初めての LoRA 追加学習【Stable Diffusion web UI Extension sd-webui-train-tools】

Signal Flag "Z"
28 Apr 202316:17

TLDRIn this video script, the presenter guides viewers through the process of adding custom images to a learning model using a method known as 'roller' learning. The tutorial covers setting up the learning environment on a PC with specific hardware requirements, installing necessary software, and troubleshooting common issues. The presenter emphasizes the importance of having a stable diffusion model and provides step-by-step instructions for creating a roller, preparing images, and training the model. The script also discusses the potential challenges and solutions in learning environments and encourages viewers to experiment with their own images for a successful learning experience.

Takeaways

  • 📚 The video discusses the process of additional learning using self-prepared images to generate stable diffusions with specific styles and characters.
  • 💻 The presenter opts for learning on their own computer instead of using cloud services, requiring a graphics card with at least 8GB of VRAM and around 32GB of main memory.
  • 🔧 The importance of having a high-performance computer for learning on your own, as well as ensuring the system drive has over 100GB of free space when using swap functionality.
  • 🛠️ The tutorial covers the steps to create a roller, including setting up the learning environment, preparing images with captions, and deciding on learning conditions.
  • 📸 Tips for preparing learning images, such as having at least 10 images, with a size of 1000 pixels or more, and ensuring the images are square-shaped for optimal results.
  • 🔄 The process of learning involves iterating through the images multiple times (epochs) and adjusting conditions or images if the results are not satisfactory.
  • 🚀 The use of Stable Diffusion WEBUI with extensions is recommended for easier learning, as it simplifies the operation and hides complex parts.
  • 💡 The video highlights the common issue of learning not working with version 0.0.16 of the transformers, and suggests creating a new Stable Diffusion WEBUI with a different version of transformers.
  • 🔍 The presenter emphasizes the importance of not showing the images you're learning with to others until you're sure the learning process is successful.
  • 🛑 The video provides troubleshooting tips, such as dealing with errors related to spaces in the path and modifying the train tool script if necessary.
  • 🎉 The presenter concludes by encouraging viewers to try the process, emphasizing that once you understand how to learn, you can then make your own adjustments and improvements.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is about performing additional learning using Stable Diffusion to generate images with specific styles and characters.

  • What type of hardware is recommended for learning on your own computer?

    -A graphics card with at least 8GB of video memory and around 32GB of main memory is recommended. However, if the main memory is less, the system can still learn using memory swap, though it may be slower.

  • What is the purpose of having at least 100GB of free space on the system drive?

    -The purpose of having at least 100GB of free space on the system drive is to accommodate the memory swap functionality, which allows the learning process to continue even with limited main memory.

  • What is the first step in creating a learner according to the video?

    -The first step in creating a learner is to set up the learning environment on your computer and prepare the learning images with appropriate captions, placing them in the correct folders.

  • What is the recommended image size for learning?

    -The recommended image size for learning is 512x512 pixels.

  • How does the video address the issue of errors during the learning process?

    -The video suggests that errors during the learning process can be due to incorrect usage of the learning program or the program not functioning properly. It advises viewers to not give up on additional learning if errors occur and to seek solutions or corrections as needed.

  • What is the role of the Stable Diffusion WEBUI extension mentioned in the video?

    -The Stable Diffusion WEBUI extension simplifies the learning process by providing a minimal operation interface that hides the more complex parts, making it easier for users to perform the necessary steps for learning.

  • What is the significance of the version number in the learning process?

    -The version number is significant as it indicates the specific iteration of the learning process. In the video, the creator uses 'v1' to denote the first version of the learning process, allowing for easy updates and changes in the future.

  • How does the video address the issue of learning rate and its impact on the learning outcome?

    -The video acknowledges that the learning rate can significantly impact the quality of learning, but it does not have a one-size-fits-all optimal value. It varies depending on what is being learned, and viewers are advised to adjust it based on their specific needs and observe the results.

  • What is the total number of learning steps mentioned in the video?

    -The total number of learning steps mentioned in the video is 1500, which is calculated by learning 10 images for 15 epochs, with each epoch consisting of 10 steps.

  • What advice does the video give for troubleshooting issues with the learning environment?

    -The video suggests that if the learning environment does not work as expected, one should check for common issues such as spaces in the path, which can cause the extension not to function. It also recommends modifying the installation script if necessary and ensuring that the system drive has enough free space.

Outlines

00:00

🌟 Introduction to Additional Learning with Custom Images

The script begins with an introduction to the process of additional learning using custom images. It explains that there are various types of additional learning, and the focus is on roller learning, which can be done on a personal computer without cloud services. The script emphasizes the need for a graphics card with at least 8GB of VRAM and suggests that 32GB of main memory is ideal, although the system can still learn with less memory using memory swap, albeit at a slower speed. It also mentions the importance of having at least 100GB of free space on the system drive. The script warns viewers about the potential difficulties of setting up the learning environment and encourages persistence despite possible errors.

05:01

🛠️ Setting Up the Learning Environment and Troubleshooting

This paragraph delves into the specifics of setting up the learning environment using the SD Script by Goya S and the Stable Diffusion WEBUI extension. It outlines the steps for creating a learning environment, including installing necessary programs and dealing with potential issues such as path spaces in the extension. The script provides a detailed guide on how to install the extension, troubleshoot common errors, and prepare the computer for the learning process. It also advises viewers to keep the installation script (install.py) and to remove it to allow for automatic updates.

10:02

🎨 Preparing Images and Starting the Learning Process

The script moves on to discuss the preparation of images for learning, emphasizing the need for at least 10 images and suggesting that the images should be square and at least 1000 dots in size. It provides tips for characters' images, recommending various poses to avoid bias in learning. The script then explains how to use the training tool to create a learning folder, select images, and set up the learning conditions. It covers aspects such as crop methods, automatic captioning, and the importance of the learning image size, which is specified as 512x512 dots.

15:05

🔧 Fine-Tuning Learning Parameters and Evaluating Results

In this paragraph, the script focuses on fine-tuning the learning parameters, such as the model choice, X-Former activation, clip skip, and save epoch. It explains the significance of these settings and how they can be adjusted based on the type of content being learned. The script then describes the learning process, including the number of epochs, batch size, and learning rate. It also touches on the concept of epochs and steps in the learning process, providing an estimate of the total learning steps and the expected duration. The paragraph concludes with an overview of how to evaluate the learning results and set up the preview conditions for the generated images.

🎉 Conclusion and Encouragement for Trying Out the Learning Process

The script wraps up by summarizing the learning process and encouraging viewers to try it out. It acknowledges the potential challenges in setting up the learning environment but reassures viewers that once this is done, the learning process is not too difficult. The script suggests that after understanding the basics of learning, viewers can experiment with different images and captions to improve their results. It ends with a call to action for viewers to subscribe to the channel and rate the video, and shares the author's personal experience of exhaustion from editing the video.

Mindmap

Keywords

💡追加学習 (additional learning)

The concept of '追加学習' refers to the process of further training or fine-tuning a machine learning model with additional data or adjustments to improve its performance. In the context of the video, it involves using self-prepared images to enhance the model's ability to generate stable and desired artistic styles or character depictions. The video outlines the steps and considerations for performing additional learning on a personal computer, emphasizing the importance of sufficient video memory and system drive space.

💡ローラー (roller)

In the context of the video, 'ローラー' or 'roller' refers to a type of machine learning model, specifically one used for image generation and manipulation. The script discusses using a roller to learn from images and captions to generate new images with specific styles or characteristics. The term is likely derived from the 'roller' model in the context of Stable Diffusion, a popular AI model for image synthesis.

💡クラウドサービス (cloud service)

Cloud services refer to the provision of computing resources and data storage over the internet, allowing users to access and manage these resources remotely. In the video, the user considers using a cloud service for machine learning but ultimately decides to perform the learning on their own computer to avoid potential issues with video memory and system drive space requirements.

💡グラフィックボード (graphics board)

A graphics board, also known as a video card or GPU (Graphics Processing Unit), is a piece of computer hardware that generates and outputs images to a display. In the context of machine learning and AI, a graphics board with a sufficient amount of video memory is crucial for processing the large amounts of data involved in training models like those used for image generation.

💡メモリスワップ (memory swap)

Memory swap refers to the process of using a portion of the hard drive as virtual memory when the physical RAM (Random Access Memory) is full. This allows the system to temporarily store data that cannot fit into the RAM, thus preventing crashes due to memory overload. However, using swap memory can significantly slow down the system as hard drives are slower than RAM.

💡学習用環境 (learning environment)

A learning environment in the context of machine learning refers to the setup and configuration of software and hardware necessary for training a model. This includes the operating system, programming languages, libraries, and the computational resources such as the graphics board and RAM. The video provides a detailed guide on creating a suitable learning environment on a personal computer for training an AI model with additional images.

💡キャプション (caption)

In the context of the video, a caption refers to a text label or description associated with an image, which is used to train the AI model to understand and generate images with specific characteristics or styles. Captions are crucial for guiding the model on what features or elements to include in the generated images.

💡エポック (epoch)

In machine learning, an epoch is a term used to describe a complete pass of the entire dataset through the neural network during the training process. Multiple epochs are used to allow the model to learn from the data more thoroughly. In the video, the user sets the number of epochs to determine how many times the learning process will cycle through the dataset.

💡バッチサイズ (batch size)

Batch size in machine learning refers to the number of samples or images processed by the model in one go during the training process. It is a crucial hyperparameter that affects the speed and stability of training. Larger batch sizes can lead to faster training but may require more memory and can sometimes impact the model's ability to learn fine details.

💡学習結果 (learning result)

The learning result refers to the output or outcome of the machine learning model after it has been trained with the provided data. In the context of the video, it involves evaluating the generated images to see if they meet the desired criteria set by the user during the training process.

💡Stable Diffusion

Stable Diffusion is a type of deep learning model used for generating images from textual descriptions. It is known for its ability to create high-quality, stable images and has been popular for various image synthesis tasks. The video script discusses using an extension for Stable Diffusion called SDwebui, which simplifies the training process for users.

💡SDwebui

SDwebui is an extension or user interface designed to simplify the process of training and using Stable Diffusion models. It provides a more user-friendly way to interact with the complex command-line interface typically associated with such AI models, making it more accessible for users who may not be as technically proficient.

Highlights

The introduction of the process for self-learning with prepared images using Stable Diffusion to generate art styles and characters.

The requirement of a graphics board with 8GB or more video memory and 32GB of main memory for learning on one's own computer without cloud services.

The necessity of having at least 100GB of free space on the system drive due to the use of memory swap functionality.

The advice to be cautious about sharing images of people in learning to avoid potential privacy issues.

The step-by-step guide on creating a learning environment, including setting up the PC environment and preparing learning images with captions.

The recommendation to use the Stable Diffusion WEBUI extension for easier learning process without directly using the original program.

The troubleshooting guide for issues like the expansion function not working due to spaces in the path, and how to fix it by editing the installation dot py script.

The mention of a common issue with version 0.0.16 where learning does not occur despite successfully creating the Lara file.

The suggestion to install a new version of Transformers and use a separate folder for the learning-only Stable Diffusion WEBUI.

The importance of preparing at least 10 images for learning, with a size of 1000 dots or more, and the recommendation to use 512x512 dots for learning.

The process of creating a learning folder using the Train Tool, including selecting images, cropping, and setting the learning size.

The explanation of terms like 'epoch' and 'batch size', and how they affect the learning process.

The guidance on setting learning conditions, including model selection, learning rate, and other parameters based on the type of learning material.

The practical advice on how to deal with bugs such as unresponsiveness after learning and the need to reload the WEBUI.

The final evaluation of the learning results and the creation of preview images to showcase the effectiveness of the learning process.

The encouragement for viewers to try the learning process themselves and the call to action for subscribing to the channel and giving high ratings.