Welcome to the Interactive Diffusion Model Course!

This application transforms a detailed report on diffusion models into a hands-on learning experience. Instead of just reading, you'll interact with the core concepts. You'll see how noise is added and removed, explore the U-Net architecture that powers these models, simulate a training process, and compare different ways to generate images. Use the navigation on the left to jump between modules and learn by doing.

Core Concepts: The Diffusion Dance

Diffusion models work in two phases. First, the **Forward Process** gradually adds random noise to a clean image until it becomes unrecognizable. Then, the **Reverse Process** learns to undo this, step-by-step, to create a new, clean image from pure noise. Move the slider below to see this process in action on a simple digit.

The Engine: U-Net Architecture

The "brain" of most diffusion models is a special neural network called a **U-Net**. Its U-shape is perfect for denoising. The left side (**Encoder**) compresses the noisy image to understand its content. The right side (**Decoder**) reconstructs the image at a higher resolution. Crucially, **Skip Connections** bridge the two sides, ensuring fine details aren't lost. Hover over the blocks to learn more.

Training Lab Simulator

Training a diffusion model involves showing it millions of noisy images and teaching it to predict the original noise. The model's performance is measured by a **loss function** (lower is better). This simulation visualizes the process. Click "Start Training" to see the loss decrease and the quality of generated samples improve over simulated epochs.

Sampling Showdown: DDPM vs. DDIM

Once a model is trained, we use a **sampler** to generate new images. Different samplers have different characteristics. Select a sampler below to see the trade-offs.

Guided Generation Playground

The real power of diffusion models is unlocked when we can guide them. By providing a text prompt, we can control the output. This is often done by combining the diffusion model with a language model like CLIP. Try giving the model a simple instruction below.

Voice Cloning Demo

Diffusion models aren't just for images; they excel at audio generation too. Voice cloning systems like Tortoise TTS use diffusion to create realistic speech from just a few seconds of a reference voice. This demo simulates the process.

Build Your Rig: Home Lab Hardware

Working with diffusion models requires a powerful computer, especially a good GPU with plenty of VRAM. This interactive guide helps you plan a home lab build based on your budget and needs. Select a tier to see recommended components and how VRAM scales.

Conclusion & Ethical Considerations