Abstract: A flat nanophotonic computational camera, which employs an array of skewed lenslets (meta-optics) and a learned reconstruction approach is disclosed herein. The optical array is embedded on a metasurface that with a height of approximately one micron, is flat and sits on the sensor cover glass at approximately 2.5 mm focal distance from the sensor. A differentiable optimization method continuously samples over the visible spectrum and factorizes the optical modulation for different incident fields into individual lenses. A megapizel image is reconstructed from a flat imager with a learned probabilistic reconstruction method that employs a generative diffusion model to sample an implicit prior. A method for acquiring paired captured training data in varying illumination conditions is proposed. The proposed flat camera design is assessed in simulation and with an experimental prototype, validating that the method is capable of recovering images from diverse scenes in broadband with a single nanophotonic layer.