Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models (2305.16322v3)

Abstract: 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}.

An In-Depth Examination of Uni-ControlNet: Unified Control for Text-to-Image Diffusion Models

The paper "Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models" introduces a robust framework that enhances the versatility of text-to-image (T2I) diffusion models by integrating multiple control modes within a single, unified model. This integration addresses notable challenges in existing models which typically struggle with detailed control and complex text processing.

Overview of Uni-ControlNet

Uni-ControlNet enables simultaneous utilization of both local and global controls in T2I diffusion models. Local controls include edge maps, depth maps, segmentation masks, and sketches, while global controls are primarily based on CLIP image embeddings. The innovative aspect of Uni-ControlNet lies in its ability to leverage this extensive range of control types without requiring a separate adapter for each condition— a limitation in prior models like ControlNet and T2I-Adapter.

Technical Contributions

The framework employs two specialized adapters upon pre-trained T2I diffusion models, eliminating the need for extensive retraining from scratch. This is made possible through dedicated adapter designs that categorize controls into local and global types, each interfacing with just two adapters. The local control adapter uses a multi-scale condition injection strategy, integrating modulation signals for versatile feature adaptation. Conversely, the global control adapter combines global condition embeddings with text prompts through cross-attention, enhancing comprehension of global cues.

Quantitative and Qualitative Evaluation

Uni-ControlNet showcases superior performance over existing methodologies in terms of fidelity, controllability, and composability. The paper presents both quantitative metrics, such as FID and CLIP scores, and qualitative analyses to substantiate these claims. By utilizing only two adapters regardless of control quantity, the model significantly reduces both fine-tuning cost and complexity, enhancing real-world applicability.

Implications and Future Developments

The implications of Uni-ControlNet are profound in various applications, including content creation, design, and more. It sets a precedent for integrating multi-modal controls into diffusion models efficiently, pointing towards a future where adaptable and composable AI models are standard. Further exploration could consider extending this model to encompass additional types of controls and adapting the approach in different diffusion model architectures.

Conclusion

The Uni-ControlNet framework represents a significant stride in the efficient unification of complex control mechanisms within text-to-image diffusion models. This work not only addresses practical challenges in current models but also highlights potential avenues for advanced model composability and control, marking a noteworthy contribution to the field of diffusion-based generative models.