Synth-SONAR
Synth-SONAR is an innovative framework for generating sonar images using advanced text-to-image diffusion models. By integrating style injection techniques and GPT prompting, Synth-SONAR enhances diversity and realism in sonar image synthesis.
Key Features:
- Controllable Image Generation: Users can guide the image creation process through text prompts, leading to more tailored and contextually appropriate outputs.
- Enhanced Diversity through Style Injection: Combines various stylistic elements into generated images for richer content.
- Dual Diffusion Models: Deploys two diffusion processes to improve the quality and detail of generated images.
- GPT Integration for Text Conditioning: Leverages GPT prompting to refine the alignment between generated images and user specifications.
Benefits:
- High-Quality Outputs: Combines multiple advanced techniques to ensure produced sonar images are realistic and detailed.
- User-Friendly: Easy setup with steps for creating environments, installing dependencies, and running generation scripts.
- Flexible Training Options: Offers both standard fine-tuning and LoRA fine-tuning options based on user preferences and computational resources.
Highlights:
- Three Core Phases: Synth-SONAR's architecture includes phases for data acquisition, image generation, and fine-tuned training, ensuring a comprehensive and effective synthesis process.
- Custom Metadata Creation: Tools provided for generating and managing metadata, which aids in improving the model's training and output accuracy.
