Diffusion Neural Network Noise Scheduling
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Solution Overview
Problem
Diffusion neural networks face performance issues in generating high-quality outputs due to suboptimal noise scheduling, which varies based on the task, such as image size, leading to undertraining of certain noise levels and impacting performance.
Innovation Solution
Modifying the noise scheduling strategy by scaling input data and using a linear noise schedule during training, optionally normalizing inputs to mitigate variance effects, thereby improving the quality of network outputs generated by the diffusion neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed noise schedule is used during training, then the training process is simple and easy to implement, but the performance deteriorates when generating outputs of different sizes and resolutions due to undertraining of certain noise levels
Solution Approach 1:
The patent implements dynamic noise scheduling where the noise level applied during training is adjusted based on the resolution and size of the input data. For higher resolution images, the system applies higher noise levels, while for lower resolution images, it applies lower noise levels. This dynamic adaptation allows a single trained model to perform optimally across multiple task variants without requiring separate training for each resolution, thus resolving the contradiction between training simplicity and task adaptability.
2Adaptability or versatility
If the noise level is increased to cover more noise levels during training, then the adaptability to different tasks improves, but the training complexity increases
Solution Approach 1:
The patent changes the noise schedule parameter dynamically based on input characteristics. Specifically, it uses a parameterized noise schedule where the noise level is determined by a function of the input image resolution or size. This approach allows the system to cover a wide range of noise levels adaptively without requiring manual configuration of multiple fixed schedules, thus improving adaptability while keeping training complexity manageable through automated parameter selection.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a network output using a diffusion neural network and for training a diffusion neural network with a modified noise scheduling strategy.