Fluorescein Fundus Angiography (FFA), although widely used to visualize the retinal vasculature to monitor Diabetic retinopathy (DR), typically requires the intravenous injection of sodium fluorescein, a fluorescent contrast agent, making it an invasive procedure. In contrast, Color Fundus (CF) images are much more accessible and cheaper to collect. Generation of FFA based on CF thus become an effective approach to address such problem. However, previous works and existing image generation methods often struggle to preserve fine-grained details and anatomical integrity in terms of FFA generation. To address this limitation, we propose the Guidance-Controllable Mamba Diffusion Model (OTMamba) capable of generating high-fidelity FFA images directly from CF photographs. In contrast to existing GAN-based and diffusion-based medical image generation approaches that suffer from high computational complexity, limited scalability, and poor transferability, our proposed method demonstrates enhanced performance and clinical relevance in ophthalmology image translation. Experimental results confirm the model’s superiority in synthesizing diagnostically valuable FFA images, providing a promising non-invasive alternative for diabetic retinopathy screening and management.