bmaltais/kohya_ss

Features

• Generative Model Fine-Tuning - Provides a comprehensive graphical workbench for fine-tuning generative diffusion models.
• Model Fine-Tuning - Provides a comprehensive suite for fine-tuning Stable Diffusion models with custom images and captions.
• Custom Diffusion Model Training - Ships a full suite of tools for configuring optimizers and datasets to train diffusion-based neural networks.
• Dataset Preparation Tools - Includes utilities for organizing images into buckets and managing caption tokens for generative AI datasets.
• Stable Diffusion Web Interfaces - Provides a graphical web interface for fine-tuning Stable Diffusion models using custom hyperparameters.
• GPU-Accelerated Training Workbenches - Provides a managed workbench for allocating VRAM and optimizing compute resources during large-scale model training.
• LoRA Training - Implements specialized tools for creating Low-Rank Adaptation weights to inject styles or characters into models.
• Low-Rank Adaptation - Enables parameter-efficient fine-tuning using low-rank adaptation to customize models with minimal hardware requirements.
• Memory Optimization - Minimizes VRAM consumption using techniques like gradient checkpointing and caching to prevent out-of-memory errors.
• Model Training Interfaces - Provides a browser-based graphical interface for configuring and executing diffusion model training scripts.
• Spatial Control Interfaces - Provides a specialized interface for training lightweight ControlNet spatial control networks.
• Lightweight Control Networks - Supports the training of lightweight ControlNet-LLLite networks using image pairs for spatial control.
• Spatial Control Networks - Provides specialized tooling for training ControlNet spatial control networks using image pairs.
• Training Optimizations - Implements training optimizations to manage VRAM and batch sizes for training on consumer hardware.
• VRAM & Compute Optimization - Deno-based balancing of memory usage and processing load via optimizer selection and batch size adjustments.
• Web-Based Configuration Dashboards - Provides a browser-based dashboard for managing training settings and configuring model hyperparameters.
• Aspect Ratio Bucketing - Implements image bucketing to maintain spatial proportions and minimize padding during the dataset preparation process.
• Caption Augmentation Strategies - Provides controls for shuffling word order and dropping captions to influence how the model learns from text descriptions.
• Image Augmentation - Increases dataset variety by applying random image flipping and color hue alterations during the training process.
• Hardware Resource Allocation - Deno-based definition of specific GPU hardware identifiers to be used during the training process.
• Gradient Checkpointing - Implements memory-saving techniques that recompute intermediate activations during backpropagation to reduce VRAM usage.
• Learning Rate Schedulers - Provides control over learning rates through various schedulers and optimizer algorithms.
• Training Utilities - Ships built-in tools for preparing training data and organizing directories for optimal training.
• Training Checkpointers - Provides mechanisms for saving and resuming model training states to ensure session continuity.
• Training Progress Monitors - Features a GPU-accelerated visualization dashboard for real-time monitoring of training metrics and logs.
• In-Training Sample Generation - Produces sample images using active weights and specific prompts during training to assess progress.
• XPU Accelerators - Provides a configuration layer that enables training on Intel GPUs via XPU-accelerated libraries.
• High Resolution Training - Enables fine-tuning of high-resolution generative models using specific resolution settings and network constraints.
• Caption Dataset Utilities - Manages image datasets by organizing them into size-based buckets and handling caption token lengths.
• GPU Device Assignment - Ships an interface for assigning specific GPU hardware identifiers to the training process.
• Intel Hardware Acceleration - Includes specific environment configurations and support for Intel GPUs via XPU-accelerated libraries.
• Resource Allocation - Allows for the allocation of GPU resources and precision levels to maximize compute performance.
• Compute Environment Toggles - Provides toggles to optimize performance across different hardware architectures and compute environments.

Historique des stars