The scalability of applications is a key requirement to improving performance in hybrid and cluster computing. Scheduling code to utilize parallelism is difficult, particularly when dealing with dependencies, memory management, data motion, and processor occupancy. The Hybrid Task Graph Scheduler (HTGS) increases programmer productivity to implement hybrid workflows that scale to multi-GPU systems. HTGS is capable of managing dependencies between tasks, represents CPU and GPU memories independently, overlaps computations with disk I/O and memory transfers, keeps multiple GPUs occupied, and uses all available compute resources. We present a prototype of HTGS and implement hybrid microscopy image stitching. Code size is reduced by 25% and shows favorable performance compared to a similar hybrid workflow implementation without HTGS. Computational functions are reused and requires no modification.