Fluorescein Angiography (FA) is a technique that employs the designatedcamera for Fundus photography incorporating excitation and barrier filters. FAalso requires fluorescein dye that is injected intravenously, which might causeadverse effects ranging from nausea, vomiting to even fatal anaphylaxis.Currently, no other fast and non-invasive technique exists that can generate FAwithout coupling with Fundus photography. To eradicate the need for an invasiveFA extraction procedure, we introduce an Attention-based Generative networkthat can synthesize Fluorescein Angiography from Fundus images. The proposedgan incorporates multiple attention based skip connections in generators andcomprises novel residual blocks for both generators and discriminators. Itutilizes reconstruction, feature-matching, and perceptual loss along withadversarial training to produces realistic Angiograms that is hard for expertsto distinguish from real ones. Our experiments confirm that the proposedarchitecture surpasses recent state-of-the-art generative networks forfundus-to-angio translation task.