How to Run Stable Diffusion in Google Colab (Free) WITHOUT DISCONNECT

AI Search
26 Dec 202314:53

TLDRIn this informative guide, the author demonstrates how to use Stable Diffusion for image generation in Google Colab without getting disconnected, a common issue with the free plan due to the prohibition of graphical interfaces. The tutorial covers the installation of necessary libraries, such as diffusers and Transformers, and provides a step-by-step process to generate images using various models and checkpoints available on Hugging Face. It also explains how to customize image parameters like height, width, and guidance scale, and how to bypass content restrictions to generate NSFW images by disabling the safety checker. The guide is beginner-friendly, ensuring that even those who are not tech-savvy can follow along and create their own images in Google Colab.


  • 🚫 Google Colab's free plan has banned the use of graphical interfaces like automatic 1111, which can lead to disconnections when attempting to run stable diffusion with a GUI.
  • πŸ’‘ You can still use stable diffusion in Google Colab without a GUI by following the steps outlined in the transcript.
  • πŸ” For documentation and code examples, visit hugging face diffusers (linked in the description).
  • πŸ“ Start by going to Google Colab, creating a new notebook, and writing the code to generate an image.
  • πŸ› οΈ Install necessary packages using `!pip install` commands in the notebook cells.
  • πŸ”— Use the provided code examples from hugging face to set up the stable diffusion pipeline and generate images.
  • πŸ–₯️ To display images in Colab, simply include an `image` cell after generating the image.
  • πŸ’» Ensure you are connected to a GPU by changing the runtime type to a T4 GPU in the runtime settings.
  • πŸ”„ The first time you run the code, it will install several dependencies, which might take some time.
  • πŸ” To use different models or checkpoints, search for them on hugging face and paste the model identifier into your code.
  • 🎨 Customize your image generation by adjusting parameters such as height, width, guidance scale, and negative prompt.
  • πŸ”“ To generate NSFW images, set the safety checker of your pipeline to none.
  • 🌐 The shared notebook will include all the steps and code, allowing you to avoid typing it from scratch.

Q & A

  • Why does Google Colab's free plan disconnect users when they try to use Stable Diffusion with a graphical interface?

    -Google Colab's free plan has banned the use of graphical interfaces like Automatic1111, which can lead to immediate disconnection when attempting to run Stable Diffusion with such interfaces.

  • How can you bypass the disconnection issue and use Stable Diffusion in Google Colab's free plan?

    -You can bypass the disconnection issue by not using a graphical interface and instead running the code directly in the notebook environment to generate images.

  • What is the first step to start using Stable Diffusion in Google Colab?

    -The first step is to go to Google Colab (, log in with your Google account, and create a new notebook where you will write and run your code.

  • What are the necessary installations needed to use Stable Diffusion in Google Colab?

    -You need to install diffusers, torch, and Transformers using the command `!pip install diffusers torch transformers`. Additionally, you may need to install 'accelerate'.

  • How do you select a specific checkpoint or model for generating images with Stable Diffusion?

    -You can select a checkpoint or model by specifying it in the pipeline code. Popular checkpoints like 'Magic Mix' or 'Cetus Mix' can be searched on Hugging Face and their corresponding model IDs can be used in the code.

  • What is the purpose of the negative prompt in Stable Diffusion?

    -The negative prompt is used to specify what you do not want to appear in the generated image, such as 'extra foot', 'missing digits', or 'deformed limbs', helping to refine the output.

  • How can you ensure that your Stable Diffusion pipeline allows for the generation of NSFW (Not Safe For Work) content in Google Colab?

    -To allow for the generation of NSFW content, you need to set the safety checker of your pipeline to 'None' by adding `pipe.safety_checker = None` in the code.

  • What is the process to change the style of the generated images in Stable Diffusion?

    -The style of the generated images can be changed by selecting different checkpoints or models. These can be found and chosen based on the desired style, such as Disney, Pixar, anime, or painting styles.

  • How can you customize the settings of the Stable Diffusion pipeline in Google Colab?

    -You can customize settings such as height, width, number of inference steps, guidance scale, and others by defining them in the pipeline code before generating the image.

  • What is the command to install packages using pip in Google Colab?

    -To install packages using pip in Google Colab, you use the command `!pip install package_name` where 'package_name' is the name of the package you want to install.

  • How do you display an image generated by Stable Diffusion in Google Colab?

    -To display an image in Google Colab, you can use the command `image.` followed by the result of the pipeline's image generation step.

  • What is the recommended runtime type to use when generating images with Stable Diffusion in Google Colab?

    -The recommended runtime type is a T4 GPU, which can be selected by going to 'Runtime' > 'Change runtime type' and choosing T4 GPU.



πŸš€ Bypassing Disconnects with Stable Diffusion in Google Colab

The video begins with an introduction to the issue of using Stable Diffusion's web UI, known as automatic1111, in Google Colab, which results in disconnections due to the ban on graphical interfaces in Colab's free plan. However, it's still possible to use Stable Diffusion without a GUI. The host guides viewers through setting up a Google Colab notebook, installing necessary libraries like diffusers and Transformers using pip with a prefix of an exclamation mark, and utilizing the hugging face diffusers documentation for code examples. The process includes importing the Stable Diffusion pipeline, selecting a model (Stable Diffusion 1.5), and generating an image from a text prompt, all within the Colab environment.


πŸ–ΌοΈ Generating Images with Custom Checkpoints and Prompts

The second paragraph explains how to generate images using different checkpoints or models that define the style of the output image, such as Disney, Pixar, anime, or realistic styles. It guides users to find and select models on and how to import them into the Colab notebook by searching and copying the model identifier from hugging face. The video also covers additional settings that can be adjusted for image generation, including image dimensions, inference steps, guidance scale, and negative prompts to exclude unwanted elements from the generated images. It demonstrates running the code to generate an astronaut riding a horse on Mars and saving the image, and briefly mentions the capability to generate NSFW (Not Safe For Work) images with the right settings.


🎨 Customizing Image Generation with Advanced Settings and Models

The third paragraph delves into customizing the image generation process further by adjusting settings like height, width, inference steps, and guidance scale within the pipeline for better control over the output. It also introduces the concept of negative prompts to refine the generation process. The host demonstrates generating an image of a beautiful female model using the Magic Mix model and explains how to modify the prompt and negative prompt for different results. Additionally, it covers generating non-human scenes by adjusting the negative prompt to exclude humans and shows how to switch to an anime style using the Cedus Mix model. The video concludes with a reminder that the Colab notebook will be shared for convenience and encourages exploring additional features of the diffusers library for more advanced image generation techniques.



πŸ’‘Stable Diffusion

Stable Diffusion is a machine learning model used for generating images from textual descriptions. It is a part of the broader field of artificial intelligence known as generative models. In the context of the video, it is the core technology that enables the creation of images in Google Colab without using a graphical interface.

πŸ’‘Google Colab

Google Colab is a cloud-based platform provided by Google that allows users to write and execute Python code in their browser, with the added benefit of free access to computing resources including GPUs. In the video, it is used as the environment to run Stable Diffusion and generate images.


In the context of the video, 'disconnect' refers to the issue where users are disconnected from Google Colab sessions when attempting to use graphical interfaces like Stable Diffusion's web UI. The video aims to provide a solution to prevent these disconnections.

πŸ’‘Graphical Interface

A graphical interface is a type of user interface that allows people to interact with electronic devices with images rather than text commands. The video discusses that Google Colab's free plan does not support graphical interfaces for certain applications, which is why an alternative method is shown.

πŸ’‘NSFW (Not Safe For Work)

NSFW is a content rating that indicates material which may not be suitable for viewing in a work environment due to its adult or offensive nature. The video mentions generating NSFW images as one of the capabilities of Stable Diffusion in Google Colab, with a method to bypass content filters.

πŸ’‘Hugging Face Diffusers

Hugging Face is a company specializing in natural language processing and offers a range of tools and libraries for AI, including 'Diffusers' which is a library for working with diffusion models like Stable Diffusion. The video references this library for the code and documentation needed to generate images.


In machine learning, a checkpoint refers to a snapshot of the model's progress at a particular point in time. Different checkpoints can define the style of the generated images, such as realistic, anime, or painting styles. The video demonstrates how to use different checkpoints to change the style of the output images.

πŸ’‘Pip Install

Pip is a package installer for Python that allows users to install libraries and dependencies. In the video, 'pip install' is used to add necessary Python packages like 'diffusers', 'torch', and 'transformers' to the Google Colab environment.

πŸ’‘GPU (Graphics Processing Unit)

A GPU is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. The video emphasizes the importance of connecting to a GPU in Google Colab for faster image generation with Stable Diffusion.


In the context of Stable Diffusion, a prompt is a textual description that guides the model to generate a specific type of image. The video explains how to input prompts to create desired images, such as an astronaut riding a horse on Mars.

πŸ’‘Negative Prompt

A negative prompt is a list of undesirable elements that the user wants to exclude from the generated image. The video demonstrates how to include a negative prompt to prevent unwanted features, like extra limbs or deformed body parts, from appearing in the output image.


Stable Diffusion can be run in Google Colab without using a graphical interface, which is banned in the free plan.

The process involves bypassing disconnection issues and generating unlimited images, including NSFW content.

Google Colab's free plan users can utilize Stable Diffusion by following specific steps outlined in the tutorial.

The tutorial provides a link to Hugging Face Diffusers for comprehensive code and examples.

Users need to install necessary packages like diffusers, torch, and Transformers using pip in the command line.

Google Colab already has Python installed, simplifying the setup process.

The tutorial demonstrates how to install and use specific checkpoints or models for different image styles.

Changing the runtime type to T4 GPU in Google Colab is essential for running the image generation process.

The process includes setting up a pipeline, feeding it a model, and using it to generate images from prompts.

Customization options are available, such as height, width, guidance scale, and negative prompts, to control the image generation.

The tutorial shows how to save generated images within Google Colab.

Sponsor mention of My Vocal AI, a tool for voice cloning and text-to-speech applications.

The video provides a step-by-step guide to using Stable Diffusion with different checkpoints for various styles, like Disney, Pixar, anime, or realistic.

To generate NSFW images, the safety checker of the pipeline must be set to none.

The tutorial offers a way to use Stable Diffusion in Google Colab without getting disconnected, even for free plan users.

The notebook used in the tutorial will be shared, allowing users to avoid typing out the entire process.

Additional features of Stable Diffusion, such as image-to-image and control net, are mentioned for further exploration.