Currently, personalized image generation methods mostly require considerable
time to finetune and often overfit the concept resulting in generated images
that are similar to custom concepts but difficult to edit by prompts. We
propose an effective and fast approach that could balance the text-image
consistency and identity consistency of the generated image and reference
image. Our method can generate personalized images without any fine-tuning
while maintaining the inherent text-to-image generation ability of diffusion
models. Given a prompt and a reference image, we merge the custom concept into
generated images by manipulating cross-attention and self-attention layers of
the original diffusion model to generate personalized images that match the
text description. Comprehensive experiments highlight the superiority of our
method.

我们提出了一种有效且快速的方法，可在不进行任何微调的情况下生成个性化图像，并保持扩散模型内在的文本到图像生成能力。通过操作原始扩散模型的交叉注意力和自注意力层，将自定义概念合并到生成图像中，以生成与文本描述相匹配的个性化图像。综合实验突出了我们方法的优越性。