Diffusion Image Generation With Saliency Noise Spatial Control
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Solution Overview
Problem
Conventional image processing systems struggle to generate new images of a target object with desired spatial attributes such as position, rotation, or pose, and fail to effectively incorporate user control or commands, leading to inefficiencies and high computational overhead.
Innovation Solution
An image processing apparatus that utilizes an inverting gradients model to generate object saliency noise based on a reference image, combined with a denoising diffusion probabilistic model (DDPM) to produce output images with targeted spatial attributes, allowing users to control these attributes through adjustments in the reference image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image processing systems are used to generate images, then image generation can be performed, but the systems cannot generate images with desired spatial attributes such as position, rotation, or pose
Solution Approach 1:
The system performs preliminary action by generating object saliency noise from the reference image before the main image generation process. This pre-processed noise contains encoded spatial attribute information (position, rotation, pose) that guides the diffusion model to produce images with desired spatial properties, thereby achieving precise spatial attribute control in the generated images
2Manufacturing precision
If diffusion models are used for conditional image generation, then images with certain attributes can be generated, but computational overhead is high
Solution Approach 1:
The system extracts only the essential spatial attribute information from the reference image by generating object saliency noise, separating this guidance information from the full image data. The diffusion model then processes only this compact noise representation along with class labels, significantly reducing computational overhead while maintaining accurate control over spatial attributes in the generated images
3Ease of operation
If users want to control target attributes in generated images, then they can adjust reference images, but the process requires complex system configuration
Solution Approach 1:
The system uses copying by generating object saliency noise that replicates the spatial attribute patterns from the reference image. Users simply need to provide a reference image with desired spatial arrangements, and the system automatically copies these spatial patterns into the generated images through the noise generation and diffusion process, making user control intuitive and eliminating complex configuration requirements
Data Source
AI summary
Systems and methods for image processing are described. Embodiments of the present disclosure receive a reference image depicting a reference object with a target spatial attribute; generate object saliency noise based on the reference image by updating random noise to resemble the reference image; and generate an output image based on the object saliency noise, wherein the output image depicts an output object with the target spatial attribute.