🔥 Holy shit... Apple just did something nobody saw coming They just dropped Pico-Banana-400K a 400,000-image dataset for text-guided image editing that might redefine multimodal training itself. Here’s the wild part: Unlike most “open” datasets that rely on synthetic generations, this one is built entirely from real photos. Apple used their internal Nano-Banana model to generate edits, then ran everything through Gemini 2.5 Pro as an automated visual judge for quality assurance. Every image got scored on instruction compliance, realism, and preservation and only the top-tier results made it in. It’s not just a static dataset either. It includes: • 72K multi-turn sequences for complex editing chains • 56K preference pairs (success vs fail) for alignment and reward modeling • Dual instructions both long, training-style prompts and short, human-style edits You can literally train models to add a new object, change lighting to golden hour, Pixar-ify a face, or swap entire backgrounds and they’ll learn from real-world examples, not synthetic noise. The kicker? It’s completely open-source under Apple’s research license. They just gave every lab the data foundation to build next-gen editing AIs. Everyone’s been talking about reasoning models… but Apple just quietly dropped the ImageNet of visual editing. 👉 github. com/apple/pico-banana-400k