AI 창작 시대! 지금 당장 배워야할 Stable Diffusion Web UI 최신 설치 및 사용법 완벽 가이드

조코딩 JoCoding
12 Nov 202250:03

TLDRThe video script introduces Stable Diffusion Web UI, a tool that leverages AI to create images from text prompts. It guides viewers through the setup process for various operating systems and explains the functionalities of the Web UI. The video also shares tips for refining images using features like inpainting and outpainting, and emphasizes the potential of AI in the creative process, showcasing how it can transform text into vivid, detailed images.


  • 🌟 Introduction to Stable Diffusion Web UI - A guide video for using the Stable Diffusion Web UI is presented, covering its background, setup, and real-life applications.
  • 📚 Background of AI and Diffusion Models - The video explains the concept of AI used for creating images and cartoons, focusing on the diffusion model and its learning process through denoising.
  • 🚀 Free Access to Stable Diffusion Model - Stability AI has made the Stable Diffusion model available for free, and it can be downloaded from Hugging Face for various uses, including fine-tuning for specific tasks.
  • 🖥️ Setting Up Stable Diffusion Web UI - Detailed instructions are provided for setting up the Stable Diffusion Web UI on different platforms, including Windows, Mac, and through online services like Google Collab.
  • 🛠️ Customizing the Environment - Users can customize their environment by selecting different models, using online services, and adjusting settings to suit their needs.
  • 🎨 Text-to-Image Functionality - The Web UI allows users to input text prompts and generate images based on those prompts, with options to adjust the level of detail and image properties.
  • 🖼️ Image-to-Image Transformation - Users can transform existing images into new ones by using the Image-to-Image tab, which allows for adjustments and modifications based on the original image.
  • 🎨 Inpainting and Outpainting - The video discusses the inpainting and outpainting features, which enable users to modify specific parts of an image or create backgrounds where none exist.
  • 🔄 Iterative Process for Improvement - The content creator emphasizes the importance of an iterative process when generating images, suggesting that users refine their prompts and settings to achieve desired results.
  • 📈 Tips and Tricks for Better Images - The video provides numerous tips for creating better images, such as using emotional prompts, specifying camera settings, and incorporating popular keywords from art communities.
  • 🔗 Community and Resources - The video encourages users to explore and utilize community resources, such as prompt books and websites like, for inspiration and to improve their image generation skills.

Q & A

  • What is the primary function of Stable Diffusion Web UI?

    -The primary function of Stable Diffusion Web UI is to provide a user-friendly interface for creating images using AI, based on text prompts or other images. It allows users to easily generate images without the need for extensive coding knowledge or high-performance computing resources.

  • How does the diffusion model work in AI image generation?

    -The diffusion model works by initially adding noise to the original image, blurring it, and then gradually decoding the noise back to the original image. This process is learned by the AI, which can then create new images from just a text prompt, without requiring an original image.

  • What is the significance of the term 'fine tuning' in the context of AI models?

    -Fine tuning refers to the process of learning additional things for a specific purpose in a model. It allows for the creation of specialized models, such as those for drawing cartoons, making furniture images, or 3D animation images, by further training the base model on specific data sets.

  • How can users access and use the Stable Diffusion model made by Stability AI?

    -Users can access and use the Stable Diffusion model made by Stability AI for free by downloading it from Hugging Face. The model can then be fine-tuned or used with various UIs like Stable Diffusion Web UI to easily generate images based on text inputs.

  • What are the main components of the Stable Diffusion Web UI?

    -The main components of the Stable Diffusion Web UI include a text input window or button, a menu for selecting the AI model, and various settings and options for adjusting the image generation process, such as the model version, sampling steps, and prompt parameters.

  • How can users install and run the Stable Diffusion Web UI on their computers?

    -Users can install and run the Stable Diffusion Web UI on their computers by following the installation instructions provided in the script, which includes installing Python, Git, and downloading the necessary repositories and model files. The process varies slightly depending on the operating system (Windows, Mac, or online environments like Google Collab).

  • What is the role of the 'negative prompt' in image generation?

    -The negative prompt is used to specify elements or characteristics that should be avoided in the generated image. By including words in the negative prompt, the AI can be guided to exclude certain features, helping to create an image that better matches the user's vision.

  • What is the purpose of the 'CFG Scale' setting in the Stable Diffusion Web UI?

    -The CFG Scale setting adjusts how closely the generated image follows the input prompt. A lower CFG Scale value results in a less prompt-adherent, more creative image, while a higher value makes the image more faithful to the prompt.

  • How can users improve the quality of images generated by Stable Diffusion Web UI?

    -Users can improve the quality of generated images by using detailed and specific prompts, referring to guides like the Dali Prompt Book, and utilizing features like inpainting and outpainting for image modification. Additionally, using keywords that enhance realism, detail, and style can significantly improve the outcome.

  • What are some tips for creating more realistic images with Stable Diffusion Web UI?

    -To create more realistic images, users can include keywords related to desired emotions, lighting, and artistic styles. Adding specific camera settings, mentioning high-resolution terms like '8K', and using phrases like 'hyper realistic' can enhance the image's realism. Additionally, experimenting with different settings and learning from community-shared tips can help achieve better results.

  • What is the role of the 'Seed' value in image generation?

    -The Seed value is a unique identifier that determines the randomness in the image generation process. By copying and pasting the same Seed value, users can recreate similar images with consistent randomness, allowing them to generate multiple images in a similar style.



📚 Introduction to Stable Diffusion Web UI

This paragraph introduces the viewer to the concept of Stable Diffusion Web UI, a tool that utilizes AI to create images based on text prompts. The speaker, Chocoding, explains that the video will serve as a comprehensive guide on setting up and using the Stable Diffusion Web UI. The background and principles of the diffusion model are briefly discussed, highlighting how AI learns to create images from noise by reversing the denoising process. The speaker also mentions the availability of the Stable Diffusion model by Stability AI, which can be downloaded for free from Hugging Face, and the various models that have been developed for different uses.


🖥️ Setting Up Stable Diffusion Web UI on Different Platforms

In this section, the speaker provides detailed instructions on how to set up the Stable Diffusion Web UI on different platforms, starting with a general guide on how to access the service through a web browser. Specific instructions are given for installing the UI on Windows and Mac operating systems, including the necessary steps and links to resources. The speaker also discusses the use of Google Collab for executing Python code in an online environment, explaining the process of connecting to Google Drive, selecting a GPU runtime, and executing the necessary code to run the AI model.


🔧 Windows PC Installation Process

This paragraph delves into the step-by-step process of installing the Stable Diffusion Web UI on a Windows PC. The speaker outlines the recommended system specifications, such as the need for a GPU with at least 4GB of VRAM. The installation process involves downloading and installing Python, Git, and the necessary repositories from GitHub. The speaker also explains how to download the model file and place it in the correct directory. Additional steps for installing GFP GAN for face restoration are provided as an optional extra.


🍎 Installation on MacBook with Apple Silicon

The speaker continues with instructions on installing the Stable Diffusion Web UI on a MacBook with Apple Silicon. The process involves installing Homebrew and using it to install necessary dependencies. The speaker guides the viewer through the command-line process, including how to download the Stable Diffusion model directly or from Hugging Face, and how to handle the model files within the Stable Diffusion Web UI folder. The paragraph concludes with troubleshooting a potential error related to a missing module and how to resolve it.


🎨 Exploring the Web UI's Features and Functions

This section provides an overview of the Web UI's main features and functions. The speaker explains the different tabs available within the UI, such as Text to Image, Image to Image, and Extras, each with its specific purpose. The paragraph also discusses the various settings and options within the UI, like the checkpoint version, model selection, and the different extensions that can be installed to enhance the functionality of the Web UI.


🖌️ Creating Images with Text-to-Image Function

The speaker demonstrates how to use the Text-to-Image function to create images from text prompts. The process involves entering a sentence or phrase in English, which can be translated using tools like Google Translate for non-English speakers. The speaker provides tips on how to improve the quality of the generated images, such as using emotional prompts, specifying camera settings, and employing various keywords to achieve a desired effect. The paragraph also touches on the use of the Prompt Matrix for experimenting with multiple prompts and the importance of using specific commands for more accurate image creation.


🌟 Enhancing and Modifying Images with Image-to-Image and Inpaint

This paragraph focuses on the Image-to-Image and Inpaint functions within the Web UI. The speaker explains how to enhance and modify existing images using these features. The Image-to-Image function allows users to transform images into new creations, while Inpaint enables the modification of specific parts of an image. The speaker provides a live demonstration of turning a character into a human-like image and adjusting the details of the image using inpainting. The paragraph also discusses the use of outpainting for creating backgrounds and the potential for combining inpainting and outpainting to achieve various modifications.


🔧 Fine-Tuning and Final Touches with Inpaint and Outpaint

The speaker continues to explore the Inpaint and Outpaint functions, showing how to fine-tune and add final touches to images. The paragraph includes a demonstration of how to use Inpaint to add features like hamster ears to an image and how to adjust the strength of the changes. The speaker also discusses the use of Outpaint to create backgrounds and the importance of feedback and iteration in achieving the desired image result. The paragraph concludes with a mention of the upcoming integration of Stable Diffusion with Photoshop, highlighting the growing accessibility and application of this technology.



💡Stable Diffusion Web UI

Stable Diffusion Web UI is a user interface designed to facilitate the use of the Stable Diffusion model, which is an AI-based system for generating images from text prompts. In the context of the video, it is the primary tool introduced for creating and manipulating images, allowing users to harness the power of AI without the need for extensive coding knowledge.

💡Diffusion Model

A diffusion model is a type of AI model used for image generation. It operates on the principle of gradually adding noise to an original image until it is fully blurred, and then learning to reverse this process, or 'denoising', to recreate the original image. This concept is central to the video, as it explains the underlying technology behind the Stable Diffusion model and how it can be used to create new images from text prompts.

💡Fine Tuning

Fine tuning is the process of training an AI model on additional data to improve its performance or adapt it to a specific task. In the video, this concept is used to describe how various models are created for different purposes, such as drawing cartoons or generating images of furniture, by fine tuning the base Stable Diffusion model.

💡Hugging Face

Hugging Face is a platform that provides access to various AI models, including the Stable Diffusion model made by Stability AI. In the video, it is mentioned as a place where users can download the Stable Diffusion model for free, which is essential for setting up and using the Stable Diffusion Web UI.

💡Google Collab

Google Collab is a cloud-based service that allows users to run Python code in an online environment provided by Google. It is mentioned in the video as a platform where users can execute the AI model without the need for a locally powerful computer, making it accessible for a wider range of users to create AI-generated images.


In the context of the video, a token is a security credential that grants access to certain features or resources, such as the AI models on Hugging Face. Users are required to create and use a token to download models from Hugging Face, which is an essential step in setting up the Stable Diffusion Web UI.


Image-to-Image is a function within the Stable Diffusion Web UI that allows users to transform or modify existing images to create new ones. It is a key feature demonstrated in the video, showcasing how users can take an initial image and apply various adjustments or effects to generate a different output.


Inpainting is a feature in image editing that allows users to modify specific parts of an image, filling in or altering selected areas to create a new version of the image. In the video, inpainting is used to demonstrate how the Stable Diffusion Web UI can be used to add or change details in an image, such as adding hamster ears to a character.


Outpainting is a technique that involves extending an existing image by generating new content that matches the style and content of the original image. In the context of the video, outpainting is a function of the Stable Diffusion Web UI that enables users to expand an image by creating a background or additional elements that fit seamlessly with the original image.


A prompt in the context of AI image generation is a text input that guides the AI in creating a specific image. It is a crucial element in the video, as it explains how users can use descriptive phrases or keywords to instruct the AI model to generate the desired visual content.


Introduction to Stable Diffusion Web UI and its capabilities in creating images from text prompts.

Explanation of the diffusion model and its process of transforming noise to a clear image through denoising.

Overview of how AI learns from original images and prompts to create new images without needing an original image.

Introduction to Stability AI's diffusion model and its availability for free download on Hugging Face.

Description of fine-tuning process for creating specialized models like cartoon, furniture, and 3D animation image generators.

Explanation of how to use Stable Diffusion Web UI for non-developers to easily generate AI-based images through a user interface.

Detailed guide on setting up Stable Diffusion Web UI using different environments, including Google Collab and local installation on Windows and Mac.

Instructions on how to install Python, Git, and other necessary dependencies for running Stable Diffusion Web UI on a Windows PC.

Process of downloading the Stable Diffusion model and additional components like GFP GAN for face restoration.

Demonstration of the Web UI's main functions, including Text to Image, Image to Image, andExtras for image processing and analysis.

Explanation of various settings within the Web UI such as prompt, negative prompt, sampling steps, and sampling methods for controlling image generation.

Introduction to using extensions like Dreambooth for fine-tuning and enhancing the functionality of the Web UI.

Tips on creating more realistic images by using specific commands, camera settings, and leveraging resources like Dali Prompt Book and

Example of transforming a character image into a human-like image using image-to-image function and fine-tuning the details.

Discussion on the potential of Stable Diffusion in creating high-quality, detailed images and its impact on the art and design industry.

Mention of Stable Diffusion Photoshop plugin as an example of the technology's integration into existing creative tools.

Encouragement for users to explore and experiment with Stable Diffusion to create their desired images and contribute to the growing body of work.