  Diffusion models excel at text-to-image generation, especially in
subject-driven generation for personalized images. However, existing methods
are inefficient due to the subject-specific fine-tuning, which is
computationally intensive and hampers efficient deployment. Moreover, existing
methods struggle with multi-subject generation as they often blend features
among subjects. We present FastComposer which enables efficient, personalized,
multi-subject text-to-image generation without fine-tuning. FastComposer uses
subject embeddings extracted by an image encoder to augment the generic text
conditioning in diffusion models, enabling personalized image generation based
on subject images and textual instructions with only forward passes. To address
the identity blending problem in the multi-subject generation, FastComposer
proposes cross-attention localization supervision during training, enforcing
the attention of reference subjects localized to the correct regions in the
target images. Naively conditioning on subject embeddings results in subject
overfitting. FastComposer proposes delayed subject conditioning in the
denoising step to maintain both identity and editability in subject-driven
image generation. FastComposer generates images of multiple unseen individuals
with different styles, actions, and contexts. It achieves
300$\times$-2500$\times$ speedup compared to fine-tuning-based methods and
requires zero extra storage for new subjects. FastComposer paves the way for
efficient, personalized, and high-quality multi-subject image creation. Code,
model, and dataset are available at
https://github.com/mit-han-lab/fastcomposer.

Computer Vision and Pattern Recognition