Conversational Image Generation System for Iterative Refinement
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Solution Overview
Problem
Current image generation solutions, such as text-to-image models, have a high abstract usage barrier, are inefficient, and do not support multiple rounds of interaction, making it difficult to meet user requirements effectively.
Innovation Solution
A conversational image generating method that allows users to interact with an image processing system in multiple rounds using natural language, determining user requirements and executing corresponding action sequences to generate or edit images without the need for specialized knowledge or learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If text-to-image models are used for image generation, then image generation capability is achieved, but the usage barrier becomes high and efficiency decreases
Solution Approach 1:
The patent introduces a conversational interface as an intermediary between the user and the text-to-image model. Users interact through natural language dialogue rather than directly configuring model parameters, which simplifies the usage process while maintaining access to powerful image generation capabilities. The conversation system translates user intents into appropriate model calls, bridging the gap between user capability and model complexity.
Solution Approach 2:
The system performs automatic understanding of user requirements through conversation analysis, eliminating the need for users to manually adjust technical parameters. The model automatically adapts to user needs by interpreting dialogue context and generating appropriate images without requiring user expertise in image generation technology.
2Adaptability or versatility
If traditional image generation models are used, then single-round image generation is possible, but multi-round interaction capability is lost
Solution Approach 1:
The patent implements a feedback mechanism where the system presents generated images to users and receives their responses through continued dialogue. Users can provide feedback such as requesting modifications, asking for variations, or expressing satisfaction, allowing the system to iteratively refine images based on user responses. This creates a multi-round interaction loop that enables progressive improvement of generated images.
Solution Approach 2:
The system transitions from static single-round generation to dynamic multi-round interaction. The conversation-based interface allows the system to adapt its behavior based on dialogue history, user preferences, and feedback received during the interaction process, making the image generation process flexible and responsive to changing user requirements.
Data Source
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AI summary
A computer-implemented image generating method includes: obtaining current dialogue data; determining a requirement type of the user in the current round of dialogue based on the current dialogue data; in response to the requirement type being an image processing requirement, determining an action sequence for implementing the image processing requirement; executing the action sequence to generate a target image; and generating response data corresponding to the user input data based on the target image.