Controlling Depth Sensitivity in Conditional Text-to-Image Generation
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Solution Overview
Problem
Conventional image generation models struggle to accurately reflect depth information in synthetic images, often resulting in images that poorly depict the intended depth features due to predominant depth guidance.
Innovation Solution
A two-step process using first and second image generation models, where the first model generates an intermediate image based on a structure input and an adherence parameter, and the second model generates the output image based on the intermediate image and adherence parameter, effectively controlling depth features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single image generation model uses predominant depth guidance to generate synthetic images, then the depth control is simplified, but the accuracy of depth feature representation deteriorates
Solution Approach 1:
The patent divides the image generation process into two separate models: a first image generation model that generates intermediate images with structure guidance, and a second image generation model that generates final synthetic images with depth guidance. This segmentation allows each model to specialize in one type of guidance, resolving the contradiction between operational simplicity and depth feature accuracy.
Solution Approach 2:
The patent introduces an intermediate image as a mediator between the structure input and the final synthetic image. The first model generates this intermediate representation that captures structural information, which then serves as input for the second model that applies depth guidance. This intermediary step enables accurate depth feature representation without sacrificing operational simplicity.
2Adaptability or versatility
If a single image generation model attempts to balance both structure and depth guidance, then the model design becomes more complex, but the overall image generation capability improves
Solution Approach 1:
The patent segments the image generation task into two distinct models with specialized functions. The first model handles structure-guided generation while the second model handles depth-guided generation. This segmentation increases adaptability and versatility for different guidance types without requiring a single complex model to handle all aspects simultaneously.
Solution Approach 2:
The two-model system provides universal functionality by handling both structure guidance and depth guidance through separate specialized models. Each model can be independently trained and applied, making the overall system versatile for different image generation scenarios while keeping individual model designs relatively simple.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the generation of synthetic images that accurately reflect depth features, improving the quality and realism of the output images by not being predominantly conditioned on depth information.
Implementation Method 1
generating, using a first diffusion process, an intermediate output based on the condition input, wherein the first diffusion process is performed for a first number of timesteps based on the adherence parameter
Implementation Method 2
generating, using a second diffusion process, a synthetic image based on the intermediate output
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining a condition input and an adherence parameter, where the condition input indicates an image attribute and the adherence parameter indicates a level of the condition input, generating an intermediate output based on the condition input and the adherence parameter, where the intermediate output includes the image attribute, and generating a synthetic image based on the intermediate output, where the synthetic image includes the image attribute based on the level indicated by the adherence parameter.