Personalized Video Generation Using Spatial-Temporal LoRA Adapters
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Solution Overview
Problem
Conventional generative artificial intelligence models face challenges in accurately reproducing specific object appearances and movements based on textual inputs, often relying on large computational resources and struggling with overfitting, and struggle to disentangle subject appearance and motion effectively.
Innovation Solution
The method involves training a generative AI model with spatial and temporal adaptation blocks using cross-attention maps to separate foreground and background content, allowing for personalized video generation by fine-tuning with spatial and temporal LoRA adapters, and applying domain randomization techniques to enhance motion customization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional generative AI models are trained with large computational resources to improve generation accuracy, then video generation quality improves, but computational overhead and training cost increase significantly
Solution Approach 1:
The model is divided into a base model and separate adapters. The base model handles general video generation tasks with standard computational resources, while lightweight adapters handle personalization and fine-tuning. This segmentation allows high-quality generation without requiring the entire model to be retrained for each task, reducing overall computational overhead.
Solution Approach 2:
Instead of retraining the entire large model for different tasks or personalizations, the patent creates copies in the form of adapters that can be trained independently. These adapters copy and extend the capabilities of the base model without requiring computational resources proportional to the full model size, significantly reducing training cost and energy consumption.
2Manufacturing precision
If the model uses large number of parameters to improve generation accuracy, then video output quality improves, but the model becomes difficult to fine-tune and prone to overfitting
Solution Approach 1:
The model parameters are segmented into the base model (which remains frozen and general) and adapters (which are trained separately for specific tasks). This segmentation prevents overfitting by limiting the number of parameters that are actively trained for each task, while still maintaining the ability to generate high-quality videos through the combination of base model and adapters.
Solution Approach 2:
Instead of training the entire large model for each task (excessive action), the patent trains only the necessary adapter parameters (partial action). This partial training approach reduces overfitting risk while maintaining sufficient performance, as the adapter leverages the pre-trained knowledge of the base model without requiring full model retraining.
3Reliability
If the model trains with extensive fine-tuning data to prevent overfitting, then model accuracy improves, but training time and data requirements increase
Solution Approach 1:
The adapter creates a parameter-efficient copy that captures task-specific patterns without requiring extensive fine-tuning data. By copying relevant features from the base model and augmenting them with minimal task-specific parameters, the system achieves high accuracy with significantly reduced training time and data requirements compared to full model fine-tuning.
Solution Approach 2:
The patent changes the parameter training strategy by freezing the base model parameters and only training adapter parameters. This parameter change approach reduces the effective number of parameters to be trained, thereby reducing training time and data requirements while maintaining model accuracy through the adapter's specialized parameters.
4Manufacturing precision
If the model uses complex attention mechanisms to separate foreground and background, then generation accuracy improves, but model complexity increases
Solution Approach 1:
The attention mechanism is segmented into spatial and temporal components, with the spatial adapter handling foreground-background separation and the temporal adapter handling motion consistency. This segmentation allows each adapter to focus on specific aspects of the generation task, improving accuracy while keeping individual adapter complexity manageable rather than requiring a single complex unified mechanism.
Data Source
AI summary
Techniques and apparatus for generating visual content according to a textual prompt input into a generative artificial intelligence model. An example method generally includes receiving an input prompt specifying a video output to be generated by a generative artificial intelligence model. Based on a spatial portion of the generative artificial intelligence model and a cross-attention map generated based on the input prompt, a spatial attention map representing a subject of the video output to be generated by the generative artificial intelligence model is generated. Based on a temporal portion of the generative artificial intelligence model and the cross-attention map, a temporal attention map representing motion to be depicted by the subject of the video output to be generated by the generative artificial intelligence model is generated. The video output is generated based on the spatial attention map and the temporal attention map, and the generated video output is output.