College GAN/Diffusion Model Project

I’m building a college project on state-of-the-art generative models—specifically GANs and the newer diffusion architectures—and I’d like an experienced deep-learning practitioner to guide the process and help me deliver a fully trained model.

Here’s the scope as I see it:
• Select or fine-tune an appropriate GAN or diffusion framework (PyTorch or TensorFlow, I’m flexible).
• Prepare the dataset with the necessary preprocessing pipelines.
• Train the model to produce high-quality synthetic outputs, validate its performance, and document the key metrics.
• Package everything in a clean, reproducible repo or notebook so I can present it confidently to my faculty.

I will supply the dataset (or we can source an open one together) and handle presentation slides—your focus stays on model architecture, training, and concise documentation.

When you apply, please highlight relevant experience with GANs, diffusion models, or adjacent generative techniques you’ve implemented before. I’m keen to collaborate and learn along the way, so clear explanations and commented code are a big plus.

Deliverable: the trained model files, training scripts/notebook, and a short read-me outlining setup and results.