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 novel approach 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 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，文本描述往往难以充分传达精细的控制信息，即使采用冗长复杂的文本组合也仍显不足。此外，近期研究还表明，这类模型在理解复杂文本并生成对应图像方面存在挑战。因此，迫切需要实现超越文本描述的更多控制模式。本文提出Uni-ControlNet，一种新颖的方法，允许在一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适配器，避免了从头训练的巨大成本。更重要的是，得益于专门设计的适配器架构，Uni-ControlNet无论使用多少局部或全局控制，都只需恒定数量（即2个）的适配器。这不仅降低了微调成本和模型规模，使其更适用于实际部署，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展现出超越现有方法的优势。代码开源地址：\url{https://github.com/ShihaoZhaoZSH/Uni-ControlNet}。