Diffusion Image Generation with Facial Feature Concatenation
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Solution Overview
Problem
Current text-to-image models require independent training for each different portrait, which is time-consuming and necessitates post-processing fine-tuning to maintain consistency, leading to a long overall generation time for target images.
Innovation Solution
An image generation method that involves feature extraction from a first source image using an image encoder and facial recognition on a second source image to concatenate features, which are then input into a trained diffusion model to generate a target image in a predetermined style with a predetermined portrait, eliminating the need for independent training and post-processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If independent training is performed for each different portrait in text-to-image models, then portrait consistency is maintained, but generation time increases significantly
Solution Approach 1:
The system performs preliminary facial feature extraction from reference images before the actual image generation process. By pre-processing and storing facial features in a structured format, the system avoids time-consuming training for each portrait while maintaining consistency. The facial recognition model extracts key features in advance, which are then readily available during generation.
Solution Approach 2:
The patent introduces an intermediate facial feature representation layer between the input image and the diffusion model. This intermediate representation (facial features extracted by recognition model) serves as a mediator that captures portrait identity without requiring full model retraining. The concatenated feature (combining style and facial features) acts as an efficient intermediary for generating consistent portraits.
2Manufacturing precision
If post-processing fine-tuning is applied to maintain portrait consistency, then generation quality improves, but overall processing time increases
Solution Approach 1:
The system extracts only the essential facial features from reference images using a dedicated facial recognition model, separating this task from the main generation process. By extracting and storing only the critical identity-preserving features rather than performing full fine-tuning, the system maintains quality while dramatically reducing processing time.
Solution Approach 2:
The patent changes the approach from modifying model parameters through fine-tuning to using fixed facial feature vectors as input conditions. Instead of adjusting weights and biases in the diffusion model, the system uses concatenated features (style + facial identity) as conditional inputs, which achieves the same consistency goal more efficiently.
3Ease of operation
If traditional text-to-image models are used without feature concatenation, then process simplicity is maintained, but portrait consistency and style control deteriorate
Solution Approach 1:
The system segments the feature representation into distinct components: style features from the first source image and facial identity features from the second source image. This segmentation allows independent optimization of each feature type while maintaining overall simplicity. The concatenated feature vector combines these segments without complex interactions.
Solution Approach 2:
The patent creates a universal feature concatenation mechanism that works across different portraits and styles without requiring separate processing pipelines. The same concatenated feature approach (style features + facial features) serves multiple functions: maintaining portrait consistency, controlling image style, and enabling efficient generation all in one unified process.
Data Source
AI summary
Embodiments of this application disclose an image generation method and apparatus, an electronic device, a storage medium, and a program product. The method includes receiving a first source image of a predetermined style and a second source image comprising a predetermined portrait; performing feature extraction on the first source image using at least one image encoder to obtain at least one image feature; performing facial recognition on the second source image using a facial recognition model to obtain a facial feature of the predetermined portrait; concatenating the at least one image feature with the facial feature to obtain a concatenated feature; and inputting the concatenated feature into a trained diffusion model to generate a target image that is in the predetermined style and that comprises the predetermined portrait.