Recent advances in text-to-image models have enabled high-quality
personalized image synthesis of 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. We
introduce AttnDreamBooth, a novel approach that addresses these issues by
separately learning the embedding alignment, the attention map, and the subject
identity in 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.

该研究针对文本到图像个性化合成过程中的限制性问题，分析了两种主要技术（文本反转和 DreamBooth）的不足之处，并引入了一种名为 AttnDreamBooth 的新方法来解决这些问题，通过在不同的训练阶段分别学习嵌入对齐、注意力图和主体身份。同时，采用交叉注意力图正则化项来增强对注意力图的学习，相较于基准方法，我们的方法在保护身份信息和文本对齐方面取得了显著改进。

AttnDreamBooth: 面向文本对齐的个性化文本与图像生成

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

There has been significant progress in personalized image synthesis with
methods such as Textual Inversion, DreamBooth, and LoRA. Yet, their real-world
applicability is hindered by high storage demands, lengthy fine-tuning
processes, and the need for multiple reference images. Conversely, existing ID
embedding-based methods, while requiring only a single forward inference, face
challenges: they either necessitate extensive fine-tuning across numerous model
parameters, lack compatibility with community pre-trained models, or fail to
maintain high face fidelity. Addressing these limitations, we introduce
InstantID, a powerful diffusion model-based solution. Our plug-and-play module
adeptly handles image personalization in various styles using just a single
facial image, while ensuring high fidelity. To achieve this, we design a novel
IdentityNet by imposing strong semantic and weak spatial conditions,
integrating facial and landmark images with textual prompts to steer the image
generation. InstantID demonstrates exceptional performance and efficiency,
proving highly beneficial in real-world applications where identity
preservation is paramount. Moreover, our work seamlessly integrates with
popular pre-trained text-to-image diffusion models like SD1.5 and SDXL, serving
as an adaptable plugin. Our codes and pre-trained checkpoints will be available
at this https URL

我们介绍了基于 InstantID 的个性化图像合成方法，通过应用 IdentityNet 模型，使用单张面部图像实现各种风格的图像个性化，同时确保高保真度，并与 SD1.5 和 SDXL 等常用预训练文本到图像扩散模型无缝集成，具有卓越的性能和高效性，在重视身份保护的实际应用中极具益处。