Every AI Image Generator Explained in 17 Minutes
From the avocado chair that broke the internet to models generating 4-megapixel photorealism in seconds — AI image generation has gone through more change in 4 years than most industries see in 40. Here's every major model, what actually makes it different, and which one you should be using right now.
📚 What you'll learn:
Why GPT Image 2 feels different from every other generator — and the autoregressive architecture that explains it
How Google's Nano Banana Pro shifted the conversation from generation to precision editing, and why e-commerce brands quietly adopted it overnight
Midjourney's full evolution from Discord spell-casting to v8 Alpha, and why "better lighting" is both its strength and its criticism
The open-source explosion Stable Diffusion started — the copyright lawsuits, the LoRA era, and why your GPU sounds like a jet engine
How Flux went from a researcher breakout to a $300M-backed model that makes you look twice at "photos"
DALL-E's full arc: avocado chairs → Craiyon chaos → prompt rewriting → quiet deprecation
ByteDance's parallel image ecosystem and why Seedream 4.5 dominates in areas Western models still fumble
Grok Imagine's Aurora engine, 1.2 billion videos in a month, and what happens when fewer guardrails meets massive scale
⏱️ Timestamps:
0:00 ChatGPT Images 2.0
2:06 Nano Banana Pro (Google)
3:50 Midjourney (v6 → v7 → v8 Alpha)
5:50 Stable Diffusion & The Open-Source Lineage
7:58 Flux (Black Forest Labs) — Flux.1 → Flux.2 Max
10:02 DALL-E 2 → DALL-E 3 → Deprecation
11:28 Seedream 4.5 & ByteDance's Image Stack
12:58 Grok Imagine (Aurora) — xAI's Uncensored Contender
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