Multi-Text Guided Image Cropping for Diffusion Models
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Solution Overview
Problem
Current CLIP-guided diffusion models are inefficient, requiring over 500 iterations to generate images, resulting in 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, using a processor to iteratively refine an initial noisy image through a CLIP-guided diffusion process, with a gradient calculator to adjust the image based on similarity scores, reducing the number of iterations and improving image quality and content safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CLIP-guided diffusion models are used to generate images, then text-to-image matching capability is improved, but the number of iterations required exceeds 500, resulting in low productivity
Solution Approach 1:
The patent segments the image generation process into multiple independent modules: diffusion model for image synthesis, CLIP model for text-image matching, and gradient estimator for optimization guidance. Each module operates independently and can be processed in parallel, reducing the total iteration time while maintaining matching accuracy.
Solution Approach 2:
The patent introduces a gradient estimator as an intermediary component that approximates the gradient calculation between CLIP losses and diffusion model parameters. This intermediary enables faster optimization by avoiding direct backpropagation through the entire diffusion process, thus improving generation speed without sacrificing text-to-image matching quality.
2Manufacturing precision
If multiple iterations are performed to refine image quality, then image quality improves, but content safety and predictability deteriorate due to computational inefficiency
Solution Approach 1:
The patent performs preliminary actions by pre-processing the input text through the CLIP text encoder to generate text embeddings before the diffusion process begins. This allows the diffusion model to receive pre-computed guidance signals, reducing the number of refinement iterations needed while maintaining image quality and improving content safety through more controlled generation.
Solution Approach 2:
The patent implements a feedback mechanism where the CLIP model continuously evaluates the generated image against the input text during the diffusion process, providing real-time gradient guidance. This feedback loop enables the system to achieve high image quality and content safety with fewer iterations by correcting deviations early in the generation process.
3Productivity
If the diffusion process is simplified to reduce iterations, then productivity improves, but text-to-image matching predictability and object location accuracy worsen
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different image regions through attention mechanisms. The diffusion model pays more attention to regions containing objects mentioned in the text, maintaining high object location accuracy even with reduced iterations. This selective focus allows the system to prioritize computationally expensive operations only where necessary.
Solution Approach 2:
The patent dynamically adjusts diffusion parameters such as noise scheduling and guidance scale during the generation process. By changing these parameters adaptively, the system maintains high text-to-image matching precision and object location accuracy while reducing the total number of iterations required, thus improving productivity without sacrificing precision.
Data Source
AI summary
Systems and methods are provided that include a processor executing a program to receive an input from a user, where the input including a first input text and a second input text. The processor is further configured to provide an initial image and, for a predetermined number of iterations, define a first and second regions of the initial image associated with the first and second input texts, respectively, define a plurality of patches of the initial image, input the initial image into a diffusion process to generate a processed image, back-propagate the processed image through a text-image match gradient calculator by generating an image embedding based on the processed image, generating a text embedding based on the region and the input text that are associated with a patch, and calculating a differential between the image embedding and the text embedding.