Enhanced Generation in Diffusion Models via Joint High-to-Low Denoising Schedule and Model Sparsity with Theoretical Guarantees

Diffusion models require learning denoising tasks across a wide range of noise levels. Standard training selects noise levels uniformly across the forward process, leading to entangled feature representations and degraded generation. Several recent methods adopt a high-to-low noise schedule during training, showing empirically faster convergence and improved generalization, yet no theoretical guarantees have been established. This paper develops a novel joint high-to-low denoising schedule and model sparsity framework and provides the first theoretical analysis linking noise level and model sparsity to the training dynamics and generalization in diffusion models. Our theoretical analysis shows that scheduling noise from high to low strengthens the implicit coarse-to-fine learning dynamics in diffusion training: the high-noise stage learns coarse features first, while the subsequent low-noise stage refines fine-grained details. Beyond the denoising schedule, our method introduces a high-to-low model sparsity strategy that progressively activates neurons: it only updates a subset of neurons during the high-noise stage and gradually activates the remaining neurons, which preserves capacity for fine-grained features without interference from early-stage high noise. Our method and analysis are supported by both theoretical guarantees and empirical results on multiple practical datasets and models, demonstrating clear advantages over standard diffusion training with uniform noise sampling.