Diffusion Model Fine-Tuning with Frozen Parameters for New Character Text
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Solution Overview
Problem
Fine-tuning diffusion models for image generation often leads to model overfitting, degrading the quality of generated images due to alterations in well-trained parameters.
Innovation Solution
A training method for an image generation model that includes obtaining a training sample set of image-text pairs, generating character representations, and adjusting parameters of the representation extraction and bypass modules based on differences between predicted and character images to avoid overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fine-tuning training is performed on the diffusion model using image-text pairs, then the model can generate predicted images corresponding to newly added text, but the well-trained parameters are altered causing overfitting and degradation of generated image quality
Solution Approach 1:
The model parameters are segmented into two groups: frozen pre-trained parameters and trainable fine-tuning parameters. The representation extraction module and bypass module parameters are updated during fine-tuning, while the main diffusion model parameters remain frozen, thus preventing overfitting while enabling adaptation to new text inputs
Solution Approach 2:
The representation extraction module is extracted as a separate trainable component that converts text inputs into representations. This module is independently updated during fine-tuning to adapt to new text without affecting the core diffusion model parameters, resolving the contradiction between adaptability and image quality
2Adaptability or versatility
If fine-tuning training updates all model parameters, then the model adapts to new text inputs, but overfitting occurs and generalization ability deteriorates
Solution Approach 1:
Different parts of the model have different training regimes: the representation extraction module and bypass module parameters are locally updated to adapt to new text, while the main diffusion model parameters remain frozen to preserve generalization ability. This local differentiation resolves the contradiction between adaptation and generalization
Data Source
AI summary
A training method includes obtaining a training sample set of an image generation model, the training sample set including at least one image-text pair each including a character name and a matching character image; inputting the character name into a representation extraction module to generate a character representation corresponding to the character name; inputting a random noise image into a forward processing module of a diffusion model to generate a latent space representation corresponding to the random noise image; inputting the character representation and the latent space representation into a backward processing module of the diffusion model and a bypass module of the image generation model, to generate a predicted image corresponding to the character name; and adjusting parameters of the representation extraction module and the bypass module based on a difference between the predicted image and the character image, to obtain a trained image generation model.