Variable-Resolution Text-to-Image Generation Without Text Cropping
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Solution Overview
Problem
Current text-to-image generation models are limited by fixed text lengths and resolutions, which restrict their application in scenarios requiring variable input lengths and resolutions.
Innovation Solution
An image generation model trained on a diverse set of images and text sequences with varying lengths and resolutions, allowing it to generate images of any resolution and accommodate text of any length without padding or cropping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If text-to-image generation models use fixed text lengths and resolutions, then the model structure is simplified and training is easier, but the adaptability to variable input lengths and resolutions is restricted
Solution Approach 1:
The patent applies dynamics by making the text sequence length and image resolution dynamic rather than fixed. The model determines text sequence length and image resolution adaptively based on the input content requirements, allowing the system to flexibly adjust to different input sizes without being constrained by predetermined fixed dimensions.
Solution Approach 2:
The patent changes the parameters of text sequence length and image resolution from fixed values to variable parameters. The model can modify these parameters dynamically during generation based on the input text and desired output requirements, enabling adaptation to different scenarios while maintaining model functionality.
2Productivity
If text sequences are padded or cropped to fixed lengths, then the model processing is simplified, but information loss or unnecessary computation occurs
Solution Approach 1:
The patent extracts and removes the unnecessary padding operations from the text processing pipeline. By eliminating the requirement for fixed-length text sequences, the model directly processes the original text input without adding or removing characters, thereby preserving all original information while avoiding wasted computational resources on padding tokens.
Solution Approach 2:
The patent avoids excessive action by not padding text sequences beyond their actual length. The model processes exactly the amount of text that is provided, neither adding unnecessary padding tokens nor cropping meaningful content,从而实现 precise and efficient processing.
3Adaptability or versatility
If images are generated at fixed resolutions, then the generation process is more controlled, but the versatility for different application scenarios is reduced
Solution Approach 1:
The patent applies dynamics to image resolution by making it a variable parameter that can be adjusted based on application requirements. The model determines the appropriate image resolution dynamically during the generation process, allowing it to produce images at different resolutions suitable for various use cases while maintaining controlled generation through the underlying diffusion model mechanics.
4Reliability
If the model is trained on diverse resolutions and text lengths, then the training data preparation is more complex, but the model's robustness and adaptability are enhanced
Solution Approach 1:
The patent applies universality by designing a training framework that handles diverse text lengths and image resolutions through a unified approach. The model is trained on a dataset with varying text sequence lengths and image resolutions without requiring separate processing pipelines for each case, making the training system multi-functional and capable of handling variable inputs through the same training procedure.
Data Source
AI summary
According to embodiments of the disclosure, a method, apparatus, a device, a medium, and a product for image generation are provided. The method includes: receiving a text sequence indicating condition information of image generation; inputting the text sequence into a trained image generation model; and generating, through the image generation model, a target image matching the condition information based on at least the text sequence. The target resolution of the target image is determined based on the text sequence. The image generation model is obtained through training based on a sample image set and a sample text sequence set. A sample image in the sample image set matches a sample text sequence in the sample text sequence set. Sample images in the sample image set have different resolutions, and sample text sequences have different text lengths.