Text-to-Image diffusion models have made tremendous progress over the past two years, enabling the generation of highly realistic images based on open-domain text descriptions. However, despite their success, text descriptions often struggle to adequately convey detailed controls, even when composed of long and complex texts. Moreover, recent studies have also shown that these models face challenges in understanding such complex texts and generating the corresponding images. Therefore, there is a growing need to enable more control modes beyond text description. In this paper, we introduce Uni-ControlNet, a unified framework that allows for the simultaneous utilization of different local controls (e.g., edge maps, depth map, segmentation masks) and global controls (e.g., CLIP image embeddings) in a flexible and composable manner within one single model. Unlike existing methods, Uni-ControlNet only requires the fine-tuning of two additional adapters upon frozen pre-trained text-to-image diffusion models, eliminating the huge cost of training from scratch. Moreover, thanks to some dedicated adapter designs, Uni-ControlNet only necessitates a constant number (i.e., 2) of adapters, regardless of the number of local or global controls used. This not only reduces the fine-tuning costs and model size, making it more suitable for real-world deployment, but also facilitate composability of different conditions. Through both quantitative and qualitative comparisons, Uni-ControlNet demonstrates its superiority over existing methods in terms of controllability, generation quality and composability. Code is available at \url{https://github.com/ShihaoZhaoZSH/Uni-ControlNet}.

翻译：文本到图像扩散模型在过去两年取得了巨大进展，能够基于开放域文本描述生成高度逼真的图像。然而，尽管取得了成功，文本描述在充分传达细粒度控制时仍显不足，即使是长文本和复杂文本也难以完全表达。此外，近期研究也表明，这些模型在理解复杂文本并生成对应图像方面面临挑战。因此，除了文本描述外，迫切需要实现更多控制模式。本文提出了Uni-ControlNet，这是一个统一框架，可在单个模型中灵活组合地同时利用不同局部控制（如边缘图、深度图、分割掩码）和全局控制（如CLIP图像嵌入）。与现有方法不同，Uni-ControlNet仅需在冻结的预训练文本到图像扩散模型上微调两个额外的适配器，无需从头训练带来的巨大成本。此外，得益于专用适配器设计，无论使用多少局部或全局控制，Uni-ControlNet仅需恒定数量（即2个）的适配器。这不仅降低了微调成本和模型规模，使其更适合实际部署，还促进了不同条件的可组合性。通过定量和定性比较，Uni-ControlNet在可控性、生成质量和可组合性方面均优于现有方法。代码发布于 \url{https://github.com/ShihaoZhaoZSH/Uni-ControlNet}。