Diffusion Model Denoising Backbone With Adapter Blocks for Lower Latency
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Solution Overview
Problem
Conventional generative models, such as diffusion models, are computationally expensive and incur substantial latency due to iterative denoising processes, consuming significant computational resources for each output generation.
Innovation Solution
Implement a denoising backbone that alternates between higher resolution blocks and adapter blocks, using lower resolution blocks only for a subset of iterations, and employs an adapter block with fewer computational operations like self-attention or cross-attention to reduce latency and computational expense.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional diffusion models use iterative denoising processes with high resolution blocks for all iterations, then generation accuracy is maintained, but computational expense and latency increase substantially
Solution Approach 1:
The patent segments the denoising iterations into two types: full-resolution iterations (using higher resolution blocks) and compressed iterations (using lower resolution blocks with adapter modules). This segmentation allows the model to maintain accuracy during critical early iterations while speeding up later iterations where less computational power is needed, thus resolving the contradiction between accuracy and speed.
Solution Approach 2:
The patent dynamically adjusts the resolution and computational complexity of processing blocks based on the iteration stage. Early iterations use full-resolution blocks for accuracy, while later iterations use compressed blocks with adapter modules for speed. This dynamic adaptation allows the system to optimize the balance between accuracy and generation speed throughout the denoising process.
2Manufacturing precision
If diffusion models process data at high resolution for all iterations, then output quality is maintained, but computational resources are consumed excessively
Solution Approach 1:
The patent applies partial action by using full-resolution processing only when necessary (early iterations) and compressed processing for the remaining iterations. The adapter modules perform partial denoising operations that are sufficient for later iterations but consume fewer computational resources, thus reducing overall energy consumption while maintaining adequate output quality.
Solution Approach 2:
The patent changes the resolution parameter dynamically during the denoising process. Early iterations operate at full resolution to ensure quality, while later iterations switch to compressed resolution with adapter modules to reduce computational resource consumption. This parameter change allows the system to maintain quality where needed while reducing energy consumption overall.
3Productivity
If adapter blocks with fewer computational operations are used for all iterations, then latency and computational expense are reduced, but generation accuracy deteriorates
Solution Approach 1:
The patent implements periodic action by alternating between full-resolution blocks and compressed blocks with adapter modules across different iterations. Full-resolution blocks are used periodically at critical stages (early iterations) to maintain accuracy, while compressed blocks are used in between to improve speed. This periodic switching ensures accuracy is maintained when needed while achieving speed improvements overall.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. During a first iteration of processing data using a first denoising backbone of a teacher diffusion machine learning model, a first latent tensor is generated using a lower resolution block of the first denoising backbone. During a first iteration of processing data using a second denoising backbone of a student diffusion machine learning model, a second latent tensor is generated using an adapter block of the second denoising backbone. A loss is generated based on the first and second latent tensors, and one or more parameters of the adapter block are updated based on the loss.