新しいLoRA学習のための拡張機能TrainTrainの紹介
TLDRThe video introduces a new extension called SDWEBUI Train Tray, created by Hamika, which allows users to train Stable Diffusion models directly from the web interface. The extension supports creating and training Lolas, a simplified version called Aleko, and copy-Lolas for specific features. The video provides a step-by-step guide on installation, setup, and the training process, emphasizing its ease of use and potential for streamlined Lola creation. The creator encourages viewers to try the extension and subscribe to the channel for more content.
Takeaways
- 📢 Introduction to a new extension called SDWEBUI Train Tray, developed by Hamika, which allows training on the stable Diffusion WEBUI interface.
- 🔍 A caution about the name 'Train Train' which might be confused with 'The Blue Hearts' song 'Train Train' when searched on Google; recommend searching for 'Laura Train Train' instead.
- 🔧 Installation process is similar to other extensions: copy the code, go to the Extensions tab in stable Diffusion WEBUI, select 'Install from URL', paste the code, and press the Install button.
- 🛠️ The extension adds a 'Train Train' tab where users can create 'Lauras',简易版 'Aleco', and 'Copy Laura' for training.
- 🎯 'Laura' creation involves selecting a network type, rank, and data directory where images for training are stored.
- 🏗️ 'Aleco' is a simplified version of 'Laura' that allows training without the need for educational images, using only prompts.
- 🔍 'Copy Laura' allows users to create 'Lauras' with specific features, such as a 'Laura' with closed eyes trained from an image with open eyes.
- 📸 Preparation of training images involves using a tool like 'DataSet Tag Editor Stand Alone' and preparing images with captions for better training results.
- 🖼️ Image selection and tagging are crucial for the training process; users should remove tags for features they don't want to learn and keep those they do.
- 🚀 The training process is straightforward: input the image folder path, set the image size, choose the training iteration, batch size, and learning rate, then start the training.
- 📈 After training, users can generate images using the trained 'Laura' by selecting it in the 'Laura' tab and using the 'Image to Girl' feature with the desired prompt.
Q & A
What is the main topic of the video?
-The main topic of the video is the introduction of a new extension called SDWEBUI Train Tray, created by Hamika, which allows users to train Stable Diffusion WEBUI models directly from the UI.
How can you avoid confusion with the search term 'Train Train'?
-To avoid confusion with the search term 'Train Train', which might lead to the song by The Blue Hearts, the video suggests searching for 'Laura Train Train' instead.
What are the basic steps to install the SDWEBUI Train Tray extension?
-To install the SDWEBUI Train Tray extension, you need to copy the provided code, navigate to the Extensions tab in Stable Diffusion WEBUI, select 'Install from URL', paste the code, and press the Install button. After installation, check the 'For' tab and apply the changes.
What are the three main functionalities provided by the SDWEBUI Train Tray extension?
-The three main functionalities are creating a Laura model through normal Laura learning, creating a simplified version called 'Areco' which allows learning without the need for educational images, and creating a 'Difference' Laura, which can learn subtle differences between images.
How does the 'Areco' feature work?
-The 'Areco' feature enables users to perform Laura learning without the need for educational images. It focuses solely on text prompts, allowing users to learn concepts without visual aids.
What is the purpose of the 'Difference' feature in the extension?
-The 'Difference' feature allows users to create a 'Sub' Laura by learning from a single image and then applying the learned features to another image, effectively learning the differences between the two.
What is the recommended image size for training with the SDWEBUI Train Tray extension?
-The recommended image size for training is 768 pixels, as mentioned in the script. However, for general Stable Diffusion learning, a size of 512 pixels is usually sufficient.
How does the video script guide users in preparing images for training?
-The script guides users to prepare images by selecting a folder containing the images, ensuring the background is white to avoid capturing unnecessary elements, and using a tool like 'Data Set Tag Editor Stand Alone' to organize and tag the images for training.
What is the role of the 'Train Iterations' setting in the extension?
-The 'Train Iterations' setting determines the number of iterations for the training process. It is suggested that a value of 1000, with 12 image inputs, results in approximately 160 epochs of training.
How does the video script address the issue of model selection for training?
-The script suggests selecting the 'Yellow' checkpoint for anime-style images, and recommends choosing a checkpoint suitable for real-image style models for different types of content.
What is the final outcome demonstrated in the video script?
-The final outcome demonstrated is the successful creation and use of a Laura model to generate images with various features, such as changing clothing and size, showing that the training process was effective and the generated images closely match the learned concepts.
Outlines
🌟 Introduction to SDWEBUI Training Extension
This paragraph introduces an extension called SDWEBUI Training Tray, created by Hamika, which allows training on the stable Diffusion WEBUI interface. The speaker, Alice, provides a brief overview of the extension's capabilities and mentions that it is still being updated, suggesting that it will continue to develop further. The speaker also warns viewers to search for 'Laura Training' instead of just 'Training' to avoid confusion with a song from The Blue Hearts.
📋 Installation and Preparation of Training Materials
The speaker explains the installation process of the SDWEBUI Training Tray extension, which is similar to installing other extensions. Detailed instructions are provided, including copying a code snippet and pasting it into the Extensions tab of the stable Diffusion WEBUI. The speaker also discusses the preparation of training materials, including selecting images and captions, and using a tool called Dataset Tag Editor Stand Alone to organize and edit tags for the training data.
🛠️ Customizing Training Settings and Starting the Process
In this paragraph, the speaker delves into the customization of training settings within the SDWEBUI interface. The speaker guides viewers on selecting the appropriate network type, adjusting the network rank, and setting the data directory for the training images. The speaker also explains how to choose the right image size for training and provides tips to avoid common issues, such as overfitting or excluding irrelevant features from the training process.
🚀 Observing Training Progress and Generating Images
The speaker monitors the training process through the command prompt and explains how to check the progress. Once training is complete, the speaker demonstrates how to generate images using the newly trained model. The speaker also discusses the ability to create sub-models, such as 'Copy Laura,' which allows for variations in the generated images. The speaker concludes by encouraging viewers to install the Training Tray extension and try creating their own models.
Mindmap
Keywords
💡SDWEBUI
💡Train Train
💡Loras
💡Recos
💡Diffusion
💡Prompts
💡Tags
💡Installation
💡Image Preparation
💡Training
💡Optimization
💡Checkpoints
Highlights
Introduction of the SDWEBUI Train Train extension by Hamika.
The extension allows training on the stable Diffusion WEBUI, Ototech 111's UI.
The extension is still being updated,预示着 future development possibilities.
Installation process is similar to other extensions, with a step-by-step guide provided.
The extension adds a 'Train Train' tab for easy access to training features.
Three main functionalities: creating a Laura, a simplified version called 'Reko', and a 'Difference' feature for creating varied Lauras.
Creating a Laura involves selecting a network type and rank, and setting the data directory.
The 'Reko' feature enables learning without the need for educational images, using only prompts.
The 'Difference' feature allows for creating specialized Lauras, such as one that learns a closed-eye expression.
Detailed instructions on preparing images and captions for training, including using Photoshop to remove backgrounds.
Use of a tool called 'DataSet Tag Editor Stand Alone' for managing tags and captions.
The importance of selecting the correct tags for learning and removing unnecessary ones.
Instructions on how to start the training process, including setting iteration numbers and batch size.
Mention of the learning rate and optimizer settings for the training process.
The use of a checkpoint called 'Yellow Laura' for anime-style image generation.
A demonstration of generating an image using the trained Laura, showcasing the results.
The video concludes with a call to action for viewers to try installing and using the Train Train extension.