Creating AI-generated images doesn’t have to mean investing in expensive hardware or juggling complicated local setups. With Google Colab’s free GPU access and Pinggy’s tunneling service, you can run ComfyUI in the cloud and share it with anyone online. This guide will walk you through the process step by step, helping you set up a fully functional AI image generation workflow without breaking the bank.
Why Use Google Colab for ComfyUI?
ComfyUI is a powerful node-based interface for Stable Diffusion and other AI image generation models. Running it locally usually requires a high-end GPU with plenty of VRAM, which can be costly. Google Colab solves this problem by providing free access to Tesla T4 GPUs with 15GB of VRAM—enough to handle most ComfyUI workflows.
This combination is ideal for artists, developers, and researchers who want to experiment with custom nodes, complex workflows, or model fine-tuning without dealing with local installations, CUDA drivers, or hardware limitations.
Enabling GPU Acceleration
Before you start, you must enable GPU acceleration in Colab. ComfyUI is designed to work with GPUs, and attempting to run it on a CPU will result in extremely slow performance.
To enable GPU:
- Go to Runtime > Change runtime type
- Select GPU as the hardware accelerator
Without a GPU, loading times will be excessive, and you may encounter memory errors or crashes.
Setting Up ComfyUI on Colab
Once GPU acceleration is enabled, start by installing essential system packages:
!apt-get update
!apt-get install -y wget aria2 libgl1-mesa-glx
Next, clone the official ComfyUI repository and install the required Python dependencies:
!git clone https://github.com/comfyanonymous/ComfyUI.git
%cd ComfyUI
!pip install -r requirements.txt
This ensures you have the exact versions of PyTorch, Transformers, and other libraries that ComfyUI relies on.
Making ComfyUI Public with Pinggy
Pinggy allows you to create public URLs for your Colab-hosted ComfyUI instance. Install Pinggy and start a tunnel:
!pip install pinggy
import pinggy
tunnel1 = pinggy.start_tunnel(forwardto="localhost:8188")
print(f"Tunnel1 started - URLs: {tunnel1.urls}")
This generates HTTP and HTTPS URLs that can be accessed from any device, anywhere, while your Colab session is active.
Launching ComfyUI
Start the ComfyUI server with the following command:
!python main.py --listen 0.0.0.0
Using --listen 0.0.0.0 allows connections from any IP, so your Pinggy URLs will work immediately. Once the server is running, you can interact with ComfyUI via the web interface and begin generating AI images.
Performance and Limitations
Using Colab’s free GPU tier comes with some caveats:
- Loading Times: Expect 5-10 minutes for ComfyUI to fully load.
- Session Limits: Free sessions may disconnect after inactivity, so save your work regularly.
- Performance: The T4 GPU handles most workflows, but complex processes may take longer.
If you want faster performance or longer sessions, Colab Pro is a good option, offering upgraded GPUs and extended runtime.
Troubleshooting Common Issues
- GPU Not Detected: Ensure GPU is enabled and restart the session.
- Out of Memory Errors: Reduce model size or batch size.
- Slow Loading: Be patient on free tier; avoid refreshing the page.
- Tunnel Issues: Confirm Pinggy is active and the server is running on port 8188.
Conclusion
Running ComfyUI on Google Colab with Pinggy makes AI image generation accessible and shareable without expensive hardware. While free resources come with limits, this setup is perfect for experimenting, testing, and exploring ComfyUI’s capabilities. For regular or intensive use, Colab Pro provides faster GPUs and longer sessions, while Pinggy’s public URLs ensure easy remote access.
With this setup, anyone—from beginners to artists and developers—can dive into AI image generation and share their creations online effortlessly.
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