Stable diffusion tutorial. ULTIMATE guide - everything you need to know!

Sebastian Kamph
3 Oct 202233:35

TLDRJoin Seb in this comprehensive Stable Diffusion tutorial to create AI-generated images. Starting with installation, Seb guides through GitHub, Python, and model setup. Learn to generate images from text prompts, refine results with settings adjustments, and explore advanced features like image-to-image and painting. Discover how to iterate and upscale your creations for stunning AI art.


  • ๐Ÿ“Œ The tutorial provides a comprehensive guide on using Stable Diffusion for AI image creation.
  • ๐Ÿ” The user is challenged to identify the real image among six, with the answer revealed at the end.
  • ๐Ÿ’ป Installation of Stable Diffusion involves using GitHub, Python, and Git for Windows users.
  • ๐Ÿ“ฆ Downloading models from Hugging Face is necessary, with detailed instructions provided in the tutorial.
  • ๐ŸŽจ The 'Text to Image' tab is used to create images from textual descriptions, with settings adjustable for progress display.
  • ๐Ÿ”„ The 'git pull' command ensures that the latest version of Stable Diffusion is used before generating images.
  • ๐Ÿ” Prompts are crucial in guiding the AI to create desired images, with recommendations to study and adapt successful prompts.
  • ๐ŸŒ is mentioned as a resource for finding inspiration and examples of effective prompts.
  • ๐Ÿ”ข The 'Sampling Steps' and 'Sampling Method' settings significantly impact the image generation process.
  • ๐Ÿ–ผ๏ธ 'Image to Image' allows for refining existing images, with a focus on denoising strength and maintaining the original's likeness.
  • ๐ŸŽจ 'Paint' feature enables selective editing of images, with the ability to add or remove elements based on a mask.
  • ๐Ÿ“ˆ 'Upscalers' are introduced for enlarging images, with SwinIR recommended for its quality and effectiveness.

Q & A

  • What is the main purpose of this tutorial?

    -The main purpose of this tutorial is to guide users on how to create AI images using Stable Diffusion, from installation to generating various types of images.

  • What is the first step in installing Stable Diffusion?

    -The first step is to download the Windows installer for Python and ensure that the box for adding Python to the PATH is checked during installation.

  • How can users acquire the models needed for Stable Diffusion?

    -Users can acquire the models by creating an account on Hugging Face, accessing the repository, and downloading the 'standard' weight file.

  • What is the role of the 'git clone' command in the installation process?

    -The 'git clone' command is used to copy the necessary files for Stable Diffusion to the user's computer.

  • How does the 'git pull' command benefit the user before running Stable Diffusion?

    -The 'git pull' command ensures that the user's local files are updated with the latest versions from GitHub before running Stable Diffusion.

  • What is the significance of the 'prompt' in creating images with Stable Diffusion?

    -The 'prompt' is crucial as it directly influences the output image. It involves specifying the desired object and adding details to refine the AI's creation.

  • What is the recommended approach for adjusting the 'sampling steps' and 'sampling method'?

    -For beginners, it is recommended to start with KLMS as the sampling method and use at least 50 sampling steps for more consistent results.

  • How can users improve the quality of the generated images?

    -Users can improve image quality by refining their prompts, adjusting settings like 'denoising strength', and using features like 'restore faces' and image upscaling with upscalers.

  • What is the 'seed' in the context of Stable Diffusion?

    -The 'seed' is a value that determines the starting point for image generation. The same seed with the same settings will produce the same image.

  • How can users find inspiration for creating AI images?

    -Users can visit platforms like, which is a search engine for Stable Diffusion images, to find prompts and styles that they can adapt for their own creations.

  • What is the ultimate goal for users learning from this tutorial?

    -The ultimate goal is to enable users to create high-quality AI-generated images that match their desired outcomes, using the various features and techniques explained in the tutorial.



๐Ÿ“š Introduction to AI Image Creation

The paragraph introduces the viewer to the world of AI-generated images, highlighting the prevalence of such images in social media and the desire to create unique pictures, such as dogs in Star Wars attire. The guide, Seb, presents a challenge to identify the real image among six, with one being AI-made, and promises to reveal the answer later. The guide also assures that creating AI images is easier than it seems and will walk the viewer through the process, starting with the installation of necessary software and tools.


๐Ÿ’ป Setting Up AI Image Creation Tools

This section provides a step-by-step guide on setting up the tools required for AI image creation. The guide instructs the viewer to download and install Python, Git, and navigate to a GitHub repository to clone the necessary files for stable diffusion web UI. The process includes creating an account on Hugging Face to download the model files and placing them in the correct folder. The guide emphasizes the importance of following the installation instructions carefully to ensure the successful creation of AI images.


๐Ÿ–ผ๏ธ Text-to-Image: Creating from Scratch

The guide introduces the text-to-image feature of stable diffusion, where images can be generated from textual descriptions. The viewer is taken through the settings and options available, such as showing the progress bar and adjusting the image creation steps. The guide demonstrates how to refine the generated images by adding more details to the text prompt, such as specifying the style and resolution. The use of a search engine for stable diffusion images is suggested to find inspiration for prompts and understand how to achieve desired results.


๐ŸŽจ Exploring Sampling Steps and Methods

This part delves into the technical aspects of image generation, focusing on sampling steps and methods. The guide explains the role of sampling steps in refining the image through iterations and the different samplers available, such as Euler ancestral and LMS, which provide varying levels of consistency. The guide advises on the optimal settings for beginners and how to achieve better results by adjusting the sampling steps and samplers. The concept of seed value is introduced, explaining how it affects the randomness of image generation and consistency in batch processing.


๐Ÿ”„ Refining Prompts and Settings

The guide discusses the importance of refining prompts and adjusting settings to achieve the desired image results. The process of adding emphasis to certain words in the prompt using parentheses is explained, as well as the impact of the scale setting on how closely the AI adheres to the prompt. The guide demonstrates how to troubleshoot issues, such as when the generated image does not meet expectations, by adjusting the scale and sampling steps. The guide also introduces the concept of exotic alien features and how they can be incorporated into the image through careful prompt manipulation.


๐Ÿ–ผ๏ธ Image-to-Image Transformation

The guide shifts focus to image-to-image transformation, where an input image is used as a base to create a new image. The process involves adjusting the denoising strength to control how much of the original image is retained and how much noise is introduced to create a new image. The guide provides practical examples of how to use this feature, including changing the background of an image and maintaining the original subject's position and angle. The guide also touches on the use of the 'in paint' feature to selectively edit parts of the image and the importance of balancing the denoising strength to achieve a satisfactory result.


๐ŸŒŸ Final Touches and Upscaling

The final paragraph covers the finishing touches to the generated images, including the use of upscalers to enlarge the images while maintaining quality. The guide compares different upscalers like SwinIR, LDSR, and ESRGAN, highlighting their strengths and weaknesses. The guide demonstrates the process of upscaling an image and emphasizes the importance of testing multiple batches to achieve the best results. The guide concludes by encouraging viewers to explore advanced features in stable diffusion and offers to provide further tutorials on the topic.



๐Ÿ’กStable Diffusion

Stable Diffusion is an AI model used for generating images from text prompts. It is the primary subject of the video, which provides a comprehensive tutorial on how to use this technology. The video explains the installation process, different features, and how to create various types of images using Stable Diffusion.


GitHub is a platform where developers store and share their code. In the context of the video, it is used to host the Stable Diffusion web UI repository. Users are guided to navigate to GitHub to find the installation instructions and download necessary files for setting up Stable Diffusion on their computers.


Git is a version control system that allows developers to track changes in their code. The video instructs viewers to install Git on their Windows computers as part of the setup process for Stable Diffusion. It is used to clone the repository containing the Stable Diffusion files onto the user's local machine.

๐Ÿ’กHugging Face

Hugging Face is a platform that provides a wide range of AI models, including Stable Diffusion. The video guides users to Hugging Face to create an account and download the model weights necessary for running Stable Diffusion. It is an essential step in the installation process and for accessing the AI capabilities required to generate images.


Prompts are the text inputs provided to Stable Diffusion to guide the generation of images. The video emphasizes the importance of crafting effective prompts to achieve desired results. It suggests that users can experiment with different prompts and adjust them based on the output to refine the images generated by the AI.

๐Ÿ’กSampling Steps

Sampling steps refer to the number of iterations the AI model goes through to refine the image during the generation process. The video discusses different sampling methods and the impact of adjusting the number of sampling steps on the final output, recommending a range of 50 to 70 for beginners to achieve consistent results.

๐Ÿ’กImage to Image

Image to Image is a feature in Stable Diffusion that allows users to input an existing image and generate a new image based on that input, while incorporating elements from the text prompt. The video demonstrates how to use this feature to modify an existing image, such as changing the background while keeping the main subject intact.

๐Ÿ’กDenoising Strength

Denoising Strength is a parameter used in the Image to Image feature of Stable Diffusion. It controls the degree to which the AI model alters the input image when generating a new image. The video explains that adjusting the denoising strength can help users retain or significantly change the original image based on their creative goals.


Upscalers are tools used to increase the resolution of an image. In the video, the presenter discusses using upscalers like SwinIR and LDSR to enlarge images generated by Stable Diffusion, with a preference for SwinIR for its ability to produce high-quality, detailed enlargements.

๐Ÿ’กRestore Faces

Restore Faces is a feature in Stable Diffusion that attempts to correct and improve the quality of generated faces. The video shows how to use this feature to address issues with the facial features in the images, such as abnormal eye shapes or other artifacts, by regenerating the image with the aim of improving the face's appearance.

๐Ÿ’กStable Fusion Web UI

Stable Fusion Web UI refers to the user interface for the Stable Diffusion model, which allows users to input text prompts and generate images. The video provides instructions on how to access and use this interface to create images, including navigating through different tabs and settings to achieve the desired results.


Stable diffusion tutorial for creating AI images.

Guide by Seb to create pictures of dogs in Star Wars clothes.

Identify the real image among six options posted by friends.

Step-by-step guide to install stable diffusion web UI.

Download Python and Git for Windows following GitHub instructions.

Access Hugging Face to download the required models.

Place the model in the correct folder for stable Fusion web UI.

Running stable diffusion locally on your computer.

Using the web UI Dash to interact with stable diffusion.

Creating images from text with adjustable settings.

Utilizing for inspiration and finding effective prompts.

Experimenting with prompts and sampling steps for better results.

Restoring faces for improved image quality.

Changing settings like width, height, and scale for customization.

Image to image functionality for creating new images from existing ones.

In-painting feature to modify specific parts of an image.

Upscaler for enlarging images with options like Swin IR and LDSR.

Finalizing images with the best results and learning to use stable diffusion from start to finish.