Title: AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation
Venue: NeurIPS 2024
Accepted: yes
Keywords: Diffusion Models

Personalized Text-to-Image Generation

Personalization
Abstract: Recent advances in text-to-image models have enabled high-quality personalized image synthesis based on user-provided concepts with flexible textual control. In this work, we analyze the limitations of two primary techniques in text-to-image personalization: Textual Inversion and DreamBooth. When integrating the learned concept into new prompts, Textual Inversion tends to overfit the concept, while DreamBooth often overlooks it. We attribute these issues to the incorrect learning of the embedding alignment for the concept. To address this, we introduce AttnDreamBooth, a novel approach that separately learns the embedding alignment, the attention map, and the subject identity across different training stages. We also introduce a cross-attention map regularization term to enhance the learning of the attention map. Our method demonstrates significant improvements in identity preservation and text alignment compared to the baseline methods.

AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation

Abstract

Recent advances in text-to-image models have enabled high-quality personalized image synthesis based on user-provided concepts with flexible textual control. In this work, we analyze the limitations of two primary techniques in text-toimage personalization: Textual Inversion and DreamBooth. When integrating the learned concept into new prompts, Textual Inversion tends to overfit the concept, while DreamBooth often overlooks it. We attribute these issues to the incorrect learning of the embedding alignment for the concept. To address this, we introduce AttnDreamBooth, a novel approach that separately learns the embedding alignment, the attention map, and the subject identity across different training stages. We also introduce a cross-attention map regularization term to enhance the learning of the attention map. Our method demonstrates significant improvements in identity preservation and text alignment compared to the baseline methods.

... as a cowboy draws

its tiny revolver in a

dusty town showdown,

surrounded by cacti

and a saloon

An oil painting of ...

dressed as a

musketeer in an old

French town

... as a knight draped

in armor, riding a

horse and galloping

through the lush

fields

Cyberpunk style of ... as a robot against the backdrop of a futuristic cityscape at night, illuminated by neon lights

... as a Jedi casting a

long shadow in a sunlit,

empty desert

... as Captain America

standing in the ruins

of the city,

surrounded by smoke

... in assassin's creed

walking on the street of

Venice, surrounded by

the crowd of merchants

... as an astronaut floats

weightlessly in a zero-

gravity environment,

surrounded by celestial

bodies and distant

galaxies

and tourists

1 Introduction

Text-to-image personalization [24, 71, 50] is the task of customizing a pre-trained diffusion model to produce images of user-provided concepts in novel scenes or styles. By providing several examples of a new concept, personalization techniques enable users to employ novel prompts to generate personalized images containing that concept. Current personalization techniques primarily fall into two categories: the first approach involves inverting the new concept into the textual embedding [24]; the second approach involves fine-tuning the diffusion model to learn the new concept [71]. Personalization techniques aim to generate high-quality images of user-provided concepts, achieving high identity preservation and text alignment. However, despite the significant progress in personalization techniques, balancing the trade-off between identity preservation and text alignment remains a challenge for current approaches.

Figure 2 shows the personalization results from Textual Inversion [24] and DreamBooth [71]. Textual Inversion tends to generate images that focus primarily on the learned concept, often neglecting other elements of the prompt. In contrast, DreamBooth appears to overlook the learned concept, producing images that are more influenced by other prompt tokens. These issues can be attributed to the incorrect learning of embedding alignment for the new concept, i.e., the embedding of the new concept is not functionally compatible with the embeddings of existing tokens.

Based on these observations, our approach aims to properly learn not only the subject identity but also the embedding alignment and the attention map for the new concept. Our key insights are as follows: 1) In the early stages of optimization, Textual Inversion effectively learns the embedding alignment but tends to overfit after extensive optimization steps; 2) DreamBooth accurately captures the subject identity but struggles with learning the embedding alignment.

In this paper, we propose a method named AttnDreamBooth, which separates the learning processes of the embedding alignment, the attention map, and the subject identity. Specifically, our approach consists of three main training stages, as illustrated in Figure 3. First, we optimize the textual embedding to learn the embedding alignment while preventing the risk of overfitting, which results in a coarse attention map for the new concept. Next, we fine-tune the cross-attention layers of the U-Net to refine the attention map. Lastly, we fine-tune the entire U-Net to capture the subject identity. Note that the text encoder remains fixed throughout all training stages to preserve its prior knowledge of contextual understanding.

Furthermore, we introduce a cross-attention map regularization term to enhance the learning of the attention map. Throughout the three training stages, we use a consistent training prompt, 'a photo of a [V] [super-category]', where [V] and [super-category] denote the tokens for the new concept and its super-category, respectively. Our attention map regularization term encourages similarity between the attention maps of the new concept and its super-category.

Input

Textual Inversion

DreamBooth

Ours

Output

'[V]'

'Drawing'

Output

'[V]'

'Drawing'

Output

'[V]'

'Drawing'

'Manga drawing of a [V] can'

Output

'[V]'

Output

'[V]'

Output

'[V]'

'A [V] toy inside a box'

RANSETLANTIC

IPA

AGAA盟

燕大西洋

photo

a

of

a

[V]

doll

Text

Encoder

ResNet

Self-Attention

Cross-Attention

ResNet

Self-Attention

Cross-Attention

❆

. . .

Diffusion U-Net

Stage 1 Trainable

Stage 2 Trainable

Stage 3 Trainable

[V]

'doll'

ℒ

!"#

To demonstrate the effectiveness of AttnDreamBooth, we compare it with four state-of-the-art baseline methods through both qualitative and quantitative evaluations. Our method achieves superior performance in terms of identity preservation and text alignment compared to the baselines. More importantly, AttnDreamBooth enables a variety of text-aligned personalized generations with complex prompts.

2 Related Work

Text-to-Image Generation. Generative models are designed to create new samples that resemble the patterns observed in their training data. There are various types of generative models, including VAEs [47, 76, 14], GANs [27, 6, 44], auto-regressive models [66, 91], flow-based models [21, 48], and diffusion models [75, 33, 59, 18]. These models can be enhanced by conditioning on text prompts, which are known as text-to-image models [68, 66, 60, 20, 23, 16, 5]. Recent advancements [73, 67, 70] in text-to-image generation, powered by training on extremely large-scale datasets, have demonstrated an impressive ability to generate diverse and generalized outputs.

Text-to-Image Personalization. Leveraging the impressive capabilities of diffusion models, textto-image personalization involves adapting pre-trained diffusion models to capture new concepts depicted in several given images. Pioneering works approach this by inverting the concept into the textual embedding [24], or by fine-tuning the diffusion model [71]. However, these methods often struggle to balance the trade-off between identity preservation and text alignment, and typically require substantial time for optimization. To overcome these limitations, some studies focus on enhancing the identity preservation of the concept [81, 1, 94, 36, 31, 43, 40], while others aim to improve text alignment [78, 3, 4, 90, 37]. Additionally, there is a growing trend of research attempting to accelerate the personalization process, either by reducing the number of tuning parameters [50, 29, 54, 34, 28, 57], or by pre-training on large datasets [85, 74, 39, 2, 25, 10, 72, 51, 87, 56, 11, 55]. Given the widespread interest in human synthesis, many studies also concentrate on the personalized synthesis of human faces [92, 62, 89, 53, 79, 83, 8, 49, 61, 45, 86, 17, 13, 82, 12].

Cross-Attention Control. The cross-attention layers [70] have been shown to play a crucial role in diffusion models. The control of cross-attention layers has proven effective in a variety of tasks, including image editing [32], compositional synthesis [22, 52, 7, 26, 46], and layoutcontrolled synthesis [63, 9, 88]. In text-to-image personalization, several studies [50, 87, 4, 78, 30, 42, 58, 93] also have explored the control of cross-attention layers. Custom Diffusion [50] illustrates how incorrect attention maps of the learned concepts can lead to unsuccessful synthesis. FastComposer [87] and Break-A-Scene [4] propose using segmentation masks of the target concepts to guide the learning of the attention maps, thereby enhancing text alignment, especially in scenarios involving multiple concepts. Perfusion [78] identifies the attention overfitting issue and addresses it by fixing the cross-attention key matrices of the target concepts to their super-category tokens.

Multi-Stage Personalization. Several studies [24, 54, 4, 41, 38] have explored combining the strengths of different methods into more efficient models through a multi-stage approach. Inspired by

PTI [69], Textual Inversion [24] investigated a two-stage approach to enhance identity preservation by first optimizing the textual embedding and then fine-tuning the diffusion model to better capture the subject identity. Similarly, MagiCapture [38] first optimizes the textual embedding and then applies LoRA [34] in the U-Net. Break-A-Scene [4] proposes initially optimizing the textual embedding with a high learning rate, followed by fine-tuning both the U-Net and the text encoder using a significantly lower learning rate. Our method differs in several aspects. First, the motivation for our first stage (i.e., optimizing the textual embedding) differs from previous methods. Specifically, we focus on learning the embedding alignment while mitigating the risk of overfitting, thus significantly reducing the optimization steps and lowering the learning rate. Second, we decompose the learning process into three stages: learning the embedding alignment, refining the attention map, and capturing the subject identity. Third, we introduce a cross-attention map regularization to guide the learning of the attention map in a self-supervised manner.

3 Preliminaries

Latent Diffusion Models. Our approach is based on the publicly available Stable Diffusion model, a type of Latent Diffusion Model (LDM) [70] for text-to-image generation. In LDM, an autoencoder is utilized to provide a lower-dimensional representational space, where an encoder E transforms an image x into a latent representation z = E ( x ) , and a decoder D reconstructs the image from this latent code, i.e., D ( E ( x )) ≈ x . Additionally, a Denoising Diffusion Probabilistic Model (DDPM) [33] is employed to produce latent codes within the latent space of the autoencoder. To generate images from text, the model leverages a conditioning vector c ( y ) , derived from a given text prompt y . The training objective of LDM is given by:

where the denoising network ε θ is tasked with recovering the original latent code z 0 from the noised latent code z t , given a specific timestep t and the conditioning vector c ( y ) .

Textual Inversion. Textual Inversion (TI) [24] personalizes a pre-trained diffusion model by encoding the target concept into the textual embedding. Given several images of a target concept, TI introduces a new token S ∗ and its associated textual embedding v ∗ to represent the concept. The learning process of TI involves initializing v ∗ with a coarse descriptor and then optimizing it to minimize the diffusion objective (Eq. 1).

DreamBooth. DreamBooth (DB) [71] learns the target concept by fine-tuning the pre-trained diffusion model. Given several images of a target concept, DB labels all the images with the prompt 'a [V] [super-category]', where [V] is a rare token in the vocabulary. The learning process of DB involves fine-tuning the entire U-Net (and possibly the text encoder) using the diffusion objective (Eq. 1) combined with a prior preservation loss [71].

4 Method

In this section, we first analyze the problems associated with Textual Inversion and DreamBooth, as discussed in Section 4.1. To address these issues, we propose a novel method named AttnDreamBooth, as detailed in Section 4.2. To further enhance text alignment, we introduce a cross-attention map regularization term in Section 4.3.

4.1 Analysis of Existing Methods

Problems and Analysis. As illustrated in Figure 2, Textual Inversion and DreamBooth encounter distinct challenges when integrating the learned concept into novel prompts. For Textual Inversion, the generated images often excessively focus on the learned concept, overlooking other prompt tokens. To investigate this issue, we present the attention map visualization using DAAM [77] for different tokens in Figure 2. This visualization reveals an embedding misalignment issue in novel compositions containing the concept, leading to incorrect attention map allocations for other tokens. A typical example is shown where the attention map corresponding to the 'drawing' token focuses on incorrect regions. This misalignment occurs because Textual Inversion tends to overfit

Input

Output

'[V]'

(a)

Before fine-tuning

After fine-tuning

(b)

the input embedding of the text encoder, responsible for managing the contextual understanding of the prompt. Conversely, images generated by DreamBooth sometimes focus solely on other prompt tokens, neglecting the learned concept. This occurs because DreamBooth uses a rare token for the new concept while keeping its textual embedding fixed, thereby leading to insufficient learning of the embedding alignment for the new concept.

A Naive Solution. As analyzed previously, Textual Inversion and DreamBooth exhibit distinct issues related to the embedding alignment: Textual Inversion tends to overfit the embedding alignment for the new concept, while DreamBooth demonstrates insufficient learning of the embedding alignment. A straightforward solution is to combine Textual Inversion with DreamBooth by jointly tuning the textual embedding and the U-Net, a method we denote as TI+DB. We observe that TI+DB enhances performance over Textual Inversion or DreamBooth individually. However, it still tends to neglect the learned concept when integrating it into new prompts. This issue arises from the slow update of the textual embedding relative to the U-Net. As illustrated in Figure 4, the learned textual embedding remains very close to its initial state. Furthermore, we calculate the cosine similarity between the learned and initial embeddings, which averages about 0.9997, indicating that TI+DB still suffers from insufficient learning of the embedding alignment.

4.2 AttnDreamBooth

To address the issues described in Section 4.1, we propose a method named AttnDreamBooth, inspired by two key observations. First, while Textual Inversion often fails to capture the subject identity and tends to overfit the embedding alignment for the new concept, it can effectively learn the embedding alignment in the very early stages of optimization. However, at these early stages, the model only learns a coarse cross-attention map for the new concept. Second, although DreamBooth fails to learn the embedding alignment, it can accurately capture the subject identity. Based on these observations, we propose to decompose the personalization process into three training stages: 1) learning the embedding alignment; 2) refining the attention map; and 3) acquiring the subject identity. An overview of our proposed AttnDreamBooth is illustrated in Figure 3.

Learning the Embedding Alignment. As previously stated, learning the embedding alignment for the new concept is critical for properly allocating the cross-attention maps for novel prompts, which in turn influences the text alignment of the personalized generation results. To achieve this, we optimize the input textual embedding of the text encoder, since the text encoder manages the contextual understanding of the prompt. However, as analyzed in Section 4.1, this approach is prone to overfitting the embedding, leading to an embedding misalignment issue. Therefore, our objective at this stage is to learn the embedding alignment while minimizing the risk of overfitting. To this end, we adapt Textual Inversion [24] with three main modifications. First, we significantly reduce the number of optimization steps (to 60 steps in our experiments) and lower the learning rate (to 10 -3 ). Second, we introduce a cross-attention map regularization (see Section 4.3) to guide the learning of the cross-attention map. Third, to facilitate the incorporation of the cross-attention map regularization, we set the training prompt as 'a photo of a [V] [super-category]'. To prevent overfitting, we stop the optimization at very early stages, thereby resulting in a coarse cross-attention map for the new concept, as depicted in Figure 5. A full analysis of attention map allocations for each token is

Input

Prompt

Stage 1

Stage 2

Stage 3

A [V] toy

inside a box

我

presented in Appendix E. The cross-attention map, as well as the subject identity, are addressed in subsequent steps.

Refining the Cross-Attention Map. To mitigate the embedding misalignment issue, our model initially learns a relatively coarse cross-attention map for the new concept. At this stage, we focus on refining the cross-attention map. Since these attention maps are embedded within the cross-attention layers, inspired by Custom Diffusion [50], we fine-tune all the cross-attention layers in the U-Net. Additionally, we employ the proposed cross-attention map regularization (see Section 4.3) to aid in refining the attention map. Furthermore, we keep the textual embedding and the text encoder fixed to prevent further embedding misalignment.

Capturing the Subject Identity. As illustrated in Figure 5, the previous stage produces images that are similar to the target concept but still exhibit significant distortions. Therefore, in the third stage, following DreamBooth [71], we unfreeze all layers of the U-Net to more accurately capture the subject identity of the target concept. We choose not to adopt the prior preservation loss [71], as we empirically find that it leads to poor identity preservation and requires significantly more training steps. A detail discussion of our models with or without the prior preservation loss could be found in Appendix H. Moreover, similar to the previous stage, we keep the textual embedding and the text encoder fixed to prevent embedding misalignment, and we continue to apply the cross-attention map regularization to guide the learning of the attention map.

4.3 Cross-Attention Map Regularization

We set the training prompt as 'a photo of a [V] [super-category]', where [V] and [super-category] denote the tokens for the new concept and its super-category, respectively. To enhance the learning of the attention map, we introduce a regularization term that encourages similarity between the attention maps of [V] and [super-category]. This regularization term serves two purposes. First, since the new concept and its super-category belong to the same object category, the attention map of the super-category token can serve as a reference for the new concept. Second, since [V] and [super-category] are used together to describe the new concept when integrating it into new prompts, the attention maps of [V] and [super-category] should refer to the same region.

Formally, for the 16 attention maps { M 1 , M 2 ..., M 16 } from 16 different cross-attention layers, we minimize the squared differences in the mean and variance of the attention map values for [V] and [super-category] as follows:

where µ ( M 1:16 ) and σ 2 ( M 1:16 ) denote the mean and variance of all the values across the 16 attention maps, respectively. This constraint helps ensure that the new concept exhibits a similar level of concentration or dispersion in the attention map as the super-category token. Note that we avoid directly applying the constraint to the attention map values themselves because we empirically find that such a constraint is too restrictive and difficult to optimize.

Input

TI+DB

ViCo

OFT

Ours

An oil painting

of a [V] sloth

dressed as

a musketeer

in an old

French town

A [V] doll as

a Jedi casting

a long shadow

in a sunlit,

empty desert

A [V] fluffy

walking in

the rainy

streets

Light seeps out

from the inside

of a clear [V]

teapot under the

moonlight on

a serene beach

A painting of

a [V] toy

floating on

the lake under

the full moon's

glow in the

Input Sample

A black [V]

toy wearing

sunglasses on

the beach

A [V] toy wearing

a chef hat in a

kitchen with meat

and vegetables on

the table

A [V] toy

wearing a

police cap in

a police car

A [V] toy as a

priest in blue robes

in the cathedral

App icon of a

laughing [V] toy

Input Sample

A purple [V]

mystical aurora

borealis in a

remote Arctic

landscape

A red [V] furby

against the back-

drop of a futuri-

stic cityscape at

night illuminated

by neon lights

A [V] furby amidst

a bustling street

market surrounded

by vibrant colors

and textures

A black [V]

the golden light

of sunset at a

serene beach

A yellow [V] furby

on a cobblestone

street in an old

European town,

with historical

architecture

Input Sample

A [V] bear atop

a high cliff

overlooking

stormy seas

A [V] bear

surrounded by

fluttering butterflies

in a meadow

A [V] bear in

the reflection

of a cracked

antique mirror

A [V] bear

perched on a

city rooftop

at sunset

A [V] bear

floating in the

weightlessness

of space

8

介

只

5 Experiments

In this section, we first present the implementation details of our method. Subsequently, we evaluate its performance by conducting a comparative analysis with four state-of-the-art personalization methods. Lastly, we conduct an ablation study to demonstrate the effectiveness of each sub-module.

5.1 Implementation and Evaluation Setup

Implementation Details. Our implementation is based on the publicly available Stable Diffusion V2.1 [70]. The textual embedding of the new concept is initialized using the embedding of the super-category token. We keep a fixed batch size of 8 across all training stages but vary the learning rates and training steps. Specifically, we train with a learning rate of 10 -3 for 60 steps in stage 1, followed by a learning rate of 2 × 10 -5 for 100 steps in stage 2, and conclude with a learning rate of 2 × 10 -6 for 500 steps in stage 3. λ µ and λ σ are set to 0.1 and 0 in stage 1, respectively, and are adjusted to 2 and 5 in subsequent stages. All experiments are conducted on a single Nvidia A100 GPU. The training process of our method takes about 20 minutes to learn a concept.

Evaluation Setup. We compare our method with four state-of-the-art personalization methods, including TI+DB [24, 71], NeTI [1], ViCo [30], and OFT [64]. The implementation details of the baseline methods are provided in Appendix A. We collect 22 concepts from TI [24] and DB [71]. For the quantitative evaluation, each method is evaluated using a set of 24 text prompts, see Appendix B for a complete list. These prompts cover background change, environment interaction, concept color change, and artistic style.

5.2 Results

Qualitative Evaluation. In Figure 6, we present a visual comparison of personalized generation for various concepts. We employ a set of complex prompts for evaluation, where one prompt simultane-

ously incorporates several editing elements such as style change (e.g., 'oil painting'), scene change (e.g., 'old French town'), and appearance change (e.g., 'dressed as a musketeer'). As observed, ViCo tends to overfit the new concept, failing to compose it in novel scenes or styles. Conversely, TI+DB sometimes overlooks the learned concept, producing images that solely reflect other prompt tokens. NeTI and OFT also struggle to achieve text-aligned generations, especially when the prompts are complex. Our method, AttnDreamBooth, is the only method that successfully generates identitypreserved and text-aligned personalized images for these complex prompts. Figures 1 and 7 show more personalized generations using complex prompts from our method. Additional qualitative results can be found in Appendix C.

Quantitative Evaluation. We conduct a quantitative evaluation of each method in terms of identity preservation and text alignment. Identity preservation is measured by the cosine similarity between the CLIP [65] embeddings of generated and real images, while text alignment is measured by the cosine similarity between the CLIP embeddings of generated images and their corresponding prompts. Each method is evaluated using 24 text prompts, generating 32 images per prompt. The results are presented in Table 1. TI+DB excels in text alignment but performs poorly in identity preservation. This is consistent with the qualitative observation that TI+DB often neglects the learned concept and generates images based solely on other prompt tokens. In contrast, ViCo achieves the best identity preservation but ranks lowest in text alignment, indicative of its tendency to overfit the new concept. Besides these two extreme cases, our approach exhibits superior performance in both identity preservation and text alignment.

Fast Version of Our Method. The average training time using our method is about 20 minutes. To reduce the training time, we developed a fast version of our method by increasing the learning rate while simultaneously decreasing both the training steps and the batch size for the third training stage. Originally, this stage involves performing 500 steps with a learning rate of 2 × 10 -6 and a batch size of 8 . The fast version now completes training in just 200 steps with a learning rate of 1 × 10 -5 and a batch size of 4 . These adjustments significantly reduce the training time from 20 minutes to an average of 6 minutes. Interestingly, this fast version maintains performance comparable to our original model, likely because the first two stages provide a convenient starting point, allowing for a higher learning rate in the third stage. Notably, the fast version performs similarly to the original model on short prompts, as shown in Table 1, but it shows slight degradation on complex prompts, as depicted in Figure 10.

User Study. We further evaluate our method by conducting a user study. Personalized images are generated using various prompts and concepts for each method. In each question of the study, participants are presented with an input image and a text prompt, along with two generated images: one from our method and another from a baseline method. Participants are asked to select the image that better achieves identity preservation and text alignment. We collected a total of 700 responses from 35 participants, as presented in Table 2. The results demonstrate a clear preference for our method.

Input

Prompt

w/o Stage 1

w/o Stage 2

w/o Stage 3

Full

An oil painting

of a [V] sloth

dressed as

a musketeer

in an old

French town

A painting of

a [V] fluffy

in the style

5.3 Ablation Study

In this section, we evaluate the effectiveness of each sub-module within our framework. Specifically, we conduct an ablation study by separately removing each training stage or the attention map regularization term. Figure 8 presents a visual comparison of personalized images generated by each variant. The results indicate that all sub-modules are crucial for achieving identity-preserved and textaligned personalized generations. Specifically, the model without optimizing the textual embedding (w/o Stage 1) tends to neglect the learned concept or generate it with significant distortions due to insufficient learning of the embedding alignment. Models without fine-tuning the cross-attention layers (w/o Stage 2) or without the regularization term (w/o Reg) suffer from degraded text alignment or identity preservation. The model without fine-tuning the U-Net (w/o Stage 3) leads to significant degradation in identity preservation. Additional ablation study results are provided in Appendix F. Similar behavior is observed in the quantitative ablation study, as detailed in Table 4.

6 Conclusions and Limitations

In this paper, we identified and analyzed the embedding misalignment issue encountered by Textual Inversion and DreamBooth. Our proposed method, named AttnDreamBooth, addresses this issue by decomposing the personalization process into three stages: learning the embedding alignment, refining the attention map, and acquiring the subject identity. AttnDreamBooth enables identity-preserved and text-aligned text-to-image personalization, even with complex prompts.

In our experiments, we used consistent training steps across different concepts; however, we observed that performance could be further improved by tuning the training steps for specific concepts. This limitation might be addressed by adopting adaptive training strategies, which we leave for future work. A second limitation is that our three-stage training method requires approximately 20 minutes on average to learn a concept, as it involves fine-tuning all parameters in the U-Net for 500 steps. To mitigate this, we introduced a fast version that reduces the training time to approximately 6 minutes while still maintaining performance comparable to the original model.

Acknowledgments

This work is supported by National Natural Science Foundation of China (No. 62176223 and No. 62302535), Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515012897 and No. 2023A1515011639), and Zhuhai Basic and Applied Basic Research Foundation (No. 2320004002745).

References

Yuval Alaluf, Elad Richardson, Gal Metzer, and Daniel Cohen-Or. A neural space-time representation for text-to-image personalization. arXiv preprint arXiv:2305.15391 , 2023.

Moab Arar, Rinon Gal, Yuval Atzmon, Gal Chechik, Daniel Cohen-Or, Ariel Shamir, and Amit H. Bermano. Domain-agnostic tuning-encoder for fast personalization of text-to-image models. arXiv preprint arXiv:2307.06925 , 2023.

Moab Arar, Andrey Voynov, Amir Hertz, Omri Avrahami, Shlomi Fruchter, Yael Pritch, Daniel Cohen-Or, and Ariel Shamir. Palp: Prompt aligned personalization of text-to-image models. arXiv preprint arXiv:2401.06105 , 2024.

Omri Avrahami, Kfir Aberman, Ohad Fried, Daniel Cohen-Or, and Dani Lischinski. Break-ascene: Extracting multiple concepts from a single image. arXiv preprint arXiv:2305.16311 , 2023.

Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Qinsheng Zhang, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, Tero Karras, and Ming-Yu Liu. ediff-i: Text-to-image diffusion models with an ensemble of expert denoisers. arXiv preprint arXiv:2211.01324 , 2022.

Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 , 2018.

Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, and Daniel Cohen-Or. Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models. In SIGGRAPH , 2023.

Li Chen, Mengyi Zhao, Yiheng Liu, Mingxu Ding, Yangyang Song, Shizun Wang, Xu Wang, Hao Yang, Jing Liu, Kang Du, and Min Zheng. Photoverse: Tuning-free image customization with text-to-image diffusion models. arXiv preprint arXiv:2309.05793 , 2023.

Minghao Chen, Iro Laina, and Andrea Vedaldi. Training-free layout control with cross-attention guidance. In WACV , 2024.

Wenhu Chen, Hexiang Hu, Yandong Li, Nataniel Rui, Xuhui Jia, Ming-Wei Chang, and William W Cohen. Subject-driven text-to-image generation via apprenticeship learning. arXiv preprint arXiv:2304.00186 , 2023.

Xi Chen, Lianghua Huang, Yu Liu, Yujun Shen, Deli Zhao, and Hengshuang Zhao. Anydoor: Zero-shot object-level image customization. arXiv preprint arXiv:2307.09481 , 2023.

Zhuowei Chen, Shancheng Fang, Wei Liu, Qian He, Mengqi Huang, Yongdong Zhang, and Zhendong Mao. Dreamidentity: Improved editability for efficient face-identity preserved image generation. arXiv preprint arXiv:2307.00300 , 2023.

Jiaxiang Cheng, Pan Xie, Xin Xia, Jiashi Li, Jie Wu, Yuxi Ren, Huixia Li, Xuefeng Xiao, Min Zheng, and Lean Fu. Resadapter: Domain consistent resolution adapter for diffusion models. arXiv preprint arXiv:2403.02084 , 2024.

Rewon Child. Very deep vaes generalize autoregressive models and can outperform them on images. In ICLR , 2021.

Riccardo Corvi, Davide Cozzolino, Giada Zingarini, Giovanni Poggi, Koki Nagano, and Luisa Verdoliva. On the detection of synthetic images generated by diffusion models. In ICASSP , 2023.

Katherine Crowson, Stella Biderman, Daniel Kornis, Dashiell Stander, Eric Hallahan, Louis Castricato, and Edward Raff. Vqgan-clip: Open domain image generation and editing with natural language guidance. In ECCV , 2022.

Siying Cui, Jiankang Deng, Jia Guo, Xiang An, Yongle Zhao, Xinyu Wei, and Ziyong Feng. Idadapter: Learning mixed features for tuning-free personalization of text-to-image models. arXiv preprint arXiv:2403.13535 , 2024.

Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. In NeurIPS , 2021.

Ganggui Ding, Canyu Zhao, Wen Wang, Zhen Yang, Zide Liu, Hao Chen, and Chunhua Shen. Freecustom: Tuning-free customized image generation for multi-concept composition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 9089-9098, 2024.

Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, et al. Cogview: Mastering text-to-image generation via transformers. In NeurIPS , 2021.

Laurent Dinh, David Krueger, and Yoshua Bengio. Nice: Non-linear independent components estimation. arXiv preprint arXiv:1410.8516 , 2014.

Weixi Feng, Xuehai He, Tsu-Jui Fu, Varun Jampani, Arjun Reddy Akula, Pradyumna Narayana, Sugato Basu, Xin Eric Wang, and William Yang Wang. Training-free structured diffusion guidance for compositional text-to-image synthesis. In ICLR , 2023.

Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv Taigman. Make-a-scene: Scene-based text-to-image generation with human priors. In ECCV , 2022.

Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618 , 2022.

Rinon Gal, Moab Arar, Yuval Atzmon, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. Encoder-based domain tuning for fast personalization of text-to-image models. TOG , 2023.

Songwei Ge, Taesung Park, Jun-Yan Zhu, and Jia-Bin Huang. Expressive text-to-image generation with rich text. In ICCV , 2023.

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In NeurIPS , 2014.

Yuchao Gu, Xintao Wang, Jay Zhangjie Wu, Yujun Shi, Yunpeng Chen, Zihan Fan, Wuyou Xiao, Rui Zhao, Shuning Chang, Weijia Wu, Yixiao Ge, Ying Shan, and Mike Zheng Shou. Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models. arXiv preprint arXiv:2305.18292 , 2023.

Ligong Han, Yinxiao Li, Han Zhang, Peyman Milanfar, Dimitris Metaxas, and Feng Yang. Svdiff: Compact parameter space for diffusion fine-tuning. arXiv preprint arXiv:2303.11305 , 2023.

Shaozhe Hao, Kai Han, Shihao Zhao, and Kwan-Yee K. Wong. Vico: Detail-preserving visual condition for personalized text-to-image generation. arXiv preprint arXiv:2306.00971 , 2023.

Xingzhe He, Zhiwen Cao, Nicholas Kolkin, Lantao Yu, Helge Rhodin, and Ratheesh Kalarot. A data perspective on enhanced identity preservation for diffusion personalization. arXiv preprint arXiv:2311.04315 , 2023.

Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control. In ICLR , 2023.

Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In NeurIPS , 2020.

Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. In ICLR , 2022.

Hexiang Hu, Kelvin CK Chan, Yu-Chuan Su, Wenhu Chen, Yandong Li, Kihyuk Sohn, Yang Zhao, Xue Ben, Boqing Gong, William Cohen, et al. Instruct-imagen: Image generation with multi-modal instruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4754-4763, 2024.

Miao Hua, Jiawei Liu, Fei Ding, Wei Liu, Jie Wu, and Qian He. Dreamtuner: Single image is enough for subject-driven generation. arXiv preprint arXiv:2312.13691 , 2023.

Mengqi Huang, Zhendong Mao, Mingcong Liu, Qian He, and Yongdong Zhang. Realcustom: Narrowing real text word for real-time open-domain text-to-image customization. arXiv preprint arXiv:2403.00483 , 2024.

Junha Hyung, Jaeyo Shin, and Jaegul Choo. Magicapture: High-resolution multi-concept portrait customization. arXiv preprint arXiv:2309.06895 , 2023.

Xuhui Jia, Yang Zhao, Kelvin CK Chan, Yandong Li, Han Zhang, Boqing Gong, Tingbo Hou, Huisheng Wang, and Yu-Chuan Su. Taming encoder for zero fine-tuning image customization with text-to-image diffusion models. arXiv preprint arXiv:2304.02642 , 2023.

Jiaxiu Jiang, Yabo Zhang, Kailai Feng, Xiaohe Wu, and Wangmeng Zuo. Mc 2 : Multi-concept guidance for customized multi-concept generation. arXiv preprint arXiv:2404.05268 , 2024.

Yuming Jiang, Tianxing Wu, Shuai Yang, Chenyang Si, Dahua Lin, Yu Qiao, Chen Change Loy, and Ziwei Liu. Videobooth: Diffusion-based video generation with image prompts. In CVPR , 2024.

Chen Jin, Ryutaro Tanno, Amrutha Saseendran, Tom Diethe, and Philip Teare. An image is worth multiple words: Learning object level concepts using multi-concept prompt learning. arXiv preprint arXiv:2310.12274 , 2023.

Maxwell Jones, Sheng-Yu Wang, Nupur Kumari, David Bau, and Jun-Yan Zhu. Customizing text-to-image models with a single image pair. arXiv preprint arXiv:2405.01536 , 2024.

Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In CVPR , 2019.

Chanran Kim, Jeongin Lee, Shichang Joung, Bongmo Kim, and Yeul-Min Baek. Instantfamily: Masked attention for zero-shot multi-id image generation. arXiv preprint arXiv:2404.19427 , 2024.

Yunji Kim, Jiyoung Lee, Jin-Hwa Kim, Jung-Woo Ha, and Jun-Yan Zhu. Dense text-to-image generation with attention modulation. In ICCV , 2023.

Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 , 2013.

Durk P Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. In NeurIPS , 2018.

Zhe Kong, Yong Zhang, Tianyu Yang, Tao Wang, Kaihao Zhang, Bizhu Wu, Guanying Chen, Wei Liu, and Wenhan Luo. Omg: Occlusion-friendly personalized multi-concept generation in diffusion models. arXiv preprint arXiv:2403.10983 , 2024.

Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu. Multiconcept customization of text-to-image diffusion. In CVPR , 2023.

Dongxu Li, Junnan Li, and Steven C. H. Hoi. Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing. arXiv preprint arXiv:2305.14720 , 2023.

Yumeng Li, Margret Keuper, Dan Zhang, and Anna Khoreva. Divide & bind your attention for improved generative semantic nursing. In BMVC , 2023.

Zhen Li, Mingdeng Cao, Xintao Wang, Zhongang Qi, Ming-Ming Cheng, and Ying Shan. Photomaker: Customizing realistic human photos via stacked id embedding. arXiv preprint arXiv:2312.04461 , 2023.

LoRA. Low-rank adaptation for fast text-to-image diffusion fine-tuning. https://github.com/cloneofsimo/lora, 2022.

Jian Ma, Junhao Liang, Chen Chen, and Haonan Lu. Subject-diffusion:open domain personalized text-to-image generation without test-time fine-tuning. arXiv preprint arXiv:2307.11410 , 2023.

Yiyang Ma, Huan Yang, Wenjing Wang, Jianlong Fu, and Jiaying Liu. Unified multi-modal latent diffusion for joint subject and text conditional image generation. arXiv preprint arXiv:2303.09319 , 2023.

Shyam Marjit, Harshit Singh, Nityanand Mathur, Sayak Paul, Chia-Mu Yu, and Pin-Yu Chen. Diffusekrona: A parameter efficient fine-tuning method for personalized diffusion model. arXiv preprint arXiv:2402.17412 , 2024.

Jisu Nam, Heesu Kim, DongJae Lee, Siyoon Jin, Seungryong Kim, and Seunggyu Chang. Dreammatcher: Appearance matching self-attention for semantically-consistent text-to-image personalization. arXiv preprint arXiv:2402.09812 , 2024.

Alex Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. In ICML , 2021.

Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741 , 2021.

Daniil Ostashev, Yuwei Fang, Sergey Tulyakov, Kfir Aberman, et al. Moa: Mixture-ofattention for subject-context disentanglement in personalized image generation. arXiv preprint arXiv:2404.11565 , 2024.

Lianyu Pang, Jian Yin, Haoran Xie, Qiping Wang, Qing Li, and Xudong Mao. Cross initialization for personalized text-to-image generation. arXiv preprint arXiv:2312.15905 , 2023.

Quynh Phung, Songwei Ge, and Jia-Bin Huang. Grounded text-to-image synthesis with attention refocusing. arXiv preprint arXiv:2306.05427 , 2023.

Zeju Qiu, Weiyang Liu, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, and Bernhard Schölkopf. Controlling text-to-image diffusion by orthogonal finetuning. In NeurIPS , 2024.

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML , 2021.

Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In ICML , 2021.

Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 , 2022.

Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee. Generative adversarial text to image synthesis. In ICML , 2016.

Daniel Roich, Ron Mokady, Amit H Bermano, and Daniel Cohen-Or. Pivotal tuning for latent-based editing of real images. TOG , 2022.

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR , 2022.

Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In CVPR , 2023.

Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Wei Wei, Tingbo Hou, Yael Pritch, Neal Wadhwa, Michael Rubinstein, and Kfir Aberman. Hyperdreambooth: Hypernetworks for fast personalization of text-to-image models. arXiv preprint arXiv:2307.06949 , 2023.

Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. In NeurIPS , 2022.

Jing Shi, Wei Xiong, Zhe Lin, and Hyun Joon Jung. Instantbooth: Personalized text-to-image generation without test-time finetuning. arXiv preprint arXiv:2304.03411 , 2023.

Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In ICML , 2015.

Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther. Ladder variational autoencoders. In NeurIPS , 2016.

Raphael Tang, Linqing Liu, Akshat Pandey, Zhiying Jiang, Gefei Yang, Karun Kumar, Pontus Stenetorp, Jimmy Lin, and Ferhan Ture. What the DAAM: Interpreting stable diffusion using cross attention. In ACL , 2023.

Yoad Tewel, Rinon Gal, Gal Chechik, and Yuval Atzmon. Key-locked rank one editing for text-to-image personalization. In SIGGRAPH , 2023.

Dani Valevski, Danny Wasserman, Yossi Matias, and Yaniv Leviathan. Face0: Instantaneously conditioning a text-to-image model on a face. arXiv preprint arXiv:2306.06638 , 2023.

Patrick von Platen, Suraj Patil, Anton Lozhkov, Pedro Cuenca, Nathan Lambert, Kashif Rasul, Mishig Davaadorj, and Thomas Wolf. Diffusers: State-of-the-art diffusion models. https://github.com/huggingface/diffusers, 2022.

Andrey Voynov, Qinghao Chu, Daniel Cohen-Or, and Kfir Aberman. p + : Extended textual conditioning in text-to-image generation. arXiv preprint arXiv:2303.09522 , 2023.

Qinghe Wang, Xu Jia, Xiaomin Li, Taiqing Li, Liqian Ma, Yunzhi Zhuge, and Huchuan Lu. Stableidentity: Inserting anybody into anywhere at first sight. arXiv preprint arXiv:2401.15975 , 2024.

Qixun Wang, Xu Bai, Haofan Wang, Zekui Qin, and Anthony Chen. Instantid: Zero-shot identity-preserving generation in seconds. arXiv preprint arXiv:2401.07519 , 2024.

Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A. Efros. Cnngenerated images are surprisingly easy to spot... for now. In CVPR , 2020.

Yuxiang Wei, Yabo Zhang, Zhilong Ji, Jinfeng Bai, Lei Zhang, and Wangmeng Zuo. Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation. arXiv preprint arXiv:2302.13848 , 2023.

Yi Wu, Ziqiang Li, Heliang Zheng, Chaoyue Wang, and Bin Li. Infinite-id: Identity-preserved personalization via id-semantics decoupling paradigm. arXiv preprint arXiv:2403.11781 , 2024.

Guangxuan Xiao, Tianwei Yin, William T. Freeman, Frédo Durand, and Song Han. Fastcomposer: Tuning-free multi-subject image generation with localized attention. arXiv preprint arXiv:2305.10431 , 2023.

Jinheng Xie, Yuexiang Li, Yawen Huang, Haozhe Liu, Wentian Zhang, Yefeng Zheng, and Mike Zheng Shou. Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion. In ICCV , 2023.

Yuxuan Yan, Chi Zhang, Rui Wang, Yichao Zhou, Gege Zhang, Pei Cheng, Gang Yu, and Bin Fu. Facestudio: Put your face everywhere in seconds. arXiv preprint arXiv:2312.02663 , 2023.

Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models. arXiv preprint arXiv:2308.06721 , 2023.

Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason Baldridge, and Yonghui Wu. Scaling autoregressive models for content-rich text-to-image generation. Transactions on Machine Learning Research , 2022.

Ge Yuan, Xiaodong Cun, Yong Zhang, Maomao Li, Chenyang Qi, Xintao Wang, Ying Shan, and Huicheng Zheng. Inserting anybody in diffusion models via celeb basis. In NeurIPS , 2023.

Yanbing Zhang, Mengping Yang, Qin Zhou, and Zhe Wang. Attention calibration for disentangled text-to-image personalization. In CVPR , 2024.

Yufan Zhou, Ruiyi Zhang, Tong Sun, and Jinhui Xu. Enhancing detail preservation for customized text-to-image generation: A regularization-free approach. arXiv preprint arXiv:2305.13579 , 2023.

A Implementation Details of Baselines

We compare our method with four baseline methods, including TI+DB [24, 71], NeTI [1], ViCo [30], and OFT [64]. For TI+DB, we implement it based on the diffusers library [80] without employing the prior preservation loss. We perform 660 training steps, which matches the total number of steps for our method, with a learning rate of 2 × 10 -6 and a batch size of 8. For the other baselines, we use the official implementations and follow the hyper-parameters described in their papers.

In the Appendix, we further compare our method with four other baseline methods, including DreamMatcher [58], FreeCustom [19], SuTI [10], and Instruct-Imagen [35]. For DreamMatcher and FreeCustom, we use their official implementations. Due to the unavailability of open-source models for SuTI and Instruct-Imagen, we rely on the examples provided in their papers for comparison.

B Text Prompts

In Table 3, we list all 24 text prompts used in the quantitative evaluation. These prompts cover a range of modifications, including background change, environment interaction, concept color change, and artistic style.

C Additional Qualitative Results

In Figures 9, 10, and 11, we provide additional qualitative comparisons to the baseline methods across a wide range of prompts. Furthermore, Figure 12 presents a qualitative comparison of our method with Textual Inversion (TI), DreamBooth (DB), and two different configurations of TI+DB: 1) first TI and then DB (TI → DB), and 2) first DB and then TI (DB → TI). In Figure 13, we provide additional qualitative results generated by AttnDreamBooth.

D Single Image Personalization

In this section, we compare AttnDreamBooth with the baseline methods when only a single image is used for training. In Figure 14, we present the generation results of each method under this challenging setting. Our method demonstrates superior text alignment and identity preservation compared to the baselines.

E Attention Maps for Each Token

In the main text, we provide the cross-attention maps of Textual Inversion and DreamBooth in Figure 2 and the cross-attention map of '[V]' for each stage in Figure 5. To vividly demonstrate the efficacy of our method in training the cross-attention layer, we present the generated images along with the cross-attention maps for each token in the prompt in Figure 15. As can be seen, our method accurately assigns the attention maps for each token, demonstrating correct embedding alignment for the new concept.

F Additional Ablation Study

In Figure 16, we provide additional ablation study results for each variant of our method. Table 4 presents the quantitative results of our ablation study. Specifically, the model without Stage 1 achieves better text alignment but significantly poorer identity preservation compared to the full model. This is because, without sufficient training of the textual embedding, the model tends to overlook the learned concept or generate it with significant distortions. Please note that the text alignment score is calculated without considering the new concept; therefore, omitting the new concept can inadvertently boost this score. Similarly, models without Stage 2 or Stage 3 also exhibit higher text alignment scores but lower identity preservation scores, due to insufficient learning of the attention maps and the subject identity, respectively. Additionally, the model without the regularization term shows degraded text alignment.

G Different Version of Stable Diffusion

A visual comparison between our models with SD1.5 or SD2.1 is presented in Figure 17. As shown, the model with SD2.1 achieves superior performance in text alignment and identity preservation. Nevertheless, our method is also effective for SD1.5, and it outperforms the baseline methods.

H Prior Preservation Loss

We present a visual comparison between models with or without the prior preservation loss in Figure 18. The results show that incorporating the prior preservation loss leads to degradation in identity preservation.

I User Study

As described in Section 5.2, we conducted a user study to evaluate our method against the baseline methods. Here, we present the details of this user study. Figure 19 shows an example question from the user study. Given a concept image and a text prompt, along with two generated images (one from our method and another from a baseline method), participants were asked to select the image that better preserves the identity of the concept image and aligns with the text prompt. The results are presented in Table 2.

J Societal Impact

Similar to existing text-to-image personalization techniques, our approach provides broader users access to effectively fine-tuning large-scale pre-trained diffusion models. By enabling users to personalize these models with their own data, our approach can be used for numerous applications, including image editing, artistic creations, and industrial production. However, the use of generative techniques comes with risks, such as the creation of misleading or false information. To mitigate these concerns, it is vital to develop effective methods for identifying fake generations [84, 15].

K Licenses for Pre-trained Models and Datasets

Our implementation is based on the publicly available Stable Diffusion V2.1 [70], which is under the CreativeML Open RAIL++-M License. The datasets used for evaluation are from TI [24] and DB [71]. The data from DB is under the Unsplash license, while the license information for the data from TI is not available online.

Input

TI+DB

ViCo

OFT

Ours

In a futuristic

cityscape,

a [V] can is

transformed

into a robotic

entity, buzzing

with neon

lights and wires

An ice

sculpture of

a [V] bird

on a beach

at night

illuminated

by neon lights

A [V] bear is

building a

sandcastle on

a sunny beach

while tiny crabs

scuttle around

and seagulls

fly overhead

A plant

grows in a

broken [V]

pot

A green [V]

bowl full of

milk on a

cobblestone

street in an

old European

town

TRANSATLANTIC

IPA

辣大西洋

EBXA

?

Input

DM

FreeCustom

Ours-fast

Ours

An oil painting

of a [V] sloth

dressed as

a musketeer

in an old

French town

A [V] doll as

a Jedi casting

a long shadow

in a sunlit,

empty desert

A [V] fluffy

walking in

the rainy

streets

Light seeps out

from the inside

of a clear [V]

teapot under the

moonlight on

a serene beach

A painting of

a [V] toy

floating on

the lake under

the full moon's

glow in the

Input

SuTI

Ours

Input

SuTI

Ours

'[V] with silver-tipped toes

kicking a football'

Input

Instruct-Img.

Ours

Input

g

'[V] sniffing a berry bowl'

Input

TI

DB

TI

→

DB

DB

→

TI

Ours

'A [V] fluffy walking in the rainy streets'

10

11

12

112

1

Input

TI+DB

ViCo

OFT

Ours

A [V] bird is

perched on the

branch of an

aged oak tree,

watching the

sunrise

A [V] bear in

a hammock

strung between

two palm trees,

swaying gently

in the tropical

breeze

A [V] wolf is

reading a

book with a

cup of hot

cocoa

nearby

A [V] clock

latte art

A [V] bird

float on the

water

12

11

11

12

6

Input

Stage 1

Output

'a'

'[V]'

'toy'

'inside'

'a""

'box'

Stage 2

A [V] toy

inside a

box

Output

'a'

'[V]'

'toy'

'inside'

'a""

'box'

Stage 3

Output

'a'

'[V]'

'toy'

'inside'

'a""

'box'

0

Input

w/o Stage 1

w/o Stage 2

w/o Stage 3

A [V] toy as

a priest in

blue robes,

in the

cathedral

A [V] doll

as a Jedi

casting a

long shadow

in a sunlit,

empty desert

Input

ViCo

OFT

Ours-SD1.5

Ours-SD2.1

'A painting of a [V] toy floating on the lake under the full moon's glow in the style of Monet'

Input

'A [V] doll as a Jedi casting a long shadow in a sunlit empty desert'

*1.Conceptimage(denotedas[V]):

Textprompt:"A[V] burns in thefire".

Pleaseselecttheimagefrom theoptionsbelowthatbetterpreservestheidentityof the

B

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new method for personalized image generation, decompose the personalization process into three training stages and introducing a cross-attention map regularization term.\n\nSTRENGTHS:\nThe manuscript is well-written.\n\nThe authors propose to address the intrinsic issues of two classical and up-to-date methods, Textual Inversion and DreamBooth, by aligning the new concept from the prompt while preserving the original subject identity. This approach tackles a significant problem.\n\nWEAKNESSES:\n1.\tSome parts of the manuscript are slightly verbose. For instance, the introduction section already introduces the existing problems of Textual Inversion and DreamBooth, along with their corresponding analysis and naïve solution. However, these points are reiterated in Section 4.1 and the first paragraph of Section 4.2 without adding any new information.\n\n2.\tWhat does the term 'this approach' refer to in Line 174?\n\n3.\tIn general, the proposed method tends to be a bag of tricks with some customized hype-parameters.\n\n4.\tImportant comparisons are lacking in the manuscript, specifically the comparison with Textual Inversion (TI) and DreamBooth (DB) individually, in addition to the comparison with the combined approach of TI+DB.\n\n5.\tThis method uses cross-attention map for regularization, which results in a high time cost; on a NVIDIA A100, training requires 660 steps, taking 20 minutes. In contrast, DreamBooth requires only 5 minutes to train for 1000 steps.\n\nQUESTIONS:\n1.\tDo the constraints imposed between [V] and [super-category] across all layers in Eq. 2 potentially restrict the diversity? Considering that [super-category] represents the original identity information and [V] introduces diversity.\n\n2.\tWhy was SD2.1 chosen, and how do other models perform?\n\n3.\tIn line 197, it is mentioned that retaining the prior preservation loss leads to poor results. Can you provide experimental results to support this?\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-11"}

comments: SUMMARY:
This paper proposes a method to enhance the performance of personalizing text-to-image models by appropriately combining textual inversion approach, which learns new embeddings, and DreamBooth approach, which fine-tunes model weights. They demonstrate the effectiveness of their approach through qualitative evaluation and human evaluation.

STRENGTHS:
* Research on appropriately combining textual inversion and DreamBooth methods has been needed in this field, and they propose a reasonable and clear method for this.
* The experimental results are good.

WEAKNESSES:
* (minor) The proposed method seems quite similar to the Magicapture [1] approach in that it separates embedding learning and weight fine-tuning and conducts regularization on attention. However, this paper does not cover a comparison with Magicapture. 
* (minor) The ablation study is not conducted quantitatively and relies on a single generation scenario.

[1] Hyung, Junha, Jaeyo Shin, and Jaegul Choo. "Magicapture: High-resolution multi-concept portrait customization." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 3. 2024.

QUESTIONS:
When conducting analysis in Figure 1, how many steps was DreamBooth trained for to obtain the results? It seems that with sufficient training steps, DreamBooth might exhibit different results.

LIMITATIONS:
Yes.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-13

comments: SUMMARY:
The author proposed a method to generate high-quality personalized images. First, a textual embedding is learned, then the cross-attention layers is  finetund to refine attention map during learning the  textual embedding, finally, the entire U-Net is trained to capture the subject identity.

STRENGTHS:
1. The paper is welll-written and easy to understand.
2. The proposed method achieve competitive results comparing with SOTAs.
3. Using the super-category attention map is an interesting idea to calibrate the [V] attention map in a self-supervised manner.

WEAKNESSES:
1. The proposed method needs a costly test-time optimization to generate personalized images. The 3 stage finetuning requires expensive computation (20 minutes in A100)
2. Even though the Attention Map Regularization idea is interesting, it seems to be the only critical contribution in this paper. The authors combine some techniques from off-the-shelf method including CostumDiffusion/DB/TI, and conducted them step by step with proposed -attention map regularization.

QUESTIONS:
1. Where does the super-category label come from? Is it a part of the annotation?
2. I am curious how the  TI+DB perform if it is optimized step by step like AttnDreamBooth, e.g. first tuning the textual embedding and then the U-Net or first tuning U-Net then optimizing the embedding.

LIMITATIONS:
N/A
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-13

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe submission proposes AttnDreamBooth for text-to-image personalization. It addresses the limitations of existing methods, Textual Inversion and DreamBooth, by separating the learning process into three stages: embedding alignment, attention map refinement, and subject identity capture. The method aims to improve identity preservation and text alignment in generated images.\n\n# Key Contributions:\n- Introduces an approach for text-to-image personalization.\n- Addresses the limitations of existing methods by separating the learning process.\n- Demonstrates improved performance in terms of identity preservation and text alignment.\n- Provides a comprehensive analysis of the proposed method through qualitative and quantitative evaluations.\n\nSTRENGTHS:\n-The paper identifies a key challenge in text-to-image personalization and provides a novel solution.\n- The proposed method, AttnDreamBooth, demonstrates superior performance in both identity preservation and text alignment compared to Textual Inversion and Dreambooth.\n- The work contributes to advancing the field of text-to-image personalization by offering a more effective approach to balancing the trade-off between identity and text alignment.\n\nWEAKNESSES:\n- My main concern is comparison with new existing work. There have been several recent works after Textual inversion and Dreambooth that have significantly advanced this area. Some of them like SuTI (NeurIPS 2023), Instruct Imagen (CVPR 2024), HyperDreambooth (CVPR 2024) are also zero-shot which do not require any fine-tuning during evaluation. \n\n- It is critical for this work to compare with the state-of-the-art papers in this area. I have listed a few above. However, I am sure that there could be more papers in the past two years in this area.\n\nQUESTIONS:\n- Comparison with multiple recent baselines is required. It would be great if the authors can provide this.\n\nLIMITATIONS:\n- While the proposed approach is novel, the core idea of decomposing the personalization process into multiple stages is not entirely groundbreaking. A more comprehensive exploration of the relationship between the proposed method and existing work would be beneficial.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-08-03"}