Building a Diffusion Model with AI Guidance
Recently, I embarked on an interesting experiment: implementing a diffusion model from scratch with the assistance of a generative AI. I wanted to see how quickly I could go from zero knowledge to a working implementation, all while having an AI guide me through the process.
The Setup
For this experiment, I used:
- An RTX 3090 GPU
- PyTorch for the initial implementation
- The classic MNIST dataset as my training data
- AI assistance for guidance on implementation details
The Implementation Journey
Starting from nothing, I began coding the diffusion model, asking the AI for help with various aspects of the implementation. As expected, my first attempts at generating images weren't particularly impressive. The initial outputs barely resembled handwritten digits.
This led to an iterative process of parameter tuning. With each adjustment suggested by the AI, I saw gradual improvements in the quality of generated images.
There were some interesting challenges along the way - at one point, the model seemed to be learning well, but then I pushed a parameter too far and ended up with too much noise in the results.
Trying the Diffusers Library
With a working implementation complete, I decided to compare my scratch-built model with one using the "diffusers" library - a popular tool designed specifically for working with diffusion models. I implemented the same MNIST generation task using this library and compared the results.
Results
The progression from my first attempts to the final versions showed a clear learning curve - both for me and for the model. The diffusers library implementation provided a good benchmark to evaluate my from-scratch code against.
Diffusers Trial with CIFAR-10
Next, I took on the challenge of generating images from the CIFAR-10 dataset using the diffusion model library Diffusers.
CIFAR-10 consists of small, 32×32 pixel RGB images across 10 equally represented classes (airplanes, automobiles, birds, cats, deer, dogs, frogs, horses, ships, and trucks). Because the images are small, it’s a great dataset for quick trials and model adjustments.
For training, I used a relatively lightweight UNet-based DDPM model and leveraged the Diffusers API. This allowed me to skip writing much of the data loader and training loop from scratch, making it possible to set up the experimental environment in a short amount of time.
The initial results are… somewhat blurry, you could say.
CIFAR-10 contains more information than MNIST, so it’s easier to observe the effects of model performance and hyperparameter choices. That made it a suitable subject for experimenting with Diffusers.
Summary
By implementing a diffusion model with the help of a generative AI, I managed to (somewhat) successfully generate MNIST data in this project, and learned a lot in the process.
Next time, I’d like to try other datasets and tackle more complex image generation tasks. This was a valuable experience that made me realize just how much working with a generative AI can accelerate the learning process.