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

Yucheng Han*, Rui Wang*†, Chi Zhang*✦, Juntao Hu, Pei Cheng, Bin Fu, Hanwang Zhang
Nanyang Technological University, Tencent
(* Equal contributions, † Project Lead, ✦ Corresponding Author)


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.


Our proposed EMMA is built upon the state-of-the-art text-conditioned diffusion model ELLA, which trains a transformer-like module, named Perceiver Resampler, to connect text embeddings from pre-trained text encoders and pre-trained diffusion models for better text-guided image generation. ELLA has strong text-to-image generation ability, and our proposed EMMA could merge information from other modalities into text features for guidance.

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In detail, to control the image generation process by modalities beyond text, EMMA incorporates our proposed Assemblable Gated Perceiver Resampler (AGPR), which leverages cross-attention to inject information from additional modalities beyond texts. In our design, the AGPR blocks are strategically interleaved with the blocks of the Perceiver Resampler of ELLA. This arrangement ensures an effective integration of multi-modal information. During training, we freeze the raw modules of ELLA to maintain the control ability of text conditions.

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Notably, EMMA is inherently designed to handle multi-modal prompts as conditions, allowing for the straightforward combination of different multi-modal configurations. This is achieved by the gate mechanism in our AGPR, which could control the way of injecting information from other modalities into the textual features. This advantage enables diverse and complex inputs to be synthesized into a unified generation framework without the need for additional training.

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Image Generation with text + portrait conditions

Here, we present additional images generated by EMMA under text + portrait conditions. Various portraits, each with unique features, adhere to the same prompts, demonstrating our model's excellent control over text conditioning and its ability to preserve individual identities.

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Image Generation with text + portrait conditions using ToonYou

Given a text prompt and a portrait, our proposed EMMA can integrate with various diffusion models to generate images in different styles. Here are the images created using EMMA in conjunction with ToonYou.

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Image Generation with text + portrait conditions using AnimateDiff

Given a portrait and a prompt, our proposed EMMA, combined with the AnimateDiff diffusion model, can generate images that preserve portrait details while adhering to text instructions.

Story Telling

Images generated by our EMMA with portrait conditions. Two sets of images are generated for two separate stories. The first set of images is about a mailing woman chased by a dog. The second set of images is about a man finding treasures.

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  title={EMMA: Your Text-to-Image Diffusion Model Can Secretly Accept Multi-Modal Prompts}, 
  author={Yucheng Han and Rui Wang and Chi Zhang and Juntao Hu and Pei Cheng and Bin Fu and Hanwang Zhang},
  primaryClass={id='cs.CV' full_name='Computer Vision and Pattern Recognition' is_active=True alt_name=None in_archive='cs' is_general=False description='Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.'}