Diffusion models often utilize specific neural network architectures to effectively generate high-quality images from noise. The most commonly used architectures are convolutional neural networks (CNNs) and recurrent neural networks (RNNs), particularly when dealing with image and sequence data, respectively. CNNs are favored because they excel in processing spatial hierarchies, which is vital in understanding images. These networks filter input images through convolutional layers, allowing the model to learn various features at different levels of abstraction—from edges and textures to more complex structures. In diffusion models, CNNs help in both learning the denoising process and generating images by progressively refining noise into coherent visual outputs.
Another architecture frequently seen in diffusion models is the U-Net, which is a specific type of CNN architecture designed for image tasks. The U-Net architecture features an encoder-decoder structure with skip connections, which allows it to combine low-level feature maps from the encoder with high-level features from the decoder. This design is particularly useful in diffusion models, as it helps capture both fine details and broader contextual information during the denoising process. For instance, in the denoising diffusion probabilistic models (DDPMs), U-Nets have shown impressive results by effectively reconstructing images at each step of the reverse diffusion process.
Additionally, transformers have started to emerge in diffusion models, especially when there's a need for attention mechanisms to understand complex dependencies in data. The self-attention mechanism used in transformers allows the model to weigh the significance of different parts of the input, which can be beneficial when generating images with intricate details or diverse content. While traditional CNNs and U-Nets dominate in many diffusion applications, the inclusion of transformers signals a growing interest in leveraging their capabilities for more complex visual tasks. Overall, a combination of CNNs, U-Nets, and transformers represents the core neural network architectures employed in diffusion models.