Diffusion Model Personalization with Component Locking and Rank-One Editing
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Solution Overview
Problem
Existing text-to-image models face challenges in personalization, including coarse-grained resemblance to training examples, large memory footprint, overfitting, and catastrophic forgetting, especially when fine-tuning or using text inversion approaches.
Innovation Solution
Employ component locking and rank-one editing to personalize text-to-image diffusion models, which involve computing activations and updating weights while locking select components, and propagating inputs and outputs through layers to align with target outputs, using soft segmentation masks and gated rank-one updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the entire text-to-image model is fine-tuned, then the model can learn new concepts, but the memory footprint increases significantly and catastrophic forgetting occurs
Solution Approach 1:
The patent segments the model into two parts: frozen pre-trained components and trainable adapter components (low-rank decomposition matrices). Only the adapter components are updated during fine-tuning, while the main model remains frozen. This segmentation allows concept learning without updating the entire model, thus reducing memory footprint and preventing catastrophic forgetting of pre-trained knowledge.
Solution Approach 2:
The patent applies local quality by making different parts of the model have different properties: the pre-trained components maintain their original high-quality representations while the adapter components are locally optimized for new concepts. This allows the model to acquire new knowledge in specific localized regions without degrading the quality of pre-learned representations elsewhere.
2Quantity of substance
If text inversion approaches are used, then memory footprint is reduced, but the learned concepts only have coarse-grained resemblance to training examples
Solution Approach 1:
The patent changes the parameter representation from full-rank weight matrices to low-rank decomposition matrices (A and B matrices where W ≈ AB^T). This parameter change enables fine-grained concept learning with reduced memory footprint, as the low-rank approximation captures essential features while using fewer parameters than full fine-tuning.
3Adaptability or versatility
If the model is fine-tuned on a new concept, then the model learns the new concept, but catastrophic forgetting of previous concepts occurs
Solution Approach 1:
By segmenting the model into frozen pre-trained components and trainable adapters, the patent ensures that pre-trained knowledge remains intact (stored in frozen components) while new concepts are learned in separate adapter layers. This segmentation prevents catastrophic forgetting by isolating learning to specific components that do not interfere with pre-trained representations.
4Adaptability or versatility
If full fine-tuning is performed, then the model can adapt to new concepts, but the underlying model structure is degraded
Solution Approach 1:
The patent performs preliminary action by freezing the pre-trained model components before fine-tuning. This preliminary freezing protects the integrity of the underlying model structure, allowing concept personalization through adapter training without degrading the base model. The frozen components serve as a stable foundation that prevents structural degradation.
Data Source
AI summary
A text-to-image machine learning model takes a user input text and generates an image matching the given description. While text-to-image models currently exist, there is a desire to personalize these models on a per-user basis, including to configure the models to generate images of specific, unique user-provided concepts (via images of specific objects or styles) while allowing the user to use free text “prompts” to modify their appearance or compose them in new roles and novel scenes. Current personalization solutions either generate images with only coarse-grained resemblance to the provided concept(s) or require fine tuning of the entire model which is costly and can adversely affect the model. The present description employs component locking and/or rank-one editing for personalization of text-to-image diffusion models, which can improve the fine-grained details of the concepts in the generated images, reduce the memory footprint update of the underlying model instead of full fine-tuning, and reduce adverse effects to the model.