Foldingdiff is a deep learning model that learns to generate novel protein backbone conformations through a denoising diffusion process. The model uses attention mechanisms and trigonometric coordinate representations to model the geometry of protein chains. It is trained on the CATH protein structure dataset and provides a pre-trained model via HuggingFace for sampling new structures directly in a browser or locally.