Denoising Diffusion Content Synthesis With Iterative Noise Reduction
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Solution Overview
Problem
Conventional denoising diffusion models require a large number of iterations and time to generate noise-free content items, and training these models often necessitates a large number of data samples, which can lead to generating content with undesirable augmentations.
Innovation Solution
A method involving iterative noise addition and reduction using a denoiser model, combined with augmented training data to enhance model training, allowing for faster and more diverse content generation without introducing unwanted augmentations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional stochastic sampling is used to generate noise-free content items, then the content quality is improved, but the generation time increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-training the denoiser model on augmented training data that includes various corruptions and augmentations. This pre-training prepares the model in advance to handle different corruption types and reduction factors, enabling faster convergence during actual content generation without requiring extensive iterative denoising steps
Solution Approach 2:
The patent utilizes parameter changes by varying the reduction factor in the denoising process and using different corruption types during training. The model is trained with multiple reduction factors (e.g., 0.1, 0.3, 0.5) and corruption types, allowing it to adapt to different denoising scenarios and reduce the number of iterations needed for high-quality content generation
2Reliability
If a large number of data samples are used to train the machine learning model, then the model's denoising capability is improved, but the training data requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the training process into two distinct phases: first training on clean data to learn fundamental patterns, then training on augmented data with various corruptions to learn robust denoising capabilities. This segmented approach allows the model to efficiently learn from smaller, targeted datasets rather than requiring massive amounts of diverse training data
Solution Approach 2:
The patent uses data augmentation as an intermediary technique to generate synthetic training samples from limited clean data. By applying various corruptions and transformations to the clean training data, the system creates a larger effective training set without requiring additional real-world data samples, thus improving denoising capability while maintaining limited data requirements
3Quantity of substance
If data samples are augmented with geometric transformations and color changes, then the training data quantity is increased, but the generated content includes undesirable augmentations
Solution Approach 1:
The patent applies partial action by selectively applying augmentations only during the training phase and not during the inference/generation phase. The model is trained on augmented data to learn robustness, but when generating content, no augmentations are applied to the input or output. This ensures the model learns from diverse training samples without transferring unwanted augmentation artifacts to the generated content
Solution Approach 2:
The patent extracts the beneficial aspects of augmentation (increased training diversity and model robustness) while removing the harmful aspects (unwanted augmentations in generated content). This is achieved by applying augmentations exclusively during training and using the model in its original form during generation, effectively separating the training enhancement from the generation output
Data Source
AI summary
Techniques are disclosed herein for generating a content item. The techniques include receiving a content item and metadata indicating a level of corruption associated with the content item; and for each iteration included in a plurality of iterations: performing one or more operations to add corruption to a first version of the content item to generate a second version of the content item, and performing one or more operations to reduce corruption in the second version of the content item to generate a third version of the content item, wherein a level of corruption associated with the third version of the content item is less than a level of corruption associated with the first version of the content item.