Using Stable Diffusion In Google Colab Github

Leo Migdal

-Nov 17, 2025, 2:38 AM

using stable diffusion in google colab github

This repository provides a guide for generating images using Stable Diffusion within Google Colab. It leverages the Hugging Face diffusers library and supports customization through LoRA. This README explains how to generate images using Stable Diffusion in Google Colab. It provides a step-by-step guide, from setup to image generation and LoRA implementation. Mount your Google Drive to save generated images and load training data. Log in to Hugging Face to access the models.

torch_dtype=torch.float16 and .to("cuda"): These options utilize your GPU for faster generation and reduce memory usage. If you encounter issues, try removing them. If your Colab instance doesn't have a GPU, remove .to("cuda"). There are many confusions in installing and running stable diffusion on Google Colab. Well, after doing lots of research on the internet we came to know that the team of Colaboratory has restricted the usage of Stable Diffusion WebUI on a Free plan. It simply means that Google Colab didn’t ban the usage of Stable Diffusion but they are just restricting the heavy usage of Web-UI that utilize the Colab environment to bypass it and run external...

So, we thought why not make things simple and use simple code by leveraging the power of Tesla T4 GPUs which are provided as a free tier in Google Colab. Don’t panic about getting those weird codes because we have written all the Python codes to run stable diffusion and we are going to explain everything in a simplified way even if you are... 0. First open your Google Colab , on the top Menu. click on File and select New Notebook. Share your technical articles, project experiences, and development insights.

Let's learn together. Google Colab, developed by the Google Research team, is a cloud-based platform that allows anyone to write and execute Python code through their browser. It’s particularly well-suited for machine learning, data analysis, and educational purposes. Technically, Colab is a hosted Jupyter notebook service that requires zero configuration and provides free access to CPU and GPU resources that might not be available locally. First, you’ll need a Google account to sign in to Google Chrome. Visit the stable-diffusion-webui-colab GitHub repository.

This repository contains stable, latest, and lite versions of Stable Diffusion WebUI. Locate your preferred version using CTRL+F to search for specific models (e.g., “anything-v3.0”). Pay attention to the three version tags in the README table: Click the above button to start generating images! A widgets-based interactive notebook for Google Colab that lets users generate AI images from prompts (Text2Image) using Stable Diffusion (by Stability AI, Runway & CompVis). This notebook aims to be an alternative to WebUIs while offering a simple and lightweight GUI for anyone to get started with Stable Diffusion.

Uses Stable Diffusion, HuggingFace Diffusers and Jupyter widgets. Improvements and new features are most welcome! Feel free to submit a PR. Have you seen all the buzz around AI-generated art and wanted to try it yourself? With powerful text-to-image models like Stable Diffusion, creating original, high-quality images is now accessible to the masses. While generating images with AI used to require extensive technical expertise and costly hardware, now almost anyone can do it right in their web browser thanks to Google Colab notebooks.

In this beginner-friendly guide, I‘ll show you step-by-step how to run Stable Diffusion in Colab and make your own AI masterpieces. Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. It was developed by Stability AI and released to the public in 2022. Unlike some other popular generative models like DALLE-2, Stable Diffusion is open source. This has led to rapid development of new versions and variants with expanded capabilities. Stable Diffusion v2, released in November 2022, improves generated image quality, supports new features like upscaling and inpainting, and mitigates some safety and ethical concerns.

At a very high level, Stable Diffusion works by learning mappings between text embeddings and image latent space, then using diffusion to denoise from pure noise into the target image matching the text description. This is an oversimplification and the actual training process involves teacher networks, perceptual image loss functions, and other complexities. But the key thing to understand is that it‘s doing iterative refinement from noise into a full image, guided by the words you provide. This is the second article in our series about Google Colab. Today, we’ll focus on setting up Stable Diffusion, generating our first images, and learning about batch processing and result management. First, we’ll install our core dependencies:

Then, add some additional libraries we’ll need: 💡 Note: These installations might take a few minutes to complete. Now comes the exciting part — setting up our image generation pipeline: TLDRDiscover how to utilize Stable Diffusion AI for free on Google Colab without needing a high-end CPU. This tutorial guides you through connecting to a T4 GPU, installing various Stable Diffusion models, and generating images using prompts. It also covers model management, upscaling images, and exploring additional options for customization.

Join the creator's WhatsApp community for more insights. -The main topic of the video is about using Stable Diffusion AI for free without needing a high-end CPU, specifically through a Google Colab notebook. -Someone might be interested in this video if they want to utilize Stable Diffusion AI without investing in high-end computer specifications. -The first step is to go to the 'Runtime' menu, select 'Change runtime type', and choose the T4 GPU instead of the default CPU. -It usually takes about 3 to 4 minutes for the initial code execution in the first cell.

Using Stable Diffusion In Google Colab Github

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