A Comprehensive Review on High-Fidelity Generative Learning using Efficient and Scalable DPM-Solver-Based Diffusion Models

Diffusion models have become a strong class of generative deep learning models that can generate high-fidelity results for scientific simulations, medical imaging, picture synthesis, and video production. Diffusion models struggle with substantial computing costs because they require many iterative denoising steps during inference, even though they offer better generative quality and training stability than Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). This restriction limits their scalability and real-time deployment in resource-constrained contexts. This study suggests an effective and scalable diffusion framework based on DPM-Solver-driven optimization methods to overcome these issues. To lower inference complexity while maintaining output fidelity, the suggested method combines adaptive noise scheduling, lightweight U-Net topologies, latent-space acceleration, and high-order DPM-Solver sampling. To reduce sampling error with minimal denoising steps, the model uses high-order numerical solvers to mathematically redefine the reverse diffusion process as an Ordinary Differential Equation (ODE). Experimental results on high-resolution image datasets show significant reductions in FLOPs, memory use, and inference delay while preserving competitive perceptual quality and structural consistency. The goal of the proposed framework is to enable practical use of diffusion models in applications such as edge-based intelligent systems, medical diagnostics, and creative artificial intelligence.