EMMA: Your Text-to-Image Diffusion Model Can Secretly Accept Multi-Modal Prompts

• URL: http://arxiv.org/abs/2406.09162v1
• Date: Thu, 13 Jun 2024 14:26:43 GMT
• Title: EMMA: Your Text-to-Image Diffusion Model Can Secretly Accept Multi-Modal Prompts
• Authors: Yucheng Han, Rui Wang, Chi Zhang, Juntao Hu, Pei Cheng, Bin Fu, Hanwang Zhang,
• Abstract summary: EMMA is a novel image generation model accepting multi-modal prompts built upon the state-of-the-art text-to-image (T2I) diffusion model, ELLA. By freezing all parameters in the original T2I diffusion model and only adjusting some additional layers, we reveal an interesting finding that the pre-trained T2I diffusion model can secretly accept multi-modal prompts.
• Score: 48.214475133206385
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advancements in image generation have enabled the creation of high-quality images from text conditions. However, when facing multi-modal conditions, such as text combined with reference appearances, existing methods struggle to balance multiple conditions effectively, typically showing a preference for one modality over others. To address this challenge, we introduce EMMA, a novel image generation model accepting multi-modal prompts built upon the state-of-the-art text-to-image (T2I) diffusion model, ELLA. EMMA seamlessly incorporates additional modalities alongside text to guide image generation through an innovative Multi-modal Feature Connector design, which effectively integrates textual and supplementary modal information using a special attention mechanism. By freezing all parameters in the original T2I diffusion model and only adjusting some additional layers, we reveal an interesting finding that the pre-trained T2I diffusion model can secretly accept multi-modal prompts. This interesting property facilitates easy adaptation to different existing frameworks, making EMMA a flexible and effective tool for producing personalized and context-aware images and even videos. Additionally, we introduce a strategy to assemble learned EMMA modules to produce images conditioned on multiple modalities simultaneously, eliminating the need for additional training with mixed multi-modal prompts. Extensive experiments demonstrate the effectiveness of EMMA in maintaining high fidelity and detail in generated images, showcasing its potential as a robust solution for advanced multi-modal conditional image generation tasks.

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