Diffusion Effect Transfer Using Reference Embeddings for Detail Preservation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image generation models struggle with accurately applying complex image effects to input images, often failing to maintain content and detail due to challenges in describing effects with text prompts and difficulties in extracting image effects from reference images.
Innovation Solution
A method and system that utilizes a pair of reference effect images to generate a synthetic image by training an effect encoder to create an effect embedding, which guides an image generator and upsampler to produce a high-resolution synthetic image, preserving the input image's content and details while applying the desired image effect.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image generation models use text prompts to describe image effects, then the model can generate synthetic images, but the accuracy of effect application deteriorates due to difficulty in describing effects with text
Solution Approach 1:
The patent introduces reference effect images as an intermediary between the text prompt and the final synthetic image. The reference effect images serve as a bridge that captures complex visual effects more accurately than text alone, allowing the model to learn and apply effects through visual examples rather than relying solely on textual descriptions.
Solution Approach 2:
The patent uses reference effect images that copy and represent the target image effect. By providing visual copies of the desired effect applied to reference images, the model can directly learn from these visual examples and replicate the effects more accurately in the generated synthetic images.
2Measurement precision
If conventional models extract image effects from reference images, then effect application is possible, but the extraction accuracy deteriorates due to difficulty in extracting image effects
Solution Approach 1:
The patent implements a feedback mechanism where the model iteratively refines its extraction of image effects from reference images. The extracted effects are used to generate synthetic images, which are then compared against the original reference effect images, allowing the model to learn from discrepancies and improve its extraction accuracy over time.
Solution Approach 2:
The patent performs preliminary extraction of image effects from reference images before generating the final synthetic image. This preliminary action allows the model to prepare and refine its understanding of the desired effects in advance, improving the overall accuracy of effect application in the final output.
3Adaptability or versatility
If the model generates synthetic images with applied effects, then the image effect is transferred, but content and detail are lost
Solution Approach 1:
The patent segments the image generation process into distinct stages: extracting image effects from reference images, generating synthetic images with these effects, and preserving original content. By separating the effect application from the content generation, the model can maintain the integrity of the original image content while applying the desired effects.
Solution Approach 2:
The patent applies different quality characteristics to different parts of the generated image. The local quality of the original image content is preserved in regions where the input image provides detailed information, while the image effect is applied globally or in specific regions where it enhances the overall appearance without compromising the underlying content.
4Loss of information
If the model maintains content and detail during effect application, then information is preserved, but image quality and resolution deteriorate
Solution Approach 1:
The patent operates in multiple dimensional spaces simultaneously - preserving the 2D content and detail information from the original image while adding a new dimension for high-resolution effect application. The model learns to maintain the original image's content and detail in one dimension while enhancing resolution and quality in another dimension through the effect application process.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining an input image and a reference effect prompt, where the reference effect prompt indicates an image effect for the input image, generating an intermediate image based on the reference effect prompt, where the intermediate image depicts the image effect applied to the input image, and generating a synthetic image based on the input image, the reference effect prompt, and the intermediate image, where the synthetic image depicts the image effect applied to the input image and has a higher resolution than the intermediate image.