Loraモデル学習をGoogle Colabで作る方法解説。Kohya LoRA Dreambooth v14.1使用。【Stable Diffusion】
TLDRThis video tutorial introduces the process of creating a Lora model using Google Colab with the Kohya Lora Dreambooth v14.1 and Stable Diffusion. The guide covers mounting Google Drive, selecting the appropriate version of Stable Diffusion, downloading necessary models, and preparing the training data. It emphasizes the importance of diverse and high-quality images for effective model training, as well as the technical settings required for optimal learning outcomes. The video also touches on the use of automatic features for captioning and tagging, the selection of network modules, and the adjustment of various parameters like epoch numbers and batch sizes. Finally, it explains the post-training steps, including model saving and testing, offering a comprehensive guide for users to create their personalized Lora models.
Takeaways
- 📚 Title: A tutorial on creating a Lora model using Google Colab, with a focus on the Kohya Lora Dreambooth v14.1 and Stable Diffusion.
- 🔄 Update: The video discusses an update in the Lora model training process and aims to introduce the new method.
- 📈 Inheritance: About 70% of the previous model remains the same, so significant changes might not be immediately visible.
- 🔗 GitHub Link: A link to the GitHub page with the tutorial is provided in the video description for easy access.
- 💻 Google Colab: The tutorial is set up to be executed in Google Colab, with a step-by-step guide from mounting Google Drive.
- 💰 Subscription: A paid Colab subscription is recommended for a smoother experience, as it alleviates concerns about GPU usage limits.
- 🌐 Stable Diffusion: The video recommends using Stable Diffusion 1.5 for both animation and live-action projects.
- 📷 Training Data: The training data should consist of a variety of images zipped together, with no重复 poses, backgrounds, or clothing.
- 🎨 Background Transparency: An option to automatically add color to transparent backgrounds in PNG images is included.
- 🔢 Batch Size: The batch size should be divisible by the number of images in the training data, with a recommendation of 8 for this tutorial.
- 🔄 Epochs and Learning: The number of epochs for learning is suggested to be around 30, with adjustments based on personal preference and results.
Q & A
What is the main topic of the video?
-The main topic of the video is creating a Lora model using Google Colab, specifically with the Kohya Lora Dreambooth v14.1 and Stable Diffusion.
What update is mentioned in the video that has changed the items completely?
-The video does not specify the exact update, but it implies that there has been a significant update that has changed about 70% of the items since the last time the user looked at it.
How can one begin setting up the Lora model on Google Colab?
-To begin setting up the Lora model on Google Colab, the user should click the 'Open in Colab' link provided in the video description, which directs to a GitHub page with the necessary files.
What is the recommended version of Stable Diffusion for this tutorial?
-The recommended version of Stable Diffusion for this tutorial is 1.5, regardless of whether the user is working with animation or live-action content.
What are the requirements for the images used in the training data?
-The images used in the training data should not have the same pose, background, or clothing. They should be well-prepared and zipped together, with no need for concern over image size as the system will automatically adjust them.
What is the purpose of the 'second-rate' checkbox in the script?
-The 'second-rate' checkbox is used to automatically add color to the background transparency of images. It is especially useful if the images are in PNG format with a transparent background.
How often should the user save epochs during the training process?
-The user can save epochs based on their preference, but the video suggests saving every 5 epochs as a starting point. Adjustments can be made based on the user's needs and GPU capacity.
What is the significance of the 'class token' in the script?
-The 'class token' is used for learning specific elements within the images. In the context of the video, it is set to represent one girl for the learning process.
What happens after the Lora model training is completed?
-Once the Lora model training is completed, the learned model will be saved in the 'Lora output' folder in the user's Google Drive.
How can the user test the learned Lora model?
-The user can test the learned Lora model by using the testing file provided in the script. However, the video suggests that the user may not need this step and can move directly to the next part of the process.
Outlines
🚀 Introduction to Kohya Lora Training Tutorial
This paragraph introduces the kohya Lora training tutorial, highlighting the significant changes since the last update. It emphasizes the importance of understanding the new features and provides a step-by-step guide on setting up the tutorial on GitHub. The speaker also discusses the inheritance of previous features and the necessity of a paid subscription for a more comfortable experience. The paragraph details the process of executing commands in Colab, mounting Google Drive, and downloading the recommended Stable Diffusion 1.5 model. It also provides instructions on preparing the training data, emphasizing the need for variety in images to avoid redundancy and ensure effective learning. The paragraph concludes with advice on model selection and execution for optimal results.
📝 Customizing Training Data and Settings
This paragraph delves into the customization of training data and settings for the Lora model. It begins by discussing the option to manually add data and the importance of having detailed and specific captions for the training process. The speaker then addresses the selection of Stable Diffusion versions and the input of project names and model paths. The paragraph provides guidance on adjusting settings for proper noun training, selecting appropriate network modules, and determining the learning rate based on the type of content being studied. It also touches on the importance of noise offset in training configuration and the selection of epochs for saving progress. The paragraph concludes with a discussion on the training batch size, GPU RAM usage, and the recommendation to consult on machine specifications for larger projects.
🧠 Lora Training Execution and Testing
The final paragraph focuses on the execution and testing of the Lora training process. It outlines the use of default settings and the option for users to modify them according to their needs. The speaker explains the process of testing the learned Lora model and the use of WebUI for automatic testing. The paragraph also discusses the saving of the trained Lora model to Google Drive and the potential need for uploading the model to platforms like GitHub. The speaker shares their intention to provide a more comprehensive understanding in the subsequent parts of the series, encouraging viewers to watch the second part for further clarification. The paragraph ends with a farewell, summarizing the learning method and inviting viewers to continue with the series for a deeper understanding.
Mindmap
Keywords
💡Loraモデル
💡Google Colab
💡Stable Diffusion
💡VAE
💡Google Drive
💡トレーニングデータ
💡学習
💡エポック
💡バッチサイズ
💡ノイズオフセット
💡WebUI
Highlights
紹介的是使用Kohya LoRA Dreambooth v14.1在Google Colab上创建Lora模型的方法。
教程的GitHub页面链接会放在视频描述中，通过点击链接在Colab中开始操作。
将GitHub页面复制到Google Drive后，从上到下依次执行每个步骤。
Google Colab的免费账户可以用来执行操作，但推荐使用付费账户以避免担心GPU使用时间。
推荐使用的Stable Diffusion版本是1.5，无论是动画还是实拍都适用。
下载模型时，需要提供一个包含训练数据的zip文件路径。
准备的图片数量没有特别要求，但建议准备8到10张不同的图片。
为了避免模型学习到相同的背景，应确保每张图片的背景、服装和发型都有所不同。
如果图片是PNG格式且背景透明，需要勾选自动填充背景颜色的选项。
学习的数据量应是批量大小的倍数，这里推荐使用8。
可以根据需要手动添加标签和描述，以提高模型训练的准确性。
项目名称、模型路径和VAE路径需要根据实际情况进行设置。
学习过程中，可以选择输出样本以监控学习进度。
网络模块的选择会影响模型的学习效果，可以根据需要尝试不同的模块。
学习次数（epochs）的默认值是10，但建议设置为30左右以获得更好的效果。
训练批次大小（batch size）应根据GPU的RAM大小进行调整，避免出现错误。
保存模型的间隔可以设置为每n个epochs保存一次，推荐设置为5。
训练完成后，可以在Google Drive的指定位置找到训练好的Lora模型。
通过测试可以确认训练好的Lora模型的效果，如果需要进一步优化可以调整相关参数。