Mean-Shift Image Normalization for Scene-Harmonized Generation
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Solution Overview
Problem
Conventional image generation models struggle to accurately incorporate new elements into an existing scene, resulting in images where the new elements are inconsistent with the semantics of the original image, often standing out due to poor normalization.
Innovation Solution
The proposed image generation model performs a channel shift process, guided by mask regions in the input image, to harmonize new elements with the existing scene, using a normalization process that ensures the noise predicted by classifier-free guidance falls within the expected distribution, enhancing the harmonization of image elements with the background.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image generation models are used to incorporate new elements into an existing scene, then the generation process is simple, but the new elements are inconsistent with the semantics of the original image and stand out due to poor normalization
Solution Approach 1:
The patent applies preliminary action by computing the mean value of the input image before the generation process and using it to normalize the predicted noise. This pre-computed normalization parameter ensures that the new elements are harmonized with the existing scene from the outset, preventing inconsistency rather than correcting it afterward.
Solution Approach 2:
The patent changes the normalization parameter by using the mean value of the input image to normalize the predicted noise. This parameter adjustment ensures that the statistical properties of the generated elements match those of the existing scene, achieving harmonization through parameter alignment rather than complex structural modifications.
2Manufacturing precision
If the image generation model performs channel shift process with mean value computation, then the new elements are harmonized with the existing scene, but the computational complexity increases
Solution Approach 1:
The patent extracts only the mean value from the input image for normalization purposes, rather than processing the entire image complexity. This extraction of the essential normalization parameter reduces computational requirements while maintaining harmonization accuracy.
Solution Approach 2:
The patent simplifies the computational burden by changing from complex multi-parameter normalization to a single mean-value-based normalization approach. This parameter simplification maintains the essential harmonization function while reducing computational power requirements.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for image generation includes obtaining an input image and an input prompt. In some cases, the input image depicts a scene and the input prompt indicates a target element to be added to the scene. The image generation model generates a normalized output based on the input image and the input prompt by performing a channel shift on a preliminary output of the image generation model. A synthetic image is generated including the scene of the input image and the target element of the input prompt that is harmonized with the scene.