Generative AI Projects You Can Run in Google Colab

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Generative AI is revolutionizing the way we create content, from writing and art to music and code. With models like GPT, DALL·E, Stable Diffusion, and MusicGen, AI can now produce human-like text, generate stunning visuals, compose music, and even write functioning code. But how do you get started?

The easiest way to begin experimenting is by using Google Colab, a free cloud-based Jupyter notebook environment. It offers access to Python, GPUs, and an intuitive interface that makes it ideal for running AI models without installing anything locally.

In this article, we’ll explore generative AI projects you can run in Google Colab, with step-by-step instructions, useful code snippets, and real-world applications.

Why Use Google Colab for Generative AI?

  • Free compute (GPU/TPU) access
  • No installation required
  • Built-in libraries and support for PyTorch, TensorFlow, Transformers
  • Easy file I/O with Google Drive
  • Sharable and collaborative notebooks

Getting Started: Setup in Google Colab

Before diving into projects, here are the basic steps:

  1. Go to https://colab.research.google.com
  2. Click “New Notebook”
  3. Rename the file (e.g., generative_ai_projects.ipynb)
  4. Go to Runtime > Change runtime type and select GPU
  5. Install dependencies using pip install in code cells

Example:

!pip install transformers diffusers torch torchvision

Now you’re ready to begin your first project!

Project 1: Text Generation with GPT-2

Goal

Generate coherent paragraphs of text based on a prompt using OpenAI’s GPT-2.

Why It’s Useful

Text generation models can assist with:

  • Creative storytelling
  • Content idea brainstorming
  • Writing code comments or documentation
  • Simulating chatbot responses

Steps

Install the required libraries:

!pip install transformers

Then use the following code:

from transformers import pipeline

generator = pipeline("text-generation", model="gpt2")
prompt = "In a distant future, humans and AI coexist peacefully."
output = generator(prompt, max_length=100, num_return_sequences=1)
print(output[0]['generated_text'])

Tips

  • Use gpt2-medium or gpt2-large for better quality (but they consume more RAM)
  • You can add custom prompt templates to control the story tone or structure
  • Consider adding randomness using temperature=0.7

Applications

  • Story generation
  • Blog post drafting
  • Conversational agents

Project 2: Image Generation with Stable Diffusion

Goal

Generate photorealistic or artistic images from natural language prompts using Stable Diffusion.

Why It’s Useful

Text-to-image generation enables:

  • AI-driven art
  • Visual prototypes for designers
  • Rapid content creation for marketing

Setup

Install dependencies:

!pip install diffusers transformers accelerate scipy safetensors

Then run the image generation code:

from diffusers import StableDiffusionPipeline
import torch

pipe = StableDiffusionPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4",
    torch_dtype=torch.float16
)
pipe = pipe.to("cuda")

prompt = "a futuristic city at sunset, digital art"
image = pipe(prompt).images[0]
image.show()

Tips

  • Use highly descriptive prompts for better results
  • Combine style keywords (e.g., “in the style of Studio Ghibli”)
  • Save outputs using image.save('filename.png')

Applications

  • AI art
  • Concept design
  • Book or game illustration

Project 3: AI-Powered Music Composition

Goal

Generate original music tracks from text prompts using MusicGen.

Why It’s Useful

Music generation is great for:

  • YouTube intros
  • Podcast background music
  • Indie game soundtracks

Setup

!pip install git+https://github.com/facebookresearch/audiocraft.git

Then run:

from audiocraft.models import MusicGen
model = MusicGen.get_pretrained('facebook/musicgen-small')
model.set_generation_params(duration=10)
output = model.generate(["a cheerful lo-fi beat with guitar"])

Note: Output is a waveform array. Use scipy.io.wavfile.write() to save to a .wav file.

Tips

  • Choose genre-specific prompts: “orchestral symphony”, “trap beat”
  • Limit duration to 10–30 seconds on free Colab tier
  • Consider upgrading to musicgen-medium for richer results

Applications

  • Background music for videos
  • Jingle generation
  • Audio branding

Project 4: Image Captioning

Goal

Automatically describe an image in human-like text.

Why It’s Useful

Image captioning is essential for:

  • Improving accessibility (e.g., alt text for screen readers)
  • Metadata generation for media libraries
  • Automated content tagging

Setup

!pip install transformers

Code Example

from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image
import requests

url = "https://huggingface.co/datasets/nateraw/image-captioning-demo/resolve/main/example.jpg"
image = Image.open(requests.get(url, stream=True).raw)

processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")

inputs = processor(images=image, return_tensors="pt")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))

Applications

  • Accessibility
  • Content tagging
  • Social media automation

Project 5: Code Generation

Goal

Use generative models to create functioning code snippets based on natural language prompts.

Why It’s Useful

Code generation models help:

  • Accelerate development
  • Generate boilerplate code
  • Learn programming syntax and logic

Setup

!pip install transformers

Example

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("replit/code-v1-3b")
model = AutoModelForCausalLM.from_pretrained("replit/code-v1-3b")

prompt = """# Python function to check if a number is prime"""
inputs = tokenizer(prompt, return_tensors="pt")
out = model.generate(**inputs, max_length=100)
print(tokenizer.decode(out[0], skip_special_tokens=True))

Tips

  • Try prompts like: “Write a Python function to calculate Fibonacci numbers”
  • Combine with error-catching or docstring generation

Applications

  • Developer assistance
  • Code autocompletion
  • Learning programming

Project 6: Text-to-Speech (TTS)

Goal

Convert written text into human-like spoken audio.

Why It’s Useful

TTS is widely used in:

  • E-learning
  • Audio guides
  • Voice-over automation

Setup

!pip install TTS

Code Example

from TTS.api import TTS

tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC")
tts.tts_to_file(text="Hello from Google Colab!", file_path="output.wav")

Tips

  • Use Google Drive to save and download audio files
  • Try expressive voices for emotional delivery

Applications

  • Audiobooks
  • Voice assistants
  • Accessibility tools

Goal

Convert text to natural-sounding speech.

Example

!pip install TTS
from TTS.api import TTS

tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC")
tts.tts_to_file(text="Hello from Google Colab!", file_path="output.wav")

Applications

  • Audiobooks
  • Voice assistants
  • Accessibility tools

Project 7: Image-to-Image Transformation with ControlNet

Goal

Transform an input image based on a prompt (e.g., style transfer, edge-to-image) using ControlNet + Stable Diffusion.

Why It’s Useful

  • Refine generated images
  • Repaint or restyle artwork
  • Enhance sketches into detailed images

Setup

!pip install diffusers transformers accelerate opencv-python

Example

from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from PIL import Image
import torch

controlnet = ControlNetModel.from_pretrained(
    "lllyasviel/sd-controlnet-canny",
    torch_dtype=torch.float16
)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    controlnet=controlnet,
    torch_dtype=torch.float16
).to("cuda")

input_image = Image.open("your_sketch.png")  # Replace with your input image
prompt = "a watercolor landscape painting"
image = pipe(prompt, image=input_image).images[0]
image.show()

Applications

  • Sketch refinement
  • Design iteration
  • AI-powered photo editing

Bonus: Combine Projects for Multimodal AI

You can combine these models to create complex, multimodal workflows.

  • Generate a story with GPT-2 → Create a cover image with Stable Diffusion → Narrate it using TTS
  • Build an AI blog writer that drafts, illustrates, and voices articles

Tips for Running Projects Smoothly

  • Restart runtime if memory issues occur
  • Use Google Drive to save models and outputs
  • Monitor token usage when using paid APIs (like OpenAI)
  • Stick to small models if you’re using the free Colab tier

Final Thoughts

These generative AI projects you can run in Google Colab are only the beginning. With a few lines of code, you can turn your notebook into a story generator, art studio, music composer, or coding assistant.

Colab makes it accessible to creators, educators, and developers alike—no GPU at home required. Whether you’re learning, prototyping, or building something unique, generative AI in Colab offers a hands-on path to innovation.

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