Image Generation Model With Dynamic Resolution Modes
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Solution Overview
Problem
Current image generation models are limited to generating images of specific resolutions, such as 512*512 or 1024*1024 pixels, failing to meet user needs for diverse resolution requirements in different scenarios.
Innovation Solution
An image generation method and device that determines a processing mode based on a target resolution, using corresponding image generation models to generate images of either a first or second resolution, allowing for flexible resolution adjustment and optimizing storage by sharing components like encoders and neural networks across different resolutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image generation models are designed to generate images of multiple resolutions, then the adaptability to different user needs is improved, but the device complexity and storage requirements increase
Solution Approach 1:
The patent implements a universal image generation model that can generate images at multiple resolutions (512x512, 1024x1024, and other custom resolutions) through a single unified architecture. The model uses adjustable processing stages and feature map resolutions to adapt to different output requirements, eliminating the need for separate specialized models for each resolution while maintaining high adaptability to user needs.
2Manufacturing precision
If separate image generation models are created for different resolutions, then the image generation quality for each resolution is optimized, but the storage space required increases
Solution Approach 1:
The patent merges multiple resolution-specific generation capabilities into a single unified model. The model shares common components including the encoder, noise injection mechanism, and base processing architecture across all resolutions. By combining these elements and making them resolution-agnostic, the patent significantly reduces storage requirements compared to maintaining separate models, while preserving the ability to generate high-quality images at different resolutions through configurable processing parameters.
3Quantity of substance
If a single image generation model is used for all resolutions, then the storage space is reduced, but the ability to meet specific resolution requirements deteriorates
Solution Approach 1:
The patent implements a dynamic image generation model where the processing architecture adapts its behavior based on the target resolution. The model dynamically adjusts the number of processing stages, feature map dimensions, and downsampling/upsampling operations according to the desired output resolution. This dynamic configuration allows the single model to flexibly meet specific resolution requirements while maintaining a compact storage footprint, as the same model weights can produce different resolutions through parameter adjustment rather than requiring separate specialized models.
Data Source
AI summary
An image generation method includes: obtaining input data and a target resolution for generating an image, the target resolution being configured to indicate a resolution of the image and being a first resolution or a second resolution; and determining a processing mode, according to the target resolution, for an image generation model, and processing the input data using the determined processing mode by the corresponding image generation model to obtain a generated image having the target resolution, wherein the first resolution and the second resolution are different and correspond to different processing modes for corresponding image generation models.