Getting Started with Stable Diffusion in 2024 for Absolute Beginners

Surfaced Studio
3 Feb 202412:56

TLDRThe video introduces stable diffusion, a popular AI-based text-to-image model, and guides viewers on how to set it up locally on their machines. It explains the process of installing Python, downloading the stable diffusion model from stability AI's GitHub, and running the model using a web-based interface. The video also touches on the capabilities of stable diffusion, such as generating various types of images and the importance of using a capable graphics card. The creator encourages exploration and creativity with this tool, while acknowledging broader discussions around AI's impact on society.


  • πŸ–ΌοΈ Stable diffusion is a popular AI-based text-to-image model used for generating creative and photo-realistic images.
  • πŸ’» To run stable diffusion locally, you need a machine with Python installed, which is the programming language it operates on.
  • 🌐 Stable diffusion models can be downloaded for free from the official website of Stability AI, the company behind the AI model.
  • πŸ” These models are trained by 'learning' from a vast database of images, thus acquiring knowledge of shapes and patterns without containing copies of images.
  • πŸ“ˆ The latest model, sdxl Turbo, is a fast version of stable diffusion, but for this guide, the presenter chose to use stable diffusion XL.
  • πŸ”— The source code for stable diffusion is open source and can be freely accessed, modified, and used online.
  • πŸ–₯️ The stable diffusion web UI is a user-friendly interface for running the AI model and can be downloaded from a GitHub repository.
  • πŸš€ To set up stable diffusion, download the required model files, install Python, and execute the web UI batch file to install dependencies.
  • 🎨 Users can input text prompts and generate images based on those descriptions using the stable diffusion web UI.
  • πŸ“Š The quality of generated images can be refined by adjusting parameters such as resolution, and the choice of model affects the output.
  • πŸ’‘ Prompting effectively with positive and negative cues can significantly influence the final image generated by stable diffusion.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is about generating AI images using Stable Diffusion, which runs locally on one's own machine.

  • What is Stable Diffusion?

    -Stable Diffusion is a popular text-to-image AI-based model that can generate photo-realistic or artistic images based on the text prompts given by the user.

  • What are some examples of images that can be generated using Stable Diffusion?

    -Examples of images that can be generated using Stable Diffusion include wallpapers, images of cats, cityscapes, people, monsters, and concept images for video games.

  • What are the advantages of running Stable Diffusion locally on your own machine?

    -Running Stable Diffusion locally allows users to generate images at their own convenience without any limitations and without the need for an internet connection or paying for a Pro Plan.

  • What is the first step to set up and install Stable Diffusion?

    -The first step to set up and install Stable Diffusion is to download and install Python, as Stable Diffusion runs on Python.

  • Where can one find the Stable Diffusion models?

    -Stable Diffusion models can be found for free online, primarily at, which is the company that makes and releases Stable Diffusion.

  • What is the latest version of the Stable Diffusion model mentioned in the video?

    -The latest version of the Stable Diffusion model mentioned in the video is the SDXL Turbo, which is a faster version of the model.

  • How can one download and install the Stable Diffusion web UI?

    -To download and install the Stable Diffusion web UI, one needs to search for 'Stable Diffusion UI' on Google, navigate to the GitHub repository, and download the code or zip file. Then, extract the files and run the appropriate executable file for the user's operating system.

  • What are the system requirements for running Stable Diffusion locally?

    -For running Stable Diffusion locally, a fairly decent graphics card is required, preferably with at least 4 GB of VRAM. Nvidia RTX cards are recommended for optimal performance.

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

    -The quality of the generated images can be improved by using a higher resolution setting, refining the prompt to include specific details like 'photo realistic', and using advanced features or models that offer better image quality.

  • What are some potential issues or considerations when using Stable Diffusion?

    -Potential issues or considerations when using Stable Diffusion include legal and copyright questions, as well as ethical concerns about the impact on the workforce and society due to the increasing capabilities of AI tools.



πŸš€ Introduction to Stable Diffusion for AI Image Generation

This paragraph introduces the concept of generating AI images using Stable Diffusion, a popular text-to-image AI model. It emphasizes the ability to run this locally on one's own machine, allowing for unlimited usage. The speaker shares their personal experience using Stable Diffusion for creating wallpapers and concept images for a video game. The paragraph also touches on the capabilities of Stable Diffusion, including its support for text-to-video and advanced features, and sets the stage for a tutorial on getting started with Stable Diffusion.


πŸ“‹ Prerequisites and Downloading Stable Diffusion Model

The speaker outlines the prerequisites for running Stable Diffusion, starting with the need to download Python, which is the programming language on which Stable Diffusion operates. The paragraph provides instructions for downloading Python from the official website and installing it on various operating systems. It then moves on to discuss the need to download a Stable Diffusion model, which contains the knowledge for image generation. The speaker clarifies misconceptions about the models and directs the audience to Stability AI for free models, highlighting that Stable Diffusion is open-source and that the source code and models are freely available.


πŸ”§ Setting Up Stable Diffusion with the UI and Model

This paragraph delves into the process of setting up Stable Diffusion using a web-based UI. The speaker guides the audience through downloading the Stable Diffusion web UI from a GitHub repository and extracting the downloaded files. They explain the need to execute the web UI batch file to install dependencies and launch the web UI. The paragraph also covers the process of selecting a Stable Diffusion checkpoint and the importance of choosing the right model for image generation. The speaker demonstrates how to generate an image using a prompt and how to replace the default model with the downloaded SDXL model for better results.

🎨 Experimenting with Prompts and Generating Images

The speaker discusses the process of refining prompts to generate better images with Stable Diffusion. They explain how different parameters and the structure of the prompt can influence the final image. The paragraph includes a demonstration of generating an image with a more detailed prompt, resulting in a photorealistic image of a cat. The speaker acknowledges that there may be imperfections in the generated images and suggests manual fixes. They encourage the audience to experiment with Stable Diffusion, play around with prompts, and have fun exploring its capabilities. The speaker also promises to cover more advanced prompts and features in future videos.



πŸ’‘stable diffusion

Stable diffusion is a popular AI-based model that generates images from text descriptions. It is open-source, meaning its code is freely available for anyone to view, download, and modify. In the video, the creator discusses how to set up and use stable diffusion to generate various types of images, such as photorealistic or artistic ones, on one's own machine.

πŸ’‘AI images

AI images refer to visual content that is generated by artificial intelligence, as opposed to being created by human artists. In the context of the video, AI images are those produced by the stable diffusion model from text prompts, which can range from realistic to artistic and conceptual.

πŸ’‘text to image

Text to image is a process where AI takes a textual description and translates it into a visual image. This technology is at the core of stable diffusion, which uses complex algorithms to understand the text prompt and create an image that corresponds to the description.

πŸ’‘local machine

A local machine refers to an individual's personal computer or device. In the video, the emphasis is on running stable diffusion locally, which means installing the AI model and its dependencies on one's own computer to generate images without the need for an internet connection or external services.


Python is a high-level programming language known for its readability and ease of use. It is the programming language on which stable diffusion runs, requiring users to install Python on their local machine to operate the AI image generation model.


GitHub is a web-based hosting service for version control and collaboration that allows developers to store and manage their code repositories. In the video, GitHub is used to host the stable diffusion model and the web UI, which are downloaded from there to set up the AI image generation locally.

πŸ’‘AI model

An AI model is a system designed to process input data and produce output predictions or decisions based on patterns learned from training data. In the case of stable diffusion, the AI model is trained to generate images from text descriptions, using a database of images to understand and replicate visual elements.

πŸ’‘stable diffusion XL

Stable diffusion XL is a specific version of the stable diffusion model that is noted for its ability to generate high-resolution images. It is one of the models provided by Stability AI and is highlighted in the video for its advanced capabilities and the detailed, photorealistic images it can produce.

πŸ’‘web-based interface

A web-based interface refers to a platform or application that is accessed and used through a web browser, rather than being installed as a software on the user's local machine. In the video, the stable diffusion web UI is a web-based interface that allows users to input text prompts and generate images using the stable diffusion model.

πŸ’‘graphics card

A graphics card is a hardware component in a computer system that processes and outputs images to the display. For AI image generation tasks like those performed by stable diffusion, a decent graphics card with sufficient video RAM (VRAM) is necessary for efficient and smooth operation.


In the context of AI image generation, a prompt is a textual description or a set of keywords that guide the AI model in creating an image. The quality and specificity of the prompt can significantly influence the output image, making it more or less accurate to the user's intent.


Introduction to generating AI images using stable diffusion, a popular text to image AI model.

Stable diffusion allows for unlimited image generation when run locally on your machine.

The versatility of stable diffusion in creating photorealistic, artistic, and creative images.

The process of generating concept images for a video game using stable diffusion.

Explanation of stable diffusion's powerful features, including the ability to use input images and support for text to video and other advanced features.

The basics of setting up and installing stable diffusion on various operating systems like Windows, Mac, and Linux.

The necessity of downloading Python and adding it to the system path for running stable diffusion.

Downloading the stable diffusion model, which is an AI-built model containing knowledge of image generation.

Clarification that stable diffusion models do not contain copies of images but rather a learned understanding of shapes and objects.

The availability of stable diffusion models for free online and the open-source nature of stable diffusion.

Instructions on obtaining the stable diffusion XL model from the official sources.

Details on downloading and installing the stable diffusion web UI for a user-friendly interface.

The importance of having a decent graphics card, preferably with at least 4 GB of VRAM, for running stable diffusion effectively.

A demonstration of the image generation process using a refined prompt and the stable diffusion XL model.

The potential for manual editing and enhancement of generated images to fix imperfections.

Encouragement to experiment with different prompts and parameters for varied image outputs.

Acknowledgment of the ongoing discussions around copyright, workforce impact, and the broader implications of AI tools like stable diffusion.

Invitation for viewers to ask questions and provide feedback for further exploration of stable diffusion in future videos.