Dependency Installation Hollowstrawberry Kohya Colab Deepwiki

Leo Migdal

-Nov 17, 2025, 1:25 AM

dependency installation hollowstrawberry kohya colab deepwiki

This page documents the technical process of installing and configuring training dependencies in the kohya-colab notebooks. The installation process clones the kohya-ss training framework, applies runtime patches, and configures the Python environment for LoRA training. For information about the wrapper scripts that interface with these dependencies, see Wrapper Scripts. For environment-related troubleshooting, see Environment Problems. The dependency installation process differs between the Standard SD 1.5 trainer and the SDXL trainer, but both follow a similar pattern: clone a kohya-based training repository, install Python packages, apply runtime patches, and configure... Installation occurs once per Colab session and is tracked via the dependencies_installed global flag.

Sources: Lora_Trainer_XL.ipynb69-70 Lora_Trainer.ipynb60-61 Sources: Lora_Trainer.ipynb232-266 Lora_Trainer_XL.ipynb331-362 This page documents common environment issues that may occur when running the kohya-colab notebooks in Google Colab, including Google Drive mounting failures, dependency installation problems, and runtime configuration issues. For dataset-related errors, see Dataset Issues. For problems that occur during the training process itself, see Training Errors. The kohya-colab notebooks require a complex environment setup involving multiple stages: Google Drive mounting, system package installation, Python dependency installation, repository cloning, and runtime patching.

Understanding this sequence helps diagnose where failures occur. Sources: Lora_Trainer_XL.ipynb331-361 Lora_Trainer.ipynb232-266 Lora_Trainer_XL.ipynb713-766 Lora_Trainer.ipynb518-558 The notebooks require Google Drive to be mounted at /content/drive to access datasets and save outputs. Mount failures prevent the entire workflow from proceeding. Sources: Lora_Trainer_XL.ipynb716-719 Lora_Trainer.ipynb521-524 This page provides a quick start guide for new users of the kohya-colab repository.

It covers the prerequisites, initial setup steps, and the basic workflow for preparing a dataset and running your first LoRA training session. For detailed information about dataset preparation techniques, see Dataset Preparation. For comprehensive training configuration options, see LoRA Training. Before beginning, ensure you have the following: The repository provides multiple notebook entry points hosted on Google Colab. Each notebook can be opened directly in your browser:

Alternatively, you can access notebooks directly via Colab URLs formatted as: All notebooks require Google Drive access for persistent storage of datasets, configurations, and trained models. The mounting process is automatic when you run the first cell of any notebook. This page documents the AI models used for automated image tagging and captioning in the kohya-colab dataset preparation pipeline. These models are accessed via HuggingFace Hub and integrated through kohya-ss/sd-scripts wrapper scripts. For information about base models used for training (SDXL, Pony Diffusion, etc.), see Model Management.

For information about the FiftyOne duplicate detection system, see Image Processing. The kohya-colab system integrates two categories of AI models during the dataset preparation phase: Both model types run during Step 4 of the Dataset Maker workflow and generate .txt files containing tags or captions alongside each image. The WD14 (Waifu Diffusion 1.4) Tagger is a specialized model trained on anime-style images to predict Danbooru-style tags. Two model variants are available: The Dataset Preparation system provides an end-to-end workflow for acquiring, curating, and annotating image datasets for LoRA training.

This document covers the overall architecture and workflow of the Dataset Maker notebooks. For specific details on individual phases, see Image Acquisition, Duplicate Detection and Curation, Image Tagging and Captioning, and Tag Management. For information about using prepared datasets in training, see LoRA Training. The Dataset Maker notebooks (Dataset_Maker.ipynb Spanish_Dataset_Maker.ipynb) automate the process of creating high-quality datasets for training LoRA models. The system handles: The output is a collection of images with corresponding text files (captions/tags) ready for consumption by the training notebooks.

Sources: Dataset_Maker.ipynb1-100 Spanish_Dataset_Maker.ipynb1-100 The Dataset Maker follows a sequential cell execution model where each step (step1_installed_flag, step2_installed_flag, etc.) must complete before subsequent steps can run. This gating mechanism prevents users from executing steps out of order. This document describes the licensing terms that govern the kohya-colab repository. It covers the GNU General Public License version 3 (GPL v3) under which this codebase is released, explains the practical implications for users and contributors, and outlines compliance requirements when modifying or redistributing the... For information about the external dependencies and their respective licenses, see External Dependencies.

For attribution and credits to projects this repository builds upon, see Credits and Attribution. The kohya-colab repository is distributed under the GNU General Public License version 3 (GPL v3), a copyleft license that guarantees end users the freedom to run, study, share, and modify the software. The complete license text is located in LICENSE1-675 The GPL v3 license applies to all original code and content in the kohya-colab repository, including: Sources: LICENSE10-69 LICENSE154-177 LICENSE589-620 This page acknowledges the projects, developers, and open-source software that the kohya-colab repository builds upon.

The notebooks in this repository serve as an accessible interface to powerful training frameworks and tools developed by others in the machine learning community. For information about the repository's license terms, see License. For details on external dependencies and their technical integration, see External Dependencies. The kohya-colab repository is fundamentally built on top of work from several key projects and developers. The repository does not implement training algorithms from scratch; instead, it provides Colab-optimized interfaces to existing frameworks. Sources: Lora_Trainer_XL.ipynb13-17 README.md1-3

Sources: Lora_Trainer_XL.ipynb13-17 README.md3 This document explains the directory organization system used across all kohya-colab notebooks. The repository supports two distinct folder organization modes that affect where datasets, outputs, configurations, and logs are stored in Google Drive. Understanding these conventions is essential for proper dataset preparation and training workflow. For information about dataset configuration (multi-folder datasets, repeats, regularization), see Dataset Configuration. For information about output file naming and epoch management, see Training Configuration.

The kohya-colab system provides two mutually exclusive folder organization strategies that users must choose when starting any notebook. This choice affects the entire directory hierarchy and must remain consistent across Dataset Maker and LoRA Trainer notebooks for the same project. The mode is selected via a dropdown parameter in every notebook's main cell and is evaluated using a simple string matching pattern: Sources: Lora_Trainer_XL.ipynb103 Lora_Trainer_XL.ipynb314-325 Lora_Trainer.ipynb99 Lora_Trainer.ipynb215-226 Dataset_Maker.ipynb68 Dataset_Maker.ipynb84-93 Accessible Google Colab notebooks for Stable Diffusion Lora training, based on the work of kohya-ss and Linaqruf. If you need support I now have a public Discord server

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Dependency Installation Hollowstrawberry Kohya Colab Deepwiki

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This Page Documents The Technical Process Of Installing And Configuring

Sources: Lora_Trainer_XL.ipynb69-70 Lora_Trainer.ipynb60-61 Sources: Lora_Trainer.ipynb232-266 Lora_Trainer_XL.ipynb331-362 This Page Documents Common

Understanding This Sequence Helps Diagnose Where Failures Occur. Sources: Lora_Trainer_XL.ipynb331-361

It Covers The Prerequisites, Initial Setup Steps, And The Basic

Alternatively, You Can Access Notebooks Directly Via Colab URLs Formatted