Neural Image Generation With IMLE-Accelerated Diffusion Training
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Solution Overview
Problem
Training denoising diffusion probabilistic models (DDPMs) for image generation is computationally expensive and time-consuming due to the large number of required steps, which affects the quality and applicability of the generated images, especially in time-sensitive applications.
Innovation Solution
Implementing Implicit Maximum Likelihood Estimation (IMLE) as a denoising step in the DDPM training process, allowing for fewer steps and a smaller latent space, which captures complex distributions, thereby reducing processing time and resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional DDPM training is used, then training stability is improved, but training time and computational cost increase significantly
Solution Approach 1:
The patent changes the training parameters by introducing implicit maximum likelihood estimation as a alternative objective function that converges faster than traditional DDPM loss functions, reducing the number of training steps required while maintaining training stability
Solution Approach 2:
The patent performs preliminary dimensionality reduction by projecting high-dimensional images into a smaller latent space before diffusion processing, which reduces the computational complexity and training time while preserving the essential features needed for stable training
2Productivity
If the number of training steps is reduced, then processing time is decreased, but image generation quality deteriorates
Solution Approach 1:
The patent introduces a latent space dimension that compresses high-dimensional image data into a lower-dimensional representation, allowing faster processing while maintaining the quality information needed for high-fidelity image generation through learned feature preservation
3Use of energy by moving object
If a smaller latent space is used, then computational resources are reduced, but the ability to capture complex distributions is compromised
Solution Approach 1:
The patent transforms the latent space parameters through learned nonlinear transformations that pack complex distribution information into a compact representation, enabling smaller latent spaces to maintain or enhance their ability to capture complex data distributions through optimized encoding schemes
Data Source
AI summary
Apparatuses, systems, and techniques are presented to generate one or more images. In at least one embodiment, one or more neural networks are used to generate one or more images based, at least in part, on one or noise values.