Diffusion Image Editing with Segmentation-Based Region Control
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Solution Overview
Problem
The processing speed of image generation is increased when a probabilistic process is performed individually for each region of the image in generative models, leading to inefficiencies in applying different generation strengths to various regions.
Innovation Solution
A method and electronic device that utilize a diffusion model to apply different image generation strengths to multiple regions in an image based on a segmentation map, using defined hyperparameters and classifier-free guidance (CFG) to combine conditional and unconditional predictions, thereby optimizing the image generation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a probabilistic process is performed individually for each region of the image to apply different generation strengths, then the image generation can reflect regional differences, but the processing speed decreases
Solution Approach 1:
The image is divided into multiple regions using a segmentation map, where each region can be assigned different generation strengths through region-specific hyperparameters (first and second hyperparameters). This allows selective application of generative processes to specific regions (e.g., object regions vs. background regions) rather than processing the entire image uniformly, thereby maintaining adaptability while reducing overall processing time.
Solution Approach 2:
Different generation strengths are applied to different regions based on their importance or type. The patent uses region-specific hyperparameters to control the degree of generative transformation locally - for example, applying stronger generation to background regions while maintaining weaker generation for object regions to preserve their identity. This local differentiation achieves regional control without requiring separate probabilistic processes for each region.
2Adaptability or versatility
If different image generation strengths are applied to multiple regions, then regional control is achieved, but the processing time increases
Solution Approach 1:
The patent combines multiple region-specific generative processes into a single unified diffusion process. Instead of executing separate probabilistic processes for each region sequentially, the system integrates region-specific hyperparameters into one joint diffusion model that processes all regions simultaneously. This merging approach maintains the ability to apply different generation strengths to different regions while significantly reducing processing time through parallel computation.
3Productivity
If a single diffusion process is used to apply different generation strengths to multiple regions, then processing time is reduced, but the complexity of controlling regional differences increases
Solution Approach 1:
The patent controls regional differences by varying hyperparameters (first and second hyperparameters) across different regions rather than creating complex structural variations. Each region is assigned specific hyperparameter values that control its generation strength, allowing the same diffusion process architecture to handle multiple regions with different characteristics. This parameter-based control simplifies the overall system complexity while maintaining productivity.
Data Source
AI summary
Provided is a method, performed by an electronic device, of editing an image, including obtaining an image, obtaining an edit prompt for the image, generating an edited image by using a diffusion model that uses the image and the edit prompt as input data, and outputting the edited image. The generating of the edited image comprises applying different image generation strengths to a plurality of regions in the image, based on a segmentation map representing the plurality of regions.