MMFace-DiT: A Dual-Stream Diffusion Transformer for High-Fidelity Multimodal Face Generation
Summary: arXiv:2603.29029v1 Announce Type: cross
Recent advancements in multimodal face generation have sought to address the limitations of traditional text-to-image diffusion models. These limitations primarily involve spatial control, which can be enhanced by incorporating various spatial priors such as segmentation masks, sketches, or edge maps. By integrating these spatial elements with text-based conditioning, researchers are now able to achieve controllable synthesis that aligns with both high-level semantic intent and low-level structural layouts.
However, the majority of existing approaches tend to extend pre-trained text-to-image pipelines by merely appending auxiliary control modules or stitching together separate unimodal networks. Such ad hoc designs often carry architectural constraints, duplicate parameters, and may falter when faced with conflicting modalities or mismatched latent spaces. These issues significantly limit their potential for synergistic fusion across both semantic and spatial domains.
Introducing MMFace-DiT
To address these challenges, we present MMFace-DiT, a unified dual-stream diffusion transformer specifically engineered for synergistic multimodal face synthesis. The core innovation of MMFace-DiT lies in its dual-stream transformer block, which processes spatial (mask/sketch) and semantic (text) tokens in parallel. This parallel processing allows for deep fusion of these elements through a shared Rotary Position-Embedded (RoPE) Attention mechanism.
This groundbreaking design effectively prevents modal dominance, ensuring a robust adherence to both text and structural priors. As a result, MMFace-DiT achieves unprecedented spatial-semantic consistency, enhancing controllable face generation significantly. The model’s architecture allows it to adapt dynamically to varying spatial conditions without necessitating retraining, thanks to the inclusion of a novel Modality Embedder.
Performance Enhancements
MMFace-DiT has demonstrated remarkable performance metrics, achieving a 40% improvement in visual fidelity and prompt alignment when compared with six state-of-the-art multimodal face generation models. This enhancement establishes MMFace-DiT as a flexible new paradigm for end-to-end controllable generative modeling, paving the way for future innovations in the field.
Conclusion
In summary, MMFace-DiT represents a significant leap forward in the realm of multimodal face generation. By addressing the limitations of existing models and introducing a unified dual-stream architecture, it not only improves visual fidelity but also enhances the ability to generate faces that are accurately aligned with both semantic and spatial inputs.
Further Information
For those interested in exploring this innovative model further, the code and dataset are available on the project page: MMFace-DiT Project Page.