Akegarasu/lora-scripts
Lora Scripts
lora-scripts is a fine-tuning toolkit designed for adapting base diffusion models to specific styles or subjects. It provides a specialized set of scripts and tools for executing low-rank adaptation and Dreambooth training jobs.
The project features a web-based graphical interface that manages the training workflow, allowing users to configure and execute jobs without manual script editing. This interface maps user inputs to hyperparameters and provides a real-time dashboard for monitoring training metrics and loss curves to track model convergence.
The system includes a dataset tagging manager for organizing and editing image labels. To ensure consistent execution across different hardware hosts, the training environment is provided as a pre-configured containerized setup.
Features
- Custom Stable Diffusion Training - Provides a complete workflow for training custom Stable Diffusion models to recognize specific entities or styles.
- LoRA Fine-Tuning Tools - Ships a comprehensive set of scripts and tools for low-rank adaptation (LoRA) of diffusion models.
- Diffusion Model Training - Implements specialized fine-tuning logic for diffusion models via standalone Python scripts.
- Diffusion Model LoRA Fine-Tuning - Provides a web interface for configuring and executing LoRA and Dreambooth fine-tuning jobs for diffusion models.
- Model Training Management Interfaces - Features a web-based dashboard for configuring hyperparameters and triggering training runs without manual scripting.
- Web-Based Command Interfaces - Translates user inputs from a graphical dashboard into command-line arguments for training scripts.
- Automated Image Tagging - Automates the labeling of image datasets using specialized vision models and editing tools.
- Automated Processing Pipelines - Provides an automated pipeline to process raw images and generate structured metadata labels for model training.
- Dataset Tagging Managers - Includes a dedicated tagging manager for organizing and editing image labels used in training.
- Image Classification Datasets - Organizes and structures image datasets with labels for training image recognition and generation models.
- Model Training Monitoring - Tracks and visualizes model convergence and loss curves in real time to monitor training quality.
- Dataset Labeling Interfaces - Provides integrated tools for tagging and editing dataset labels to improve training accuracy.
- Training Progress Monitors - Provides a real-time dashboard for visualizing training metrics and loss curves to track model convergence.
- Item Tagging and Organization - Includes built-in editors and automated processes for organizing and managing image tags in datasets.
- Real-Time Metric Visualization - Features a web dashboard that renders live convergence curves by polling training logs and loss data.
