An attempt to explore the integration of Efficient Hybrid Diffusion–GAN Model for High-Resolution Image Generation. The the paper will try to answer the question of the paper “ Does Diffusion Beat GAN in Image Super Resolution?”
This report presents a fast hybrid super-resolution system which operates on CPUs while maintaining performance within an 8-20 second execution time. The proposed method is attemting to unite core components from GAN and diffusion models to achieve better results at a cost that does not exceed affordable computational needs. The QualityHybridSRModel consists of a fundamental SR backbone which works together with an edge-conditioned refinement module. The research investigates previous prototype results and evaluates our design against current hybrid systems while exploring the question of whether diffusion models outperform GANs in super-resolution tasks. The experimental results together with current benchmark results demonstrate that neither diffusion nor GANs dominate all other methods but our proposed hybrid approach delivers practical results for CPU-based systems.
The first attempt employed RRDBNet with TinyDiffusionRefiner to produce excellent results, yet it did not fulfill the required CPU performance standards. The FastHybridSRModel achieved fast operation, but its perceptual quality remained insufficient. The ImprovedSRModel with BalancedRefiner achieved a balanced solution which delivered good quality results and acceptable CPU performance.
The design process of our method maintains simplicity as its core focus which differentiates it from previous hybrid approaches.
The above shows the architeture of the proposed hybrid model
The research developed an efficient hybrid SR system optimized for CPU hardware. The model combines a small SR backbone with an edge-conditioned refiner and latency-aware execution. Future work can explore perceptual losses, addition of short multi-step refinement, and embedded platform testing.