Multimodal Diffusion Model Training for Detail-Controlled Image Generation
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Solution Overview
Problem
Existing Text to Image (T2I) diffusion models struggle to accurately capture complex details and nuances in image generation due to the inherent simplicity of text descriptions, often failing to fully represent the intended scenario or entity.
Innovation Solution
A model training method utilizing multimodal data, including sample texts and entity images, to generate initial multimodal features, which are then used to train N diffusion networks, enhancing the model's ability to process richer descriptive information and improve controllability in image generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text descriptions are used as input for image generation, then the model can generate images from textual prompts, but the model fails to accurately capture complex details and nuances in image generation
Solution Approach 1:
The patent introduces entity images as an intermediary between text descriptions and the diffusion model. These entity images serve as visual mediators that bridge the gap between textual prompts and detailed image generation, allowing the model to capture complex details and nuances that text alone cannot convey. The entity images are processed through an image-text encoder to extract multimodal features that enrich the generation process.
Solution Approach 2:
The patent combines multiple data modalities (text features and image features) into composite multimodal features. By fusing text embeddings with entity image embeddings through the image-text encoder, the model creates enriched feature representations that contain both semantic information from text and visual details from images, thereby improving detail capture accuracy without losing information.
2Adaptability or versatility
If multimodal data processing is implemented, then the model can process richer descriptive information, but the device complexity increases
Solution Approach 1:
The patent employs a universal image-text encoder that can process both text inputs and image inputs through the same architectural framework. This multi-functional component handles text embedding, image embedding, and their fusion operations, reducing the need for separate specialized modules and thereby limiting the increase in device complexity while maintaining high adaptability for processing diverse multimodal data.
3Reliability
If entity images are inserted after corresponding entity texts in multimodal features, then the model can maintain proper alignment between text and image features, but the processing complexity increases
Solution Approach 1:
The patent performs preliminary organization of multimodal features by inserting entity images after their corresponding entity texts in the feature sequence before processing. This pre-alignment step ensures that text and image features are properly matched and ordered, improving feature alignment accuracy. By establishing the correct structure in advance, the model avoids complex real-time alignment operations during diffusion processing.
Data Source
AI summary
Provided is a target model training method, a multimodal data processing method, and devices therefor, relating to the field of artificial intelligence technology, and in particular to the fields of computer vision, deep learning, large model and other technologies. The target model training method includes: inputting sample data into a preset model to obtain initial multimodal features of the sample data; and using the initial multimodal features and a preset noise feature to perform model training on N diffusion networks in the preset model to obtain a target model when parameters of an image-text encoder of the preset model are fixed.