U-Net

FLUX adopts a DiT (Diffusion Transformer) architecture, replacing the traditional U-Net structure with a pure Transformer.

Diffusion Transformer (DiT) is a class of generative backbone networks that introduces the Transformer architecture into diffusion models, cutting noisy images into patches and linearly embedding them into token sequences, and using multi-layer Transformer blocks to predict noise or velocity, replacing the U-Net in traditional diffusion models.

The reverse process of diffusion models typically uses U-Net or DiT (Diffusion Transformer) architectures to predict the amount of noise to be removed at each step.

Text-conditioned vectors are injected into the U-Net denoising network via a cross-attention mechanism, with image spatial features serving as Query and text encodings serving as Key and Value.

Compared to earlier U-Net-based diffusion models, the DiT architecture has stronger temporal consistency when processing long-sequence video frames.

U-Net was proposed by Ronneberger et al. in 2015, originally for medical image segmentation tasks.

一个Checkpoint文件通常包含完整的U-Net去噪网络权重，文件大小在2-7GB之间

早期视频生成模型基于U-Net架构，而扩散变换器将Transformer的注意力机制引入扩散过程

Reverse generation learns a denoising network that starts from pure noise, gradually predicts and removes noise, and ultimately generates clear samples.

Traditional diffusion models mostly use U-Net as the denoising network, whereas DiT replaces U-Net with a pure Transformer, splitting the image into a patch sequence as input and modeling global dependencies through self-attention.

The image generation field is undergoing an architectural paradigm shift from U-Net to Transformer; traditional diffusion models such as Stable Diffusion 1.x and 2.x primarily rely on a U-Net encoder-decoder structure.

The DiT architecture replaces the traditional U-Net with a Transformer as the diffusion model backbone, enabling the capture of long-range cross-frame dependencies at the patch level.

U-Net's network structure consists of an encoder (downsampling path) and a decoder (upsampling path), with fine-grained features transmitted between them via skip connections.

The study uses a general U-Net backbone and, through pixel-level contrastive learning, produces heatmaps with greater discriminative power than traditional heatmap regression.

Traditional diffusion models mostly use U-Net as the denoising network, whereas DiT replaces U-Net with a Transformer and has achieved performance superior to U-Net on image generation tasks.

Image segmentation is divided into semantic segmentation (such as U-Net and DeepLab) and instance segmentation (such as Mask R-CNN).

HiPredict adopts a U-Net architecture to learn the nonlinear spatiotemporal-spectral relationship between the inputs and the target channel.

U-Net is an encoder-decoder convolutional neural network originally used for biomedical image segmentation, which preserves fine spatial details through skip connections.

Mainstream segmentation APIs are usually based on U-Net or Transformer architectures and estimate the alpha value at the edges while removing the background, rather than doing a simple binary cut.

Modern before/after image differencing methods increasingly use deep convolutional neural networks (such as U-Net, ResNet, etc.) for semantic segmentation, classifying image pixels into categories such as buildings, roads, water bodies, and vegetation, and then determining whether the state of each category has undergone abnormal changes.