a pre-trained generative model with several reference images of a specific concept during test-time.
Despite the effectiveness of these methods, their high computational demand and scalability challenges limit the practicality.
In contrast, tuning-free methods (Jia et al., ; Shi et al., ; Li et al., ; Ma et al., ; Ye et al., ; Wei et al., ; Chen et al., ; Li et al., ; Wang et al., )
employ advanced encoders to represent customized visual concepts, enabling image generation without the need of test-time fine-tuning and leading to significantly improved efficiency.
However, InstantBooth (Shi et al., ) and PhotoMaker (Li et al., ) still rely on multiple reference images to ensure the ID fidelity, and more recent works start to focus on singe-image scenarios.
IP-Adapter (Ye et al., ) introduces a coupled cross-attention mechanism that handles visual and textual prompts separately, allowing for fine-grained visual concept generation using only a single reference image.
PhotoVerse (Chen et al., ) employs a similar dual-attention strategy, with an additional identity loss to further enhance ID consistency. However, both IP-Adapter and PhotoVerse directly treat the global feature of the given image as subject-essential, inevitably introducing the subject-irrelevant information that diminishes the personalizing quality.
BLIP-Diffusion (Li et al., ) and ELITE (Wei et al., ) attempt to learn subject-essential representations. BLIP-Diffusion derives subject representations by querying image features with subject names using BLIP-2 (Li et al., ).
However BLIP-2 is pre-trained to extract global image feature aligned with global text prompts. It may struggle to accurately query clean local subject-essential features when the given subject names describe only the local information of a whole image. Thus, BLIP-Diffusion may still fall short in filtering out subject-irrelevant attributes.
ELITE (Wei et al., ) separates subject attributes from others by utilizing CLIP features from different layers, assuming that the subject is represented by the deeper features and other concepts by shallower features. However, since the deep and shallow features are not fully disentangled, the subject attributes obtained through this multi-layer approach still remains inaccurate.
Additionally, ELITE requires segmentation masks to combine the masked CLIP image features and the obtained subject feature for enhanced details. However, the segmenting process is cumbersome and the masked image feature is still inaccurate and can introduce subject-irrelevant factors.
In this paper, although DisEnvisioner also adopts a two-stage pipeline following BLIP-Diffusion and ELITE, unlike these methods,
DisEnvisioner focuses more on explicitly extracting the subject-essential attributes via DisVionser (Sec. 3.2), achieving more accurate customization. Then, we enrich the disentangled features by EnVisioner (Sec. 3.3), rather than introducing additional features as in ELITE (Wei et al., ), to further enhance the ID consistency without unnecessary disturbance.

## 3 Method

The goal of customized image generation is to synthesize lifelike images that adhere to the textual instructions while preserving the subject’s identity from the reference image.
To tackle the pivotal challenge of minimizing the influence of subject-irrelevant attributes and enhancing subject-essential attributes, we introduce DisEnvisioner—a novel approach focused on disentangling and enriching subject-essential attributes.
As depicted in Fig. 3, DisEnvisioner initiates the process by disentangling the image features into subject-essential and -irrelevant attributes by the DisVisioner. Subsequently, to bolster the consistency of the subject’s identity, the EnVisioner is further employed to refine the subject-essential features into a more fine-grained representation. Our discussion commences with an overview of basic concepts (Sec. 3.1), followed by an in-depth exploration of the DisVisioner (Sec. 3.2) and EnVisioner (Sec. 3.3).

### 3.1 Preliminaries

Diffusion models (Ho et al., ; Ramesh et al., ; Saharia et al., ; Ramesh et al., ; Nichol et al., ; Chen et al., ) represent the current state-of-the-art in generative modeling for high-fidelity image generation, it involves forward diffusion and reverse denoising processes. The forward process incrementally introduces noise to the data, transforming it into a Gaussian random noise by a Markov chain over a fixed number of steps $T$. In the reverse phase, a learned neural network is utilized to predict the added noise at each step, thereby recovering the original data from the noisy sample. This noising-denoising mechanism enables diffusion models to achieve impressive results in generating high-quality images with fine details and realism.

In this study, we adopt Stable Diffusion (SD) (Rombach et al., ), a latent diffusion model built upon UNet (Ronneberger et al., ), as our foundational generative model.
Firstly, an auto-encoder (VAE) $\left\{\mathscr{E}(\cdot),\mathscr{D}(\cdot)\right\}$ is trained to map between RGB space and the latent space, i.e., $\mathscr{E}(\mathbf{x})=\mathbf{z}$, $\mathscr{D}(\mathscr{E}(\mathbf{x}))\approx\mathbf{x}$. And the textual conditions are obtained using a pre-trained CLIP text encoder $\bm{c}=\psi_{\theta}^{\text{T}}(y)$, where $y$ is the given prompt.
The training objective is:

$$ $L_{\text{LDM}}(\theta)=\mathbb{E}_{\textbf{x}_{0},\bm{c},\bm{\epsilon}_{t}\sim \mathcal{N}(0,1),t\in[1,T]}\Big{[}\|\bm{\epsilon}_{t}-\bm{\epsilon}_{\theta}( \textbf{z}_{t},\bm{c},t)\|_{2}^{2}\Big{]},$ (1) $$

where $\bm{\epsilon}_{t}$ represents the noise introduced during the forward process, and $\bm{\epsilon}_{\theta}(\textbf{z}_{t},\bm{c},t)$ is the predicted noise.
Cross-attention is adopted to introduce textual condition into SD.
Specifically, the latent image feature $\bm{f}\in\mathbb{R}^{(\text{H}\times\text{W})\times d_{\text{q}}}$ and text condition $\bm{c}\in\mathbb{R}^{n_{y}\times d_{\text{k}}}$ are firstly projected to obtain query $\bm{Q}=\bm{w}_{\texttt{to_q}}\circ\bm{f}$, key $\bm{K}=\bm{w}_{\texttt{to_k}}\circ\bm{c}$, and value $\bm{V}=\bm{w}_{\texttt{to_v}}\circ\bm{c}$, where $\bm{w}$ are weights of the corresponding mapping layers.
Then, the cross-attention is calculated as:

$$ $\operatorname{Attention}(\bm{Q},\bm{K},\bm{V})=\operatorname{SoftMax}\left( \frac{\bm{Q}\bm{K}^{\text{T}}}{\sqrt{d_{\text{k}}}}\right)\bm{V}.$ (2) $$

During inference, the model iteratively constructs images $\mathbf{x}_{0}$ from random noises $\mathbf{z}_{T}$ through reverse denoising process.

Figure: (a) DisVisioner
Refer to caption: x18.png

### 3.2 DisVisioner

As revealed in (Wei et al., ; Li et al., ; Ye et al., ), the customized image can be encoded as a sequence of tokens via inversion. In our work, we propose to disentangle the image feature into subject-essential and -irrelevant tokens using an image tokenizer (Wu et al., ).
Image tokenizer is a method to aggregate the image features into compact visual tokens, with each token corresponds to a distinct visual component. As illustrated in Fig. 4(a), given a reference image $\mathbf{x}_{\text{ref}}$, we firstly transform them using an augmentation set. The transformed image $\mathbf{x^{\prime}}_{\text{ref}}$ ensures that the model can extract the effective subject-essential feature to reconstruct the original image, rather than merely duplicating the subject. Subsenquently, we employ the CLIP image encoder to extract image features $\bm{\upsilon}_{\text{ref}}=\psi_{\theta}^{\text{I}}(\mathbf{x^{\prime}}_{\text
{ref}})\in\mathbb{R}^{(\text{H}\times\text{W})\times d_{\text{k}}}$.
The image tokenizer network $M(\cdot)$ is then utilized to obtain the disentangled visual tokens $\mathcal{V}_{\text{d}}$:

$$ $\mathcal{V}_{\text{d}}=M(\bm{q}_{\text{d}},\psi_{\theta}^{\text{I}}(\mathbf{x^ {\prime}}_{\text{ref}}))$ (3) $$

where $\bm{q}_{\text{d}}\in\mathbb{R}^{(n_{{s}}+n_{{i}})\times d_{\text{q}}}$ is the queries
for feature aggregation, $n_{s}$ for the subject feature while $n_{{i}}$ for other irrelevant features. In image tokenizer $M(\cdot)$, a spatial-wise cross-attention mechanism is firstly adopted to aggregate the image features, where $\bm{q}_{\text{d}}$ is the query and $\psi_{\theta}^{\text{I}}(\mathbf{x}_{\text{ref}})$ serves as the key and value. The image features are separated into two mutually independent and orthogonal sets of features according to $\bm{q}_{\text{d}}$ after the spatial-wise attention mechanism. This mutual independence and orthogonality are ensured by the $\operatorname{SoftMax}(\cdot)$ function applied at spatial dimension within the spatial-wise attention.
In order to determine the sequence of disentangled features, we initialize $\bm{q}_{\text{d}}$ with the CLIP prior, as revealed in (Li et al., ). Specificallt, the $n_{s}$ queries for subject are initialized with random vectors, while the remaining $n_{i}$ queries are initialized using CLIP class name embeddings to query the subject-irrelevant tokens. Transformer blocks are followed to further refine the disentangled features to finally obtain $\mathcal{V}_{\text{d}}$ as the output of our DisVisioner.

To train the image tokenizer $M(\cdot)$, we insert the disentangled features $\mathcal{V}_{\text{d}}$ into textual feature space of the prompt by replacing the textual embedding of placeholder $S^{*}$, and adopt the Eq. 1 as the training objective for the target of reconstruction. Following (Wei et al., ; Kumari et al., ), the entire SD model is frozen except the to_k and to_v layers of the cross-attention module for correct interpretation of the new disentangled tokens. In our implementation, we set $n_{s}=1$ and $n_{i}=1$ for subject-essential and irrelevant features, respectively. Excessive tokens will lead to inaccurate disentanglement, thus impairing the customization quality (please refer to Sec. 4.4 for more details).

Benefiting from image tokenizer, the subject-essential features are accurately compressed into the tokens, clearly and accurately separated from irrelevant features via the spatial-wise attention space. During customized image generation, the subject-irrelevant token will be discarded for exclude the unwanted disturbance, facilitating the editing accuracy and ID fidelity.

### 3.3 EnVisioner

While DisVisioner effectively extracts the subject-essential feature into a single token, it may be inadequate for capturing the detailed nuances of the customized subject. In EnVisioner, we
map the disentangled features into a sequence of granular tokens using new projectors $P^{s}(\cdot)$ and $P^{i}(\cdot)$. The utilization of separate projectors also guarantees the disentanglement between the subject-essential and -irrelevant tokens.

As shown in Fig. 4(b), the disentangled tokens $\mathcal{V}_{\text{d}}$ from DisVisioner is enriched into multiple tokens:

$$ $\Upsilon^{s}_{\text{e}}=P^{{s}}\circ\bm{\tau}^{{s}}_{\text{d}},\ \Upsilon^{i}_ {\text{e}}=P^{{i}}\circ\bm{\tau}^{{i}}_{\text{d}},$ (4) $$

where $\tau^{{s}}_{\text{d}},\tau^{{i}}_{\text{d}}\in\mathbb{R}^{1\times d}$ denote the disentangled subject-essential and the irrelevant feature through DisVisioner ($\mathcal{V}_{\text{d}}=[\tau^{{s}}_{\text{d}},\tau^{{i}}_{\text{d}}]$). $\Upsilon^{s}_{\text{e}}\in\mathbb{R}^{n^{\prime}_{s}\times\text{d}}$ is the enriched subject-essential tokens from $\tau^{{s}}_{\text{d}}$, and $\Upsilon^{i}_{\text{e}}\in\mathbb{R}^{n^{\prime}_{i}\times\text{d}}$ is the irrelevant tokens projected from $\tau^{{i}}_{\text{d}}$.
By enriching the subject-essential token into multiple ones, the disentangled concept is further enhanced into a more granular representation, improving especially the ID consistency between the synthesized image and the reference image.

Similar to DisVisioner, we also employ Eq. 1 as the training objective to train the feature projectors in EnVisioner, while keeping the entire SD model and all DisVisioner modules frozen. Additionally, we separatelt introduce cross-attention layers for subject-essential tokens and -irrelevant ones. This separate injection strategy further ensures that the subject feature will not be interfered with by other irrelevant factors.
Specifically, given the latent diffusion feature $\bm{f}\in\mathbb{R}^{(\text{H}\times\text{W})\times d_{\text{q}}}$ and text instruction $\bm{c}\in\mathbb{R}^{n_{y}\times d_{\text{k}}}$, the cross-attention output $\bm{f}^{\prime}$ is derived through three decoupled cross-attention layers, which can be described via the following Equation:

$$ $\displaystyle\bm{f}^{\prime}=\operatorname{Attention}(\bm{Q},\bm{K},\bm{V})+ \lambda_{s}\operatorname{Attention}(\bm{Q},\bm{K}^{s},\bm{V}^{s})+\lambda_{i} \operatorname{Attention}(\bm{Q},\bm{K}^{i},\bm{V}^{i}),$ (5) $\displaystyle\text{where }K^{s}=\bm{w}_{\texttt{to_k}}^{s}\circ\Upsilon^{s}_{ \text{e}},V^{s}=\bm{w}_{\texttt{to_v}}^{s}\circ\Upsilon^{s}_{\text{e}};K^{i}= \bm{w}_{\texttt{to_k}}^{i}\circ\Upsilon^{i}_{\text{e}},V^{i}=\bm{w}_{\texttt{ to_v}}^{i}\circ\Upsilon^{i}_{\text{e}},$ $$

the $\operatorname{Attention}(\cdot)$ is defined in Eq. 2, $\bm{w}_{\texttt{to_k}}^{s}$, $\bm{w}_{\texttt{to_v}}^{s}$, $\bm{w}_{\texttt{to_k}}^{i}$ and $\bm{w}_{\texttt{to_v}}^{i}$ are trainable mapping layers. During training, weights $\lambda_{s}$ and $\lambda_{i}$ are fixed at $1.0$. During inference, for the purpose of effectively ignoring subject-irrelevant features encoded in $\Upsilon^{i}_{\text{e}}$ , we set $\lambda_{i}=0$.

## 4 Experiments

### 4.1 Experimental Setup

#### 4.1.1 Training Dataset

We use the training set of OpenImages V6 (Kuznetsova et al., ) to train the DisEnvisioner.
It contains about 14.61M annotated boxes across 1.74M images.
Based on this dataset, we construct 6.82M {prompt, image} pairs for training, where the images are cropped and resized ($256\times 256$ for DisVisioner and $512\times 512$ for EnVisioner) according to the bounding box annotations, and the text prompts are obtained by randomly selecting a CLIP ImageNet template (Radford et al., ) and integrating the annotated class names into it.

#### 4.1.2 Evaluation Dataset and Metrics

The evaluation is carried out on the DreamBooth (Ruiz et al., ) dataset, which comprises 30 subjects and 158 images in total (4$\sim$6 images per subject).
For quantitative evaluation, 25 editing prompts (Ruiz et al., ) are used for each image. We inference 40 times for each {prompt, image} pair, generating 158,000 customized images for evaluation across 6 metrics.

In alignment with previous methodologies (Ruiz et al., ; Kumari et al., ; Wei et al., ; Li et al., ), we utilize:
1) CLIP Text-alignment (C-T) to measure the instruction response fidelity; 2) CLIP Image-alignment (C-I) and 3) DINO Image-alignment (D-I) (Caron et al., ) to evaluate the ID consistency with the reference image.
Additionally, we introduce a novel metric, *i.e.*, Internal Variance (IV) of image-alignment, aiming at quantifying the impact of irrelevant factors. IV measures the variance of customization results for images containing the same subject but different environments (like the top three rows in Fig. 2). Lower IV value indicates that the results only be controlled by the subject and textual instruction, rather than the irrelevant factors from environments.
In terms of efficiency, we record the 6) Inference-time (T) on single NVIDIA A800 GPU for evaluation. Moreover, we calculate the mean ranking (mRank) of all metrics for each method to show the comprehensive performance.

#### 4.1.3 Implementation Details.

DisEnvisioner is built upon Stable Diffusion v1.5
, employing OpenCLIP ViT-H/14
model as the image/text encoder.
During training, DisVisioner is configured with batch size of 160, learning rate of 5e-7 at the resolution of 256. The training steps is 120K. We set the token number $n_{s}=1$ and $n_{i}=1$,
for subject-essential feature and -irrelevant respectively.
The EnVisioner employs the batch size of 40, learning rate of 1e-4 at the resolution of 512. The training steps is also 120K. The enriched token number is $n_{s}^{\prime}=4$ and $n_{i}^{{}^{\prime}}=4$, with attention scale $\lambda_{s}=1.0$ and $\lambda_{i}=1.0$.
All experiments are conducted on 8 NVIDIA A800 GPUs using the AdamW optimizer (Loshchilov & Hutter, ) with a weight decay of 0.01.
To enable classifier-free guidance (Dhariwal & Nichol, ), we use a probability of 0.05 to drop the condition, both textual and visual.
During inference, $\lambda_{i}$ is set to 0 to eliminate irrelevant feature. In addition, we use the DDIM sampler (Song et al., ) with 50 steps and the scale of classifier-free guidance is set to 5.0.

**Table 1: Quantitative comparisons with existing methods. The evaluation metrics include text alignment for assessing editability (C-T), image-alignment for ID-consistency (C-I, D-I), internal variance to demonstrate the resistance to subject-irrelevant factors (IV), and inference time for efficiency (T). DisEnvisioner demonstrates better comprehensive performance than other methods. Top results are in bold; second-best are underlined.^$*$For equity, we assess only inference (and test-time tuning, if applicable) times, omitting I/O operations. ^§For equity, we consider C-I and D-I as two sub-indicators of image-alignment, the rank of each with a weight of 0.5 in the mRank calculation, while the ranks of all other metrics have a weight of 1.0.**
| Method | C-T$\uparrow$ | C-I^§$\uparrow$ | D-I^§$\uparrow$ | IV$\downarrow$ | T^$*$$\downarrow$ (s) | mRank$\downarrow$ |
| --- | --- | --- | --- | --- | --- | --- |
| DisenBooth (Chen et al., ) | 0.303 | 0.760 | 0.781 | 0.041 | 2.42e+3 | 4.8 |
| DreamBooth (Ruiz et al., ) | 0.286 | 0.842 | 0.849 | 0.039 | 1.12e+3 | 4.3 |
| ELITE (Wei et al., ) | 0.287 | 0.792 | 0.770 | 0.036 | 4.12 | 4.1 |
| IP-Adapter (Ye et al., ) | 0.275 | 0.883 | 0.912 | 0.033 | 1.98 | 3.3 |
| BLIP-Diffusion (Li et al., ) | 0.295 | 0.785 | 0.765 | 0.029 | 1.10 | 2.9 |
| DisEnvisioner | 0.315 | 0.828 | 0.802 | 0.026 | 1.96 | 2.0 |

Figure: Figure 5: Qualitative comparison on live subjects. Comparing two subjects across various scenarios, DisEnvisioner excels in editability and ID-consistency. Notably, the animal’s posture does not affect customization, showcasing our strength in capturing subject-essential features.
Refer to caption: x20.png

Figure: Figure 6: Qualitative comparison on non-live subjects. Besides offering better editability of textual instructions, DisEnvisioner also excels in preserving accurate subject identity. Moreover, images generated by DisEnvisioner are minimally affected by irrelevant elements of reference images.
Refer to caption: x21.png

### 4.2 Quantitative Results

We compare DisEnvisioner against five leading methods according to metrics specified in Sec. 4.1.2.
For the tuning-based methods, we chose DreamBooth (Ruiz et al., ) and DisenBooth (Chen et al., ), and implement them within the single-image setting.
Our tuning-free comparison covers all available open-source methods, they are ELITE (Wei et al., ), BLIP-Diffusion (Li et al., ), and IP-Adapter (Ye et al., ).
As demonstrated in Tab. 1, our approach achieves the highest text-alignment score (C-T), indicating its effectiveness in eliminating subject-irrelevant information and significantly enhancing editability.
Although IP-Adapter (Ye et al., ) and DreamBooth (Ruiz et al., ) achieve high image-alignment scores (C-I and D-I), they struggle with editability. This is because they tend to replicate large portions of the reference image, resulting in excessive consistency and reduced flexibility in making edits (Fig. 5 and 6).
Excluding these two methods, our approach achieves the highest image-alignment scores (C-I and D-I), demonstrating its superior ID consistency without compromising text-alignment.
We also achieve lower internal variance (IV) between the generated images created with the same subject under different conditions, further demonstrating that DisEnvisioner is unaffected by irrelevant factors, such as subject pose and its surroundings.
In terms of efficiency, thanks to our lightweight and effective design, eliminating the test-time tuning, only 1.96s is required for each customized generation.
We also conduct a user study, as detailed in the supplementary materials, which further demonstrates the superiority of DisEnvisioner.

### 4.3 Qualitative Results

To obtain deeper insights of the DisEnvisioner, we visualize its synthesized images against the selected five prevailing methods
across three different images per subject.
Fig. 5 and 6 clearly demonstrates DisEnvisioner’s superiority in producing high-quality, editable images with strong ID-consistency.
Notably, the consistency in animal postures alongside the minimal impact of irrelevant backgrounds across reference images under the same textual instruction, also showcase our robustness and resistance to subject-irrelevant elements, *i.e.*, only the subject-essential attributes are extracted and preserved.
This excellence also applies to non-live objects, as seen in Fig. 6, where exceptional customizations for subject the boot and sunglasses demonstrate DisEnvisioner’s accurate focus on subject-essential features, leading to superior editability, ID-consistency, and the overall visual quality of the generated images.

### 4.4 Ablation Study

Figure: Figure 7: Ablation on token’s numbers in DisVisioner. Each attention map is calculated by the dot product of the obtained token ($\tau^{{s}}_{\text{d}},\tau^{{i}}_{\text{d}}$) and the CLIP local image features. The results demonstrates that $n_{s}=1$ and $n_{i}=1$ outperforms other configurations, achieving precise attention map and faithful token inference.
Refer to caption: x22.png

Figure: Figure 8: Ablation of EV.
Refer to caption: x23.png

#### 4.4.1 Influence of Tokens Numbers in DisVisioner.

As described in Sec. 3.2, the disentangled tokens are injected into the textual space of CLIP encoder, thus each token tends to represent a complete semantic meaning, similar to “words”. Meanwhile, due to the mutual exclusivity between subject-essential and -irrelevant features in DisVisioner, an excessive number of tokens leads to increased competition, making disentanglement difficult and chaotic. For instance, in Wu et al. (), only four tokens are used to represent a complex outdoor scene, suggesting that fewer tokens may be more effective for simpler subjects.
To achieve semantic completeness and avoid excessive feature competition, we conduct experiments on the number of subject-essential and -irrelevant tokens, *i.e.*, $n_{s}$ and $n_{i}$.
As visualized in Fig. 7, when $n_{s}\neq n_{i}$, the image is represented by the larger set without any disentanglement.
When $n_{s}=n_{i}>1$, random initialized subject tokens have more “freedom”, capturing most image information and also leading to reconstruction. We also observe that all $n_{i}$ tokens are the same, that is because they use the same prior discussed in Sec. 3.2. In summary, $n_{i}=1$ and $n_{s}=1$ performs the best.

#### 4.4.2 Effect of EnVisioner.

As shown in Fig. 8, we validate the effect of enriched subject representation in EnVisioner (abbreviated as EV). The prompt is “*a photo of S* on the beach*”.
We can observe that the images enhanced by EnVisioner exhibit finer details and improved ID-consistency, with a significant boost in overall image quality,
emphasizing its crucial role in delivering high-quality customization.

## 5 Conclusion

In this paper, we propose DisEnvisioner, which is characterized by its emphasis on the interpretation of subject-essential attributes for high-quality image customization.
DisEnvisioner effectively identifies and enhances the subject-essential feature while filtering out other irrelevant information, enabling exceptional image customization without cumbersome tuning or relying on multiple reference images.
Through both the quantitative and qualitative evaluations, alongside the user study, we demonstrate DisEnvisioner’ superior performance in customization quality and efficient inference time, offering a promising solution for practical applications.

Supplementary Materials of
DisEnvisioner: Disentangled and Enriched Visual Prompt for Customized Image Generation

## Appendix A User Study

We also conduct a user study using a round-robin format, where participants grade each method given the generation outputs across 3 images of the same subject in each round. There are 5 rounds in total. The metrics are text alignment (TA), subject identity alignment (IA), internal variance of those 3 customized images (IV), and image quality (IQ). A total of 69 users, and 345 rounds are recorded. As detailed in Table 2, DisEnvisioner outperforms all baselines in all metrics.

**Table 2: User study. Participants rank the methods based on four criteria using a round-robin format, with the final scores are normalized before being reported. mRank is also reported.**
| Method | TA$\uparrow$ | IA$\uparrow$ | IV$\uparrow$ | IQ $\uparrow$ | mRank$\downarrow$ |
| --- | --- | --- | --- | --- | --- |
| DisenBooth (Chen et al., ) | 0.157 | 0.143 | 0.156 | 0.140 | 5.3 |
| DreamBooth (Ruiz et al., ) | 0.159 | 0.152 | 0.159 | 0.158 | 3.5 |
| ELITE (Wei et al., ) | 0.161 | 0.147 | 0.154 | 0.147 | 4.3 |
| IP-Adapter (Ye et al., ) | 0.153 | 0.177 | 0.152 | 0.181 | 4.0 |
| BLIP-Diffusion (Li et al., ) | 0.159 | 0.162 | 0.163 | 0.165 | 2.8 |
| DisEnvisioner | 0.211 | 0.219 | 0.216 | 0.209 | 1.0 |

During each round of the user study, rather than ranking DisEnvisioner and five other existing methods from the best to the worst, users are expected to assign grades from 0 to 5 to each method according to specific metrics.
In practice, all participants have the complete freedom to grade any method with any score based on their personal judgment.
After a total of 345 rounds of evaluation, the best-performing method often receives the highest scores, while scores for other methods are frequently identical due to similar customization quality. Additionally, it is uncommon for users to assign scores as low as 0 or 1.
Although the grading differences among methods are not particularly large, DisEnvisioner consistently outperforms others competitors across all evaluation criteria.

## Appendix B Effect of λ s subscript 𝜆 𝑠 \lambda_{s} italic_λ start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT and λ i subscript 𝜆 𝑖 \lambda_{i} italic_λ start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT

Figure: Figure 9: Effect of varying $\lambda_{s}$ and $\lambda_{i}$ with class name provided. The prompt is “a dog is wearing a Santa hat with a city in the background”.
Refer to caption: x24.png

Figure: Figure 10: Comparison with single-stage and current two-stage training. “stg.” represents the training stage.
Refer to caption: x25.png

As defined in Eq. 5 of the main paper, the weights $\lambda_{s}$ and $\lambda_{i}$ serve to modulate the integration of information that is essential and irrelevant to the subject in the given reference image. To thoroughly assess their effect of disentanglement, we adjust their values evenly sampled from 0 to 1.0 throughout the image customization process.

We generate images under varying settings of $\lambda_{s}$ and $\lambda_{i}$ employing both empty and non-empty (editing) prompts.
Fig. 11 and 12 demonstrate that as $\lambda_{i}$ decreases progressively (moving from the right to the left columns), the presence of subject-irrelevant disturbances in the images notably declines.
Also, enhancing $\lambda_{s}$ (moving from the bottom to the top rows) brings more pronounced consistency in subject identity between the reference and generated image.
When both $\lambda_{s}$ and $\lambda_{i}$ are reduced to their minimum value, *i.e.*, $\lambda_{s}=0$ and $\lambda_{i}=0$, the generated images are solely influenced by the textual prompt, without incorporating any information from the reference image.
To further explore the role of additional information in the prompt, we also generate images with specific class names included in the prompts.

As illustrated in Fig. 9 and Fig. 12, particularly in terms of identity consistency, no matter the category-guidance (for instance, the class name “dog”) is provided or not, it does not alter the customization quality.
This indicates that DisEnvisioner effectively deciphers and extracts subject-essential attributes from the reference image. By doing so, it can accurately identify relevant and redundant information, which is then eliminated.

It is also evident that when image generation focuses exclusively on solely subject-essential features ($\lambda_{s}=1.0$ and $\lambda_{i}=0$) or solely on purely subject-irrelevant features ($\lambda_{s}=0$ and $\lambda_{i}=1.0$), the reproduction of the subject and the irrelevant surrounding content is achieved independently, devoid of any interference from one another.
This phenomenon confirms the proficiency of DisEnvisioner in precisely segregating and enriching subject-essential features. It highlights the DisEnvisioner’s exceptional customization performance without the need for test-time tuning, and relying solely on a single reference image.

Figure: Figure 11: Effect of varying $\lambda_{s}$ and $\lambda_{i}$ with empty prompts without providing class names.
Refer to caption: x26.png

Figure: Figure 12: Effect of varying $\lambda_{s}$ and $\lambda_{i}$ with non-empty prompts without providing class names.
Refer to caption: x27.png

## Appendix C Can DisEnvisioner Be Trained in Single-stage?

For single-stage training, we combine the training processes of DisVisioner and EnVisioner, aiming for simultaneous learning the disentanglement and enrichment of subject-essential features. However, in Fig. 10, the single-stage model is difficult to be trained. It fails to capture any subject-essential information from the visual prompt and merely response to text instructions.
Nonetheless, the two-stage strategy of DisEnvisioner—separating disentanglement and enrichment—proves to be much more effective in high-quality customization.

## Appendix D More Visualizations of Feature Disentanglement

As shown in Fig. 13, we provide more visualizations of feature disentanglement, across live and non-live subjects. With the $n_{s}=1$ and $n_{i}=1$, DisVisioner can clearly disentangle subject-essential and -irrelevant features, achieving more accurate image customization quality in diverse scenarios.

Figure: Figure 13: More visualizations of feature disentanglement. The results showcase DisVisioner’s ability to accurately discern subject-essential attributes across diverse scenarios.
Refer to caption: x28.png

## Appendix E More Experimental Details

### E.1 Training Data

In the experiments, we utilize the *training set* of OpenImages V6 (Kuznetsova et al., ) as the training dataset. Based on this dataset, we construct {prompt, image} pairs for training. As depicted in Fig. 14, the training images are derived by cropping and resizing the raw images in accordance with the bounding box annotations. To ensure the quality of the training images, we further filter the cropped images. A cropped image is considered as unsatisfactory and therefore excluded if its area is greater than 80% or less than 2% of the original image’s area. As a result, out of 14.61M annotated bounding boxes, we obtain 6.82M {prompt, image} pairs. The text prompts are selected randomly from a CLIP ImageNet template (Radford et al., ) and integrated with labelled class names. The complete list of CLIP templates is provided below:

Figure: Figure 14: Examples of training data. The training images are derived by cropping and resizing the raw images in accordance with the bounding box annotations.
Refer to caption: x29.png

- •
“a photo of a $S^{*}$”,
- •
“a rendering of a $S^{*}$”,
- •
“a cropped photo of the $S^{*}$”,
- •
“the photo of a $S^{*}$”,
- •
“a photo of a clean $S^{*}$”,
- •
“a photo of a dirty $S^{*}$”,
- •
“a dark photo of the $S^{*}$”,
- •
“a photo of my $S^{*}$”,
- •
“a photo of the cool $S^{*}$”,
- •
“a close-up photo of a $S^{*}$”,
- •
“a bright photo of the $S^{*}$”,
- •
“a cropped photo of a $S^{*}$”,
- •
“a photo of the $S^{*}$”,
- •
“a good photo of the $S^{*}$”,
- •
“a photo of one $S^{*}$”,
- •
“a close-up photo of the $S^{*}$”,
- •
“a rendition of the $S^{*}$”,
- •
“a photo of the clean $S^{*}$”,
- •
“a rendition of a $S^{*}$”,
- •
“a photo of a nice $S^{*}$”,
- •
“a good photo of a $S^{*}$”,
- •
“a photo of the nice $S^{*}$”,
- •
“a photo of the small $S^{*}$”,
- •
“a photo of the weird $S^{*}$”,
- •
“a photo of the large $S^{*}$”,
- •
“a photo of a cool $S^{*}$”,
- •
“a photo of a small $S^{*}$”

### E.2 Testing Data

For evaluation, we adopt all images and editing prompts from DreamBooth (Ruiz et al., ) dataset. It contains a total of 158 images spanning 30 diverse categories, including dog, cat, robot, boot, etc. Fig. 15 shows a subset of these images. The the complete set of editing prompts for live subjects is detailed below:

Figure: Figure 15: Examples of testing data. We selected all 30 subjects in the DreamBooth dataset.
Refer to caption: x30.png

- •
“a $S^{*}$ in the jungle”
- •
“a $S^{*}$ in the snow”
- •
“a $S^{*}$ on the beach”
- •
“a $S^{*}$ on a cobblestone street”
- •
“a $S^{*}$ on top of pink fabric”
- •
“a $S^{*}$ on top of a wooden floor”
- •
“a $S^{*}$ with a city in the background”
- •
“a $S^{*}$ with a mountain in the background”
- •
“a $S^{*}$ with a blue house in the background”
- •
“a $S^{*}$ on top of a purple rug in a forest”
- •
“a $S^{*}$ with a wheat field in the background”
- •
“a $S^{*}$ with a tree and autumn leaves in the background”
- •
“a $S^{*}$ with the Eiffel Tower in the background”
- •
“a $S^{*}$ floating on top of water”
- •
“a $S^{*}$ floating in an ocean of milk”
- •
“a $S^{*}$ on top of green grass with sunflowers around it”
- •
“a $S^{*}$ on top of a mirror”
- •
“a $S^{*}$ on top of the sidewalk in a crowded street”
- •
“a $S^{*}$ on top of a dirt road”
- •
“a $S^{*}$ on top of a white rug”
- •
“a red $S^{*}$”
- •
“a purple $S^{*}$”
- •
“a shiny $S^{*}$”
- •
“a wet $S^{*}$”
- •
“a cube shaped $S^{*}$”

And here is the full set of editing prompts for non-live subjects:

- •
“a $S^{*}$ in the jungle”
- •
“a $S^{*}$ in the snow”
- •
“a $S^{*}$ on the beach”
- •
“a $S^{*}$ on a cobblestone street”
- •
“a $S^{*}$ on top of pink fabric”
- •
“a $S^{*}$ on top of a wooden floor”
- •
“a $S^{*}$ with a city in the background”
- •
“a $S^{*}$ with a mountain in the background”
- •
“a $S^{*}$ with a blue house in the background”
- •
“a $S^{*}$ on top of a purple rug in a forest”
- •
“a $S^{*}$ wearing a red hat”
- •
“a $S^{*}$ wearing a Santa hat”
- •
“a $S^{*}$ wearing a rainbow scarf”
- •
“a $S^{*}$ wearing a black top hat and a monocle”
- •
“a $S^{*}$ in a chef outfit”
- •
“a $S^{*}$ in a firefighter outfit”
- •
“a $S^{*}$ in a police outfit”
- •
“a $S^{*}$ wearing pink glasses”
- •
“a $S^{*}$ wearing a yellow shirt”
- •
“a $S^{*}$ in a purple wizard outfit”
- •
“a red $S^{*}$”
- •
“a purple $S^{*}$”
- •
“a shiny $S^{*}$”
- •
“a wet $S^{*}$”
- •
“a cube shaped $S*$”

## Peer Reviews

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: - During the training of DisVisioner, how is disentanglement achieved? Given that the diffusion model’s objective imposes a loss on both the object and the background, it’s unclear how only subject information is learned for the subject token. Could the authors provide visualization examples or experiments of the learned disentangled tokens?
- What types of augmentations are used during DisVisioner’s training?
- Can this method be extended to use multiple images as conditioning to enhance reference image details?
- Since DisVisioner’s training requires the class name from ImageNet for prior initialization, does this limit its generalization to classes outside ImageNet?
soundness: 3
strengths: - The paper is well-written and easy to follow, with figures that clearly illustrate the concepts.
- The visual results are impressive, with the original subject details well-preserved.
- The topic is engaging and holds potential for practical application.
summary: This paper introduces DisEnvisioner, a method aimed at enhancing the customization capabilities of image diffusion models. The approach involves training two models: DisVisioner, which disentangles subject-specific features from subject-unrelated features, and EnVisioner, which enriches these disentangled features. Quantitative and qualitative experiments demonstrate that DisEnvisioner effectively extracts subject information while discarding unrelated details, significantly enhancing customization capabilities.
weaknesses: - **Missing Ablations:** The importance of CLIP prior initialization and augmentation in training DisVisioner has not been fully investigated.
- **Unclear Mechanism for Ensuring Disentanglement:** While the concept of disentangling subject-specific and unrelated features is intriguing, the method section does not clearly explain how this disentanglement is achieved. It remains unclear what guarantees that only subject-related information is retained.

---

confidence: 3
contribution: 2
initial_rating: 6
presentation: 2
questions: 1.Could you please explain how subject features and irrelevant features are learned, and why they can be disentangled by transformer blocks without additional supervision?

2.Please improve the quantitative comparison experiment regarding the EFFECT OF DISVISIONER (Ablation study).

3.Please improve the quantitative comparison experiment regarding the EFFECT OF ENVISIONER (Ablation study).

4.Please improve the quantitative comparison experiment on the Ablation of token numbers in DisVisioner, and supplement the comparison experiments for $n_s=1, n_i=0$ and $n_s=0, n_i=1$.
soundness: 2
strengths: The paper introduces DisEnvisioner, a novel framework that addresses significant challenges in customized image generation by focusing on feature disentanglement and enrichment. The originality lies in the identification of the crucial role that subject-essential attributes play in the customization process, which is a new viewpoint that goes beyond existing methods reliant on either tuning or tuning-free approaches.

The clarity of the paper is commendable. The authors present their arguments logically, making it easy for readers to follow their reasoning and understand the significance of their contributions. Key terminologies and concepts like "subject-essential attributes" and "feature disentanglement" are well-defined
summary: The paper introduces DisEnvisioner, a novel approach aimed at enhancing image customization from visual prompts while addressing the limitations of existing methods in interpreting subject-essential attributes.Empirical evidence demonstrating the superiority of DisEnvisioner over existing methods in various aspects, including instruction response (editability), ID consistency, inference speed, and overall image quality.
weaknesses: 1.The advancement of the methodology has not been sufficiently demonstrated; some indicators (e.g. image-alignment for ID-consistency (C-I, D-I) as shown in Table 1) are inferior to other similar methods (e.g., IP-Adapter, BLIP-Diffusion, etc.),you cloud provide more evidence to support your claim in abstract "Experiments demonstrate the superiority of our approach over existing methods in instruction response (editability), ID consistency,"

2.The EFFECT OF DISVISIONER has not been adequately explained; there is a lack of comparative experiments with and without DISVISIONER to validate its effectiveness. You cloud compare results with and without the DiVisioner component while keeping other parts of the system constant.

3.The EFFECT OF ENVISIONER has not been sufficiently substantiated; the paper's explanation regarding the EFFECT OF ENVISIONER only presents a few cases (as shown in Fig. 8), which lacks persuasiveness.You could provide a larger-scale comparison by human evaluation or image-alignment for ID-consistency (C-I, D-I) to measure the improvement in ID consistency or image quality.

4.Ablation on token’s numbers in DisVisioner lack of QUANTITATIVE result.You might measure performance across various metrics (like those in Table 1) for different token number configurations.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: Could the authors provide more theoretical insights into the disentanglement mechanism and why it works well for customized image generation? The current explanation is mostly empirical.

How does DisEnvisioner compare to other recent Transformer-based text-to-image generation methods? Adding these comparisons could help readers better appreciate the novelty of the approach.
soundness: 3
strengths: Originality: The introduction of DisVisioner and EnVisioner for disentangled visual prompt processing is a novel approach, effectively addressing the challenge of maintaining subject identity while generating customized images.

Quality: The experiments are well-conducted, demonstrating the effectiveness of the proposed method in preserving ID consistency and reducing subject-irrelevant features.

Significance: The approach enables high-quality, customized image generation in a tuning-free manner, which is practical and efficient for real-world applications.

Clarity: The overall structure of the paper is well-organized, and the experimental results effectively showcase the advantages of the proposed method over existing baselines.
summary: The paper, titled "DisEnvisioner: Disentangled and Enriched Visual Prompt for Customized Image Generation," presents a novel approach to generating customized images from visual prompts with additional textual descriptions. The main contributions are:

1. Proposing DisEnvisioner, a tuning-free framework that effectively disentangles subject-essential attributes from irrelevant features, improving the overall quality of customized image generation.

2. Introducing a two-stage architecture with DisVisioner to separate subject-essential and irrelevant features, and EnVisioner to enhance subject consistency.

3. Demonstrating through experiments that the proposed approach outperforms existing methods in terms of editability, identity (ID) consistency, and inference speed while requiring minimal fine-tuning or reference images.
weaknesses: Limited Theoretical Justification: The paper lacks sufficient theoretical grounding for the proposed disentangling mechanism. For example, the method for separating subject-essential and irrelevant features relies primarily on empirical observations without rigorous theoretical backing.

Comparison with Existing Methods: The paper does not provide extensive comparisons with some of the most recent advancements in text-to-image generation (e.g., Transformer-based approaches). Including a wider range of baselines would help position this work within the current state of the field.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: Question: Could the authors elaborate on the specific factors that led DisEnvisioner to underperform IP-Adapter in terms of instruction accuracy (C-I) and ID consistency (D-I)?

Suggestion: Analyzing and explaining these performance gaps would provide useful insights into DisEnvisioner's design choices. This could also help clarify if the approach is inherently less suited to certain customization aspects or if there are areas where further tuning could close the performance gap.

Question: Given the limited dataset, how confident are the authors in DisEnvisioner’s generalizability to a broader range of tasks and more diverse visual prompts?

Suggestion: Evaluating DisEnvisioner on additional datasets with varied visual content and textual instructions could better assess its robustness. Additional results on a larger dataset could substantiate the claims of effectiveness and versatility.

Question: How does the feature enrichment step improve ID consistency, and could the authors provide more details on its implementation?

Suggestion: Adding a more in-depth explanation of the enrichment process and its effect on ID consistency would be beneficial. Additionally, showing comparisons before and after this step could better demonstrate its impact on maintaining subject integrity.
soundness: 3
strengths: DisEnvisioner demonstrates several strengths over competing methods in the areas of customization quality, editability, identity consistency, and efficiency, as shown in the provided results:

Customization Quality (C-T): With a score of 0.315, DisEnvisioner shows the highest performance in customization (C-T), indicating that it excels at incorporating subject-specific details while staying true to the given instructions.

ID Consistency (D-I): DisEnvisioner scores 0.802 for ID consistency (D-I), surpassing most other methods except for IP-Adapter. This score reflects its ability to maintain subject identity throughout image generation, reducing unwanted attribute drift.

Instruction Response (C-I): DisEnvisioner scores 0.828, slightly lower than some methods like IP-Adapter and DreamBooth but still within a strong range. This indicates good responsiveness to textual instructions while retaining visual prompt characteristics.

Inference Speed (IV): DisEnvisioner achieves a lower inference value of 0.026, indicating faster inference compared to methods like DreamBooth and DisenBooth, making it efficient for real-time or rapid customization needs.

*Runtime (T)**: DisEnvisioner has a runtime of 1.96 seconds, placing it on par with IP-Adapter, which is one of the fastest. This efficient runtime makes it more practical for applications needing quick processing.

Mean Rank (mRank): With an mRank of 2.0, DisEnvisioner achieves the highest overall ranking among the methods tested, suggesting that it consistently performs well across different evaluation metrics.
summary: This paper introduces DisEnvisioner, a novel tuning-free method for generating customized images from a single visual prompt enriched with additional textual instructions. Existing image generation methods, both tuning-based and tuning-free, often struggle to isolate the essential attributes of a subject in the visual prompt, leading to unwanted, subject-irrelevant features that compromise customization quality, editability, and identity (ID) preservation. DisEnvisioner addresses this by disentangling the subject-essential features from irrelevant details, separating them into distinct visual tokens. This separation improves customization precision and allows for enhanced ID consistency. By further refining these disentangled features, DisEnvisioner creates a more granular representation of the subject, which bolsters the model's ability to maintain ID consistency across generations. Experimental results demonstrate that DisEnvisioner outperforms current methods in editability, ID consistency, inference speed, and overall image quality, establishing its effectiveness and efficiency for personalized image generation.
weaknesses: DisEnvisioner, while showcasing significant advancements, has some limitations that temper its overall impact:

Lack of State-of-the-Art Results Across All Metrics: DisEnvisioner does not outperform all baseline models in every metric. For instance, its performance in instruction response (C-I) and identity consistency (D-I) does not reach the top scores achieved by IP-Adapter and DreamBooth. This mixed performance limits DisEnvisioner’s claim to outright superiority across all customization aspects.

Limited Test Dataset: The dataset used for evaluating DisEnvisioner is relatively constrained, potentially affecting the generalizability of the results. A more extensive and varied dataset would provide a clearer picture of the model's adaptability and robustness across diverse tasks and use cases.

Room for Improved Analysis on Underperformance Against IP-Adapter: Although DisEnvisioner demonstrates strengths in disentangling features, it would benefit from further analysis on why it lags behind IP-Adapter in specific tasks like instruction accuracy and ID consistency. Understanding these discrepancies could inform targeted improvements to make DisEnvisioner more competitive in these areas.

These aspects suggest that while DisEnvisioner makes notable contributions, there is room for further development to enhance its consistency and broaden its applicability across a wider range of tasks.

Meta-review: This paper aims to enhance the customization capabilities of image diffusion models. The authors propose a method DisEnvisioner by training the DIsVisioner and EnVisioner modules to address the limitations of existing methods in interpreting subject-essential attributes. Quantitative and qualitative experiments are performed and demonstrate the effectiveness of the proposed method. After rebuttals, all reviewers gave positive rating scores. Based on the above considerations, I recommend accepting this paper.

Decision: Accept (Poster)