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.

AutoStudio 是一个训练免费的多代理框架，通过使用大型语言模型生成高质量图像，并且能够与用户进行多次交互来生成一系列多主题图像。

AutoStudio: 多回合互动图像生成中一致主题的构建

AutoStudio: Crafting Consistent Subjects in Multi-turn Interactive Image  Generation

Given two images depicting a person and a garment worn by another person, our
goal is to generate a visualization of how the garment might look on the input
person. A key challenge is to synthesize a photorealistic detail-preserving
visualization of the garment, while warping the garment to accommodate a
significant body pose and shape change across the subjects. Previous methods
either focus on garment detail preservation without effective pose and shape
variation, or allow try-on with the desired shape and pose but lack garment
details. In this paper, we propose a diffusion-based architecture that unifies
two UNets (referred to as Parallel-UNet), which allows us to preserve garment
details and warp the garment for significant pose and body change in a single
network. The key ideas behind Parallel-UNet include: 1) garment is warped
implicitly via a cross attention mechanism, 2) garment warp and person blend
happen as part of a unified process as opposed to a sequence of two separate
tasks. Experimental results indicate that TryOnDiffusion achieves
state-of-the-art performance both qualitatively and quantitatively.

本研究提出了一种基于扩散的架构，统一了两个并行 UNet，旨在在保留服装细节的同时，通过扭曲服装并令其适应不同的身体姿态和形状变化，生成着装效果图。实验结果表明，该方法在质量和多个评估指标上均取得了最先进的表现。