Diffusion Prior Image Editing With Text-Guided Conceptual Control
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Solution Overview
Problem
Conventional digital image editing systems are inefficient, inflexible, and inaccurate, requiring additional inputs, fine-tuning, and optimization processes, often leading to unrealistic or inaccurate image generation.
Innovation Solution
The diffusion prior image editing system utilizes a diffusion prior neural network for text-guided image editing, performing conceptual and structural edits without additional inputs or model fine-tuning, using a trained text-image encoder and diffusion models to generate modified images efficiently and flexibly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional generative models are used for image editing, then image generation capability is achieved, but flexibility and accuracy are insufficient
Solution Approach 1:
The patent introduces a text encoder as an intermediary component that converts text prompts into embedding vectors, which then guide the diffusion model's generation process. This mediator enables flexible text-controlled editing while maintaining accuracy through the structured embedding space that captures semantic relationships between text and image features.
Solution Approach 2:
The patent utilizes parameter changes in the diffusion model by adjusting the text embedding weights and guidance scales during generation. By dynamically modifying these parameters based on the input text prompt, the system achieves both flexibility in responding to different text inputs and accuracy in generating semantically consistent images.
2Measurement precision
If additional inputs and fine-tuning are applied to improve image editing accuracy, then editing precision increases, but processing efficiency and simplicity decrease
Solution Approach 1:
The patent performs preliminary action by pre-training the text encoder and diffusion model on large-scale datasets before deployment. This pre-training establishes robust text-to-image mappings and semantic understanding, enabling the system to achieve high editing precision without requiring additional fine-tuning or optimization during actual image editing operations, thus maintaining processing efficiency.
3Adaptability or versatility
If conventional systems require fine-tuning and optimization, then model adaptability improves, but computational complexity and time increase
Solution Approach 1:
The patent implements self-service through the diffusion model's inherent ability to generate images from text prompts without requiring external fine-tuning or optimization processes. The model adapts to different editing tasks automatically by processing the text embedding inputs through its pre-trained parameters, eliminating the need for complex computational adjustments and reducing overall system complexity.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing a diffusion prior neural network for text guided digital image editing. For example, in one or more embodiments the disclosed systems utilize a text-image encoder to generate a base image embedding from the base digital image and an edit text embedding from edit text. Moreover, the disclosed systems utilize a diffusion prior neural network to generate a text-image embedding. In particular, the disclosed systems inject the base image embedding at a conceptual editing step of the diffusion prior neural network and condition a set of steps of the diffusion prior neural network after the conceptual editing step utilizing the edit text embedding. Furthermore, the disclosed systems utilize a diffusion neural network to create a modified digital image from the text-edited image embedding and the base image embedding.