Text-to-Image Diffusion Prior for Quality-Aligned Generation
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Solution Overview
Problem
Conventional image generation models face challenges in accurately generating images that align with the specified image quality level described in the input prompt, often requiring extensive computational resources and unstable training processes, leading to inconsistent performance and unreliable outcomes.
Innovation Solution
The use of a diffusion prior model trained using upside-down reinforcement learning (UDRL) to convert a text embedding into an image embedding, which directly correlates the image quality level with visual information, enabling the image generation model to produce synthetic images that accurately reflect the intended quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image generation models are used to generate images aligned with specified quality levels, then image generation capability is achieved, but computational resources required are excessive and training process is unstable
Solution Approach 1:
The model is segmented into two distinct components: a diffusion prior model for generating image embeddings from text prompts, and an image generation model for creating final images. This segmentation allows each component to specialize, improving overall reliability while optimizing resource usage compared to monolithic conventional models
Solution Approach 2:
An image embedding is introduced as an intermediary representation between the text prompt and the final generated image. The diffusion prior model generates this embedding which captures image quality characteristics, enabling the image generation model to produce accurate results with reduced computational requirements by operating in this intermediate feature space
2Reliability
If conventional image generation models are used to generate images aligned with specified quality levels, then image generation capability is achieved, but training process stability deteriorates
Solution Approach 1:
Dividing the training process into two separate stages - first training the diffusion prior model on text-image pairs to learn quality embeddings, then training the image generation model using these embeddings - improves training stability by avoiding the complexities of joint training while maintaining image quality alignment
Solution Approach 2:
The diffusion prior model is trained in advance to generate image embeddings that encode quality information before the image generation model is trained. This preliminary action prepares stable, pre-processed features that facilitate more stable and reliable training of the final generation model
3Use of energy by moving object
If diffusion prior model with image embedding is used, then computational resources are reduced, but model complexity increases
Solution Approach 1:
The diffusion prior model extracts essential image quality information from text prompts and encodes it into compact image embeddings. This extraction approach reduces the computational burden on the image generation model by providing pre-processed, concentrated features, thereby reducing overall computational resources despite the added component
Solution Approach 2:
Instead of directly processing full-resolution images through the generation model, the system works with compressed image embeddings that capture essential quality characteristics. This copying of essential features into a reduced representation space lowers computational requirements while maintaining generation accuracy
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining an input prompt including an image quality level and a description of an object, generating an image embedding based on the input prompt, where the image embedding represents the object and the image quality level in a vector space, and generating a synthetic image based on the image embedding, where the synthetic image depicts the object and has the image quality level.