Diffusion Model Sampling with Higher-Order ODE Distillation
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Solution Overview
Problem
Denoising diffusion generative models (DDMs) require numerous iterative deep neural network calls for data synthesis, leading to slow sampling and high resource usage due to the complexity of solving the generative ordinary differential equation (ODE), which is typically approximated by a score function learned with a neural network.
Innovation Solution
Employ a higher-order truncated Taylor method (TTM) ODE solver, such as a second-order solver, to capture local curvature, combined with a separate light-weight neural network to predict higher-order terms, reducing the number of steps and neural network calls required for image synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a denoising diffusion generative model uses iterative step-wise denoising from random noise, then synthesis quality and sample diversity are improved, but sampling speed deteriorates due to requiring many iterative deep neural network calls
Solution Approach 1:
The patent changes the mathematical parameters of the ODE solver by using higher-order truncated Taylor methods (second-order or higher) instead of standard first-order solvers. This allows capturing local curvature of the score function, enabling larger integration steps while maintaining synthesis quality, thus reducing the number of iterative calls required
Solution Approach 2:
The patent employs neural network approximations to model the score function and its higher-order derivatives. By training neural networks to predict these functions, the system can efficiently evaluate the ODE solver without requiring direct computation of complex mathematical operations at each step, thereby accelerating sampling
2Manufacturing precision
If a denoising diffusion generative model performs iterative denoising steps, then synthesis accuracy is improved, but computational resource usage and cost increase
Solution Approach 1:
The patent changes the step size parameter in the ODE integration process by using higher-order methods that allow larger steps. This reduces the total number of iterations needed to reach the synthesized sample, directly decreasing computational resource usage and energy consumption while maintaining accuracy
Solution Approach 2:
The patent extracts and pre-computes higher-order derivative terms through neural network approximations during training. By storing these pre-computed representations, the system avoids recalculating complex higher-order derivatives during each iterative step, reducing real-time computational burden and resource usage
3Adaptability or versatility
If a score function is learned and approximated with a neural network to solve the generative ODE, then synthesis capability is improved, but the complexity of solving the ODE increases
Solution Approach 1:
The patent uses neural networks to create computational copies of the score function and its higher-order derivatives. Instead of directly solving the complex ODE analytically, the system trains neural networks to approximate these functions, transforming the problem into a series of neural network evaluations that are computationally more manageable
Solution Approach 2:
The patent introduces neural network approximations as intermediary functions between the input noise and the final synthesized sample. These neural networks act as mediators that capture the complex relationships in the data distribution, simplifying the overall ODE solving process by breaking it down into manageable neural network computations
Data Source
AI summary
Approaches presented herein provide for the generation of synthesized data from input noise using a denoising diffusion network. A higher order differential equation solver can be used for the denoising process, with one or more higher-order terms being distilled into one or more separate efficient neural networks. A separate, efficient neural network can be called together with a primary denoising model at inference time without significant loss in sampling efficiency. The separate neural network can provide information about the curvature (or other higher-order term) of the differential equation, representing a denoising trajectory, that can be used by the primary diffusion network to denoise the image using fewer denoising iterations.