OminiControl: Minimal and Universal Control for Diffusion Transformer (2411.15098v5)

Abstract: We present OminiControl, a novel approach that rethinks how image conditions are integrated into Diffusion Transformer (DiT) architectures. Current image conditioning methods either introduce substantial parameter overhead or handle only specific control tasks effectively, limiting their practical versatility. OminiControl addresses these limitations through three key innovations: (1) a minimal architectural design that leverages the DiT's own VAE encoder and transformer blocks, requiring just 0.1% additional parameters; (2) a unified sequence processing strategy that combines condition tokens with image tokens for flexible token interactions; and (3) a dynamic position encoding mechanism that adapts to both spatially-aligned and non-aligned control tasks. Our extensive experiments show that this streamlined approach not only matches but surpasses the performance of specialized methods across multiple conditioning tasks. To overcome data limitations in subject-driven generation, we also introduce Subjects200K, a large-scale dataset of identity-consistent image pairs synthesized using DiT models themselves. This work demonstrates that effective image control can be achieved without architectural complexity, opening new possibilities for efficient and versatile image generation systems.

• The paper introduces OmniControl, a framework that reuses DiT components to incorporate image conditions with only a 0.1% increase in parameters.
• The paper employs a novel multi-modal attention mechanism to handle both spatially and non-spatially aligned tasks within a unified architecture.
• The paper demonstrates improved performance over UNet-based models in tasks like edge-guided and depth-aware generation, validated using the Subjects200K dataset.

An Analysis of OmniControl: A Parameter-Efficient Framework for Image Conditioning in Diffusion Transformers

The paper "OmniControl: Minimal and Universal Control for Diffusion Transformer" introduces a novel framework designed to efficiently integrate image conditions into pre-trained Diffusion Transformer (DiT) models. The core innovation of OmniControl lies in its parameter reuse mechanism, which leverages existing model components to encode image conditions, effectively minimizing additional parameter requirements while expanding the model's capacity to handle a wide array of image conditioning tasks.

OmniControl stands out by incorporating image conditions into DiT models using their inherent architecture, eschewing the need for additional, complex modules typically seen in current methods. This approach integrates multi-modal attention processors within the DiT, significantly enhancing its ability to process spatially-aligned image conditions, such as edges and depth, alongside non-spatially aligned tasks like subject-driven generation, all within a singular, unified architecture. This is achieved with an impressively low parameter overhead of approximately 0.1%, showcasing substantial improvements in parameter efficiency.

Experimentally, OmniControl demonstrates superior performance over UNet-based models and variants adapted for DiT, particularly in tasks requiring subject-driven generation and spatial alignment. The authors provide comprehensive evaluations, elucidating the framework's effectiveness across a spectrum of tasks including edge-guided generation, depth-aware synthesis, and identity-consistent generation. A significant contribution of the paper is the introduction of the Subjects200K dataset, comprised of over 200,000 identity-consistent images to further advance the research in subject-consistent generation.

From a methodological perspective, OmniControl advances the field by addressing limitations inherent in previous models. Conventional models typically impose significant parameter overhead through the addition of encoder modules and elaborate architectures, often requiring architecture-specific adjustments for either spatially or non-spatially aligned tasks. OmniControl's innovative use of the VAE encoder for image condition processing within the DiT provides a robust and unified solution for multi-modal image generation tasks.

Practically, this research has significant implications for applications requiring high-quality, conditional image generation with minimal computational resources, presenting opportunities for advancements in areas such as digital content creation and automated design systems. Theoretically, the reduction in parameters combined with the enhanced performance invites further investigation into optimization techniques within transformer-based architectures, possibly leading toward more efficient and scalable models in AI research.

Future research could explore extending this approach to other large-scale transformer based generative models, further scrutinize the interplay of positional encoding strategies in varied contexts, or even adapt OmniControl's methodology to other domains that benefit from conditional generation models. As transformer models evolve, OmniControl's insights might serve as a foundational framework for efficient and flexible image conditioning in broader applications.