Tencent EMMA

Preview：

Introduce：

EMMA is a new image generation model based on the cutting-edge text-to-image diffusion model ELLA, capable of accepting multimodal cues and effectively integrating text and complementing modal information through an innovative multimodal feature connector design. By freezing all parameters of the original T2I diffusion model and adjusting only a few extra layers, the model reveals an interesting property that pre-trained T2I diffusion models can secretly accept multimodal cues. Easily adapted to different existing frameworks, EMMA is a flexible and effective tool for generating personalized and context-aware images and even videos.

Stakeholders:
The target audience includes researchers, developers, and artists in the field of image generation who need a tool that can understand and incorporate multiple inputs to create high-quality images. EMMA’s flexibility and efficiency make it ideal for these users, especially when they need to quickly adapt to different generation frameworks and conditions.
Usage Scenario Examples:

• Use EMMA and ToonYou to generate images of different styles
• Combine the AnimateDiff model to generate images that retain portrait detail
• Generate a set of images with a story, such as the story of a woman being chased by a dog

The features of the tool:

• Accept multimodal prompts such as text and reference images
• Integrate text and supplement modal information through special attention mechanisms
• Freeze the original T2I diffusion model parameters and adjust only the extra layers to accommodate the multi-modes
• Handle different multimodal configurations without additional training
• Generate high fidelity and detailed images
• Suitable for generating personalized and context-aware images and videos

Steps for Use:

• 1. Visit the EMMA product page for a basic introduction
• 2. Read the technical documentation to understand how the model works and features
• 3. Download and install necessary software dependencies, such as the Python environment and related libraries
• 4. Follow sample code or documentation guidance to write your own multimodal prompts
• 5. Run EMMA model, enter text and reference images and other prompts
• 6. Wait for the model to generate images, evaluate the generated results and make necessary adjustments
• 7. Apply the generated images to artistic creation or research projects as needed

Tool’s Tabs: Image generation, multimodal