#Sampling

DPM-Solver offers fast sampling for diffusion probabilistic models without retraining, providing quality results in 10-20 function evaluations. The updated DPM-Solver++ enhances image quality with dynamic thresholding, widely used in platforms like Stable-Diffusion for faster processing. It is utilized in major diffusion frameworks, making it suitable for real-time applications across various AI and media models.

The project provides an improved PyTorch implementation for scalable diffusion models with transformers, focusing on optimizing training and memory efficiency. It features pre-trained class-conditional models on ImageNet (512x512, 256x256) and tools for both sampling and training. Enhancements like gradient checkpointing and mixed precision training lead to notable performance gains. Resources such as Hugging Face Space and Colab notebooks facilitate easy deployment and model training. Evaluation tools support metrics computation like FID and Inception Score for thorough analysis.