[SD 01] Stable Diffusion 설치부터 응용까지 전 과정을 시리즈로 제작하려고 합니다.

조피디 연구소 JoPD LAB
2 Jan 202409:01

TLDRThe video script outlines a series on Stable Diffusion, starting from installation to application. It emphasizes the importance of graphic cards, recommending at least an RTX 2080, and specifies system requirements like 8GB RAM and 10GB hard disk space. The script details the installation process of Python 3.10.6 and Git, followed by Stable Diffusion. It introduces the concept of checkpoints, which are pre-trained models for generating images, and explains how different models affect the style of the generated images. The video also discusses the process of downloading additional models from a platform, highlighting the significance of checking licenses before use. The script concludes with a demonstration of image generation using the Stable Diffusion web interface and encourages viewers to look forward to future videos on advanced usage and settings.


  • 📌 The video series aims to teach viewers how to use Stable Diffusion from installation to application, enabling them to create and utilize images like professionals.
  • 💻 Before installation, ensure your computer meets the required specifications: a minimum of NVIDIA VM 6 or higher, 8GB+ RAM (16GB recommended), and at least 10GB of free hard disk space.
  • 🔧 Install Python and Git, which are essential for running Stable Diffusion, and make sure to install Python version 3.10.6 to avoid errors.
  • 🔄 Download and install Stable Diffusion using the latest version available at the time of the video (version 1.7 released two weeks prior).
  • 🖼️ Choose a checkpoint model within the Stable Diffusion web UI to generate images; the choice of model affects the overall style of the generated images.
  • 🔍 Use the filter option when browsing available models to narrow down the selection based on the version of Stable Diffusion you have installed.
  • 🎨 Select and download checkpoint models based on their popularity and suitability for creating realistic or stylized images.
  • 📝 Carefully review the license agreement before using a downloaded model to ensure compliance with the terms, especially if you plan to sell or merge the images for profit.
  • 🔄 Download both the full and optimized versions of the selected model, with the optimized version being preferred for its reduced size and removed redundant data.
  • 📂 Save the downloaded images in the correct folder structure within the Stable Diffusion installation directory to ensure they are recognized by the application.
  • 🚀 By following the video series, viewers can expect to see an improvement in the quality of generated images and gain a deeper understanding of Stable Diffusion's features and settings.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is about Stable Diffusion, covering its installation, basic usage, and application in creating images.

  • What is the recommended minimum graphics card specification for Stable Diffusion?

    -The recommended minimum graphics card specification is NVIDIA RTX 2080 or higher.

  • How much memory is required for Stable Diffusion?

    -A minimum of 8GB of RAM is required, with 16GB or more being recommended.

  • What is the minimum hard disk space needed for Stable Diffusion installation?

    -At least 10GB of free space is needed for the installation.

  • Which version of Python is advised for Stable Diffusion?

    -Python version 3.10.6 is advised for Stable Diffusion.

  • What is the significance of the Checkpoint in Stable Diffusion?

    -The Checkpoint in Stable Diffusion is crucial as it contains pre-trained Stable Diffusion weights for generating images. The choice of Checkpoint can affect the overall style of the generated images.

  • How can users find and download additional Checkpoints in Stable Diffusion?

    -Users can access the website mentioned in the script to browse, filter, and download additional Checkpoints that are popular and suitable for their needs.

  • What is the recommended approach for selecting a Checkpoint model in Stable Diffusion?

    -The video suggests sorting models by the most downloads and recommends specific models for creating realistic and diverse images of different subjects.

  • What is the importance of checking the license when downloading Checkpoint models?

    -Checking the license is important to ensure that users understand the usage rights and restrictions, such as the requirement to credit the creator and the limitations on selling or merging the models.

  • How does the video demonstrate the creation of an image using Stable Diffusion?

    -The video demonstrates the creation of an image by selecting a Checkpoint, entering a description in the prompt, and clicking the generate button. It also shows how to apply negative prompts to exclude certain elements from the image.

  • What can users expect to learn in the subsequent videos of the series?

    -In the subsequent videos, users can expect to learn about the meaning and usage of various options in Stable Diffusion, as well as advanced settings and environmental configurations.

  • How does the video conclude?

    -The video concludes by encouraging viewers to like and subscribe for more advanced content on Stable Diffusion and its applications.



🚀 Introduction to Stable Diffusion A Series

This paragraph introduces a new video series on Stable Diffusion A, a deep learning model for image generation. The series aims to guide viewers from installation to application, enabling them to create professional-level images. The speaker emphasizes the importance of a powerful graphics card, recommending at least an RTX 2080, and sufficient computer specifications such as 8GB+ RAM and 10GB+ hard disk space. The process begins with installing Python and Git, followed by Stable Diffusion A. The speaker provides a detailed walkthrough of the installation steps, including downloading Python, checking specific version requirements, and executing the installation files. The video aims to help viewers improve their skills through practical exercises and various application examples, with the support and encouragement of the audience.


🌟 Exploring Checkpoint Models and Image Generation

This paragraph delves into the selection and utilization of checkpoint models in Stable Diffusion A for image generation. The speaker explains the role of checkpoint models in determining the style of the generated images, likening them to the 'brain' of the process. The video covers how to choose a checkpoint model, with a focus on the most popular and highly downloaded models. It provides guidance on downloading models, including understanding license requirements and ensuring the合法 use of the models. The speaker also demonstrates how to download and install a model, integrate it into the Stable Diffusion A environment, and use it to generate images. The paragraph concludes with a showcase of images created using different checkpoint models, highlighting the unique color schemes and styles each model brings to the images.



💡Stable Diffusion

Stable Diffusion is a type of artificial intelligence (AI) model used for generating images from textual descriptions. In the context of the video, it is the primary software being discussed and installed, with a focus on its capabilities to transform text prompts into visual content. The video outlines the process of installing Stable Diffusion and emphasizes its importance in creating high-quality images, as it is the foundation for the tutorials and practical applications that follow.


Python is a high-level programming language that is widely used for various types of software development. In the video, Python is highlighted as a necessary component for running Stable Diffusion, indicating its role as a foundational software that supports the AI model. The version of Python specified in the script (3.10.6) is crucial to ensure compatibility and prevent errors during the installation process.

💡Graphics Card

A graphics card is a hardware component in a computer system that renders images, video, and animations. It is essential for tasks requiring intensive graphical processing like gaming, video editing, and AI image generation. The video script specifies a minimum requirement of a graphics card from NVIDIA, recommending at least an RTX 2080 for optimal performance with Stable Diffusion.

💡Memory (RAM)

Memory, or RAM (Random Access Memory), is the primary storage area for a computer's short-term data and program instructions. It allows the system to multitask and run applications smoothly. In the context of the video, sufficient memory is crucial for the efficient operation of Stable Diffusion, with a minimum of 8GB recommended and 16GB suggested for better performance.

💡Hard Disk Space

Hard disk space refers to the amount of storage available on a computer's hard drive for saving files and applications. The video emphasizes the need for ample hard disk space, specifically 10GB or more, to accommodate the installation files and the data generated by the Stable Diffusion AI model.


Checkpoints in the context of AI models like Stable Diffusion refer to pre-trained weights or states of the model that can be used to generate images. These checkpoints are crucial as they contain the learned patterns and features necessary for the AI to produce images from textual descriptions. The video discusses selecting and downloading various checkpoints to enhance the image generation capabilities of Stable Diffusion.


Prompts are textual descriptions or inputs provided to the Stable Diffusion AI model to guide the generation of specific images. They are a key component in the creative process, as they directly influence the output of the AI. The video emphasizes the importance of crafting effective prompts to achieve desired results in image generation.

💡Negative Prompts

Negative prompts are instructions given to the AI model to avoid including certain elements in the generated image. They are used to refine the output by specifying what should not be present. In the video, negative prompts are discussed as a tool to guide the AI in creating images that align more closely with the user's vision.

💡Image Quality

Image quality refers to the resolution, clarity, and overall visual appeal of the images produced by the AI model. In the context of the video, improving image quality is a primary goal, with the tutorial aiming to teach viewers how to enhance their generated images through various settings and options within Stable Diffusion.

💡Model Selection

Model selection involves choosing the appropriate pre-trained AI model or checkpoint for the task at hand. Different models can produce varying styles and qualities of images, making the selection process crucial for achieving desired outcomes. The video script emphasizes the importance of selecting the right model based on the type of image to be generated.


A license in the context of software and AI models grants users certain permissions and restrictions on how they can use, modify, and distribute the software or the generated content. The video script stresses the importance of checking the license agreement before downloading and using a model to ensure compliance with the terms set by the model's creator.


Introduction to the series on Stable Diffusion A, covering the entire process from installation to application.

The importance of having a graphics card with at least 6GB VRAM, with RTX 2080 or higher recommended.

The necessity of at least 8GB of RAM, with 16GB or more being recommended for optimal performance.

A minimum of 10GB free hard disk space is required for the software.

Instructions on installing Python 3.10.6 and Git, which are essential for running Stable Diffusion A.

Downloading and installing Stable Diffusion A using the latest version available at the time of the video.

The process of downloading source code and extracting it for installation.

Running the application and dismissing the PC protection warning for the first execution.

An overview of the Stable Diffusion A web interface, including model selection and prompt input.

The role of checkpoints in generating images and the availability of pre-trained models.

Explanation of how to select and use checkpoints, such as the SD 1.5 base model.

Recommendations for choosing models based on the type of images to be generated, like realistic vision for Western faces or a mix of models for Asian faces.

Instructions on downloading and installing recommended models from the SIAI website.

The importance of checking the license before using a model for generating images.

How to download and use optimized models to reduce redundancy and improve efficiency.

A demonstration of generating an image using the Stable Diffusion A web interface and a pre-selected model.

A comparison of images generated using different checkpoints, showcasing the unique style and color palette of each.

An encouragement for viewers to follow along with the series to experience the progression of image quality.

A closing statement thanking viewers for their support and promising more advanced content in future videos.