How to Run Stable Diffusion in Google Colab (Free) WITHOUT DISCONNECT
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.
Takeaways
- ๐ซ 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.
- ๐ก You can still use stable diffusion in Google Colab without a GUI by following the steps outlined in the transcript.
- ๐ For documentation and code examples, visit hugging face diffusers (linked in the description).
- ๐ Start by going to Google Colab, creating a new notebook, and writing the code to generate an image.
- ๐ ๏ธ Install necessary packages using `!pip install` commands in the notebook cells.
- ๐ Use the provided code examples from hugging face to set up the stable diffusion pipeline and generate images.
- ๐ฅ๏ธ To display images in Colab, simply include an `image` cell after generating the image.
- ๐ป Ensure you are connected to a GPU by changing the runtime type to a T4 GPU in the runtime settings.
- ๐ The first time you run the code, it will install several dependencies, which might take some time.
- ๐ To use different models or checkpoints, search for them on hugging face and paste the model identifier into your code.
- ๐จ Customize your image generation by adjusting parameters such as height, width, guidance scale, and negative prompt.
- ๐ To generate NSFW images, set the safety checker of your pipeline to none.
- ๐ 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 (colab.research.google.com), 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.
Outlines
๐ 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.
๐ผ๏ธ 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 ci.com 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.
๐จ 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.
Mindmap
Keywords
๐กStable Diffusion
๐กGoogle Colab
๐กDisconnect
๐กGraphical Interface
๐กNSFW (Not Safe For Work)
๐กHugging Face Diffusers
๐กCheckpoint
๐กPip Install
๐กGPU (Graphics Processing Unit)
๐กPrompt
๐กNegative Prompt
Highlights
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.
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