Diffusion Image Synthesis With Independent Color and Style Control
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Solution Overview
Problem
Conventional image editing tools require extensive training and resources to independently control color and style attributes of reference images, leading to decreased user experience and content quality.
Innovation Solution
A training-free image processing apparatus that uses diffusion models to generate separate color and style conditioned images, allowing independent control over attributes by combining them to form a synthetic image, using time-step constrained transformations and self-attention feature manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image editing tools are used to independently control color and style attributes, then manufacturing precision of image attributes is improved, but device complexity and training resources increase
Solution Approach 1:
The patent segments the image generation process into two independent diffusion models: one dedicated to color attribute control and another dedicated to style attribute control. This segmentation allows each model to specialize in specific attributes, achieving independent control without requiring a single complex model to handle all attributes simultaneously, thereby reducing overall system complexity and training requirements.
Solution Approach 2:
The patent creates a universal framework where two specialized diffusion models work together to achieve multi-attribute control. Each model serves a specific function (color or style) but together they provide comprehensive control over multiple image attributes, eliminating the need for separate tools or extensive retraining for different attribute combinations.
2Adaptability or versatility
If extensive training is performed to control multiple attributes, then adaptability of image generation is improved, but loss of time and computational resources increases
Solution Approach 1:
The patent performs preliminary action by pre-training two specialized diffusion models on specific attributes (color and style) before actual image generation. This preliminary specialization allows the models to be rapidly deployed for attribute-controlled generation without requiring extensive retraining for each new attribute combination, significantly reducing time loss while maintaining high adaptability.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for image generation includes obtaining a color input indicating a color attribute, a style input indicating a style attribute, and a content input indicating an image element. A first image generation model generates a color conditioned image based on the color input and the content input, wherein the color conditioned image depicts the image element with the color attribute. A second image generation model generates a style conditioned image based on the style input and the content input, wherein the style conditioned image depicts the image element with the style attribute. A synthetic image is generated by combining the color conditioned image and the style conditioned image, wherein the synthetic image depicts the image element with the color attribute and the style attribute.