ObjectComposer: Consistent Generation of Multiple Objects Without
  Fine-tuning
Alec Helbling Evan Montoya Duen Horng Chau
Recent text-to-image generative models can generate high-fidelity images from
text prompts. However, these models struggle to consistently generate the same
objects in different contexts with the same appearance. Consistent object
generation is important to many downstream tasks like generating comic book
illustrations with consistent characters and setting. Numerous approaches
attempt to solve this problem by extending the vocabulary of diffusion models
through fine-tuning. However, even lightweight fine-tuning approaches can be
prohibitively expensive to run at scale and in real-time. We introduce a method
called ObjectComposer for generating compositions of multiple objects that
resemble user-specified images. Our approach is training-free, leveraging the
abilities of preexisting models. We build upon the recent BLIP-Diffusion model,
which can generate images of single objects specified by reference images.
ObjectComposer enables the consistent generation of compositions containing
multiple specific objects simultaneously, all without modifying the weights of
the underlying models.