Diffusion Model Fine-Tuning via Spectral Shifts for Stable Personalization
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Solution Overview
Problem
Existing diffusion-based generative neural networks for text-to-image generation face challenges such as overfitting and language-drifting due to large parameter spaces, and struggle with efficiently learning multiple personalized concepts, particularly for semantically similar categories.
Innovation Solution
A fine-tuning system that utilizes a compact 'spectral shift' parameter space by adjusting the singular values of weight matrices through singular value decomposition, allowing for efficient adaptation to specific tasks and user preferences, while mitigating overfitting and language-drifting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the entire model is fine-tuned for personalization, then the model can learn specific user preferences and personalized concepts, but the large parameter space leads to overfitting and language-drifting
Solution Approach 1:
The patent segments the weight matrix into singular vectors and singular values, freezing the singular vectors while only training the spectral shifts of singular values. This segmentation allows personalization without requiring the entire model to be retrained, thus preventing overfitting and language-drifting while maintaining personalization capability.
Solution Approach 2:
The patent extracts and isolates the spectral shifts of singular values as the only trainable parameters, separating them from the frozen singular vectors. This extraction enables efficient personalization with minimal parameters, avoiding the overfitting issues that arise when training the entire large parameter space.
2Adaptability or versatility
If the entire model is fine-tuned for personalization, then the model can learn specific user preferences, but the large number of parameters is inefficient for model storage
Solution Approach 1:
The patent extracts only the spectral shifts of singular values as trainable parameters, eliminating the need to store and train the entire weight matrix. This extraction reduces the parameter space from millions of weights to just a few spectral shift values, making storage and computation highly efficient.
Solution Approach 2:
The patent changes the parameterization approach by representing weight matrices through their spectral decomposition, where only the spectral shifts (changes in singular values) are trained rather than the full weight matrices. This parameter change dramatically reduces the quantity of parameters while maintaining personalization capability.
3Adaptability or versatility
If the entire model is fine-tuned for multiple personalized concepts, then the model can learn diverse subjects, but it struggles with semantically similar categories due to overfitting
Solution Approach 1:
The patent segments the personalization process into independent spectral shift parameters for each concept, allowing the model to learn multiple personalized concepts simultaneously without interference. This segmentation prevents overfitting to any single concept while maintaining discrimination accuracy across semantically similar categories.
Solution Approach 2:
The patent changes from training full weight matrices to training only spectral shifts, which provides a regularized parameter space that prevents overfitting. This parameter change enables the model to learn multiple diverse concepts including semantically similar categories while maintaining concept discrimination accuracy.
Data Source
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Figure 1C~1D
Figure 1E
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for fine-tuning diffusion-based generative neural networks in compact parameter spaces for text-to-image generation. In one aspect, a method performed by one or more computers for fine-tuning a diffusion-based generative neural network to obtain a fine-tuned version of the diffusion-based generative neural network is described. The method includes: for each of a number of neural network layers of the diffusion-based generative neural network: obtaining an initial weight matrix including a number of pre-trained weights parametrizing the neural network layer; performing a singular value decomposition on the initial weight matrix; and re-parametrizing the neural network layer with new weights that depend on spectral sifts; and training the spectral shifts of each of the number of neural network layers of the diffusion-based generative neural network to obtain the fine-tuned version of the diffusion-based generative neural network.