A complete collection of UNET models

Stable Diffusion UNET Model Resources

UNET architecture and its variants

1. Standard UNET

Paper: UNet: Convolutional Networks for Biomedical Image Segmentation

Introduction: UNET is a convolutional neural network architecture for image segmentation. It consists of a shrinking path (encoder) and an extended path (decoder), which resembles the letter "U". UNET was originally designed for biomedical image segmentation, but is now widely used in a variety of image processing tasks.

2. UNet++ (UNet Extended)

Paper: UNet++: A Nested U-Net Architecture for Medical Image Segmentation

Introduction: UNet++ is an improved version of UNET that introduces nested and dense jump connections. This design aims to reduce semantic gaps and improve segmentation accuracy, especially in the field of medical image segmentation.

3. Attention UNet

Paper: Attention U-Net: Learning Where to Look for the Pancreas

Introduction: Attention UNet introduces attention mechanisms based on standard UNET. This allows the model to focus better on the relevant parts of the image when generating the output, improving the accuracy of segmentation, especially when dealing with complex or small targets.

4. Residual UNet

Introduction: Residual UNet combines UNET architecture with residual connections. Residual connections help solve the problem of gradient vanishing in deep networks, making it possible to train deeper networks, thereby improving the performance and expression of the model.

UNET and its variants play an important role in image segmentation, generation and processing tasks. In image generation models such as Stable Diffusion, UNET is used as one of the core components, responsible for gradually generating high-quality images from noise. Understanding these architectures helps us better understand and apply these powerful deep learning tools.