U-Net

A convolutional neural network architecture originally developed for biomedical image segmentation, characterized by a symmetric encoder-decoder structure with skip connections that directly link corresponding layers in the encoder and decoder. The skip connections preserve fine-grained spatial detail that would otherwise be lost during encoding, making U-Net highly effective for tasks requiring precise localization. It has since become widely used in diffusion models for image generation.