Latent Adversarial Diffusion Distillation for Data-Scarce Training
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Solution Overview
Problem
Machine learning models face challenges in training due to limited availability of high-quality real-world data, data curation complexity, and resource-intensive processing, which is exacerbated in resource-constrained environments.
Innovation Solution
The approach involves generating synthetic data for training reverse diffusion transformer models without encoding prompts or content into vector space, enabling training within the latent space and using fewer sampling steps, thereby reducing computational resources and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world data is used for training reverse diffusion transformer models, then model training quality is improved, but data availability is limited and curation complexity increases
Solution Approach 1:
The patent creates synthetic training data by copying and transforming existing data through diffusion processes. A teacher model generates synthetic data that mimics real-world data characteristics, which then serves as training data for student models. This copying approach resolves the contradiction by providing abundant training data without requiring additional real-world data collection or curation.
2Reliability
If traditional training methods are used for reverse diffusion transformer models, then model accuracy is maintained, but computational resources and processing time increase significantly
Solution Approach 1:
The patent segments the training process into teacher model training and student model training phases. The teacher model is trained once on real or synthetic data to generate training samples, which are then used to train multiple student models. This segmentation resolves the contradiction by amortizing the computational cost across multiple student models, significantly improving efficiency while maintaining accuracy through the teacher-student knowledge transfer mechanism.
Solution Approach 2:
The patent performs preliminary action by having the teacher model generate synthetic training data in advance. This pre-generated data is then used to train student models without requiring real-time access to real-world data or repeated expensive training processes. The preliminary generation of training samples resolves the contradiction by preparing all necessary training materials beforehand, reducing computational resources and time for subsequent student model training.
3Reliability
If more sampling steps are used during inference, then generated content quality is improved, but latency and processing time increase
Solution Approach 1:
The patent changes the parameter of sampling steps by training student models to achieve accurate generation with fewer sampling steps. The student models learn from the teacher model's outputs and can produce high-quality results more efficiently. This parameter change resolves the contradiction by reducing the number of sampling steps required during inference, thereby decreasing latency while maintaining generated content quality through the learned representations from teacher-student training.
Data Source
AI summary
A method including receiving a first representation of an image in a first latent space of a first machine learning model. The method further includes generating, by a second machine learning model based at least in part on the first representation, a second representation of the image in a second latent space of the second machine learning model. The method further includes updating, without generating an output image corresponding to the image, a set of weights of the second machine learning model based at least in part on the first representation and the second representation.