Fast Personalized Text-to-Image Syntheses With Attention Injection
Yuxuan Zhang Yiren Song Jinpeng Yu Han Pan Zhongliang Jing
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.