The field of text-to-image (T2I) generation has made significant progress in
recent years, largely driven by advancements in diffusion models. Linguistic
control enables effective content creation, but struggles with fine-grained
control over image generation. This challenge has been explored, to a great
extent, by incorporating additional user-supplied spatial conditions, such as
depth maps and edge maps, into pre-trained T2I models through extra encoding.
However, multi-control image synthesis still faces several challenges.
Specifically, current approaches are limited in handling free combinations of
diverse input control signals, overlook the complex relationships among
multiple spatial conditions, and often fail to maintain semantic alignment with
provided textual prompts. This can lead to suboptimal user experiences. To
address these challenges, we propose AnyControl, a multi-control image
synthesis framework that supports arbitrary combinations of diverse control
signals. AnyControl develops a novel Multi-Control Encoder that extracts a
unified multi-modal embedding to guide the generation process. This approach
enables a holistic understanding of user inputs, and produces high-quality,
faithful results under versatile control signals, as demonstrated by extensive
quantitative and qualitative evaluations. Our project page is available in
https://any-control.github.io.

我们提出了 AnyControl，这是一个支持多种不同控制信号任意组合的多控制图像合成框架，它使用了多模态的嵌入来引导生成过程，实现了对用户输入的整体理解，并通过广泛的定量和定性评估展示了高质量、忠实的生成结果。