Saliency-Guided Reduced-Precision Models for Generative Graphics
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Solution Overview
Problem
Existing generative models for image, video, and 3D content generation using machine learning techniques require high precision formats like fp32, bfloat16, and int8, which can be computationally expensive and inefficient, especially for regions that do not require high quality, leading to suboptimal performance and resource utilization.
Innovation Solution
Implementing reduced precision models, such as 1-bit quantized networks for imperceptible regions and switching to higher precision when necessary, utilizing saliency and attention models to optimize performance, and merging low and high-bit generation pipelines for improved computational efficiency and real-time streaming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high precision formats (fp32, bfloat16, int8) are used for generative model computation, then image quality and model accuracy are improved, but computational cost and resource consumption increase significantly
Solution Approach 1:
The patent applies local quality by using different precision levels for different regions of the generated content. Saliency models identify important regions that require high precision (fp32, bfloat16, or int8), while non-salient regions use lower precision (int4, int2, or 1-bit). This resolves the contradiction by maintaining high image quality only where necessary, significantly reducing overall computational cost and resource consumption.
Solution Approach 2:
The patent implements dynamic precision adjustment during the generation process. The system dynamically determines which regions require high precision based on saliency analysis and attention mechanisms, switching between precision formats adaptively. This dynamic approach optimizes the balance between image quality and computational efficiency, avoiding the static overhead of using high precision throughout the entire image.
2Reliability
If high precision formats are used throughout the entire generation process, then model accuracy is maintained, but inference speed and real-time performance deteriorate
Solution Approach 1:
By applying high precision only to salient regions identified by saliency models and attention mechanisms, the patent maintains model accuracy for important content while dramatically speeding up inference for non-critical regions. This local differentiation resolves the contradiction between reliability and productivity.
Solution Approach 2:
The patent segments the generation process into different precision pathways based on region importance. Salient regions follow high-precision computation paths while non-salient regions use optimized low-precision paths. This segmentation enables parallel processing at different precision levels, improving overall inference speed without sacrificing accuracy where needed.
3Stability of the object's composition
If uniform high precision is applied to all regions, then consistent quality is achieved, but resource utilization becomes suboptimal
Solution Approach 1:
The patent replaces uniform quality application with local quality differentiation. By using saliency models and attention mechanisms to identify important regions, the system applies high precision only where necessary and low precision elsewhere. This resolves the contradiction by achieving quality consistency in perceptually important areas while optimizing resource utilization across the entire system.
Data Source
AI summary
Systems and methods are for reduced precision models for generative graphics are provided. In one example, information is received that is indicative of saliency of one or more portions of an image that is to be generated. The image is then generated by, for each portion of the one or more portions of the image: (i) based on the information indicative of saliency, selecting a generative model from among multiple pre-trained generative models (e.g., diffusion models) quantized at different precision levels (e.g., using various weights of between 32-bits and 1-bit, inclusive); and (ii) applying the selected generative model to pixels associated with the portion.