OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows

• URL: http://arxiv.org/abs/2412.01169v2
• Date: Fri, 21 Mar 2025 07:14:34 GMT
• Title: OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows
• Authors: Shufan Li, Konstantinos Kallidromitis, Akash Gokul, Zichun Liao, Yusuke Kato, Kazuki Kozuka, Aditya Grover,
• Abstract summary: We introduce OmniFlow, a novel generative model designed for any-to-any generation tasks such as text-to-image, text-to-audio, and audio-to-image synthesis.<n>It outperforms previous any-to-any models on a wide range of tasks, such as text-to-image and text-to-audio synthesis.
• Score: 21.677178476653385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce OmniFlow, a novel generative model designed for any-to-any generation tasks such as text-to-image, text-to-audio, and audio-to-image synthesis. OmniFlow advances the rectified flow (RF) framework used in text-to-image models to handle the joint distribution of multiple modalities. It outperforms previous any-to-any models on a wide range of tasks, such as text-to-image and text-to-audio synthesis. Our work offers three key contributions: First, we extend RF to a multi-modal setting and introduce a novel guidance mechanism, enabling users to flexibly control the alignment between different modalities in the generated outputs. Second, we propose a novel architecture that extends the text-to-image MMDiT architecture of Stable Diffusion 3 and enables audio and text generation. The extended modules can be efficiently pretrained individually and merged with the vanilla text-to-image MMDiT for fine-tuning. Lastly, we conduct a comprehensive study on the design choices of rectified flow transformers for large-scale audio and text generation, providing valuable insights into optimizing performance across diverse modalities. The Code will be available at https://github.com/jacklishufan/OmniFlows.

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