👉Cài đặt Stable Diffusion AI trên PC Windows thành công 100% | Setups SD on PC

Xây Dựng Thực Hành
19 Feb 202446:05

TLDRThe video script offers a comprehensive guide on installing and utilizing the stable diffusion AI tool for various applications, particularly in the field of construction and design. It addresses common installation issues, outlines system requirements, and provides step-by-step instructions for setting up the software, including Python installation, model and checkpoint downloads, and GPU driver optimization. The script also discusses the AI's capabilities in generating images, editing real-life structures, and creating diverse styles, emphasizing its potential in enhancing design processes and offering clients visual options. The guide aims to help users successfully install and operate stable diffusion, enabling them to harness its AI capabilities for creative and practical applications.


  • 🖥️ The video provides a detailed guide on installing and troubleshooting Stable Diffusion on a PC, addressing common issues like Python error 9009 and graphics card recognition problems.
  • 🌐 Stable Diffusion is a popular free and open-source AI model that has various applications, particularly in the field of construction and design, such as creating interior and exterior designs, and visualizing architectural concepts.
  • 💡 The video emphasizes the importance of having a powerful enough PC to run the AI model smoothly, with recommended specifications including a minimum of 16 GB RAM, a dedicated GPU (NVIDIA GTX 1050 Ti or higher), and at least 15 GB of stable storage.
  • 🔧 The installation process involves several steps, including checking the PC's configuration, installing Git for software management, and Python for running the AI model, as well as downloading the necessary models and checkpoints from sources like cv.com and huggingface.co.
  • 📂 The video explains how to properly configure the downloaded files, including renaming and placing them in the correct directories to ensure the AI model functions correctly.
  • 🔄 The guide also covers updating the AI model's source code using Git commands and the importance of having the correct Python path set up to avoid installation errors.
  • 🎨 The application of Stable Diffusion in design is showcased through examples like generating images of people, products, and architectural concepts, highlighting its versatility and potential in the creative process.
  • 🖌️ The video mentions the use of control nets and style transfer to refine the AI's output, allowing users to create images that match specific styles or preferences.
  • 🚀 The video encourages viewers to explore additional resources and tutorials for mastering Stable Diffusion, suggesting that further learning is necessary to fully utilize the AI model in various projects.
  • 💻 The video script includes a step-by-step walkthrough of the installation process, from checking system requirements to optimizing the AI's performance settings.
  • 🛠️ The guide provides practical advice on troubleshooting, such as checking the graphics card driver and CUDA installation for optimal performance, and adjusting settings in the user configuration file for a successful setup.

Q & A

  • What is the main issue users face when installing stable diffusion on their PC?

    -The main issue users face is encountering errors such as error 9009 or errors related to the graphics card, which often occur due to the use of an outdated stable diffusion version.

  • What is stable diffusion and how can it be beneficial in the construction field?

    -Stable diffusion is a free, open-source AI image generation model that has become quite popular recently. It can be beneficial in the construction field by generating design concepts, interior and exterior views, and helping clients visualize different styles before proceeding with detailed work.

  • What are the minimum system requirements for running stable diffusion?

    -The minimum system requirements include a dedicated graphics card with at least 4 GB of VRAM, 16 GB of RAM, and a stable and smooth-running operating system, with at least 15 GB of storage space.

  • How can users check their PC's configuration?

    -Users can check their PC's configuration by pressing Ctrl + R, typing 'System' in the search bar, and clicking 'OK'. This will open the System window displaying details such as memory (RAM) and graphics card information.

  • Why is it important to install Git when setting up stable diffusion?

    -Git is important because it helps in automatically installing, updating, and managing the software and source code required for running stable diffusion, including updates to the AI model itself.

  • Which version of Python is recommended for running stable diffusion?

    -The recommended version of Python for running stable diffusion is 3.10.6 to 3.10.9, as these versions have been tested and found to work successfully without errors.

  • What are checkpoints or models in the context of stable diffusion?

    -Checkpoints or models are large files that have been trained on various data sets. They represent the AI's learned knowledge and can be applied to generate images in different styles or specific fields, such as construction.

  • What is the role of the control net (ve) in stable diffusion?

    -The control net (ve) helps to refine the image generation process, making the output more detailed and accurate, especially for features like eyes and textures. It is particularly useful for achieving a more realistic style in the images.

  • How can users who encounter error 9009 when installing Python resolve the issue?

    -To resolve error 9009, users need to ensure that they have correctly entered the Python installation path in the system's environment variables. They should also check that they have selected the option to add Python to the PATH during installation.

  • What is the significance of the 'user.chb' file in the stable diffusion setup?

    -The 'user.chb' file is a user configuration file that allows customization of the stable diffusion setup. It is where users can input commands and settings to control the AI's behavior and output, such as image resolution, style, and other parameters.



💻 Installation Issues and Stable Diffusion Overview

This paragraph discusses common installation problems users face with stable diffusion on their PCs, such as Python error 9009 or issues with graphics card recognition. It introduces the video's purpose, which is to guide users through the installation process to achieve a 100% success rate. The paragraph also mentions an existing blog post on the website that provides a detailed guide on stable diffusion installation. The introduction to stable diffusion highlights its benefits and applications in the construction field, emphasizing its role as a free, open-source AI model that has become popular recently.


🔧 System Requirements and Python Installation

The paragraph outlines the system requirements necessary to run stable diffusion, recommending specific hardware configurations such as a minimum of 4GB VRAM for the GPU and 16GB RAM. It provides a method for users to check their system specifications using a command in the system settings. The section also covers the installation of Git, explaining its function in downloading and updating software packages, and provides a link for downloading the appropriate version of Python, emphasizing the importance of choosing a compatible version to avoid installation errors.


📦 Model and Checkpoint Downloading

This section guides users through the process of downloading models or checkpoints, which are essential for the stable diffusion AI to function. It directs users to sources like cv.com or huggingface.co for downloading checkpoints and explains the difference between full and compact versions. The paragraph also touches on the role of 'vae' in enhancing image details, particularly the eyes and texture, and provides guidance on downloading separate 'vae' files if needed.


🛠️ Stable Diffusion Web UI Setup and Configuration

The paragraph details the steps for setting up the stable diffusion web UI, including cloning the repository from GitHub and configuring the environment. It emphasizes the importance of following the correct procedure to ensure the AI model runs smoothly and mentions the need to update the source code for optimal performance. The section also provides instructions for copying necessary files into the correct directories and touches on the installation of drivers for the graphics card.


🔄 Optimizing Graphics Card Driver and CUDA Installation

This part focuses on the installation and optimization of the graphics card driver and CUDA, which are crucial for the AI model to utilize the GPU effectively. It provides a link for downloading the appropriate NVIDIA driver and explains the process of checking the driver installation through the NVIDIA Control Panel. The paragraph also covers the installation of CUDA and cuDNN, offering guidance on selecting the correct versions and verifying the installation process.


📝 Customizing User Settings and Testing the AI

The paragraph discusses the customization of user settings in the 'user.chb' file to ensure the stable diffusion AI functions correctly. It highlights the need to modify specific lines in the file to update the source code and adjust settings for optimal performance. The section also describes the process of testing the AI by running commands and troubleshooting potential errors, such as the 9009 error, by ensuring the correct Python path is set.


🎨 Exploring Creative Applications of Stable Diffusion

This section delves into the creative applications of stable diffusion, demonstrating how it can be used to generate images based on user input. It covers the process of inputting commands to create detailed images, such as a beautiful girl, and discusses the capabilities of the AI in understanding and applying various styles. The paragraph also touches on the potential of using stable diffusion in construction, design, and other fields, emphasizing its versatility and usefulness in creating concepts and transforming sketches into detailed designs.


🖼️ Rendering Styles and Interior Design Visualization

The paragraph showcases the AI's ability to render different styles based on an existing interior design image. It explains how users can apply various styles to visualize the interior design in unique ways. The section also highlights the AI's capability to understand and apply user-defined styles, such as 'Hoàng Hôn' or 'Bình Minh,' to create stunning visualizations. The paragraph concludes with an encouragement for users to explore the AI's potential in transforming their design concepts and enhancing their creative projects.


📢 Final Thoughts and Encouragement for Further Learning

In the final paragraph, the speaker encourages viewers to like and subscribe to the channel for more content on stable diffusion and other topics related to construction and design. The speaker also invites users to visit the website for additional information and resources, emphasizing the value of learning about tools like AutoCAD, Excel, and construction-related skills to enhance their capabilities in the field.



💡Stable Diffusion

Stable Diffusion is an open-source AI model that generates images from text descriptions. In the context of the video, it is used for various applications such as architectural design, creating concept art, and editing existing images to enhance or alter their visual appearance. The video provides examples of how Stable Diffusion can be utilized in the construction field to create interior and exterior designs.


Python is a high-level programming language that is widely used for web development, data analysis, and artificial intelligence projects. In the video, Python is essential for running the Stable Diffusion model, as it provides the necessary coding environment to execute the AI's functionalities and generate images based on user input.


Git is a version control system that allows developers to manage and track changes in their codebase. In the context of the video, Git is used to download and update the Stable Diffusion model and its source code, ensuring that users have the latest features and bug fixes.


In the context of AI models like Stable Diffusion, a checkpoint refers to a pre-trained model or a specific state of the model that has been saved for later use. Checkpoints can be loaded to perform various tasks without the need for extensive training from scratch. The video discusses downloading different checkpoints for Stable Diffusion to enable users to generate images in various styles.


Video RAM (VRAM) is the memory used by graphics cards to store图像 data for rendering. In the context of running AI models like Stable Diffusion, having sufficient VRAM is crucial as it allows for faster processing and generation of images. The video emphasizes the importance of having a graphics card with enough VRAM to support the computational demands of the AI model.


CUDA is a parallel computing platform and programming model developed by NVIDIA that allows developers to use the GPU for general purpose processing. In the video, CUDA is necessary for optimizing the performance of Stable Diffusion by enabling the AI to leverage the GPU for image generation, which can significantly speed up the process.


cuDNN is a GPU-accelerated library for deep neural networks that is part of the CUDA platform. It provides highly optimized primitives for deep learning. In the context of the video, cuDNN is installed alongside CUDA to enhance the performance of the Stable Diffusion model by allowing it to run more efficiently on the GPU.

💡Command Line

The command line, also known as the command prompt or terminal, is a text-based interface for interacting with the operating system. In the video, the command line is used to execute various commands for installing and setting up the Stable Diffusion model, as well as for cloning the model's repository from GitHub.

💡Configuration File

A configuration file is a file used to store settings and options for a software application. In the context of the video, the configuration file for Stable Diffusion contains important settings such as the Python path and other parameters that the AI model uses during its operation. Users are instructed to edit this file to ensure proper setup and functioning of the model.

💡Image Generation

Image generation refers to the process of creating visual content using AI models, like Stable Diffusion. It involves inputting text descriptions or other data into the model, which then produces corresponding images based on the input. The video showcases the capabilities of Stable Diffusion in generating images, such as creating a beautiful girl or designing architectural elements.


The video provides a comprehensive guide on installing and setting up stable diffusion on a PC, addressing common issues such as Python error 9009 and graphics card recognition problems.

Stable diffusion is a powerful AI image generation tool with wide applications in various fields, including construction and design, making it valuable for creating concept designs and visualizing architectural projects.

The video emphasizes the importance of having a sufficiently powerful PC to run stable diffusion, recommending specific hardware configurations such as a minimum of 4GB VRAM for the GPU and 16GB RAM.

A step-by-step guide is provided for installing necessary software like Git and Python, with specific version recommendations to ensure compatibility and avoid installation errors.

The video explains how to download and utilize various models or checkpoints for stable diffusion from sources like cv.com and huggingface.co, highlighting the benefits of using pre-trained models for specific tasks.

The process of importing and applying vector files (ve) for additional features such as detailed eyes and textures is discussed, improving the quality and realism of generated images.

Detailed instructions are given for configuring the user settings file to optimize stable diffusion's performance, including setting the correct Python path and adjusting parameters for image quality and generation speed.

The video demonstrates how to use stable diffusion for architectural visualization, showing how to generate detailed interior and exterior designs based on sketches or 3D models.

The importance of training stable diffusion with specific styles or laora for accurate image generation is emphasized, with the video showing how to integrate user-defined styles for personalized results.

The video also covers how to use control nets and style transfer to modify existing images or photos, allowing users to apply desired styles to their projects.

Practical applications of stable diffusion in fashion design and concept art are discussed, showcasing the tool's versatility beyond architectural and construction uses.

The video provides troubleshooting tips for common errors encountered during installation, such as Python path configuration and graphics card driver issues.

A demonstration of the stable diffusion interface and its features is given, including how to select models, input text prompts, and generate images based on user specifications.

The video concludes with a reminder to follow the channel for more tutorials and updates on stable diffusion and its applications in various fields, encouraging continuous learning and exploration.