The field of diffusion models is rapidly advancing, with a focus on improving style imitation and time series generation. Recent developments have introduced innovative approaches to address the challenges of inconsistent word spacing, distributional bias, and lack of interpretability in diffusion models. The use of multi-scale attention features, conditional diffusion models, and style-guided kernels has shown promising results in generating high-quality synthetic data. Furthermore, the integration of adversarial and autoregressive refinement techniques has enhanced the temporal coherence and fidelity of generated time series. Noteworthy papers include: Layout Stroke Imitation, which proposes a conditional diffusion model for stroke generation guided by calligraphic style and word layout. DS-Diffusion, which develops a style-guided diffusion framework to reduce distributional bias and improve interpretability. Training-Free Multi-Style Fusion, which enables controllable fusion of multiple reference styles in diffusion models. One-shot Embroidery Customization, which proposes a contrastive learning framework for fine-grained style transfer. TIMED, which integrates a denoising diffusion probabilistic model with a supervisor network and a Wasserstein critic for high-quality time series generation.