Denoising Diffusion Model With Dynamic Error Threshold
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Solution Overview
Problem
Existing diffusion-based image generation models require long generation times, and attempts to reduce generation time often result in decreased image quality, with lower latency leading to worse alignment between text prompts and generated images.
Innovation Solution
A generative AI system using a denoising diffusion model that performs iterative denoising steps in parallel, with a dynamic error threshold that adjusts based on denoising performance, to balance latency and image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the number of iterative generation steps is reduced, then generation time is reduced, but image quality and alignment between text prompt and generated image deteriorate
Solution Approach 1:
The patent pre-calculates and stores optimal denoising parameters and intermediate results for different text prompts during an offline training phase. When generating images, the system retrieves these pre-computed parameters and performs only a few iterative refinement steps, achieving high-quality results without requiring many forward diffusion steps.
Solution Approach 2:
The system dynamically adjusts the number of iterative steps and denoising parameters based on the specific text prompt and desired image characteristics. Rather than using a fixed number of iterations, the method adapts the generation process to achieve optimal results with variable step counts, reducing time while maintaining quality.
2Manufacturing precision
If more iterative denoising steps are performed, then image quality improves, but generation latency increases
Solution Approach 1:
The system pre-computes and caches optimal denoising schedules, noise schedules, and intermediate latent representations for various text prompts during training. During inference, it leverages these pre-computed resources to achieve high image quality with significantly fewer iterative steps, thereby reducing generation latency.
Solution Approach 2:
The patent changes key parameters such as noise schedule, diffusion timestep, and denoising strength dynamically based on the text prompt and desired output quality. By optimizing these parameters in advance and adapting them during generation, the system achieves high image quality without requiring excessive iterative steps, thus reducing latency.
Data Source
AI summary
Apparatuses, systems, and techniques to generate an image using a neural network based model using a variable error threshold. In at least one embodiment, one or more neural networks are used to generate a final output image by iteratively removing noise from an initial image based, at least in part, on one or more variable error threshold values.