Phase 2: Diffusion Models · ~45 minutes · Python
Video Generation
An image is a 2-D tensor. A video is a 3-D one. The theory is the same; the compute is 10-100x harder. OpenAI's Sora (Feb 2024) proved it was possible. By 2026 Veo 2, Kling 1.5, Runway Gen-3, Pika 2.0, and WAN 2.2 ship production video from text at 1080p — and the open-weights stack (CogVideoX, HunyuanVideo, Mochi-1, WAN 2.2) is 12 months behind.
What you will learn
- Implement video generation from scratch
- Understand the math and intuition behind the algorithm
- Use production libraries for the same task
- Ship a reusable artifact
Introduction
- Type: Build
- Languages: Python
- Prerequisites: Latent Diffusion & Stable Diffusion, Vision Transformers (ViT) (Transformers & LLMs from Scratch), Diffusion Models — DDPM from Scratch
- Time: ~45 minutes
The Problem
A 10-second 1080p video at 24fps is 240 frames of 1920×1080×3 pixels. That's ~1.5 GB of raw data per clip. Pixel-space diffusion is infeasible. You need:
- Spatiotemporal compression. A VAE that encodes videos, not frames, into a sequence of spatial-temporal patches.
- Temporal coherence. Frames need to share content, lighting, and object identity over seconds. The net has to model motion.
- Compute budget. Video training is 10-100x more expensive than image for the same model size.
- Conditioning. Text, image (first-frame), audio, or another video. Most production models accept all four.
The architecture that solved this is the Diffusion Transformer (DiT) applied to spatiotemporal patches, trained on huge (prompt, caption, video) datasets. Same diffusion loss as Diffusion Models — DDPM from Scratch.
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