CLD Diffusion Modeling for Faster Synthetic Data Sampling
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Solution Overview
Problem
Existing score-based generative models (SGMs) face challenges with low sampling rates due to their iterative denoising process, often compromising data distribution coverage for acceleration, while generative adversarial networks (GANs) are difficult to train and may fail to model all parts of a data distribution.
Innovation Solution
Implementing a critically-damped Langevin diffusion (CLD) in a joint data-velocity space within SGMs, using a score matching objective that learns the conditional distribution of velocity given data, and employing a hybrid score matching method to improve synthesis quality and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If score-based generative models use iterative denoising process, then synthesis quality is improved, but sampling rate becomes low
Solution Approach 1:
The patent applies parameter changes by transforming the diffusion process into a velocity-space formulation where the score function operates on velocity fields rather than direct data space. This changes the parameter space in which the iterative denoising occurs, allowing for faster convergence while maintaining synthesis quality. The velocity-space transformation enables the model to capture data distribution characteristics more efficiently, reducing the number of iterations needed.
Solution Approach 2:
The patent introduces a velocity dimension to the traditional data space formulation. By formulating the diffusion process in joint data-velocity space, the model adds an auxiliary dimension that captures temporal or dynamic information. This dimensional extension allows the score function to leverage additional structural information about the data distribution, thereby improving sampling efficiency without sacrificing synthesis quality.
2Productivity
If score-based generative models accelerate sampling rate, then productivity is improved, but data distribution coverage is compromised
Solution Approach 1:
The velocity field acts as an intermediary that mediates between the data distribution and the sampling process. Instead of directly manipulating data points in the traditional denoising approach, the model operates on velocity fields that encode the structure of the data distribution. This intermediary representation allows for accelerated sampling while preserving coverage of complex data distributions, as the velocity field captures essential statistical properties without requiring exhaustive iteration through the entire distribution space.
3Manufacturing precision
If generative adversarial networks are used, then synthesis quality is improved, but training difficulty increases
Solution Approach 1:
The patent extracts the adversarial training component from the generative modeling framework and replaces it with a score-based approach. By removing the adversarial objective and focusing solely on learning the score function (gradient of log probability) from data, the model eliminates the training instability and mode collapse issues inherent in GANs. The score function can be learned through straightforward maximum likelihood estimation, significantly simplifying the training process while maintaining high synthesis quality.
Data Source
AI summary
Systems and methods described relate to the synthesis of content using generative models. In at least one embodiment, a score-based generative model can use a stochastic differential equation with critically-damped Langevin diffusion to learn to synthesize content. During a forward diffusion process, noise can be introduced into a set of auxiliary (e.g., “velocity”) values for an input image to learn a score function. This score function can be used with the stochastic differential equation during a reverse diffusion denoising process to remove noise from the image to generate a reconstructed version of the input image. A score matching objective for the critically-damped Langevin diffusion process can require only the conditional distribution learned from the velocity data. A stochastic differential equation based integrator can then allow for efficient sampling from these critically-damped Langevin diffusion models.