Diffusion Image Translation Without Retraining Across Domains
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Solution Overview
Problem
Conventional Generative Adversarial Networks (GANs) require retraining when the domain to be translated changes, and they produce low-quality translated images.
Innovation Solution
A diffusion model-based system and method that utilizes a deep learning computer vision algorithm for image translation without retraining, using a text-to-image generation diffusion model with a text encoder, U-Net for denoising, and an image decoder, and employs a Denoising Diffusion Implicit Model (DDIM) process to generate high-quality images by preserving context information with a segmentation mask.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If Generative Adversarial Network (GAN) is used for image translation, then generated images closely mimic the distribution of real images, but retraining is required when domain changes and translation quality is low
Solution Approach 1:
The patent changes the fundamental parameters of the translation system by switching from GAN architecture to diffusion model architecture, and from direct image-to-image translation to text-prompt guided translation. This parameter change enables the system to achieve both high translation quality and domain versatility without retraining, as the diffusion model can adapt to different domains through text prompt modifications rather than architectural retraining.
Solution Approach 2:
The patent introduces text prompts as an intermediary between the source image and target image translation. Instead of direct GAN-based image-to-image translation, the system uses text descriptions as a mediator that guides the diffusion model to generate translations across different domains. This intermediary enables flexible domain adaptation without requiring model retraining.
2Manufacturing precision
If GAN retraining is performed for domain changes, then translation quality may improve, but time consumption and computational cost increase
Solution Approach 1:
The patent performs preliminary action by pre-training a diffusion model on a large dataset (e.g., LAION-5B) that encompasses multiple domains. This pre-training enables the model to learn general translation capabilities that can be applied to any domain without retraining. When domain changes occur, the system simply modifies text prompts rather than retraining the model, thus eliminating retraining time while maintaining high translation quality.
3Manufacturing precision
If diffusion model is trained with large dataset, then translation quality improves, but training complexity and computational resources increase
Solution Approach 1:
The patent segments the translation process into distinct components: (1) pre-training phase on large dataset to learn general patterns, (2) inference phase where text prompts guide specific domain translations. This segmentation allows the complex task of multi-domain translation to be divided into a one-time pre-training step followed by simple prompt-based inference, reducing ongoing training complexity while maintaining high quality.
Solution Approach 2:
The patent creates a universal diffusion model that can perform translation across multiple domains simultaneously through text prompt guidance. Instead of training separate models for different domains (increasing complexity), a single pre-trained model handles all domains by interpreting different text prompts, thus achieving multi-functionality without proportional increases in training complexity.
Data Source
AI summary
Disclosed are a diffusion-based image translation system and method without requiring retraining. The diffusion-based image translation method without requiring retraining includes (a) generating a prompt using information obtained from an image dataset, (b) training a text-to-image generation diffusion model using the prompt, and (c) performing image translation using the text-to-image generation diffusion model.