  Benefiting from large-scale pre-trained text-to-image (T2I) generative
models, impressive progress has been achieved in customized image generation,
which aims to generate user-specified concepts. Existing approaches have
extensively focused on single-concept customization and still encounter
challenges when it comes to complex scenarios that involve combining multiple
concepts. These approaches often require retraining/fine-tuning using a few
images, leading to time-consuming training processes and impeding their swift
implementation. Furthermore, the reliance on multiple images to represent a
singular concept increases the difficulty of customization. To this end, we
propose FreeCustom, a novel tuning-free method to generate customized images of
multi-concept composition based on reference concepts, using only one image per
concept as input. Specifically, we introduce a new multi-reference
self-attention (MRSA) mechanism and a weighted mask strategy that enables the
generated image to access and focus more on the reference concepts. In
addition, MRSA leverages our key finding that input concepts are better
preserved when providing images with context interactions. Experiments show
that our method's produced images are consistent with the given concepts and
better aligned with the input text. Our method outperforms or performs on par
with other training-based methods in terms of multi-concept composition and
single-concept customization, but is simpler. Codes can be found at
https://github.com/aim-uofa/FreeCustom.
