The field of generative models is moving towards more efficient and scalable methods for image and data synthesis. Researchers are focusing on developing novel techniques to accelerate sampling processes, reduce computational costs, and improve the quality of generated samples. One of the key directions is the use of diffusion models, which have shown remarkable success in generating high-quality images and videos. However, these models often suffer from high computational costs due to their iterative sampling process. To address this, several papers have proposed innovative methods to accelerate diffusion models, including the use of parallel processing, knowledge distillation, and novel ODE solvers. Another area of research is the development of more efficient generative models for discrete data, such as discrete flow-based models. These models have shown great promise in generating high-quality discrete data, but often suffer from slow sampling speeds. New methods have been proposed to address this, including the use of rectified discrete flows and interval splitting consistency. Notable papers in this area include:

• Efficient Burst Super-Resolution with One-step Diffusion, which proposes a novel method for efficient burst super-resolution using a diffusion model.
• CHORDS: Diffusion Sampling Accelerator with Multi-core Hierarchical ODE Solvers, which presents a novel framework for accelerating diffusion sampling using multi-core parallelism.
• SADA: Stability-guided Adaptive Diffusion Acceleration, which proposes a novel paradigm for accelerating diffusion models using a stability criterion.
• CompactFusion, which presents a compression framework for reducing communication overhead in parallel diffusion model serving.