新しいLoRA学習のための拡張機能TrainTrainの紹介

AI is in wonderland
20 Jan 202416:25

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

00:00

🌟 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.

05:02

📋 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.

10:02

🛠️ 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.

15:04

🚀 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

SDWEBUI refers to a specific user interface for the Stable Diffusion web application, which is a platform for generating AI-based images. In the context of the video, it is the base upon which the new extension, 'Train Train,' is built to facilitate the training of AI models directly within the UI.

💡Train Train

Train Train is an extension for the SDWEBUI that allows users to train AI models, specifically 'Loras' and 'Recos,' without the need for additional software or extensive coding knowledge. It simplifies the process of AI model training by providing an intuitive interface within the existing SDWEBUI platform.

💡Loras

Loras, short for Stable Diffusion Loras, are pre-trained AI models that can be fine-tuned with new data to generate specific types of images. In the video, the process of creating and training Loras is detailed, emphasizing the customization of AI-generated content.

💡Recos

Recos, or Stable Diffusion Recos, are简易版 (simplified versions) of Loras that require less data for training. They are designed to be more accessible for users who want to quickly train AI models without the need for extensive datasets, allowing for faster and more straightforward AI customization.

💡Diffusion

Diffusion, in the context of the video, likely refers to the diffusion process in generative AI models, such as Stable Diffusion, where images are progressively refined through a series of noise reduction and image enhancement steps. This process is fundamental to the creation of AI-generated images and is a key component of the AI training discussed in the video.

💡Prompts

Prompts are the text inputs or descriptions that guide the AI in generating specific types of images. They are crucial in the training of AI models like Loras and Recos, as they help the AI understand the desired output and learn to generate images accordingly.

💡Tags

Tags are labels or keywords associated with images during the training process of AI models. They help categorize and filter images, allowing the AI to learn and generate content based on these categories. In the video, tags are used to organize and select images for training the AI model.

💡Installation

Installation refers to the process of setting up and configuring software or extensions on a computer. In the context of the video, it involves the steps required to add the 'Train Train' extension to the SDWEBUI, enabling users to train AI models directly from the user interface.

💡Image Preparation

Image preparation involves selecting, editing, and organizing images for use in training AI models. This process is critical for ensuring that the AI learns from the correct data and can generate the desired types of images. The video script includes instructions on how to prepare images and their associated captions for this purpose.

💡Training

Training, in the context of AI, refers to the process of teaching the AI model to recognize patterns, features, and associations through exposure to data. In the video, training involves using prepared images and prompts to teach the AI to generate specific types of content.

💡Optimization

Optimization in AI training refers to the process of adjusting parameters and settings to improve the efficiency and effectiveness of the AI model. This includes fine-tuning the learning rate, batch size, and other factors to achieve better performance and more accurate image generation.

💡Checkpoints

Checkpoints in AI training are saved states of the model at various stages of the learning process. They allow users to resume training from a specific point or to use the trained model for generating images without starting the training process from scratch.

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.