How I Built an AI Product Photography Tool With FastAPI and Flux Models

I spent $6,000 last year on product photography for my ecommerce store. 60 SKUs, $200-500 per shoot, a week turnaround each time, and half the shots were unusable. I'm also a developer. So I built PixelPanda — upload a phone snap of any product, get 200 studio-quality photos in about 30 seconds. This post breaks down the technical architecture, the AI pipeline, and the tradeoffs I made building it as a solo developer. Architecture Overview Client (Jinja2 + vanilla JS) | FastAPI (Python) | +----------------------------------+ | Replicate API | | +- Flux Kontext Max (product) | | +- Flux 1.1 Pro Ultra (avatar) | | +- BRIA RMBG-1.4 (bg removal) | | +- Real-ESRGAN (upscaling) | +----------------------------------+ | Cloudflare R2 (storage) | MySQL (metadata) The whole thing runs on a single Ubuntu VPS behind Nginx with Supervisor managing the process. Total infra cost: ~$50/month. Why FastAPI Over Django or Express Three reasons: Async by default. Image generation calls take 5-30 seconds.