【有料級】簡単！最後までできる！kohya’GUIを使ったLora作成！【Stable Diffusion】 【Lora学習】
TLDRIn this comprehensive guide, the assistant introduces a straightforward method for creating a custom AI model named 'Lora' using a graphical user interface (GUI) developed by 'Koyama' and 'BMR'. The tutorial is designed for beginners and those familiar with Stable Diffusion, covering the installation of necessary software, selection of training images, and the training process itself. The assistant uses the popular character 'Paimon' from the game 'Genshin Impact' as the subject for the Lora model, providing detailed steps for image selection, tagging, and model training. The video concludes with testing the trained Lora model in various scenarios, demonstrating its effectiveness in generating images of Paimon with different styles and emphasizing the importance of respecting copyright when using web-sourced images.
Takeaways
- 🌟 Introduction to creating a custom AI model called 'Lora' using a simple GUI developed by 'Koyama' and 'BMR'.
- 💻 System requirements for the setup include a Windows 11 PC with an NVIDIA GPU and at least 6GB of VRAM, though 24GB is recommended.
- 📋 Installation prerequisites involve Python 3.10, git, and Visual Studio's redistributable package.
- 🔗 The video provides links to installation guides and additional tutorials for those unfamiliar with the setup process.
- 🎨 Selection of training images is crucial, with a focus on high-quality, character-only images in JPEG or PNG format.
- 🗂️ Organization of image folders and naming conventions are detailed for clarity and ease of use during the training process.
- 🛠️ The use of 'Stable Diffusion's Image to Image feature for preprocessing images and the 'Waifu Diffusion 1.4 Target' extension for tagging images is discussed.
- 📊 Adjustment of training parameters such as batch size, epochs, and save frequency to optimize the learning process.
- 📈 Explanation of the model selection process, including the choice of 'Ein Lora' checkpoint for anime-style images.
- 🏋️♂️ Training progress is monitored through the command prompt, with an example of a 10-epoch training session.
- 🖼️ Post-training, the effectiveness of the 'Lora' model is tested using various Stable Diffusion models and prompts.
- 🎥 The video concludes with a call to action for viewers to try Lora training and encourages channel subscription for future content.
Q & A
What is the main topic of the video?
-The main topic of the video is a tutorial on how to create a custom AI model called 'Lola' using a GUI (Graphical User Interface) developed by a user named BMR.
What are the system requirements for the video tutorial?
-The system requirements include a Windows 11 PC with an NVIDIA GPU, at least 6GB of VRAM, and 24GB of VRAM is recommended for this tutorial.
What software and tools are needed for the installation process?
-The necessary software and tools for the installation process are Python 3.10, git, and a registry version of Visual Studio.
How does the video address the issue of copyright when using images for training?
-The video acknowledges the delicate issue of copyright when using images for training and suggests selecting images where the size is 512x512 pixels or larger and the image contains only one character to avoid confusion for the AI.
What is the purpose of the 'Stable Diffusion Image to Image' feature used in the video?
-The 'Stable Diffusion Image to Image' feature is used to process and prepare the images for training by cropping and converting webp files into compatible formats like JPEG or PNG.
What is the role of the 'Waifu Diffusion 1.4 Target' extension in the video?
-The 'Waifu Diffusion 1.4 Target' extension is used to batch tag images, which is an essential step in preparing the images for training the AI model.
How does the video demonstrate the effectiveness of the trained 'Lola' model?
-The video demonstrates the effectiveness of the trained 'Lola' model by generating images using different models and comparing the results, showing that the 'Lola' model successfully learns and produces images of the character 'Paimon' from the game Genshin Impact.
What is the significance of the 'Training Parameters' section in the GUI?
-The 'Training Parameters' section in the GUI is where users can adjust various settings such as the type of 'Lola' to create, batch size, number of epochs, and save intervals, which directly affect the training process and the outcome of the AI model.
What is the 'Dimension Network Rank' and how does it affect the training?
-The 'Dimension Network Rank' is a parameter in the advanced configuration settings that can be adjusted according to the user's needs. It is suggested that a value of 128 is optimal based on research, but smaller values might also work.
How does the video address the issue of 'Out of Memory' errors during training?
-The video suggests not checking the 'Memory Efficient Attention' option for those who are not confident in their GPU's VRAM capacity, as it can make the training process less likely to encounter out-of-memory issues, although it might slow down the learning speed.
What is the final outcome of the video tutorial?
-The final outcome of the video tutorial is the successful creation of a 'Lola' model trained on the character 'Paimon' from Genshin Impact. The model is tested using various settings and models to demonstrate its effectiveness in generating images of 'Paimon'.
Outlines
🌟 Introduction to Creating Lola with AI
The video begins with an introduction to the process of creating a custom AI model called Lola. The assistant, Alice, explains that the tutorial will cover a simple method for creating Lola, suitable for beginners who have not yet started learning about Lola or using GUI. The content is aimed at those who want to see the actual workflow of creating Lola. The assistant mentions that a specific GUI developed by a user named Takanoyama will be introduced, which simplifies the training process of Lola. The video also outlines the technical requirements such as a Windows 11 PC with an NVIDIA GPU and a minimum of 6GB VRAM. The assistant provides a detailed guide on installing necessary software like Python 3.10, git, and Visual Studio's registry editor. The video emphasizes that the content is for those who have some experience with Stable Diffusion and recommends watching other tutorial videos for beginners.
🛠️ Installation and Setup Process
This paragraph details the installation process for creating Lola using the GUI. It explains the steps to clone the repository, install necessary software, and set up the environment. The assistant provides instructions on how to navigate to the BMR体质's homepage for installation instructions, and how to use the command prompt to clone the repository and install the GUI. The process includes setting up the directory, running commands to install the GUI, and waiting for the setup menu to appear. The assistant also discusses the selection of options for GPU compatibility and the importance of choosing the correct settings for the installation to proceed smoothly.
🎨 Preparing Images for Lola Training
The assistant discusses the selection of images for Lola training, emphasizing the importance of choosing high-quality, 512x512 pixel images that represent the character well. The chosen character for the tutorial is Paimon from the popular game Genshin Impact. The assistant provides tips on selecting images that are clear, represent the character alone, and avoid images with multiple characters or unsupported file formats. The process includes searching for images online, saving them in a designated folder, and ensuring that the images are in JPEG or PNG format. The assistant also mentions the need to process webp files using Stable Diffusion's Image to Image feature.
📂 Organizing Images and Folders for Training
The assistant explains how to organize the selected images into specific folders for the Lola training process. It details the creation of an 'Input Images' folder and subfolders for each training cycle, including the character's name and date. The assistant provides a step-by-step guide on renaming the image files in sequence, moving the processed images into the correct folders, and using the Stable Diffusion extension 'Waifu Diffusion 1.4 Tagger' to batch tag the images with the character's name. The assistant also discusses the importance of editing the tags to remove unnecessary prompts and retain only the essential elements for the AI to learn.
🚀 Starting the Lola Training
The assistant guides the viewer through the process of starting Lola training using the GUI. It covers selecting the custom model, setting the training folder, and choosing the output folder. The assistant provides instructions on downloading and installing a specific checkpoint model, 'Enny Lola,' which is suitable for anime-style characters. The video also explains how to adjust training parameters such as the batch size, epochs, save frequency, and caption extension. The assistant emphasizes the importance of selecting the correct folder one level up from the image folder and provides tips on saving the configuration for future use.
🖼️ Testing Lola with Different Models
After completing the Lola training, the assistant demonstrates testing the trained Lola with various models, including Anything v4.5 and Counterfeit v4.5. The video shows the process of generating images using the trained Lola, adjusting the intensity of Lola in the prompts, and observing the results. The assistant compares the outputs from different models and notes the differences in the generated images. The video also includes testing the trained Lola with real-image models and discusses the impact of Lola on the generated images. The assistant concludes by reflecting on the effectiveness of the Lola training and suggests further training for stronger results.
🎉 Conclusion and Future Training
The assistant wraps up the tutorial by summarizing the Lola training process and its outcomes. The video highlights the successful creation of a Lola model using the Genshin Impact character, Paimon, and the generation of images with the trained Lola. The assistant encourages viewers to try Lola training themselves and reminds them to respect copyright laws when using images. The video ends with a call to action for viewers to subscribe and like the channel for more helpful content in the future.
Mindmap
Keywords
💡Stable Diffusion
💡Lora Training
💡GUI (Graphical User Interface)
💡Paimon
💡Character Customization
💡Image Processing
💡Command Prompt
💡Batch Size
💡Epochs
💡VRAM (Video RAM)
💡Tagging Images
💡Output Folder
Highlights
Introduction to creating a custom AI model using the Red Team's GUI, specifically for character creation.
Explanation of the required software and hardware prerequisites, including Windows 11, NVIDIA GPU, and Python 3.10.
Step-by-step guide on installing necessary components like Python, git, and Visual Studio's redistributable.
Detailed instructions on cloning the Red Team's GUI repository and setting up the environment.
Discussion on selecting images for training the AI model, emphasizing the importance of image quality and character representation.
Process of preparing images for training, including resizing and converting webp files to compatible formats.
Demonstration of organizing training data into structured folders for clarity and ease of use.
Explanation of tagging images with captions to guide the AI learning process.
Overview of launching the GUI and selecting the appropriate model checkpoint for training.
Customization of training parameters such as batch size, epochs, and save frequency.
Discussion on the importance of selecting the correct folder for training to avoid common mistakes.
Presentation of the training progress and how to monitor it through the command prompt.
Explanation of generating images using the trained model and adjusting the model's sensitivity to achieve desired results.
Comparison of different models' reactions to the trained character, demonstrating the model's effectiveness.
Conclusion summarizing the successful creation and application of a custom AI model for character generation.
Encouragement for viewers to try their hand at AI model training and a recommendation for the game Genshin Impact.
Call to action for viewers to subscribe and like the video for more helpful content.