We describe a system to remove real-world reflections from images for consumer photography. Our system operates on linear (RAW) photos, and accepts an optional contextual photo looking in the opposite direction (e.g., the "selfie" camera on a mobile device). This optional photo helps disambiguate what should be considered the reflection. The system is trained solely on synthetic mixtures of real-world RAW images, which we combine using a reflection simulation that is photometrically and geometrically accurate. Our system comprises a base model that accepts the captured photo and optional context photo as input, and runs at 256p, followed by an up-sampling model that transforms 256p images to full resolution. The system can produce images for review at 1K in 4.5 to 6.5 seconds on a MacBook or iPhone 14 Pro. We test on RAW photos that were captured in the field and embody typical consumer photos, and show that our RAW-image simulation yields SOTA performance.