Guidance Embedding Interpolation for Diverse Prompted Images
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Solution Overview
Problem
Existing image generation models struggle with limited diversity in generated images due to reliance on user-provided prompts and lack of effective methods for interpolating between prompt embeddings, leading to inconsistent results based on user expertise.
Innovation Solution
An image generation system that incorporates a diversity parameter to control the level of adherence to a prompt, using a prompt encoder to interpolate between prompt and expanded prompt embeddings, and combines these with a noise tensor to generate synthetic images with varied levels of diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system strictly adheres to the user prompt, then the accuracy of prompt representation is improved, but the diversity of generated images deteriorates
Solution Approach 1:
The system dynamically adjusts the balance between prompt adherence and image diversity by allowing users to control the interpolation parameter. The guidance embedding is not fixed but can be adjusted along a continuum from strict prompt following to more diverse interpretations, making the system adaptable to different user needs.
Solution Approach 2:
The invention changes the parameter of prompt embedding by interpolating between the original prompt embedding and an expanded prompt embedding. This parameter adjustment allows the system to generate images with varying degrees of diversity while maintaining control through the interpolation factor.
2Productivity
If the system uses simple prompt encoding, then the computational efficiency is improved, but the quality and variability of generated images deteriorates
Solution Approach 1:
The system performs preliminary action by pre-computing both the original prompt embedding and the expanded prompt embedding before image generation. This allows the interpolation to be done efficiently during generation without compromising quality, as the complex embedding work is already completed.
Solution Approach 2:
The invention introduces an intermediary mechanism - the interpolated guidance embedding - that mediates between simple prompt encoding and complex diverse image generation. This intermediary allows the system to achieve both efficiency and quality by blending information from both simple and expanded prompts.
3Adaptability or versatility
If the system requires iterative prompt adjustments, then the diversity of results is improved, but the time required for generation increases
Solution Approach 1:
The system performs the diversity adjustment in advance by computing the expanded prompt embedding and setting up the interpolation parameter. This eliminates the need for iterative adjustments during generation, as the diversity control is already configured before the actual image synthesis begins.
Solution Approach 2:
The invention segments the prompt processing into two distinct components: the original prompt embedding and the expanded prompt embedding. By separating these and allowing independent control through interpolation, the system achieves diversity without requiring multiple iterative generations.
Data Source
AI summary
Embodiments include obtaining a prompt and a diversity input indicating a level of adherence to the prompt. The diversity input may be implemented as a graphical user interface (GUI) element, such as a slider or field. Embodiments then generate a guidance embedding based on the prompt and the diversity input. Embodiments update the guidance embedding based on the diversity input. Subsequently, embodiments generate a synthetic image based on the guidance embedding, wherein the synthetic image depicts an element of the prompt based on the level of adherence from the diversity input.