DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation

The article introduces "DiffusionBlocks," a framework for block-wise training of transformer-based neural networks that allows independent training of model blocks, significantly reducing memory requirements associated with end-to-end backpropagation. By leveraging the score matching objective and the properties of residual connections, DiffusionBlocks enables scalable training across various architectures, including vision and generative models, while maintaining competitive performance. This approach is particularly relevant for practitioners looking to optimize memory usage and efficiency in large-scale AI model training.