Diffusion Model Training Data Generation via Gradient Estimation
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Solution Overview
Problem
Current CLIP-guided diffusion models are inefficient, requiring over 500 iterations to generate images, and suffer from low content safety, low image quality, and unpredictable text-to-image matching and object location, making them unsuitable for real-time applications.
Innovation Solution
The system employs a multi-algorithm diffusion sampling process, dynamic value clipping, and multi-text guided image cropping to generate images efficiently by using a smaller gradient estimator model, back-propagating through a text-image match gradient calculator, and applying gradients to refine images within fewer iterations, while ensuring content safety and quality through curated sentence and artistic phrase datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CLIP-guided diffusion models are used to generate images, then image generation capability is achieved, but the number of iterations required exceeds 500, reducing efficiency
Solution Approach 1:
The patent segments the diffusion process into two distinct stages: a coarse generation stage using a smaller gradient estimator model for rapid initial image creation, and a refinement stage using the full diffusion model for detailed optimization. This segmentation allows the system to achieve high-quality images in fewer total iterations by performing coarse generation efficiently first, then refining only the critical aspects.
Solution Approach 2:
The patent introduces a gradient estimator model as an intermediary component that provides initial guidance during the diffusion process. This intermediary model, being smaller and faster than the full diffusion model, enables rapid initial iterations while maintaining sufficient guidance quality, thereby reducing the total number of iterations needed when combined with the refinement stage.
2Reliability
If standard diffusion models are used, then image generation is possible, but content safety is low and image quality is poor
Solution Approach 1:
The patent applies preliminary action by using the gradient estimator model to generate initial images and compute initial gradients before the refinement stage. This preliminary generation incorporates safety filters and quality controls from the outset, ensuring that only safe and high-quality images proceed to refinement, thereby maintaining high reliability without sacrificing generation speed.
Solution Approach 2:
The patent implements feedback mechanisms through the refinement stage, where the full diffusion model processes the initial images and adjusts them based on quality and safety evaluations. This feedback loop ensures that only images meeting safety and quality criteria are finalized, maintaining high reliability while the two-stage process preserves generation efficiency.
3Measurement precision
If diffusion models generate images from text, then text-to-image matching is achieved, but matching accuracy is unpredictable and object location is inaccurate
Solution Approach 1:
The patent segments the text-to-image generation process into two stages: initial generation using the gradient estimator model with simplified text encoding, and refinement using the full diffusion model with enhanced text-image alignment. This segmentation allows the system to achieve high matching accuracy in the refinement stage while keeping the overall process manageable through the simpler initial stage.
Solution Approach 2:
The patent applies parameter changes by transitioning from simplified text encoding and fewer attention heads in the gradient estimator model to enhanced text encoding and more attention heads in the full diffusion model. This progressive parameter enhancement allows the system to achieve high matching accuracy without requiring the complex model to handle all tasks simultaneously, thereby managing overall system complexity.
Data Source
AI summary
Systems and methods are provided that include a processor executing a program to match sentences from a sentence dataset with artistic phrases from an artistic phrase dataset to generate a plurality of safe phrases. The processor is further configured to, for each of the safe phrases, generate a safe image by, for a predetermined number of iterations, performing steps to input an initial image into a diffusion process to generate a processed image, wherein the diffusion process includes a first diffusion model, back-propagate the processed image through a text-image match gradient calculator to calculate a gradient against the safe phrase, and update the initial image by applying the gradient to the processed image. The processor is further configured to pair each of the generated safe images with their respective safe phrase to form a plurality of safe phrase-image pairs.