Instructional Image Editing With Human Feedback for Prompt Alignment
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Solution Overview
Problem
Current image editing models struggle to consistently align images with text prompts, resulting in images that fail to adhere to user instructions.
Innovation Solution
An instructional image editing framework that employs human feedback to fine-tune image editing models, using human annotation to train a reward model which predicts a reward score for the alignment between images and text instructions, and then fine-tunes a diffusion model based on this score to generate images that better align with user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If current image editing models are used to generate images from text prompts, then image generation is achieved, but the alignment between the generated images and text prompts is inconsistent and poor
Solution Approach 1:
The patent implements a feedback mechanism where human annotators evaluate the alignment between generated images and text prompts, providing reward scores that are used to fine-tune the diffusion model. This closed-loop feedback system continuously improves alignment precision by adjusting model parameters based on evaluation results, directly resolving the contradiction between achieving image generation and ensuring consistent prompt alignment.
2Manufacturing precision
If human feedback is used to fine-tune the image editing model, then alignment between images and text prompts is improved, but computational resources and training time are increased
Solution Approach 1:
The patent applies partial action by using a subset of training data and selective fine-tuning rather than complete retraining. The diffusion model is fine-tuned on specific datasets with human annotations focused on alignment tasks, rather than processing all possible training data. This approach achieves improved alignment precision while limiting computational resource consumption to only the necessary portions of training.
3Reliability
If larger datasets are used to train the image editing model, then model performance is improved, but training time and computational requirements are increased
Solution Approach 1:
The patent employs preliminary action by pre-processing and curating training datasets before fine-tuning the diffusion model. Human annotations and alignment evaluations are prepared in advance, creating a ready-to-use training corpus that accelerates the fine-tuning process. This preliminary preparation allows the model to achieve better performance without proportionally increasing training time, as the data is optimized for efficient learning.
Data Source
AI summary
Embodiments described herein provide a feedback based instructional image editing framework that employs a diffusion process to follow user instruction for image editing. A diffusion model is fine-tuned using a reward model, which may be trained via human annotation. The training of the reward model may be done by having the image editing model output a number of images, which a human annotator ranks based on their alignment with the original image and a given instruction.