Diffusion Image Generation With Text-Guided Geometric Constraints
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Solution Overview
Problem
Existing diffusion models struggle to accurately control geometric constraints in generated images for complex perception tasks such as object detection and pose estimation, particularly in controlling the location and size of bounding boxes.
Innovation Solution
A data processing method that incorporates text information indicating location constraints into a diffusion model, using a noise addition module and a denoising model to generate images with precise geometric control, and employs a text encoder for feature representation and attention mechanism-based interaction to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional diffusion models are used for image generation, then content creation performance is excellent, but geometric constraint control (bounding box location and size) is inaccurate
Solution Approach 1:
The patent introduces a text encoder as an intermediary component that processes geometric constraint information (bounding box location and size) and transforms it into feature representations. This mediator bridges the gap between the input image and the diffusion model, enabling precise geometric control without modifying the core diffusion model architecture. The text encoder acts as a translator that converts geometric parameters into a format compatible with the diffusion model's processing.
Solution Approach 2:
The patent segments the image processing task into distinct components: the original image processing stream and the geometric constraint processing stream. The geometric constraints are extracted and encoded separately through the text encoder, then fused with the image features through attention mechanisms. This segmentation allows independent optimization of geometric control while preserving the content generation capabilities of the diffusion model.
2Measurement precision
If text encoder with attention mechanism is introduced to improve geometric control, then location constraint accuracy improves, but system complexity increases
Solution Approach 1:
The patent employs a pre-trained text encoder that serves multiple functions: it processes geometric constraint information, extracts feature representations, and integrates with the diffusion model through attention mechanisms. This universal component handles both the encoding of geometric parameters and the fusion with image features, reducing the need for separate specialized modules and thereby limiting the increase in system complexity.
Solution Approach 2:
The text encoder leverages its pre-trained capabilities to automatically process geometric constraint information without requiring extensive task-specific customization. The attention mechanism self-adapts to fuse the encoded features with image features, reducing the need for manual feature engineering and complex architecture design. This self-service approach minimizes the complexity overhead while achieving improved location constraint accuracy.
3Reliability
If geometric constraints are tightly controlled in generated images, then object detection performance improves, but generation flexibility decreases
Solution Approach 1:
The patent implements dynamic control over geometric constraints through the attention mechanism. The model can adaptively adjust the strength of geometric constraint application based on the input image characteristics and desired output. This dynamic approach allows the system to maintain high object detection performance when needed while preserving generation flexibility when geometric constraints are less critical, resolving the contradiction between reliability and ease of operation.
Data Source
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AI summary
A data processing method, which is applied to the artificial intelligence field, includes: obtaining a first image and text information, where the text information indicates a location constraint of at least one object in an image, and the first image is an image obtained by performing noise addition using a noise addition module in a diffusion model; processing the text information based on a text encoder to obtain a first feature representation; and obtaining a second image based on a fusion result of the first image and the first feature representation by using a denoising model in the diffusion model, where an object included in the second image meets the location constraint indicated by the text information.