【簡単！】Stable Diffusion Web UIをインストールしよう！CPUのみで動く方法も解説！
TLDRIn this informative video, the presenter guides viewers through the process of installing and running Stable Diffusion WebUI on a PC without a GPU, utilizing only the CPU. The video covers the installation of Python 3.10.6, necessary Windows kits, and Git, as well as downloading and configuring the Stable Diffusion WebUI repository. The presenter also demonstrates how to change models within the WebUI, experimenting with different settings and options to ensure smooth operation on a CPU-only environment. The video concludes with a successful demonstration of image generation using a popular model, highlighting the adaptability and versatility of the Stable Diffusion WebUI.
Takeaways
- 📜 The video is a tutorial on running Stable Diffusion WEBUI on a PC without a GPU, using only the CPU.
- 💻 The prerequisite for the tutorial is having Python 3.10.6 installed, with paths configured and Git installed.
- 🔄 The video covers the installation process of Python, the Windows SDK, and the Stable Diffusion WEBUI repository using git clone.
- 🛠️ The user needs to ensure they are using Windows 10 or higher as Windows 7 is not supported.
- 📂 The video provides guidance on choosing the right installer for Python and where to install it.
- 🖥️ The installation of the Windows SDK involves selecting components, but the video suggests proceeding with default options for simplicity.
- 🔗 The video demonstrates how to download and set up the Stable Diffusion WEBUI repository from GitHub.
- 🚀 The video shows how to run the WEBUI User Dot Bat file and troubleshoot common errors, such as skipping GPU checks with command line arguments.
- 🎨 The tutorial includes changing the model used by Stable Diffusion WEBUI by downloading and integrating a new model file.
- 🔄 The presenter attempts to generate an image using the new model and discusses the evolution of the product and its capabilities.
- 📸 The video ends with a mention of future content, including additional learning and exploration of Stable Diffusion WEBUI features.
Q & A
What is the main topic of the video?
-The main topic of the video is about running a stable version of the Diffusion WEBUI on a PC without a GPU, using only the CPU, and also covering the process of changing models within the application.
Which version of Python is required for the installation?
-Python 3.10.6 is required for the installation, as mentioned in the script.
What is the recommended operating system for running the Diffusion WEBUI?
-The recommended operating system is Windows 10 or higher, as Windows 7 or earlier versions are not supported.
How can one install Git as part of the setup process?
-Git can be installed by downloading the latest version from the official website and choosing the 64-bit Windows installer. The installation process can be followed with default settings unless the user has specific requirements.
What is the command used to clone the repository for the Diffusion WEBUI?
-The command used to clone the repository is 'git clone' followed by the URL of the Diffusion WEBUI repository.
What is the purpose of the 'Skip Torch Check' command?
-The 'Skip Torch Check' command is used to bypass the GPU existence check, which is helpful when running the application on a system without a GPU.
How can one change the model used in the Diffusion WEBUI?
-The model can be changed by specifying the desired model using the 'ckpt' command in the command line and editing the WEBUI user.bat file accordingly.
What is the significance of the 'No Half Use CPU All' option?
-The 'No Half Use CPU All' option is used to run the application entirely on the CPU, ensuring compatibility with systems without a GPU.
What kind of models can be found in the 'Models' folder?
-The 'Models' folder contains various model files, typically with 'safetensors' extension, which are used in the Diffusion WEBUI for generating images with different styles and characteristics.
How long does it typically take for the Diffusion WEBUI to start and generate an image?
-The time it takes for the Diffusion WEBUI to start and generate an image can vary significantly depending on the system's CPU performance and the complexity of the model being used.
What is the process for downloading and installing a new model?
-To download and install a new model, one should find a suitable model file, typically with a 'safetensors' extension, download it, and place it in the appropriate directory within the Diffusion WEBUI's 'Models' folder. The application should then recognize the new model and allow the user to select it for image generation.
Outlines
📝 Installing Stable Diffusion Web UI on CPU-only PC
The paragraph outlines the process of installing Stable Diffusion Web UI on a PC without a GPU, relying solely on the CPU. It begins with the installation of Python 3.10.6, emphasizing the prerequisites such as having Python installed, the path set up, and git installed. The speaker guides through the installation process, including selecting the correct version of Python from the release page and the importance of choosing the 64-bit version for Windows. The paragraph also covers the installation of the necessary kit and the steps to download and execute the Stable Diffusion Web UI repository using git clone. The speaker encounters and addresses an error related to GPU checks, providing a workaround by adding a skip flag to the command line.
🖥️ Running the Stable Diffusion Web UI
This section details the execution of the Stable Diffusion Web UI on a CPU-only machine. The speaker navigates through potential errors, suggesting the use of a skip flag to bypass GPU-related checks. The process involves running the 'webui-user.bat' file and waiting for the application to set up, which can take a significant amount of time. The speaker also discusses the generation of a web interface and the initial testing of the application by inputting a prompt and observing the output. The goal is to demonstrate the application's ability to generate images using the CPU.
🔄 Changing Models in Stable Diffusion Web UI
The speaker explores the process of changing models within the Stable Diffusion Web UI. They discuss the use of command-line arguments to specify the model and the location of the model files. The speaker attempts to download a popular model and replace the existing model with the new one. They also touch upon the challenges of selecting the right model and the potential risks of using certain models due to their outputs. The speaker successfully updates the model and tests it with a simple prompt, noting the visible change in the generated output.
🚀 Exploring Model Evolution and Updates
In this part, the speaker expresses a desire to explore the evolution of models and updates within the Stable Diffusion Web UI. They discuss the potential for the web interface to have been updated since their last check and the importance of staying current with the latest models. The speaker plans to download a model with a high popularity and size, indicating a likely high quality. They also mention the intention to test the model by pasting it into the Stable Diffusion Web UI and observing if it reads and applies correctly.
🎉 Successful Model Change and Future Topics
The speaker concludes by summarizing the successful change of models in the Stable Diffusion Web UI and the ability to generate images with the updated model. They express excitement about the rapid evolution of the product and its open-source nature. The speaker then shifts focus to the next topic, which involves additional learning (likely referring to further training or fine-tuning of the model). They invite viewers to subscribe and rate the video for future content, indicating a continuation of the series.
Mindmap
Keywords
💡Stable Diffusion
💡Web UI
💡GPU and CPU
💡Python
💡Git
💡Installation
💡Command Line
💡Error Handling
💡Model Switching
💡Checkpoints
💡Image Generation
Highlights
The speaker discusses the process of running Stable Diffusion WebUI on a PC without a GPU, using only the CPU.
The prerequisite for the installation is having Python 3.10.6 installed, with paths properly configured and Git installed.
The speaker provides a detailed guide on installing Python 3.10.6 from the official release page, including selecting the correct version and the 64-bit Windows installer.
Instructions for installing the Windows Kit are given, including choosing the right components and settings for the user's needs.
The process of downloading the Stable Diffusion WebUI repository using git clone is explained, with emphasis on choosing the right directory for the download and execution.
The speaker encounters an error when running the WebUI User.bat file and provides a solution by skipping the GPU check using a command-line argument.
The speaker successfully generates an image using the CPU-only setup, demonstrating the practical application of the technology.
The process of changing the model used by the WebUI is discussed, including the use of the 'ckpt' command-line argument.
The speaker attempts to download and use a popular model, showcasing the flexibility and updatable nature of the Stable Diffusion WebUI.
The speaker provides a step-by-step guide on how to download, install, and use the Stable Diffusion WebUI, making it accessible for users with different levels of technical expertise.
The video includes troubleshooting tips, such as disabling path length limit, to prevent potential issues during the installation process.
The speaker emphasizes the importance of using the correct version of Python and the Windows Kit to ensure compatibility and smooth installation.
The video demonstrates the practical use of command-line arguments to modify the behavior of the WebUI, such as skipping GPU checks.
The speaker's attempt to generate an image with a playful prompt shows the creative potential of the Stable Diffusion WebUI.
The video provides insights into the evolving nature of AI models, with the speaker searching for and attempting to use a recent and popular model.
The speaker's content is educational, offering a clear guide for users interested in exploring AI and machine learning applications without the need for advanced hardware.