Generative AI — Images, Video & Audio

Traditional AI classifies or predicts from existing data — "is this a cat or dog?" Generative AI creates new data — "generate an image of a cat wearing a spacesuit."

The key insight: if you train a model to understand the relationship between descriptions and images deeply enough, you can run it in reverse — give it a description and ask it to create the matching image.

This works because generative models learn the statistical patterns of what things look like, sound like, or read like — and can sample from those patterns to create new examples.

The dominant approach for image generation is diffusion. The intuition:

Training: Take millions of real images. Add random noise to each image progressively until it becomes pure static. Train a neural network to predict and remove the noise at each step.

Generation: Start with pure random noise. Run the denoising network in reverse — gradually removing noise according to a text prompt that guides the direction. After 20-50 steps, a coherent image emerges.

This is why generation takes multiple "steps" — you literally watch the model sculpt an image out of noise, guided by your prompt. Flux.1, Stable Diffusion XL, and Midjourney v6 all use variations of this approach.

Modern TTS (text-to-speech) uses neural networks trained on thousands of hours of human speech. The model learns to map text → audio patterns, including prosody (rhythm and emphasis), emotion, and speaker identity.

Voice cloning works by extracting a "voice embedding" — a numerical fingerprint of a specific voice from just a few seconds of audio. The TTS model then generates speech in that voice from any text.

ElevenLabs, Kokoro, and Coqui TTS all use variations of this approach. The open-source Kokoro model (82M parameters) can run in real-time on a laptop CPU and produces near-professional quality.

Video generation is dramatically harder than image generation because every frame must be consistent with every other frame — maintaining identity, physics, and lighting across time.

Modern video models like Wan 2.1, LTX Video, and HunyuanVideo extend the diffusion approach to 3D (width × height × time) or use transformer architectures that process video tokens similarly to text tokens.

The "consistent character" problem — keeping a face identical across scenes — is one of the hardest open problems. Current solutions use IP-Adapter (face identity embeddings) or fine-tuning on a specific subject, but the results are still imperfect.

Sora, Runway Gen-4, and the open-source alternatives are all making rapid progress — expect video quality to match image quality within 1-2 years.