Akegarasu/lora-scripts
Lora Scripts
lora-scripts 是一个微调工具包，旨在将基础扩散模型适配到特定的风格或主题。它提供了一套专门的脚本和工具，用于执行低秩自适应（LoRA）和 Dreambooth 训练任务。
该项目具有一个基于 Web 的图形界面，用于管理训练工作流，允许用户在无需手动编辑脚本的情况下配置和执行任务。此界面将用户输入映射到超参数，并提供一个实时仪表板，用于监控训练指标和损失曲线以跟踪模型收敛情况。
该系统包括一个用于组织和编辑图像标签的数据集标记管理器。为了确保在不同硬件主机上执行的一致性，训练环境以预配置的容器化设置提供。
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.
