👉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 stable diffusion, a powerful AI image generation tool. It addresses common installation issues, outlines system requirements, and provides step-by-step instructions for setting up the software. The video also highlights the tool's versatility in various fields, such as architecture and product design, and explains how to optimize settings for best results. Additionally, it touches on the importance of using the correct Python version and updating the software to ensure smooth operation.


  • 💻 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 GPU recognition problems.
  • 🖼️ Stable diffusion is highlighted as a powerful AI image generation tool with wide applications in various fields, including construction and design, offering benefits like quick visualization of architectural sketches.
  • 📈 The importance of having a sufficiently powerful PC to run stable diffusion is emphasized, with recommended specifications including a minimum of 4GB VRAM for the GPU and 16GB RAM.
  • 🔄 The process of checking PC configuration is detailed, instructing viewers on how to use system information commands to ensure their hardware meets the requirements.
  • 🔧 Installation of necessary software like Git and Python is discussed, with specific version recommendations to ensure compatibility with stable diffusion.
  • 🔗 The video provides links and guidance for downloading required models and checkpoints from platforms like cv.com and huggingface.co, with detailed instructions on selecting and using different versions.
  • 🎨 The application of stable diffusion in design is showcased through examples like generating an image of a beautiful girl with a simple command, illustrating the tool's potential for creating detailed and styled images.
  • 🏠 An example of using stable diffusion in construction is given, where the AI can help visualize interior design options for clients by creating various styles based on a hand-drawn sketch or 3D model.
  • 🚀 The video emphasizes the importance of updating the AI model and source code for stable diffusion to ensure the latest features and bug fixes are utilized.
  • 📋 The script mentions the need to edit the user settings file for stable diffusion to include the correct Python path and other configurations for optimal performance.
  • 🎥 The presenter shares personal experiences and tips, such as using the 'realistic Vision' model for creating more lifelike images and the potential need for additional training with tools like laora for specific styles.

Q & A

  • What is the main issue faced by users 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 prevent successful installation.

  • 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 popular recently. In the construction field, it can be applied to create preliminary sketches or 3D drafts, visualize interior and exterior designs, and assist clients in choosing preferred styles before proceeding with detailed development and construction.

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

    -The minimum hardware requirements include a dedicated GPU with at least 4 GB VRAM, such as an NVIDIA GTX 1050 Ti or higher, and 16 GB of RAM. Additionally, at least 15 GB of stable and fast storage space is needed.

  • How can users check their PC's hardware configuration?

    -Users can check their PC's hardware configuration by pressing Ctrl + R, typing 'System' in the search bar, and clicking 'System'. This will display a summary of the system's specifications, including RAM, GPU, and VRAM.

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

    -Git is essential for downloading and updating the stable diffusion software and its source code. It automates the process of fetching the latest updates and managing different versions of the software.

  • 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. It is advised to avoid using the latest version, as it may cause installation errors with stable diffusion.

  • What is a checkpoint in the context of stable diffusion?

    -A checkpoint is a trained AI model file with a large storage capacity that has been trained on various data sets. It can be downloaded and used for different applications, including construction, design, and other fields.

  • 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 features more accurate, especially for eyes and textures. It is a separate component that needs to be downloaded if the chosen checkpoint does not include it.

  • How can users ensure their graphics card driver and CUDA are properly installed for stable diffusion?

    -Users should download and install the appropriate NVIDIA graphics card driver from the NVIDIA website and the compatible version of CUDA from the CUDA website. They should also copy the necessary files into the CUDA directory to avoid runtime errors.

  • What changes should be made to the user config file for stable diffusion to avoid errors?

    -Users should edit the user config file to include the correct Python path, enable auto updates, and adjust settings like image quality, steps, and batch size according to their hardware capabilities and desired output.

  • How can users utilize stable diffusion for creating images based on their design concepts?

    -Users can input keywords or phrases related to their design concepts, such as style preferences, colors, or specific features, into the stable diffusion interface. The AI will then generate images based on the provided information.



💻 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 speaker references a previous article on the topic and suggests combining the video with that resource for the best results. The paragraph also provides an overview of stable diffusion, highlighting its benefits and applications in various fields, particularly in construction and design.


🔧 System Requirements and Git Installation

The speaker outlines the minimum system requirements needed to run stable diffusion, recommending specific hardware configurations such as a certain amount of RAM and a capable GPU. The paragraph then delves into the installation of Git, a software essential for managing and updating the software packages required by stable diffusion. The speaker provides a step-by-step guide on how to download and install Git, emphasizing the importance of this step in the overall setup process.


🐍 Python Installation and Model Download

This section focuses on the installation of Python, which is necessary for running stable diffusion. The speaker advises on the appropriate Python version to install and provides a link for downloading. The paragraph also covers the process of downloading models or checkpoints from sources like cv.com and huggingface.co. The speaker explains the difference between full and compact versions of these models and provides guidance on selecting the right one based on the user's needs.


📂 File Preparation and Web UI Setup

The speaker instructs viewers on how to prepare the downloaded checkpoint files and where to place them within the stable diffusion directory structure. The paragraph details the process of setting up the web UI for stable diffusion, including cloning the necessary code from GitHub and configuring the environment. The speaker emphasizes the importance of following these steps carefully to ensure the smooth operation of stable diffusion.


🖥️ Graphics Card Driver and CUDA Installation

This paragraph addresses the installation of graphics card drivers and CUDA, which are critical for optimizing the performance of stable diffusion. The speaker provides guidance on downloading and installing the appropriate NVIDIA drivers and CUDA version, as well as the cuDNN library. The speaker also offers tips on checking the installation and ensuring that the graphics card is recognized correctly to avoid runtime errors.


🔧 Final Configuration and Stability Enhancements

The speaker discusses the final steps in configuring stable diffusion, including optimizing the graphics card settings for better performance. The paragraph covers the process of updating the system's environment variables and making necessary adjustments to the user configuration file for stable diffusion. The speaker also emphasizes the importance of these steps in preventing common errors and ensuring the stability of the software.


🎨 Exploring Creative Applications and Commands

This section delves into the creative potential of stable diffusion, demonstrating how it can be used to generate images based on user input. The speaker provides examples of commands that can be used to create detailed images, such as a beautiful girl or a specific architectural style. The paragraph also touches on the ability to refine and improve existing images or designs, showcasing the versatility and power of stable diffusion in various creative tasks.


🛠️ Advanced Techniques and Custom Styles

The speaker introduces advanced techniques for using stable diffusion, including the use of control nets and custom styles. The paragraph discusses the potential for users to train stable diffusion with their own styles, allowing for the creation of unique and personalized images. The speaker also mentions the importance of understanding and utilizing these features to fully harness the capabilities of stable diffusion in creative projects.



💡Stable Diffusion

Stable Diffusion is an open-source AI model that specializes in generating images from textual descriptions. It is widely used in various fields, including construction and design, to create visual concepts and enhance existing images. In the video, the speaker discusses the installation and application of Stable Diffusion for generating images and improving design projects.


Python is a high-level programming language that is often used for AI and machine learning applications. In the context of the video, Python is a necessary component for running the Stable Diffusion model, as it provides the coding framework for the AI to operate.


Git is a version control system that allows developers to manage and track changes in their code. In the video, the speaker mentions installing Git to facilitate the automatic updating of the Stable Diffusion model and its source code.


A checkpoint in the context of AI models like Stable Diffusion refers to a pre-trained model or a snapshot of the model's training progress. These checkpoints can be downloaded and used to generate images without the need for extensive initial training. The video discusses downloading various checkpoints for Stable Diffusion.


Video RAM (VRAM) is the memory used to store image data for the graphics processor. In the video, the speaker emphasizes the importance of having sufficient VRAM, such as 4 GB, to ensure that the Stable Diffusion model runs smoothly and can handle high-quality image generation tasks.


CUDA is a parallel computing platform and application programming interface model created by Nvidia. It allows developers to use Nvidia GPUs for general purpose processing. In the video, the speaker instructs viewers to install CUDA to optimize the performance of the Stable Diffusion model on Nvidia graphics cards.

💡Control Net

Control Net is a feature within the Stable Diffusion model that allows users to guide the generation process by providing specific instructions or styles. This can result in more accurate and customized image outputs based on the user's requirements. The video briefly mentions the use of Control Net for advanced image generation.


Image-to-Image is a term used in the context of AI image generation, referring to the process of converting one image into another with specific characteristics or styles. In the video, the speaker describes how Stable Diffusion can be used to transform sketches or existing images into detailed, stylized versions.

💡Style Transfer

Style Transfer is a technique in AI where the style of one image is applied to another, resulting in a new image that combines the content of one with the artistic style of the other. The video discusses using Stable Diffusion for style transfer, allowing users to apply various styles to their images or designs.


Configuration in the context of the video refers to the process of setting up and adjusting the parameters of the Stable Diffusion model to achieve desired results. This includes selecting the right checkpoint, adjusting VRAM usage, and modifying user settings for optimal performance and image generation.


Introduction to the video's purpose: addressing common errors encountered during Stable Diffusion installation, such as Python error 9009, graphics card recognition issues, and outdated Stable Diffusion versions, promising a 100% success rate in installation.

Background information on Stable Diffusion as a popular, open-source AI tool for image generation, its applications in various industries including construction, and its ability to generate multiple styles for project visualization.

Explanation of pre-installation requirements: ensuring the PC's hardware is capable of running Stable Diffusion, with recommendations for CPU, GPU, RAM, and storage specifications.

Step-by-step guide on how to install Git, Python, and manage versions to avoid compatibility issues, emphasizing the importance of using Python versions that are known to work well with Stable Diffusion.

Instructions on downloading and setting up the correct Stable Diffusion model, highlighting the importance of choosing the right model version and the process of registering on specific platforms to access model files.

Detailed walkthrough for configuring Python environment variables to ensure successful Stable Diffusion operation, addressing common installation errors.

Guide on updating graphics drivers and installing CUDA Toolkit to optimize performance for Stable Diffusion, focusing on compatibility with NVIDIA graphics cards.

Comprehensive explanation on editing configuration files to customize the Stable Diffusion UI, adjust image generation settings, and address specific user needs.

Practical demonstration of generating an image using Stable Diffusion, showing the command structure and parameters for creating high-quality, detailed images.

Introduction to advanced features of Stable Diffusion, such as controlling the level of detail, adjusting image dimensions, and managing GPU memory usage for efficient processing.

Showcase of Stable Diffusion's capabilities in transforming simple sketches into detailed architectural visuals, demonstrating its potential in design and construction.

Highlighting the versatility of Stable Diffusion across different fields, from character design to product visualization and fashion design, underscoring its wide-ranging applications.

Tips for troubleshooting common installation issues, offering solutions to ensure a smooth setup process for Stable Diffusion.

Emphasis on the role of community contributions and open-source models in enhancing Stable Diffusion's functionality and accessibility.

Encouragement for viewers to explore further applications of Stable Diffusion in their work, suggesting a future filled with creative possibilities powered by AI.