The remarkable advancement in text-to-image generation models significantly
boosts the research in ID customization generation. However, existing
personalization methods cannot simultaneously satisfy high fidelity and
high-efficiency requirements. Their main bottleneck lies in the prompt image
encoder, which produces weak alignment signals with the text-to-image model and
significantly increased model size. Towards this end, we propose a lightweight
Inv-Adapter, which first extracts diffusion-domain representations of ID images
utilizing a pre-trained text-to-image model via DDIM image inversion, without
additional image encoder. Benefiting from the high alignment of the extracted
ID prompt features and the intermediate features of the text-to-image model, we
then embed them efficiently into the base text-to-image model by carefully
designing a lightweight attention adapter. We conduct extensive experiments to
assess ID fidelity, generation loyalty, speed, and training parameters, all of
which show that the proposed Inv-Adapter is highly competitive in ID
customization generation and model scale.

利用预训练的文本图像模型通过 DDIM 图像反转提取 ID 图像的传播域表示，不需要额外的图像编码器，然后通过精心设计的轻量级注意力适配器将提取的 ID 提示特征和文本图像模型的中间特征有效地嵌入到基础文本图像模型中，从而为 ID 定制生成和模型规模上提出的 Inv-Adapter 在 ID 忠实度、生成忠诚度、速度和训练参数等方面进行了广泛的实验评估表明其具有很高的竞争力。

Inv-Adapter：基于图像反转和轻量级适配器的 ID 个性化生成

Inv-Adapter: ID Customization Generation via Image Inversion and  Lightweight Adapter

Alignment is the most critical step in building large language models (LLMs)
that meet human needs. With the rapid development of LLMs gradually surpassing
human capabilities, traditional alignment methods based on human-annotation are
increasingly unable to meet the scalability demands. Therefore, there is an
urgent need to explore new sources of automated alignment signals and technical
approaches. In this paper, we systematically review the recently emerging
methods of automated alignment, attempting to explore how to achieve effective,
scalable, automated alignment once the capabilities of LLMs exceed those of
humans. Specifically, we categorize existing automated alignment methods into 4
major categories based on the sources of alignment signals and discuss the
current status and potential development of each category. Additionally, we
explore the underlying mechanisms that enable automated alignment and discuss
the essential factors that make automated alignment technologies feasible and
effective from the fundamental role of alignment.

通过系统回顾最近出现的自动对齐方法，本文旨在探索如何在大型语言模型的能力超越人类之后实现有效、可扩展的自动对齐。我们将现有的自动对齐方法分为四个主要类别，并讨论每个类别的当前状态和潜在发展。此外，我们探讨了使自动对齐技术可行和有效的基本因素，从对齐的基本作用角度阐述了自动对齐的潜在机制。