Exquisite demand exists for customizing the pretrained large text-to-image model, $\textit{e.g.}$, Stable Diffusion, to generate innovative concepts, such as the users themselves. However, the newly-added concept from previous customization methods often shows weaker combination abilities than the original ones even given several images during training. We thus propose a new personalization method that allows for the seamless integration of a unique individual into the pre-trained diffusion model using just $\textbf{one facial photograph}$ and only $\textbf{1024 learnable parameters}$ under $\textbf{3 minutes}$. So as we can effortlessly generate stunning images of this person in any pose or position, interacting with anyone and doing anything imaginable from text prompts. To achieve this, we first analyze and build a well-defined celeb basis from the embedding space of the pre-trained large text encoder. Then, given one facial photo as the target identity, we generate its own embedding by optimizing the weight of this basis and locking all other parameters. Empowered by the proposed celeb basis, the new identity in our customized model showcases a better concept combination ability than previous personalization methods. Besides, our model can also learn several new identities at once and interact with each other where the previous customization model fails to. The code will be released.

翻译：针对预训练大规模文本到图像模型（如Stable Diffusion）的定制化需求日益精细化——例如生成用户本人等创新概念。然而，现有定制化方法即便在训练中提供多张图像，其新增概念往往仍弱于原始概念的组合能力。为此，我们提出一种新型个性化方法，仅需**一张面部照片**、**1024个可学习参数**且**耗时3分钟以内**，即可将特定个体无缝融入预训练扩散模型。由此可基于文本提示，轻松生成该人物任意姿态位置、与任何人互动、执行任何可想象动作的惊艳图像。为实现这一目标，我们首先从预训练大规模文本编码器的嵌入空间中分析并构建了一个明确定义的名人基座。随后，以单张面部照片作为目标身份，通过优化该基座权重并锁定其他所有参数，生成其专属嵌入。得益于所提出的名人基座，定制化模型中的新身份展现出优于过往个性化方法的概念组合能力。此外，本模型还能同时学习多个新身份并实现彼此交互——这是以往定制化模型无法做到的。相关代码将开源。