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保研问题

StyleAR: Customizing Multimodal Autoregressive Model for Style-Aligned Text-to-Image Generation

使得生成图片的风格和参考图片高度一致；与prompt对应的语义高度一致

摘要

点出自回归模型在风格一致的文生图上的困难，特别是数据获取方面
需要text-image-image三元组
获取这样的三元组比获取传统的用于训练模型的数据困难的很多

motivation：处理上述困难，将专门设计的数据管理方法和提出的AR模型相结合，有效的利用t2i二元数据来生成样式对齐的图像

Introduction

AR模型遇到的困难：
1.严重依赖三元数据
2.通过image tokenizer提取的图像特征不够好

key contribution
1.新的数据获取方法，使用二元数据
2.提出了一个AR框架，
3.样式增强token技术，解决内容泄漏和提高指令依从一致性

Method

主要贡献在数据获取部分

审稿

Summary

本文提出了StyleAR，一种用于风格一致文本到图像生成的自回归（AR）模型框架。它针对该类任务中AR模型因依赖难以获取的三元组数据而性能受限的关键问题，提出了解决方案。

在数据策略上，StyleAR 设计了一种新颖的数据策划方法，摆脱了对复杂三元组数据的依赖。该方法利用现有的扩散模型（如 InstantStyle）生成风格化图像，但仅保留“提示文本”和“生成的风格化图像”来构建更易于获取的二元数据对。

在模型设计上，StyleAR采用 CLIP 图像编码器与 Perceiver Resampler将参考图像转换为风格 Token，这样有效利用二元数据并抑制内容泄露。在训练时，该方法将风格化图像与原始图像进行混合训练提升模型提取丰富风格特征的能力，从而改善最终的风格一致性。在推理时，通过结合 SAM 分割机制与高斯噪声注入的“风格增强 Token”技术，精确解耦参考图中的无关语义内容，有效抑制内容泄露，确保生成图像遵循文本提示。

This paper proposes StyleAR, an autoregressive (AR) framework for style-aligned text-to-image generation. It addresses a key limitation of existing AR models—namely, their reliance on hard-to-collect image–text–image triplets—by offering an alternative solution.

Data strategy. StyleAR introduces an innovative curation pipeline that eliminates the need for complex triplet data. It first leverages an existing diffusion model (e.g., InstantStyle) to create stylized images, but keeps only the text prompt and its corresponding stylized image, forming easy-to-obtain image–text pairs.

Model design. StyleAR combines a frozen CLIP image encoder with a Perceiver Resampler to convert the reference image into style tokens, enabling effective use of the paired data while suppressing content leakage. During training, stylized images are mixed with raw images to enrich style features and enhance overall style consistency. At inference time, StyleAR further employs a style-enhanced token technique—injecting Gaussian noise and incorporating SAM segmentation—to strip away irrelevant semantics from the reference image, mitigating content leakage and ensuring that the generated image faithfully follows the text prompt.

Strengths And Weaknesses

优点

1.文章提出利用二元组数据在自回归模型上进行训练的方法，不仅具有良好的可扩展性，还有效缓解了对高成本三元组数据的依赖，从而减轻了训练过程中的数据资源压力。

2.CLIP + Perceiver Resampler 的组合有助于将风格信息嵌入 AR token 空间；图3中提出的风格增强 token 机制在减轻语义泄露方面具备实际技术贡献。

3.风格化与原始图像混合训练在概念与实证上均被充分验证

不足

1.在模型架构上面缺少创新性，作者并未使用大量三元组数据在自回归模型上进行训练，不清楚风格一致性的提升源自数据方案还是 AR 架构本身的固有优势

2.在图4，图6中的Reference风格图片展示中，其分布有些相近，无法判断模型在复杂风格上的泛化性

Strength

1.The paper proposes a method that trains autoregressive models using binary data pairs, which not only offers strong scalability but also effectively reduces the reliance on costly triplet data, thereby alleviating the data resource burden during training.

2.The combination of a frozen CLIP image encoder and a Perceiver Resampler successfully injects style information into the AR token sequence, while the style-enhanced token mechanism in Figure 3 makes a concrete contribution to reducing semantic leakage.

3.The idea of mixing stylized and raw image–text pairs is both conceptually sound and empirically validated in the experiments.

Weaknesses

1.The model architecture itself lacks major innovation. The authors did not train the autoregressive model using a large amount of triplet data, and it remains unclear whether the observed gains in style consistency stem mainly from the new data pipeline or from inherent advantages of the AR framework.

2.In the reference style images shown in Figures 4 and 6, many styles appear quite similar, making it hard to judge how well the model will generalize to more diverse or complex styles.

Quality

Clarity

Significance

Originality

Questions

1.作者能否说明为何自回归模型的训练每个 epoch 仅使用约 6 万对图文数据？该数据规模是否足以充分发挥模型的潜力？

2.文章指出当前自回归模型在该任务中的性能上限主要受到扩散模型（如 InstantStyle）生成数据能力的限制。希望作者能进一步提供相关的理论依据或实证支持，以增强这一论断的说服力。

3.作者是否评估过模型复杂风格内容上的泛化能力？如有相关分析或定性结果，将有助于进一步强化实验部分的完整性。目前尚不清楚论文中展示风格是否能代表更广泛的艺术风格，还是偏重于特定美学类型

1.Could the authors clarify why the training of the autoregressive model uses approximately 60k image–text pairs per epoch? Is this dataset size sufficient to fully exploit the model’s potential?

2.The paper suggests that the performance ceiling of AR models in this task is due to the limitations of diffusion-based data generation (e.g., InstantStyle). It would be helpful if the authors could provide additional justification or empirical evidence to support this claim.

3.Has the model’s generalization ability to complex or diverse styles been evaluated? If the paper includes such analysis or qualitative results, it would help strengthen the completeness of the experimental section. At present, it remains unclear whether the styles showcased in the paper represent a broad range of artistic domains or are biased toward specific aesthetic categories.

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