Akegarasu/lora-scripts
Lora Scripts
lora-scripts ist ein Fine-Tuning-Toolkit, das für die Anpassung von Basis-Diffusionsmodellen an spezifische Stile oder Motive entwickelt wurde. Es bietet eine spezialisierte Reihe von Skripten und Tools zur Ausführung von Low-Rank-Adaptation- und Dreambooth-Trainingsjobs.
Das Projekt verfügt über eine webbasierte grafische Oberfläche, die den Trainings-Workflow verwaltet und es Benutzern ermöglicht, Jobs ohne manuelle Skriptbearbeitung zu konfigurieren und auszuführen. Diese Oberfläche bildet Benutzereingaben auf Hyperparameter ab und bietet ein Echtzeit-Dashboard zur Überwachung von Trainingsmetriken und Verlustkurven, um die Modellkonvergenz zu verfolgen.
Das System enthält einen Dataset-Tagging-Manager zum Organisieren und Bearbeiten von Bild-Labels. Um eine konsistente Ausführung über verschiedene Hardware-Hosts hinweg zu gewährleisten, wird die Trainingsumgebung als vorkonfiguriertes, containerisiertes Setup bereitgestellt.
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.
