Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models
By Shihao Zhao, Dongdong Chen, Yen-Chun Chen, Jianmin Bao, Shaozhe Hao, Lu Yuan, Kwan-Yee Kenneth Wong. Neural Information Processing Systems, 2023
472 citations recorded by the source corpus.
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}.
View Paper: https://www.semanticscholar.org/paper/a483ff9557f29d21fe780b3dd969a037a3ffc3ed
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Citations in the corpus
- IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models (2023, methodology): “To reduce the fine-tuning cost, Uni-ControlNet [12] presents a multi-scale condition injection strategy to learn an adapter for various local controls.”
- VideoComposer: Compositional Video Synthesis with Motion Controllability (2023, background): “Recently, research efforts on image synthesis have shifted from utilizing GANs [17], VAEs [29], and flow models [13] to diffusion models [21, 50, 54, 73] due to more stable training, enhanced sample quality, and increased flexibility in a conditional generation.”
- OminiControl: Minimal and Universal Control for Diffusion Transformer (2024, unknown): “…show clear task bias – they typically work well for either spatially aligned controls (e.g., edge-guided or depth-guided generation [2, 15, 29, 30, 37, 42, 52, 61, 63]) or spatially un-aligned ones (e.g., style transfer or subject-driven generation [18, 27, 28, 50, 60][14, 33]), but rarely both.”
- InstantID: Zero-shot Identity-Preserving Generation in Seconds (2024, background): “Additionally, adaptations for style or content referencing, such as the T2I-adapter’s style adapter and Uni-ControlNet’s[26] global controller, have made strides in this area.”