In early design, the building morphology and floor plan determine its daylighting performance. However, climate-based daylight simulations are often computationally expensive and therefore their participation in fast iterative design is limited. Nowadays, image-to-image translation algorithms are promising since climate-based daylighting metrics are often visualized as a floorplan grid that can be represented in a pixel bitmap. The pix2pix, conditional generative adversarial networks (cGANs), can quickly generate corresponding images based on the input images encoded with information. In our work, this method uses pix2pix as a proxy model to provide daylighting results with high accuracy while requiring little computing resources and running in a short time. Hence, using this method, designers can achieve accurate instant daylight performance feedback to polish the design outcome. We have wrapped our work as a new tool in Grasshopper, ArchiGAN, and organized a general workflow to predict daylighting performance for floorplans based on pix2pix.