LoRAの作り方(2023年11月版)【Stable Diffusion 初心者】

4 Nov 202325:16

TLDRThe script introduces viewers to the process of creating a 'Lora,' a custom AI model that can be used with Stable Diffusion for generating images. It explains the concept using a cooking analogy, comparing Lora to a sauce that adds flavor to the base model. The tutorial walks through the steps of preparing the 'ingredients' (images and captions), setting up the 'oven' (AI environment), and 'baking' (training) the Lora. The script emphasizes the potential of Lora for various applications, such as costume transformations, and encourages users to explore and experiment with creating their own Loras for different purposes.


  • 💻 The script discusses creating custom training data for Stable Diffusion to automatically generate art, implying a way to achieve passive income through AI-generated content.
  • 🧑‍🎨 It highlights the concept of 'Lola' as an advanced but challenging tool for beginners, suggesting that it allows users to add personalized 'flavors' to their AI models similar to adding sauce to food.
  • 🔍 Explains that 'Lola' operates by incorporating a small, additional set of trained data to influence the generated art, showing that specific features can be reflected in the output.
  • 🛠️ Provides a detailed step-by-step guide on how to prepare for and execute the training of a 'Lola', including installing necessary tools and setting up the environment.
  • 📸 Emphasizes the importance of preparing images and caption texts for training, suggesting that clear, focused images lead to better training outcomes.
  • 📚 Discusses tweaking the training process by editing keywords in captions, indicating a method to refine what the AI learns and produces.
  • 📗 Mentions the use of a 'Dataset Tag Editor' for organizing and editing training data, hinting at the complexity and the need for precise control over the training process.
  • 🔧 Offers technical advice on overcoming common issues encountered during the setup and training phases, showing practical problem-solving steps.
  • 🔬 Highlights the creative potential of custom 'Lolas', showcasing examples like automatically transforming all clothing in an image to pajamas.
  • 🚀 Suggests that mastery of 'Lola' can greatly enhance the capabilities of AI models, but also acknowledges the steep learning curve and the effort required for setup.

Q & A

  • What is the main goal of creating a 'Lora' in the context of the script?

    -The main goal of creating a 'Lora' is to produce a unique file that can be used with Stable Diffusion to generate images based on the learning data provided.

  • What is the significance of the 'Lora' compared to other AI learning models mentioned in the script?

    -The 'Lora' is likened to a sauce in the context of food, suggesting that it adds flavor or unique characteristics to the AI-generated images, much like how sauce enhances a dish.

  • How does the script describe the process of learning from images?

    -The script describes the process as being similar to baking a pie, where the images and their captions are the ingredients, and the learning parameters are the cooking instructions.

  • What kind of images and captions are used to create a 'Lora'?

    -The images used for creating a 'Lora' are typically of a specific subject, such as a character, and the captions are one-to-one text files that describe the images.

  • What is the role of 'Stable Diffusion' in the 'Lora' creation process?

    -Stable Diffusion is the platform on which the 'Lora' is created and used to generate images. It is mentioned as a tool that can be used by beginners with the help of a 'Lora'.

  • Why is it important to have a high-performance 'Orb' for learning in the script?

    -A high-performance 'Orb', which refers to the AI processing capability, is crucial for handling the computational demands of the learning process, ensuring efficient and effective image generation.

  • What is the purpose of the 'Training' folder in the 'Lora' creation process?

    -The 'Training' folder is where the learning images and their corresponding captions are placed for the AI to learn from during the 'Lora' creation process.

  • How does the script suggest organizing the learning images and their captions?

    -The script suggests creating a hierarchical folder structure with a 'Training' folder containing subfolders for the images, each with a specific number of repetitions for learning.

  • What is the significance of the 'Output' folder in the script?

    -The 'Output' folder is where the completed 'Lora' and the generated images from the learning process are saved.

  • What is the role of the 'Model' folder in the 'Lora' creation process?

    -The 'Model' folder is where the models used during the learning process are stored. These models are essential for the 'Lora' to function correctly.

  • How does the script describe the process of setting up the learning parameters?

    -The script describes setting up the learning parameters as a meticulous process involving the adjustment of various settings such as batch size, epochs, and saving intervals, which are crucial for the efficiency and outcome of the learning process.



🤖 Introduction to Creating Custom Stable Diffusion Models

This paragraph introduces the concept of creating custom learning data for Stable Diffusion models. It discusses the potential complexity for beginners but emphasizes the possibility of creating models that reflect personal preferences, similar to making a sauce to enhance a dish's flavor. The paragraph also touches on the idea of using these models for various applications, such as generating images or altering existing ones, and sets the stage for a detailed tutorial on creating a custom 'Laura' model.


🛠️ Setting Up the Environment for Model Training

The second paragraph delves into the technical setup required for training a Stable Diffusion model. It covers the installation process, including deciding on the installation folder, cloning repositories, and setting up the necessary packages. The paragraph also discusses the importance of choosing the right graphics card settings and provides a step-by-step guide on how to prepare the environment for model training.


🎨 Preparing Images and Captions for Training

This section focuses on the preparation of images and captions for the training process. It explains the need to create specific folders for training, output, and model data. The paragraph also discusses the selection of images, the creation of captions, and the use of a data set tag editor to organize and edit the training data effectively.


🔧 Customizing and Editing Training Data Tags

The paragraph describes the process of customizing and editing tags for the training data. It covers the use of a data set tab editor to refine the captions and select specific keywords for the model to learn. The section also explains the importance of choosing the right keywords and setting up trigger words for the model, which will be used to invoke specific features during the model's use.


🚀 Launching the Training Process

This part outlines the actual training process of the Stable Diffusion model. It details the steps to launch the training, including setting up the model, specifying the training image folder, and configuring the training parameters. The paragraph also discusses the importance of selecting the appropriate model for training and provides insights into the expected outcomes of the training process.


📦 Reviewing the Training Results and Applications

The final paragraph reviews the results of the training process and explores the applications of the newly created model. It discusses the use of the model to generate images and the impact of the training on the final output. The paragraph also reflects on the potential for future improvements and the excitement of exploring new technologies in the field of AI and machine learning.

💬 Closing Remarks and Invitation for Feedback

The video script concludes with a call to action for viewers to provide feedback and engage with the content. It invites viewers to rate, subscribe, and comment on the video, emphasizing the channel's focus on AI-generated synthetic voices and related technologies.



💡Stable Diffusion

Stable Diffusion is an AI model used for generating images from textual descriptions. In the context of the video, it is mentioned as a tool that can be utilized with the custom 'Laura' model created by the user. It is a key component in the process of learning and generating images, as it allows for the creation of unique visual outputs based on the user's input and preferences.


Laura is a custom AI model created by the user for image generation, as described in the video. It is likened to a sauce or seasoning that can be added to existing models to give them specific characteristics or flavors. The term 'Laura' is used to represent the user's personalized touch to the AI model, allowing them to generate images that reflect their desired features and styles.

💡AI Learning

AI Learning, as mentioned in the script, refers to the process of training an AI model to recognize and produce specific features or styles in image generation. This involves feeding the AI with data, such as images and corresponding captions, to teach it how to generate desired outputs. The learning process is crucial for creating a 'Laura' that can produce images according to the user's preferences.

💡Image and Caption Preparation

Image and Caption Preparation is a critical step in the AI learning process described in the video. It involves collecting images and creating corresponding text files, or captions, that describe the images. These captions serve as instructions for the AI model during the learning process, guiding it to generate images with specific features. The quality and relevance of the images and captions directly impact the outcomes of the AI-generated images.

💡Training Folders

Training Folders are the designated locations where the user places the images and other data for the AI to learn from. These folders are organized in a specific structure to facilitate the learning process. In the context of the video, the user creates different folders such as 'Train', 'Output', and 'Model' to organize the learning materials, store the generated 'Laura' models, and manage the output of the AI learning process.

💡Data Set Tag Editor

The Data Set Tag Editor is a tool used to edit and organize the tags or captions associated with the images in the data set. It allows the user to refine the descriptions and keywords that will guide the AI learning process. By using this tool, the user can ensure that the AI model focuses on learning the desired features and styles from the provided images.

💡Trigger Words

Trigger Words are specific keywords that are used to invoke particular features or characteristics in the AI-generated images. They serve as prompts for the AI model during the image generation process, guiding it to produce outputs that align with the user's intentions. In the context of the video, trigger words are carefully selected and added to the captions to ensure that the AI model learns to generate images with the desired features.

💡Custom Model

A Custom Model refers to a personalized AI model that has been trained with specific data sets and parameters to generate images according to the user's preferences. In the video, the user creates a custom 'Laura' model by training it with their own set of images and captions, which allows for a unique and tailored image generation experience.


VRAM, or Video RAM, is the memory used by graphics processing units (GPUs) to store图像 data. In the context of AI image generation and training models like 'Laura', having sufficient VRAM is crucial for handling the large amounts of data involved in the process. The script mentions checking the VRAM capacity to ensure that the user's system can support the AI learning and image generation tasks.

💡Command Prompt

The Command Prompt is a text-based user interface used for executing commands in a command-line interface (CLI) environment. In the video, it is used to install and set up the necessary tools and packages for creating and training the 'Laura' model. The user interacts with the Command Prompt to navigate through the file system, clone repositories, and run installation scripts.


JSON罗德 (JSON罗德) is a term mentioned in the context of saving settings for the AI learning process. It refers to a file in JSON format that contains the configuration settings for the AI model, including parameters like learning rate, batch size, and other training-specific details. This file is crucial for saving and reusing the model's configuration, ensuring consistency in the learning process.

💡AI-generated Images

AI-generated Images are the visual outputs created by AI models like 'Laura' based on the input data and learning parameters provided by the user. These images are designed to reflect the user's desired features and styles, as learned from the training data. The quality and accuracy of these images are a direct result of the effectiveness of the AI model and the relevance of the training data.