Multi-Resolution Image Token Generation for Detail-Rich Output
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Solution Overview
Problem
Existing image generation methods struggle to produce high-resolution images with rich details, as they often lack effective mechanisms for quality control during the generation process.
Innovation Solution
An image generation method that discretizes text into tokens, generates image tokens at multiple resolutions, and evaluates and corrects them progressively to improve image quality, using a resolution sequence with incremental steps and a unified generation framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image generation is performed using conventional single-resolution methods, then the generation process is simple and fast, but the image quality and detail richness are insufficient
Solution Approach 1:
The image generation process is segmented into multiple resolution stages, where image tokens are generated at different resolutions (e.g., 8x8, 16x16, 32x32) sequentially. Each resolution level captures features at its appropriate scale, with lower resolutions capturing global structure and higher resolutions capturing detailed features. This segmentation allows the system to achieve high image quality by combining information from multiple resolution levels without overwhelming complexity in a single generation step.
Solution Approach 2:
The patent introduces a resolution dimension to the image generation process, transforming it from a single-resolution approach to a multi-resolution approach. By adding the resolution dimension as a new axis of generation, the system can capture image information at multiple scales simultaneously, thereby improving image quality and detail richness while maintaining manageable process complexity through structured progression.
2Manufacturing precision
If image tokens are generated at multiple resolutions progressively, then image information richness and quality are improved, but the generation time and computational cost increase
Solution Approach 1:
The system performs preliminary action by generating image tokens at lower resolutions first, establishing the basic structure and global features before progressing to higher resolutions. This preliminary generation at coarser resolutions provides a foundation that guides subsequent higher-resolution token generation, ensuring that time and computational resources are invested in a structured manner that maximizes quality improvement while managing generation time through efficient progression.
Solution Approach 2:
The multi-resolution generation process maintains continuity of useful action by seamlessly transitioning between resolution levels. Each resolution level builds upon the previous one, with higher-resolution tokens being generated based on the context and structure established at lower resolutions. This continuous progression ensures that generation time is efficiently utilized, as each step contributes meaningfully to the final image quality without unnecessary interruptions or redundant computations.
3Reliability
If conventional image generation methods are used, then the process is straightforward, but quality control and correction capabilities are insufficient
Solution Approach 1:
The patent implements feedback mechanisms at each resolution level, where generated image tokens are evaluated and corrected before proceeding to the next resolution stage. This feedback loop allows the system to identify and correct quality issues at early stages, preventing propagation of errors to higher resolutions. By integrating feedback into the multi-resolution generation process, the system achieves reliable quality control while managing complexity through structured evaluation and correction at each stage.
Data Source
AI summary
An image generation method, an apparatus, an electronic device and a storage medium are provided. The method includes: discretizing a target text to obtain a plurality of text tokens; obtaining a resolution sequence based on an initial resolution and a target resolution, wherein the resolution sequence comprises a plurality of resolutions, and a difference between two adjacent resolutions of the plurality of resolutions is a preset increment; generating image tokens corresponding respectively to the plurality of resolutions based on the plurality of text tokens and the resolution sequence; and fusing all the image tokens to obtain a target image corresponding to the target text.