  As cutting-edge Text-to-Image (T2I) generation models already excel at
producing remarkable single images, an even more challenging task, i.e.,
multi-turn interactive image generation begins to attract the attention of
related research communities. This task requires models to interact with users
over multiple turns to generate a coherent sequence of images. However, since
users may switch subjects frequently, current efforts struggle to maintain
subject consistency while generating diverse images. To address this issue, we
introduce a training-free multi-agent framework called AutoStudio. AutoStudio
employs three agents based on large language models (LLMs) to handle
interactions, along with a stable diffusion (SD) based agent for generating
high-quality images. Specifically, AutoStudio consists of (i) a subject manager
to interpret interaction dialogues and manage the context of each subject, (ii)
a layout generator to generate fine-grained bounding boxes to control subject
locations, (iii) a supervisor to provide suggestions for layout refinements,
and (iv) a drawer to complete image generation. Furthermore, we introduce a
Parallel-UNet to replace the original UNet in the drawer, which employs two
parallel cross-attention modules for exploiting subject-aware features. We also
introduce a subject-initialized generation method to better preserve small
subjects. Our AutoStudio hereby can generate a sequence of multi-subject images
interactively and consistently. Extensive experiments on the public CMIGBench
benchmark and human evaluations show that AutoStudio maintains multi-subject
consistency across multiple turns well, and it also raises the state-of-the-art
performance by 13.65% in average Frechet Inception Distance and 2.83% in
average character-character similarity.
