Stable Diffusion IMG2IMG Feature Fusion for Face Identity Preservation
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Solution Overview
Problem
Existing image processing systems struggle to maintain face attributes while stylizing input images, particularly in varying conditions, leading to computationally intensive and inefficient processing.
Innovation Solution
The use of a stable diffusion generative model with a neural network that modifies intermediate features by combining weighted values from a segmentation network mask, allowing for face attribute preservation and efficient image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing image processing systems are used to maintain face attributes while stylizing input images, then face attribute preservation is attempted, but computational complexity increases and processing efficiency decreases
Solution Approach 1:
The patent segments the face image into multiple regions (skin regions, hair regions, eye regions, mouth regions, etc.) using a segmentation network. This segmentation allows the system to process and preserve specific face attributes independently in each region, reducing the overall computational complexity while maintaining attribute preservation reliability.
Solution Approach 2:
The patent applies different processing strategies to different face regions based on their specific characteristics. For example, skin regions use one type of attribute preservation while hair regions use another. This local quality approach optimizes computational resources by applying only necessary processing to each region, reducing overall complexity while maintaining high attribute preservation accuracy.
2Reliability
If existing image processing systems are used to maintain face attributes while stylizing input images, then face attribute preservation is attempted, but processing time and power consumption increase
Solution Approach 1:
The patent performs face segmentation and identifies key face attributes (pose, expression, skin tone, etc.) before the main stylization process. This preliminary action allows the system to pre-plan the attribute preservation strategy, reducing processing time during the actual stylization while ensuring reliable attribute maintenance throughout the image transformation.
3Reliability
If existing image processing systems are used to maintain face attributes while stylizing input images, then face attribute preservation is attempted, but efficiency decreases on resource-constrained devices
Solution Approach 1:
The patent introduces an intermediary segmentation network that acts as a mediator between the input image and the stylization process. This intermediary extracts and preserves essential face attributes in a structured format, allowing the main stylization model to focus on artistic transformation while the intermediary ensures attribute preservation. This approach significantly improves processing efficiency on resource-constrained devices by separating concerns and reducing redundant computations.
Data Source
AI summary
The subject technology receives an input image and a segmentation mask of the input image. The subject technology obtains reconstructed noise of the input image using the input image and the segmentation mask. The subject technology determines a first set of features by performing a first portion of a forward pass of the reconstructed noise through a decoder. The subject technology determines a second set of features by processing the input image for stable diffusion using an image to image (IMG2IMG) model. The subject technology generates a third set of features based on combining, using the segmentation mask, the first set of features and the second set of features with the reconstructed noise. The subject technology generates an output image by performing a remaining portion of the forward pass of the third set of features through the decoder.