1. Bibliographic Information

1.1. Title

A Survey on Personalized Content Synthesis with Diffusion Models

1.2. Authors

Xulu Zhang, Xiaoyong Wei, Wentao Hu, Jinlin Wu, Jiaxin Wu, Wengyu Zhang, Zhaoxiang Zhang, Zhen Li, Qing Li

1.3. Journal/Conference

This paper is a pre-print available on arXiv. Surveys published on arXiv are typically comprehensive reviews of a specific field, intended to synthesize current knowledge, identify trends, and highlight open problems. Given the affiliations (The Hong Kong Polytechnic University, Chinese Academy of Sciences, University of Chinese Academy of Sciences) and the corresponding authors' backgrounds (x1wei@polyu.edu.hk; zhen.lei@ia.ac.cn), the paper is from reputable academic institutions and targets a broad academic audience interested in generative AI.

1.4. Publication Year

2024

1.5. Abstract

This paper surveys the rapidly evolving field of Personalized Content Synthesis (PCS) using diffusion models. PCS tailors content to specific user-provided subjects, learned from a small set of examples. With over 150 methods emerging in two years, the paper addresses a gap in existing surveys, which primarily focus on general text-to-image generation. It categorizes PCS into two main frameworks: test-time fine-tuning (TTF) and pre-trained adaptation (PTA), analyzing their strengths, limitations, and key techniques. Specialized PCS tasks like object, face, and style personalization are explored, highlighting their unique challenges and innovations. The survey also discusses ongoing challenges, including overfitting and the trade-off between subject fidelity and text alignment, and proposes future research directions.

1.6. Original Source Link

https://arxiv.org/abs/2405.05538 PDF Link: https://arxiv.org/pdf/2405.05538v5.pdf Publication Status: Pre-print on arXiv.

2. Executive Summary

2.1. Background & Motivation

The core problem addressed by the paper is the inability of advanced generative models, such as ChatGPT and text-to-image diffusion models, to fulfill specific, personalized user requirements. While these models excel at general content creation, they often struggle with tasks like accurately depicting a user's portrait or answering domain-specific queries. This limitation highlights a significant gap: the lack of Personalized Content Synthesis (PCS) capabilities, which allow users to customize models for their unique tasks and data.

This problem is critically important in the current field because achieving Artificial General Intelligence (AGI) requires models to adapt to individual user needs. The growing number of companies releasing products that support personalized content creation (e.g., fine-tuning language models with Reinforcement Learning) further underscores its practical relevance. The rapid emergence of diffusion models has significantly advanced text-guided content generation, leading to an explosion of PCS methods. Following key innovations like DreamBooth and Textual Inversion in August 2022, over 150 methods have been introduced in a remarkably short period. However, existing surveys primarily focus on text-to-image generation broadly, with few providing up-to-date, comprehensive summaries specifically on PCS. This gap motivates the current survey.

The paper's entry point is to provide a structured and comprehensive overview of this burgeoning field, categorizing the diverse methodologies, analyzing their underlying techniques, exploring specialized applications, and identifying persistent challenges and future directions.

2.2. Main Contributions / Findings

The paper makes several primary contributions to the understanding and advancement of Personalized Content Synthesis (PCS):

1. Comprehensive Categorization of Frameworks: It introduces and thoroughly analyzes two overarching frameworks for PCS: test-time fine-tuning (TTF) and pre-trained adaptation (PTA). TTF methods fine-tune a model for each personalization request during inference, prioritizing visual fidelity. PTA methods train a unified model on large datasets to handle a wide range of subjects without per-inference parameter updates, prioritizing efficiency. This categorization provides a foundational structure for understanding the diverse approaches in the field.

2. Analysis of Key Techniques: The survey dissects the common techniques employed across PCS methods into four primary areas: attention-based operations, mask-guided generation, data augmentation, and regularization. For each, it explains how these techniques enhance subject fidelity, minimize redundant semantics, improve generalization, and prevent overfitting.

3. Exploration of Specialized Personalization Tasks: The paper highlights the expanding scope of PCS by examining specialized tasks beyond general object customization, including human faces, artistic styles, actions, video generation, and 3D representations. It details the unique challenges and innovations pertinent to each domain.

4. Identification of Challenges and Future Directions: It critically discusses unresolved challenges such as the pervasive overfitting problem (producing rigid outputs or including irrelevant elements), the inherent trade-off between subject fidelity and text alignment, the lack of robust evaluation metrics, and the absence of standardized test datasets. Based on this analysis, the paper proposes future research directions, including exploring multimodal autoregressive frameworks and interactive personalization workflows.

5. Introduction of a New Benchmark: To address the lack of standardized evaluation, the paper introduces Persona, a new test dataset for common personalized object and face personalization tasks, and provides a benchmark evaluation of 22 representative PCS methods against this dataset using metrics like CLIP-T and CLIP-I, offering a clear comparison and promoting future research.

   In summary, the paper synthesizes the rapidly growing field of PCS, offering a structured overview, technical analysis, and critical assessment of its current state and future potential, thereby serving as a valuable resource for researchers and practitioners.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To understand Personalized Content Synthesis (PCS) within diffusion models, it's essential to grasp the core mechanics of how these generative models work. The paper provides a concise yet fundamental overview in Section 2, covering Denoising Diffusion Probabilistic Models (DDPMs), Stochastic Differential Equations (SDEs), Ordinary Differential Equations (ODEs), and Conditional Generation Mechanisms.

3.1.1. Denoising Diffusion Probabilistic Models (DDPMs)

DDPMs are a class of generative models that learn to reverse a gradual noising process. The core idea is to define a forward diffusion process that slowly adds Gaussian noise to an image, transforming it into pure noise. Then, a reverse process is learned to iteratively remove this noise, reconstructing the original image.

• Forward Diffusion Process: This process systematically perturbs data distributions. Starting with an original data point $\mathbf{x}_0$ (e.g., an image), Gaussian noise is added over $T$ discrete time steps. Each step $t$ generates $\mathbf{x}_t$ from $\mathbf{x}_{t-1}$. A key property is that $\mathbf{x}_t$ can be directly sampled from $\mathbf{x}_0$ at any step $t$:

  $$\mathbf { x } _ { t } = \sqrt { \bar { \alpha } _ { t } } \mathbf { x } _ { 0 } + \sqrt { 1 - \bar { \alpha } _ { t } } \epsilon , \quad \bar { \alpha } _ { t } = \prod _ { s = 1 } ^ { t } \alpha _ { s }$$ Where:

  • $\mathbf{x}_t$: The noised data at diffusion step $t$.
  • $\mathbf{x}_0$: The original, un-noised data.
  • $\epsilon$: A sample from a standard Gaussian noise distribution, $\mathcal{N}(0, \mathbf{I})$.
  • $\alpha_s$: A parameter governing the noise scheduling policy at step $s$.
  • $\bar{\alpha}_t$: The cumulative product of $\alpha_s$ up to step $t$, which controls the signal-to-noise ratio.

• Reverse Process (Generation): The goal is to learn the reverse transitions, $p(\mathbf{x}_{t-1} \mid \mathbf{x}_t)$, to go from noise back to data. DDPMs approximate this reverse transition using a neural network, typically a U-Net, which learns to predict the noise $\epsilon$ that was added. The reverse transition is achieved via Bayesian reconstruction:

  $$\begin{array} { r l } & { \quad p _ { \theta } \left( \mathbf { x } _ { t - 1 } \mid \mathbf { x } _ { t } \right) = } \\ & { \mathcal { N } \left( \mathbf { x } _ { t - 1 } ; \frac { 1 } { \sqrt { \alpha _ { t } } } \left( \mathbf { x } _ { t } - \frac { \beta _ { t } } { \sqrt { 1 - \bar { \alpha } _ { t } } } \epsilon _ { \theta } ( \mathbf { x } _ { t } , t ) \right) , \beta _ { t } I \right) } \end{array}$$ Where:

  • $p_\theta(\mathbf{x}_{t-1} \mid \mathbf{x}_t)$: The parameterized reverse transition probability, approximating the true reverse probability.
  • $\mathcal{N}(\cdot; \mu, \Sigma)$: A normal distribution with mean $\mu$ and covariance $\Sigma$.
  • $\epsilon_\theta(\mathbf{x}_t, t)$: The neural network (e.g., U-Net) that predicts the noise component added at step $t$, given $\mathbf{x}_t$ and $t$.
  • $\beta_t$: A hyperparameter related to the noise variance at step $t$.

• Training Objective: The neural network $\epsilon_\theta$ is trained to predict the noise $\epsilon$ added to $\mathbf{x}_0$ to get $\mathbf{x}_t$. The objective is a simple mean squared error (MSE) loss:

  $$\mathcal { L } = \mathbb { E } _ { \mathbf { x } _ { 0 } , t , \epsilon } \left[ \lVert \epsilon - \epsilon _ { \theta } ( \mathbf { x } _ { t } , t ) \rVert _ { 2 } ^ { 2 } \right]$$ Where:

  • $\mathcal{L}$: The loss function.
  • $\mathbb{E}[\cdot]$: Expectation over original data $\mathbf{x}_0$, time step $t$, and noise $\epsilon$.
  • $\lVert \cdot \rVert_2^2$: The squared $L_2$ norm (MSE).

3.1.2. Stochastic Differential Equations (SDEs)

SDEs generalize DDPMs by formulating the diffusion process in continuous time, offering more flexibility in noise scheduling and potentially improving computational efficiency. The forward process is described by an SDE:

$$d \mathbf { x } = \mathbf { f } ( \mathbf { x } , t ) d t + g ( t ) d \mathbf { w }$$ Where:

• $d\mathbf{x}$: Infinitesimal change in the data $\mathbf{x}$.

• $\mathbf{f}(\mathbf{x}, t)$: Drift coefficient, encoding the deterministic part of the process (how $\mathbf{x}$ changes over time without noise).

• g(t): Diffusion coefficient, modulating the amount of stochastic noise added.

• $d\mathbf{w}$: An infinitesimal Wiener process (or Brownian motion), representing the random noise component.

  The reverse process of an SDE also follows an SDE:

$$\begin{array} { r } { d \mathbf { x } = \left[ \mathbf { f } ( \mathbf { x } , t ) - g ( t ) ^ { 2 } \nabla _ { \mathbf { x } } \log p _ { t } ( \mathbf { x } ) \right] d t + g ( t ) d \overline { { \mathbf { w } } } } \\ { ( } \end{array}$$ Where:

• $\nabla_{\mathbf{x}} \log p_t(\mathbf{x})$: The score function, which indicates the direction of highest probability density of the perturbed data $\mathbf{x}_t$. This function is typically learned by the neural network.
• $d\overline{\mathbf{w}}$: Reverse-time Wiener increments.

3.1.3. Ordinary Differential Equations (ODEs)

SDEs involve inherent randomness, which can make sampling slow. ODEs provide a deterministic alternative for faster sampling by removing the stochastic term from the SDE formulation, essentially tracing the mean path of the probability flow:

$$d \mathbf { x } = \left[ \mathbf { f } ( \mathbf { x } , t ) - { \frac { 1 } { 2 } } g ( t ) ^ { 2 } \nabla _ { \mathbf { x } } \log p _ { t } ( \mathbf { x } ) \right] d t$$ Where:

• The terms are the same as in SDEs, but the Wiener process term ($g(t)d\mathbf{w}$) is removed, and the score function term is modified. This allows for faster sampling (e.g., 5-10x faster than SDEs) using adaptive ODE solvers like DPM-Solver.

3.1.4. Conditional Generation Mechanisms

Conditional generation is crucial for practical applications, allowing users to guide the generation process with specific inputs (e.g., text, images, masks). It extends the unconditional DDPM objective:

$$\mathcal { L } = \mathbb { E } _ { \mathbf { x } _ { 0 } , t , \epsilon } \left[ \| \epsilon - \epsilon _ { \theta } ( \mathbf { x } _ { t } , t , c ) \| _ { 2 } ^ { 2 } \right]$$ Where:

• $c$: The conditioning signal (e.g., a text prompt, subject embedding, anatomical mask).
• $\epsilon_\theta(\mathbf{x}_t, t, c)$: The neural network now predicts the noise conditioned on $\mathbf{x}_t$, $t$, and the conditioning signal $c$. Conditioning can be integrated via cross-modal attention (e.g., in Stable Diffusion) or spatial modulation. This conditional paradigm is fundamental to PCS, enabling precise user control over customized content.

3.2. Previous Works

The paper highlights DreamBooth [4] and Textual Inversion [7] as key innovations that catalyzed the rapid expansion of PCS research following their release in August 2022.

• Textual Inversion [7]: This method allows users to personalize text-to-image diffusion models by learning new "words" (or token embeddings) that represent novel visual concepts from just a few example images. These learned embeddings can then be used in prompts to generate images of the personalized subject in various contexts. It's known for its parameter efficiency (only a few kilobytes of additional parameters) but can sometimes struggle with detail preservation or require longer training times compared to methods that fine-tune more model parameters. The core idea is to find a textual embedding in the model's vocabulary space that, when used in a prompt, consistently evokes the visual features of the provided subject.

• DreamBooth [4]: This approach proposes fine-tuning the entire diffusion model (or a significant portion of its parameters) on a small set of images of a specific subject (e.g., a user's pet). To prevent overfitting and language drift (where the model forgets common concepts due to fine-tuning on a narrow dataset), DreamBooth introduces a regularization dataset (e.g., other images of "dogs" if the subject is a dog) and a unique identifier (e.g., a rare token) to represent the subject. This allows the model to generate the subject in novel contexts while preserving its identity, often achieving superior visual fidelity compared to Textual Inversion but at a higher computational and storage cost.

3.3. Technological Evolution

The evolution of content generation has moved from general, unconstrained image synthesis to highly controllable and personalized output.

1. Early Generative Models: Generative Adversarial Networks (GANs) were foundational for high-quality image generation, but often lacked precise user control.

2. Text-to-Image Generation: The advent of large-scale text-to-image models like DALLE and Stable Diffusion marked a major leap, allowing users to generate diverse images from natural language prompts. This represented a shift towards semantic control.

3. Personalized Content Synthesis (PCS): Despite the power of text-to-image models, they struggled with instance-level personalization—generating a specific object, person, or style across various scenes. This led to the development of PCS, exemplified by Textual Inversion and DreamBooth, which focused on learning unique subject characteristics from minimal user-provided examples.

4. Specialized PCS Tasks: The field further diversified into object, face, style, video, and 3D personalization, each presenting unique challenges (e.g., identity preservation for faces, temporal consistency for videos) and requiring specialized techniques.

5. Addressing Challenges: Current research is focused on mitigating issues like overfitting, improving the fidelity-alignment trade-off, and enhancing computational efficiency, leading to innovations in parameter-efficient fine-tuning, reference-aware architectures, and advanced regularization techniques.

   This paper's work fits within this timeline by comprehensively surveying the PCS phase, specifically focusing on how diffusion models are adapted for personalization. It synthesizes the vast number of methods that have emerged in a very short period, providing a structured understanding of their diverse approaches and identifying the critical path forward.

3.4. Differentiation Analysis

Compared to prior surveys that primarily focused on general text-to-image generation, this paper's core innovation lies in its dedicated and up-to-date focus on Personalized Content Synthesis (PCS).

• Targeted Scope: While other surveys might touch upon personalization as a sub-topic of text-to-image, this paper makes PCS its central theme. It acknowledges the shift from generating any image described by text to generating images of a specific, user-defined subject described by text.

• Structured Frameworks: The introduction of the Test-time fine-tuning (TTF) and Pre-trained adaptation (PTA) frameworks provides a novel and systematic way to categorize and understand the diverse PCS methods. This distinction is crucial because it highlights the fundamental trade-offs between customization flexibility/fidelity (TTF) and inference efficiency (PTA).

• Detailed Technique Breakdown: The paper goes beyond broad categories to analyze specific techniques like attention-based operations, mask-guided generation, data augmentation, and regularization within the PCS context. This granular analysis is essential for researchers looking to develop new methods or improve existing ones.

• Comprehensive Task Coverage: It explicitly explores various specialized PCS tasks (object, face, style, video, 3D), detailing their unique requirements and solutions. This is more in-depth than general surveys that might only briefly mention a few personalization applications.

• Identification of PCS-Specific Challenges: The survey zeroes in on challenges that are particularly acute in PCS, such as overfitting (especially given limited reference images) and the subject fidelity vs. text alignment trade-off. These are often less prominent or framed differently in general text-to-image surveys.

• New Benchmark and Evaluation: A significant differentiator is the introduction of the Persona dataset and a benchmark evaluation of current State-of-the-Art (SoTA) PCS methods. This directly addresses a critical need for standardized evaluation within the PCS community, which is often lacking in rapidly evolving sub-fields.

  In essence, this paper serves as a specialized guide, disentangling the complexities of PCS from the broader text-to-image domain, providing a much-needed structured analysis for this burgeoning area.

4. Methodology

The paper introduces a structured approach to understand Personalized Content Synthesis (PCS) by categorizing methods into two main frameworks: Test-time Fine-tuning (TTF) and Pre-trained Adaptation (PTA). It then details common techniques applied across both.

4.1. Principles

The core idea behind PCS is to enable diffusion models to generate images of a specific Subject of Interest (SoI) (e.g., a particular cat, a unique art style, a user's face) based on a small set of user-provided reference images. The model should then be able to render this SoI in various new contexts, poses, or styles specified by a text prompt, while maintaining its identity and visual characteristics. The theoretical basis is rooted in adapting the powerful generative capabilities of diffusion models to learn instance-specific features from limited data, often by modifying latent representations or fine-tuning network weights, and then controlling generation through text.

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

4.2.1. Test-time Fine-tuning (TTF) Framework

The TTF framework represents a foundational approach where the generative model is adapted specifically for each new SoI during the inference phase. This means that for every personalization request, a part of the model undergoes an optimization process.

4.2.1.1. Test-time Adaptation

For each collection of reference images $\mathbf{X}_{SoI}$ belonging to a specific Subject of Interest, the TTF method adjusts a subset of the model's parameters, denoted as $\theta'$, to reconstruct these reference images. This process is essentially a fine-tuning step performed "at test-time" (or more accurately, prior to the actual generation for a given subject). The fine-tuning objective is defined by a reconstruction loss:

$$\mathcal { L } = \mathbb { E } _ { \mathbf { x } _ { 0 } \in \mathbf { X } _ { S o I } , t , \epsilon } \left[ \left\| \epsilon - \epsilon _ { \theta ^ { \prime } } ( \mathbf { x } _ { t } , t , c ) \right\| _ { 2 } ^ { 2 } \right]$$ Where:

• $\mathcal{L}$: The mean squared error (MSE) loss, aiming to minimize the difference between the actual noise $\epsilon$ and the noise predicted by the model $\epsilon_{\theta'}$.

• $\mathbb{E}[\cdot]$: Expectation taken over samples $\mathbf{x}_0$ from the SoI reference set $\mathbf{X}_{SoI}$, random time steps $t$, and random noise samples $\epsilon$.

• $\mathbf{x}_0 \in \mathbf{X}_{SoI}$: An original reference image of the SoI.

• $t$: A randomly sampled time step for the diffusion process.

• $\epsilon$: The Gaussian noise added to $\mathbf{x}_0$ to obtain $\mathbf{x}_t$.

• $\epsilon_{\theta'}(\mathbf{x}_t, t, c)$: The U-Net (or denoising network) with updated parameters $\theta'$, predicting the noise at step $t$ given the noised image $\mathbf{x}_t$ and a condition signal $c$.

• $c$: The condition signal, typically a text caption describing the reference image (e.g., "a photo of a cat").

  Compared to large-scale pre-training (Equation 7 in the paper, which is the conditional generation objective mentioned in Section 2.4), the key differences are:

1. Training Data: Limited to the SoI references, sometimes supplemented with a regularization dataset to prevent overfitting (e.g., in DreamBooth).
2. Learnable Parameters $\theta'$: Only a subset of the model parameters are updated, as discussed in Section 4.2.1.3.

4.2.1.2. Test-time Generation

Once the model has been fine-tuned and its parameters $\theta'$ are optimized for the specific SoI, the generation phase can begin. This involves composing new input prompts that incorporate a unique identifier for the SoI (explained next) along with desired new contexts. The adapted model then synthesizes diverse images of the SoI that adhere to these new textual descriptions while preserving the subject's distinct characteristics.

The overall TTF framework is illustrated in Figure 4 from the original paper:

该图像是示意图，展示了测试时微调（TTF）和生成过程的框架。在推理阶段，模型通过重建每个主题对象（SoI）组的参考图像来微调其参数，使用独特的修饰符 $V ^ { * }$ 来生成个性化图像。

Figure 4 depicts the TTF process:

1. Reference Images: A small set of images of the SoI (e.g., a specific cat) is provided.
2. Fine-tuning: A diffusion model is fine-tuned using these references. This involves reconstructing the references, adapting the model's parameters (e.g., U-Net weights or token embeddings). A unique modifier (e.g., $V^*$) is learned or associated with the SoI.
3. Generation: During inference, a user provides a novel prompt that includes the unique modifier (e.g., "$V^*$ on the beach"). The fine-tuned model then generates new images of the SoI in the specified context.

4.2.1.3. Unique Modifier

A unique modifier is a textual token or a short phrase that serves as a unique textual representation for the SoI. It allows users to refer to their personalized subject within prompts for flexible content generation. There are three main categories:

1. Plain Text: Uses explicit descriptive words (e.g., "cat") to represent the SoI. This often requires fine-tuning diffusion model components (like U-Net or Transformer blocks) to associate the SoI's visual features with this general term. It's user-friendly and injects prior knowledge but can overspecialize common terms.
2. Rare Token: Employs an infrequently used token to minimize interference with common vocabulary. The embeddings of these rare tokens typically remain unchanged during fine-tuning. However, they lack useful prior information and can still cause weak interference.
3. Learnable Token Embedding: Adds a new token and its corresponding embedding vector to the tokenizer's dictionary. This new token's weights are adjustable during fine-tuning, while pre-defined dictionary embeddings remain fixed. This method is parameter-efficient (few kilobytes) but can lead to long convergence times and potential loss of detail due to compressing complex features into low-dimensional embeddings.

4.2.1.4. Training Parameter Selection

The choice of which parameters to train is critical for subject fidelity, training efficiency, and model storage.

1. Token Embedding: Only token embeddings (as introduced in Textual Inversion [7]) are optimized. This is highly parameter-efficient but can struggle with detail preservation and requires longer training.
2. Existing Model Parameters: Directly optimizes components of the pre-trained model like the text encoder, U-Net blocks, or transformer layers (e.g., DreamBooth [4]). This benefits from the advanced representation capacity of these modules, leading to faster convergence and superior visual fidelity, but incurs significant storage overhead.
3. Parameter-Efficient Extensions: Introduces small, trainable components like LoRA (Low-Rank Adaptation) modules or adapter modules into the base model (e.g., LoRA [25, 26, 27], adapters [23, 24]). These achieve comparable performance to full fine-tuning with drastically reduced storage.
4. Combined Strategy: Different learning rates and training phases are allocated to various parameter types (e.g., fine-tuning token embeddings as an initialization for subsequent model weight fine-tuning, or optimizing both simultaneously with different rates).

4.2.1.5. Prompt Engineering

The construction of training prompts for SoI samples is crucial. Simple prompts like "Photo of $V^*$" can lead to long training times and unsatisfactory performance. DreamBooth [4] improved this by incorporating the unique modifier with a class noun (e.g., "Photo of $V^*$ cat"). More precise captions (e.g., "Photo of $V^*$ cat on the chair") can further improve disentanglement between SoI characteristics and irrelevant concepts, aligning with the observation that high-quality captions aid accurate text control.

4.2.2. Pre-trained Adaptation (PTA) Framework

The PTA framework aims to overcome the computational burden of per-request fine-tuning seen in TTF by enabling single-pass personalization during inference. This is achieved through large-scale pre-training of a unified model.

4.2.2.1. Pre-training

During this phase, the PTA framework learns direct mappings between reference characteristics (e.g., facial features, object textures) and synthesized outputs. Reference inputs are processed by feature extractors and fused with text prompts to guide generation. The model is optimized using a reconstruction loss that enforces alignment with a large-scale dataset $X_{data}$:

$$\mathcal { L } = \mathbb { E } _ { \mathbf { x _ { 0 } } \in \mathbf { X } _ { d a t a } , t , \epsilon } \left[ \| \epsilon - \epsilon _ { \theta ^ { \prime } } ( \mathbf { x } _ { t } , t , c ^ { \prime } ) \| _ { 2 } ^ { 2 } \right] , \quad c ^ { \prime } = \mathcal { F } ( c , \mathbf { x _ { 0 } } )$$ Where:

• $\mathcal{L}$: The MSE loss.

• $\mathbb{E}[\cdot]$: Expectation over original data $\mathbf{x}_0$ from the large-scale dataset $\mathbf{X}_{data}$, random time steps $t$, and random noise samples $\epsilon$.

• $\mathbf{x}_0 \in \mathbf{X}_{data}$: An original image from the large-scale training dataset.

• $t$: A randomly sampled time step.

• $\epsilon$: Gaussian noise.

• $\epsilon_{\theta'}(\mathbf{x}_t, t, c')$: The denoising network with parameters $\theta'$, predicting noise given $\mathbf{x}_t$, $t$, and a fused condition $c'$.

• $c'$: The fused condition, which is the result of the fusion operation $\mathcal{F}(c, \mathbf{x}_0)$.

• $\mathcal{F}$: The fusion operation combining a text condition $c$ (e.g., a caption for $\mathbf{x}_0$) and the reference image $\mathbf{x}_0$ itself (used as a reference during training).

  The tunable parameters $\theta'$ can include visual encoder weights, text encoder components, diffusion modules, and injected adapter modules.

4.2.2.2. Inference

During inference, a reference image is passed through its pre-trained visual encoder to extract discriminative features. These features are then fused with text embeddings from the text encoder using a pre-trained conditioning module. This fused representation directly guides the diffusion model to generate personalized outputs, eliminating the need for test-time optimization and ensuring fast generation.

The overall PTA framework is illustrated in Figure 5 from the original paper:

该图像是个示意图，展示了个性化图像合成中的PTA方法。该框架通过预训练阶段利用大规模数据集进行训练，结合图像和文本编码器处理混合输入。在推理阶段，用户上传的图像与上下文合并，生成个性化图像。

Figure 5 shows the PTA process:

1. Reference Input: A reference image and text prompt are provided.
2. Feature Extraction: A visual encoder extracts features from the reference image, and a text encoder extracts features from the text prompt.
3. Feature Fusion: These features are combined through a conditioning module to create a hybrid input.
4. Diffusion Model: This hybrid input guides a pre-trained diffusion model to generate the personalized image. Crucially, no fine-tuning occurs at this stage; the model is already trained to handle such hybrid inputs.

4.2.2.3. Subject Feature Extraction

Extracting salient and representative features of the SoI is paramount.

• Encoders: Pre-trained models like CLIP [42] and BLIP [43] are commonly used. While good at global features, they can capture irrelevant information (e.g., background).
• Prior Knowledge: To focus on the SoI, additional prior knowledge is incorporated:
  • SoI-specific masks [44, 32, 38, 45, 34, 35]: Used to exclude background influence by discarding background pixels during reconstruction.
  • Facial landmarks [6]: For human face customization, these help improve identity preservation.
• Multiple References: To handle multiple input references, techniques involve averaging or stacking features extracted from several images to form a composite SoI representation (e.g., [6, 46]).

4.2.2.4. Subject Feature Fusion

Combining heterogeneous visual and textual features is a critical challenge in PTA.

1. Concatenation-based Fusion [47, 46, 5, 48, 49, 50]: A placeholder token (acting as a unique modifier) encapsulates visual subject characteristics. Its embedding (initialized with image features) is concatenated with text embeddings. This combined representation guides generation via standard cross-attention layers.
2. Cross-attention Fusion [51, 52, 6, 53, 54, 55, 56]: Extends the U-Net with specialized attention mechanisms that process visual and textual conditions jointly. For instance, IP-Adapter [51] uses decoupled cross-attention layers with separate query projections for image and text features.
3. Multimodal Encoder Fusion [57, 58, 59, 60, 61]: Leverages powerful multimodal encoder architectures (e.g., BLIP-2 [62]) to jointly embed visual and textual subject descriptors into a compact subject prompt embedding (e.g., BLIP-Diffusion [57]).
4. Hybrid Fusion [63, 64]: Integrates multiple strategies, such as combining concatenation and cross-attention fusion, to leverage the strengths of each.

4.2.2.5. Training Data

PTA models require large-scale datasets.

1. Triplet Data (Reference Image, Target Image, Target Caption): This format directly aligns with PCS objectives, relating a reference to personalized content. It's often constructed via data augmentation (e.g., foreground segmentation and background placement [57]) or synthetic sample generation (using TTF models to create variations [52]).
2. Dual Data (Reference Image, Reference Caption): A simplified version where the personalized content is the original image itself. More accessible (e.g., LAION [66], LAION-FACE [67]), but training might over-focus on reconstruction, potentially limiting responsiveness to complex text prompts.

4.2.3. Hybrid Framework

Some recent works combine TTF and PTA to leverage their respective strengths. For example, HyperDreamBooth [26] develops a PTA network for general handling, followed by subject-driven fine-tuning (TTF) for fine-grained detail preservation. DreamTuner [39] pre-trains a subject encoder for reconstruction, then fine-tunes for identity. SuTI [52] uses TTF methods to generate synthetic data, which then trains a PTA model.

4.2.4. Techniques in Personalized Content Synthesis

These techniques are applicable to both TTF and PTA frameworks, addressing challenges like subject fidelity, redundant semantics, generalization, and overfitting.

4.2.4.1. Attention-based Operation

Attention mechanisms are crucial for feature processing in diffusion models.

1. Explicit Attention Weight Manipulation: Focuses on restricting the influence of the SoI token within attention layers.

   • Mix-of-Show [25]: Uses region-aware cross-attention where global prompt features are replaced with regional features for each entity, preventing misalignment.
   • DreamTuner [39]: Designs a self-subject-attention layer to refine subject identity using generated image features as query and reference features as value.
   • Layout-Control [69]: Adjusts attention weights around specific layouts without extra training.
   • Cones 2 [70]: Defines negative attention areas to penalize illegal occupation for multiple object generation.
   • VICO [71]: Inserts a new attention layer with a binary mask to selectively obscure the attention map between noisy latent and reference image features.

2. Implicit Attention Guidance: Uses localization supervision in cross-attention modules by training them with coordinate-aware loss functions, forcing attention maps to align with annotated subject positions (e.g., [5, 22, 38, 63, 45, 72, 37]).

4.2.4.2. Mask-guided Generation

Masks provide crucial prior information about the SoI's position and contour, minimizing the influence of irrelevant visual elements.

1. Pixel-level Mask:
   • Utilizes advanced segmentation (e.g., SAM [73]) to isolate the SoI. Many studies [44, 32, 38, 45, 34, 35, 37, 74, 75] discard background pixels to focus reconstruction loss on the target object.
   • [49] adds masked background reconstruction for better disentanglement.
   • Layouts indicated by pixel masks can supervise attention modules to adaptively adjust attention concentration (e.g., [5, 22, 38, 63, 45, 72, 37]).
   • Masks can stitch feature maps to construct informative semantic patterns [47, 76, 63].
2. Feature-level Mask: Extends masking to the feature space.
   • DisenBooth [77]: Defines an identity-irrelevant embedding with a learnable mask to adaptively exclude redundant information, preserving subject appearance.
   • AnyDoor [78]: Uses a high-frequency mask to store detailed SoI features as a condition.
   • FaceDiffuser [79]: Determines a mask through augmentation from noise predicted by a pre-trained text-to-image model and a PTA personalized model, combining their outputs via mask-guided concatenation.

4.2.4.3. Data Augmentation

Addresses the issue of limited reference images by enriching SoI diversity.

1. Compositional Augmentation: Enhances data diversity through classical image augmentation.
   • SVDiff [22]: Manually constructs mixed images of multiple SoIs as new training data.
   • BLIP-Diffusion [57]: Segments foreground subjects and composes them into random backgrounds, expanding text-image pairs.
   • StyleAdapter [82]: Shuffles image patches to break irrelevant subjects while preserving style.
   • PACGen [75]: Uses rescaling, center crop, and relocation as spatial augmentation solutions to disentangle position from identity.
2. Synthetic Data: Generates large amounts of training resources.
   • SuTI [52]: Uses a cascaded pipeline where TTF models generate diverse SoI variations to train the target PTA model.
   • DreamIdentity [48]: Leverages pre-trained knowledge of celebrities to generate source and edited face images.
   • StyleDrop [24] and GAL [19]: Implement iterative refinement pipelines, incorporating high-quality synthetic outputs into subsequent training rounds.
3. External Sources: Leverages web resources.
   • COTI [83]: Adopts a scorer network to progressively expand the training set by selecting semantically relevant and aesthetically high-quality samples from web-crawled data.

4.2.4.4. Regularization

Regularization techniques prevent overfitting and enhance generalization.

1. Auxiliary Data Regularization:
   • DreamBooth [4]: Uses an additional dataset of images from the same category as the SoI (e.g., other cats) to ensure the personalized model can generate diverse instances of the class while adapting to the target subject, mitigating rigid replication.
   • StyleBoost [84]: Introduces auxiliary style-specific data to separate content and aesthetic adaptation.
   • [40]: Curates datasets with detailed textual prompts to improve disentanglement between subject characteristics and background features.
2. Text Embedding Constraints: Uses the semantic richness of pre-trained text (e.g., subject class name) as a regularization signal.
   • Perfusion [21]: Constrains key projections toward class noun embeddings while learning value projections from subject images.
   • Compositional Inversion [87]: Employs semantically related tokens as anchors to constrain token embedding search, inspired by coached active learning.
   • [88, 34]: Regularize learnable token offsets relative to pre-trained CLIP embeddings to achieve better text alignment.
   • Cones 2 [70]: Minimizes offset by reconstructing features of sentences containing the class noun.
   • [33]: Optimizes learnable tokens towards the mean textual embedding of well-known names.
   • Domain-Agnostic [89]: Uses a contrastive loss to guide the SoI text embedding closer to its nearest CLIP tokens.
   • VICO [71]: Leverages an L2 loss to reduce the difference in attention similarity logits between the SoI token and the $end-of-text token (<|EOT|>)$ for semantic consistency.

5. Experimental Setup

5.1. Datasets

The paper discusses existing evaluation datasets and introduces a new benchmark dataset called Persona.

• DreamBench [4]:

  • Source: Developed for DreamBooth.
  • Scale: 30 diverse subjects.
  • Characteristics: Includes various categories such as backpacks, animals, vehicles, and toys. Each subject has 25 unique prompts.
  • Purpose: Primarily for evaluating object personalization.

• DreamBench-v2 [52]:

  • Source: Expansion of DreamBench.
  • Scale: Same 30 subjects, but significantly expanded prompts.
  • Characteristics: Adds 220 test prompts for each subject, increasing the scope of evaluation.

• Custom-10 [20]:

  • Source: Used in Custom Diffusion.
  • Scale: 10 subjects.
  • Characteristics: Each subject has 20 specific test prompts. Also includes tests for multi-subject composition with 5 pairs of subjects and 8 prompts per pair.
  • Purpose: Evaluates personalization and multi-subject generation.

• Custom-101 [20]:

  • Source: Latest released dataset by authors of Custom Diffusion.
  • Scale: 101 subjects.
  • Characteristics: Provides a broader evaluation scope than Custom-10.

• Stellar [35]:

  • Purpose: Specifically targets human-centric evaluation.
  • Scale: 400 human identities.
  • Characteristics: Features 20,000 prompts.

• Persona (New Benchmark Dataset introduced in this survey):

  • Purpose: Addresses the lack of a standardized benchmark across a large number of personalized generation tasks. It's designed for common personalized object and face personalization.
  • Object Component:
    • Source: 47 subjects collected from available resources (Textual Inversion [7], DreamBooth [4], Custom Diffusion [20]).
    • Characteristics: Categorized into 10 pets and 37 various objects.
    • Prompts: Uses text prompts from DreamBooth [4]:
      • Objects: 20 recontextualization prompts and 5 property modification prompts.
      • Pets: 10 recontextualization, 10 accessorization, and 5 property modification prompts. Total 25 prompts per category.
  • Face Component:

    • Source: 15 subjects from Celeb-A [159].

    • Prompts: Uses 40 prompts for evaluation: 10 accessory prompts, 10 style prompts, 10 action prompts, and 10 context prompts.

      These datasets were chosen to rigorously validate the methods' ability to preserve subject identity across various contexts and modifications, and to assess text alignment. Persona aims for a more comprehensive and standardized evaluation across diverse PCS tasks.

5.2. Evaluation Metrics

The paper highlights two primary aspects of evaluation for PCS: text alignment and visual fidelity.

5.2.1. Text Alignment Metrics

These quantify how precisely generated outputs reflect the semantics of the input text prompts.

• CLIP-T (CLIP Text Similarity):

  1. Conceptual Definition: Measures the semantic similarity between the generated image and its corresponding text prompt. A higher value indicates better alignment of the generated content with the textual description. It leverages the multimodal understanding capabilities of CLIP.
  2. Mathematical Formula: $$\mathrm{CLIP-T} = \mathrm{cosine\_similarity}(\mathrm{CLIP\_encode\_image}(I_{gen}), \mathrm{CLIP\_encode\_text}(P_{text}))$$
  3. Symbol Explanation:
     • $\mathrm{cosine\_similarity}(\cdot, \cdot)$: The cosine similarity function, which measures the cosine of the angle between two vectors. Values range from -1 (opposite) to 1 (identical).
     • $\mathrm{CLIP\_encode\_image}(I_{gen})$: The embedding vector of the generated image $I_{gen}$ obtained from the CLIP image encoder.
     • $\mathrm{CLIP\_encode\_text}(P_{text})$: The embedding vector of the text prompt $P_{text}$ obtained from the CLIP text encoder.

• ImageReward [152], HPS Score (v1/v2) [153, 154], PickScore [155]:

  1. Conceptual Definition: These are learned metrics trained on human judgments or preferences. They aim to correlate better with human perception of quality and text-to-image alignment than simpler metrics like CLIP-T. They reflect human preference and aesthetic quality.
  2. Mathematical Formula: The exact mathematical formulas for these metrics are complex and involve the internal workings of the learned models themselves. They are not typically expressed as a single, simple closed-form equation but rather as the output of a neural network model, $M(\text{image}, \text{text})$, where $M$ is the trained reward model. For example, a generic representation for a reward model might be: $$\mathrm{Score} = M(\mathrm{Image}, \mathrm{Text})$$
  3. Symbol Explanation:
     • $M$: A neural network model (e.g., ImageReward, HPS Score, PickScore) trained to predict human preference or quality scores.
     • $\mathrm{Image}$: The generated image.
     • $\mathrm{Text}$: The input text prompt.

5.2.2. Visual Fidelity Metrics

These assess how closely the generated subject resembles the Subject of Interest (SoI) from the reference images.

• CLIP-I (CLIP Image Similarity):

  1. Conceptual Definition: Measures the semantic similarity between the generated image and the reference images of the SoI using CLIP image embeddings. A higher value indicates better preservation of the SoI's visual identity. The paper notes that optimal values require a balance: high scores are good, but excessively high scores might indicate overfitting (where the model ignores text guidance and just replicates the reference).
  2. Mathematical Formula: $$\mathrm{CLIP-I} = \mathrm{cosine\_similarity}(\mathrm{CLIP\_encode\_image}(I_{gen}), \mathrm{CLIP\_encode\_image}(I_{ref}))$$
  3. Symbol Explanation:
     • $\mathrm{cosine\_similarity}(\cdot, \cdot)$: The cosine similarity function.
     • $\mathrm{CLIP\_encode\_image}(I_{gen})$: The embedding vector of the generated image $I_{gen}$ from the CLIP image encoder.
     • $\mathrm{CLIP\_encode\_image}(I_{ref})$: The embedding vector of the reference image $I_{ref}$ (of the SoI) from the CLIP image encoder.

• DINO-I [156] (DINO Image Similarity):

  1. Conceptual Definition: Provides a complementary assessment of visual similarity, particularly effective for object-level similarity, using features from DINO (self-supervised Vision Transformer). DINO features are known for capturing fine-grained visual details and local instance-aware information better than some other general-purpose embeddings.
  2. Mathematical Formula: Similar to CLIP-I, it typically involves cosine similarity between DINO embeddings: $$\mathrm{DINO-I} = \mathrm{cosine\_similarity}(\mathrm{DINO\_encode\_image}(I_{gen}), \mathrm{DINO\_encode\_image}(I_{ref}))$$
  3. Symbol Explanation:
     • $\mathrm{cosine\_similarity}(\cdot, \cdot)$: The cosine similarity function.
     • $\mathrm{DINO\_encode\_image}(I_{gen})$: The embedding vector of the generated image $I_{gen}$ obtained from the DINO image encoder.
     • $\mathrm{DINO\_encode\_image}(I_{ref})$: The embedding vector of the reference image $I_{ref}$ (of the SoI) obtained from the DINO image encoder.

• Fréchet Inception Distance (FID) [157]:

  1. Conceptual Definition: Quantifies the statistical similarity between the distribution of generated images and the distribution of real (reference) images. Lower FID scores indicate that the generated images are more realistic and diverse, resembling the real data distribution. It uses features from the Inception-V3 model.
  2. Mathematical Formula: $$\mathrm{FID} = \|\mu_1 - \mu_2\|^2_2 + \mathrm{Tr}(C_1 + C_2 - 2\sqrt{C_1 C_2})$$
  3. Symbol Explanation:
     • $\mu_1$: The mean of the Inception-V3 feature vectors for the real (reference) images.
     • $\mu_2$: The mean of the Inception-V3 feature vectors for the generated images.
     • $C_1$: The covariance matrix of the Inception-V3 feature vectors for the real images.
     • $C_2$: The covariance matrix of the Inception-V3 feature vectors for the generated images.
     • $\|\cdot\|^2_2$: The squared Euclidean distance (L2 norm).
     • $\mathrm{Tr}(\cdot)$: The trace of a matrix (sum of its diagonal elements).
     • $\sqrt{C_1 C_2}$: Generalized matrix square root.

• FaceNet Identity Similarity:

  1. Conceptual Definition: For face generation tasks, this metric specifically measures how well the identity of the person in the generated image is preserved compared to the reference images. It involves detecting faces and computing pairwise identity similarity using a face recognition model like FaceNet. Higher values indicate better identity preservation.
  2. Mathematical Formula: Not explicitly provided in the paper for FaceNet but generally involves: $$\mathrm{FaceNet\_Similarity} = \mathrm{cosine\_similarity}(\mathrm{FaceNet\_encode}(F_{gen}), \mathrm{FaceNet\_encode}(F_{ref}))$$
  3. Symbol Explanation:
     • $\mathrm{FaceNet\_encode}(F)$: The embedding vector of a face $F$ obtained from a FaceNet model.
     • $F_{gen}$: The detected face from the generated image.
     • $F_{ref}$: The detected face from the reference image.

5.2.3. Specialized Metrics

• LyCORIS [36]: Proposes a 5-dimensional assessment covering fidelity, controllability, diversity, base model preservation, and image quality.
• Stellar [35]: Develops six human-centric metrics: soft-penalized CLIP text score, Identity Preservation Score, Attribute Preservation Score, Stability of Identity Score, Grounding Objects Accuracy, and Relation Fidelity Score.

5.3. Baselines

The paper evaluates 22 representative PCS methods using its Persona dataset. These methods serve as the baselines for comparison in the benchmark provided in Table 1. They span both TTF and PTA frameworks and utilize various backbones (e.g., SD 1.4, SD 1.5, SD 2.1, SDXL, Imagen, LDM, Muse, FLUX).

Examples of prominent baselines for object personalization include:

• Textual Inversion [7]: A foundational TTF method optimizing token embeddings.

• DreamBooth [4]: Another foundational TTF method, fine-tuning the diffusion model (or parts of it).

• Custom Diffusion [20]: A TTF method focusing on parameter-efficient fine-tuning of cross-attention layers.

• ELITE [47]: An early PTA method using encoding visual concepts into textual embeddings.

• BLIP-Diffusion [57]: A PTA method leveraging pre-trained subject representation.

• IP-Adapter [51]: A PTA method that uses an image prompt adapter for text-to-image diffusion models.

  For face personalization, baselines include:

• Face2Diffusion [162]: A PTA method for fast and editable face personalization.

• FastComposer [5]: A PTA method for tuning-free multi-subject image generation with localized attention.

• PhotoMaker [46]: A PTA method for customizing realistic human photos via stacked ID embedding.

• InstantID [6]: A PTA method for zero-shot identity-preserving generation.

  These baselines are representative because they cover a wide range of algorithmic approaches (token embedding optimization, full model fine-tuning, parameter-efficient fine-tuning, various PTA architectures), represent key milestones in the development of PCS, and are often cited as State-of-the-Art in their respective sub-areas of PCS. Comparing against them allows for a comprehensive assessment of current PCS capabilities.

6. Results & Analysis

6.1. Core Results Analysis

The core experimental results are presented in Table 1, which evaluates 22 representative PCS methods across object and face personalization tasks using the newly introduced Persona evaluation dataset. The evaluation focuses on CLIP-T for text alignment and CLIP-I (or FaceNet similarity for faces) for visual fidelity.

The results clearly indicate a significant challenge in PCS: no single method excels simultaneously in both visual fidelity and text alignment. This highlights an inherent trade-off between preserving the subject's exact visual characteristics and allowing the text prompt to flexibly edit or recontextualize the subject.

• Object Personalization:

  • Methods like NeTI [30] achieve a high CLIP-I (0.801), suggesting strong visual fidelity, but their CLIP-T (0.283) is not the highest, indicating potential limitations in text alignment.
  • MoMA [59] shows a relatively high CLIP-T (0.322), implying good text alignment, but its CLIP-I (0.748) is not among the top for visual fidelity.
  • IP-Adapter [51] demonstrates very high CLIP-I (0.825), indicating excellent subject preservation, but its CLIP-T (0.272) is lower than some other PTA methods, potentially leading to less prompt adherence.
  • DreamBooth [4] (0.286 CLIP-T, 0.772 CLIP-I) and Textual Inversion [7] (0.199 CLIP-T, 0.749 CLIP-I) represent foundational methods, with DreamBooth generally outperforming Textual Inversion in both metrics, as expected due to its more extensive fine-tuning.

• Face Personalization:

  • InstantID [6] achieves the highest FaceNet similarity (0.707), indicating superior identity preservation, but its CLIP-T (0.278) is moderate.

  • PhotoMaker [46] shows a good CLIP-T (0.311) but a lower FaceNet similarity (0.547), suggesting it's better at following prompts but might compromise identity slightly.

  • It's generally observed that FaceNet scores are lower than CLIP-I for objects, possibly due to the stricter requirements for identity preservation in faces and the specific nature of FaceNet features.

    The analysis also points out that higher visual fidelity does not always equate to better performance. In some cases, high visual fidelity metrics might result from the model overfitting to the reference images, causing it to produce outputs that rigidly mirror the references and ignore prompt guidance. This means the model might perfectly reconstruct the subject but fail to embed it in the new context or apply requested modifications. This phenomenon highlights a key limitation of relying solely on CLIP-I for evaluation without considering text alignment and human perception of generated image quality. The ability of the model to generalize and integrate the subject into novel scenarios, rather than just replicating it, is crucial.

6.2. Data Presentation (Tables)

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

Note: The original table contains a repetition of "IP-Adapter [51]" for Face with slightly different CLIP-T values (0.292 vs 0.292) but different CLIP-I values (0.462 vs 0.642). This suggests a potential evaluation on different backbones or settings, but is not explicitly clarified in the table. The entry "IP-Adapter [51] PTA SDXL 0.292 0.642" is the one immediately preceding PhotoMaker [46] in the original table's Face section.

6.3. Ablation Studies / Parameter Analysis

The paper itself is a survey and does not conduct original ablation studies or parameter analyses on the proposed methods. Instead, it synthesizes the findings and observations from the original research papers it surveys. However, it implicitly discusses aspects of ablation studies when it details the impact of different techniques:

• Overfitting Rationale: The discussion in Section 8.1 on the overfitting problem touches upon observations from Compositional Inversion [87] regarding learned token embeddings being out-of-distribution, and from [87, 76, 71] suggesting that unique modifiers dominate cross-attention layers. These observations are akin to insights gained from analyzing the impact of specific model components or training choices.

• Trade-off between Fidelity and Alignment: The consistent finding across the benchmark results (Table 1) that methods struggle to achieve both high CLIP-I and high CLIP-T is an empirical demonstration of the fidelity-alignment trade-off. The paper highlights methods like Perfusion [21] and [163] that explicitly try to address this trade-off, which represents a form of algorithmic analysis to balance these competing objectives.

• Impact of Regularization and Data Augmentation: The entire Section 4 discusses how regularization and data augmentation techniques are designed to mitigate overfitting and improve generalization, which are common goals of ablation studies in individual papers. For example, the use of auxiliary data regularization in DreamBooth [4] was a specific design choice to prevent the model from rigidly replicating references.

  While the survey doesn't present new experimental ablations, its structured analysis of various techniques (e.g., different types of unique modifiers, parameter-efficient fine-tuning vs. full fine-tuning, mask-guided generation) implicitly summarizes the results of numerous ablation studies conducted in the original papers it references. This meta-analysis helps understand how different design choices impact performance in terms of subject fidelity, text alignment, and computational efficiency.

7. Conclusion & Reflections

7.1. Conclusion Summary

This survey provides a comprehensive and timely review of Personalized Content Synthesis (PCS) with diffusion models, focusing particularly on 2D image customization. It successfully categorizes the diverse landscape of over 150 methods into two principal frameworks: Test-time Fine-tuning (TTF) and Pre-trained Adaptation (PTA), thoroughly analyzing their mechanisms, strengths, and limitations. The paper further delves into the specific technical approaches, including attention-based operations, mask-guided generation, data augmentation, and regularization, and examines the expansion of PCS into specialized areas like object, face, style, video, and 3D synthesis. A key contribution is the identification of persistent challenges, such as overfitting, the inherent trade-off between subject fidelity and text alignment, and the lack of standardized evaluation metrics and datasets. To address the latter, the survey introduces a new benchmark dataset, Persona, and evaluates classical methods, offering a valuable comparative analysis for the community. The paper concludes by proposing future directions to foster further innovation and collaboration in PCS.

7.2. Limitations & Future Work

The authors highlight several ongoing challenges that serve as limitations of current PCS systems and define promising avenues for future research:

1. Overfitting Problem:

   • Limitation: PCS systems frequently suffer from overfitting due to the limited number of reference images. This manifests as models rigidly mirroring the SoI from references (loss of SoI editability) or including irrelevant semantic elements (e.g., background) from the references in the generated output. The paper notes that learned token embeddings often reside out-of-distribution, and unique modifiers can dominate cross-attention layers, leading to a lack of other semantic appearances.
   • Future Work: While many existing techniques (masking, attention manipulation, regularization, data augmentation) contribute to alleviation, the problem is far from solved, especially for non-rigid SoIs or when context prompts have semantic correlations with irrelevant reference elements. Effective strategies and robust evaluation metrics are still needed to tackle this.

2. Trade-off on Subject Fidelity and Text Alignment:

   • Limitation: There's an inherent conflict between achieving high subject fidelity (accurate reconstruction of SoI details) and text alignment (flexible adaptation of SoI based on diverse textual descriptions). Minimizing reconstruction loss for fidelity often compromises the model's flexibility to adapt to new prompts.
   • Future Work: This remains a critical area for exploration. Solutions might involve enhanced model architectures, innovative training methodologies, and more dynamic data handling strategies to better balance these competing demands.

3. Standardization and Evaluation:

   • Limitation: The field lacks standardized test datasets and robust evaluation metrics that accurately capture PCS performance. Current metrics like CLIP image similarity can misleadingly exaggerate performance when models overfit.
   • Future Work: Future efforts should focus on creating comprehensive and widely accepted benchmarks that can evaluate visual fidelity, subject editability, and other aspects of PCS models accurately.

4. Multimodal Autoregressive Frameworks:

   • Future Work (Emerging Direction): The paper suggests exploring multimodal autoregressive models (e.g., Emu3) as a novel solution. These models can natively handle image-text-video sequences and potentially offer seamless integration of multimodal references while maintaining contextual coherence. They might also inherently support subject editing via multi-round chat, addressing overfitting in diffusion-based models.

5. Interactive Personalization Workflow:

   • Future Work (Emerging Direction): The evolution of interactive generation systems and multi-round interactive generation presents another promising direction. Conversational PCS systems (e.g., Gemini-2.0-flash) could allow users to iteratively refine and accurately define SoIs through natural language dialogue, bridging the gap between abstract intent and concrete outputs.

7.3. Personal Insights & Critique

This survey is an extremely valuable resource for anyone entering or working within the Personalized Content Synthesis domain. Its rigorous categorization of methods into TTF and PTA frameworks, followed by a detailed breakdown of underlying techniques, provides much-needed structure to a rapidly evolving and often fragmented field. The inclusion of Persona, a new benchmark, directly addresses a critical community need for standardized evaluation, which is vital for fostering objective progress.

Inspirations and Applications:

• Structured Development: The TTF vs. PTA dichotomy is powerful. It allows researchers to quickly identify where a new method fits and what trade-offs it implicitly makes (e.g., speed vs. fidelity). This can guide the design of hybrid systems or specialized solutions.
• Technique Cross-Pollination: The detailed section on techniques (attention, mask, data augmentation, regularization) highlights that many solutions are modular. This encourages researchers to combine and adapt techniques from different papers to address specific challenges. For instance, a PTA model could incorporate TTF-inspired regularization during its pre-training or leverage advanced masking techniques to improve SoI disentanglement.
• Beyond 2D: The expansion into video and 3D personalization is particularly inspiring. It demonstrates the versatility of diffusion models and PCS principles across modalities, hinting at a future where users can personalize complex digital assets with ease. The challenges in these areas (e.g., temporal consistency in video, multi-view consistency in 3D) also highlight fertile ground for novel research.
• Ethical Considerations: The brief mention of attack and defense strategies against misuse (e.g., deepfakes) is crucial. As PCS becomes more powerful, its ethical implications grow, and research into safeguarding mechanisms is paramount.

Potential Issues, Unverified Assumptions, or Areas for Improvement:

• Quantifying the Fidelity-Alignment Trade-off: While the paper effectively identifies this trade-off as a major challenge, a more quantitative analysis or a proposed meta-metric that explicitly balances these two aspects could be beneficial. Currently, researchers might prioritize one over the other based on application, but a universal way to measure this balance is elusive. The current CLIP-I versus CLIP-T comparison hints at this, but a single "trade-off score" could simplify comparisons.

• Computational Cost as a Metric: The paper discusses training efficiency and storage requirements, especially when comparing TTF and PTA. However, a comprehensive benchmark table that includes training time, inference time, and model size for each method (even if approximate or based on reported values from original papers) would greatly enhance the practical utility of the survey. This is particularly relevant for PTA methods, which claim faster inference.

• User Experience (UX) Metrics: The survey touches upon user-friendliness (e.g., plain text vs. learnable tokens). Future evaluation could benefit from incorporating human studies or UX-centric metrics to assess how intuitive and effective PCS methods are for end-users, beyond purely technical performance.

• "Collapse" Analysis: The paper mentions "collapse" as a failure state in the trade-off figure (Figure 3), but it isn't explicitly elaborated in the challenge section as a distinct problem with its own set of solutions or root causes. A deeper dive into why models "collapse" (fail to meet either fidelity or alignment) could offer more insights.

• Dynamic Nature of the Field: As noted, over 150 methods emerged in two years. Any survey, no matter how comprehensive, will inevitably become partially outdated rapidly. A potential future improvement could be a living document or an online platform that continuously updates PCS methods and benchmarks.

  Overall, this survey is a landmark effort in structuring and understanding the complex landscape of Personalized Content Synthesis. It not only summarizes past achievements but also critically points towards the most pressing challenges and exciting future directions, making it an indispensable guide for further research and development in this dynamic field.