Conditional Image Generation via Latent Diffusion Masking
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Solution Overview
Problem
Existing image generation techniques using diffusion models can produce variable results, making it time-consuming to achieve acceptable images for inpainting or outpainting, especially for users without familiarity with the process.
Innovation Solution
The method involves generating a condition image from an input image, applying noise to specific portions of the condition image to create a latent image, and then using a latent diffusion model to generate an image based on the latent image, allowing for efficient inpainting or outpainting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If diffusion models are used for image generation with inpainting or outpainting, then image generation capability is achieved, but results are variable and require significant user expertise and time to obtain acceptable results
Solution Approach 1:
The patent introduces an intermediary processing system that automatically generates masks and condition images from user inputs. This intermediary layer translates simple user interactions into the complex parameters required by diffusion models, eliminating the need for users to manually create masks or understand diffusion model parameters. The system mediates between user intent and model requirements, making the process accessible to non-experts while maintaining consistent results.
Solution Approach 2:
The patent performs preliminary actions by automatically generating masks and condition images before the actual diffusion model inference. The system pre-processes user inputs to create properly formatted condition images with appropriate masking, ensuring that the diffusion model receives optimally prepared inputs. This preliminary preparation eliminates the need for users to perform complex setup tasks and ensures consistent results from the start.
2Manufacturing precision
If users manually adjust parameters and retry generation to achieve acceptable results, then image quality can be improved, but time consumption increases significantly
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate high-quality condition images and masks without requiring user intervention or manual parameter adjustment. The automated pipeline processes user inputs, generates appropriate masks, creates condition images, and passes them to the diffusion model in a single operation. This self-service approach eliminates iterative manual adjustments while maintaining high image quality, significantly improving productivity.
3Manufacturing precision
If complex mask creation and condition image generation are required, then precise control over generation areas is achieved, but device complexity and operational difficulty increase
Solution Approach 1:
The patent merges multiple complex operations into a unified automated process. Instead of requiring users to separately create masks, generate condition images, and configure diffusion parameters, the system combines these operations into a single integrated pipeline. The mask generation and condition image creation are merged into an automated workflow that processes user inputs and generates all necessary components in sequence, maintaining precise control while eliminating process complexity.
Data Source
AI summary
Computer implemented methods for generating an image are described. In some embodiments the methods are applied to outpainting of an image. In some embodiments the methods are applied to inpainting of an image. A data processing system may be configured to perform one or both of the outpainting and inpainting. Non-transient or non-transitory computer-readable storage storing instructions for a data processing system are also described, which are configured to perform the methods for generating an image.