diffusion model

1. Background Introduction Since its introduction by Vaswani et al. in 2017, the Transformer model has made revolutionary progress in the field of natural language processing. However, as the complexity of generative tasks increases, traditional Transformer models face challenges. The proposal of Diffusion Transformers (DiTs) aims to combine the generative capabilities of the diffusion process with the self-attention mechanism of Transformers to address these challenges. 2. Diffusion Models 2.1. Background of Diffusion Models Diffusion Models are a new and advanced type of generative model used to produce data similar to training data, capable of generating various high-resolution images. 2.2. Definition of Diffusion Models Diffusion Models are a new and advanced type of generative model used to produce data similar to training data, capable of generating various high-resolution images. 2.3. Core Idea of Diffusion Models Inspired by non-equilibrium thermodynamics, Diffusion Models are generative models whose core idea is to gradually add noise to data through a diffusion process and then learn to reverse this process to construct the desired data samples from the noise. 2.4. Detailed Explanation of the Diffusion Process The diffusion process typically includes two phases: the forward process and the reverse process. In the forward process, the model gradually guides the data towards a simple noise distribution; in the reverse process, the model reverses this process, gradually removing noise to recover the original data. 3.1. Overview of Transformers Transformers are models based on the self-attention mechanism that can process sequential data and capture long-distance dependencies. They consist of an encoder and a decoder, implementing information transfer and processing through self-attention layers and feed-forward neural network layers. 3.2. Self-Attention Mechanism The self-attention mechanism allows the model to consider all elements of a sequence simultaneously while processing it, thereby capturing the global context. 4.1. Key Concepts of DiTs 4.1.1. Definition of DiT A Diffusion Transformer is a diffusion model combined with the Transformer architecture, used for image and video generation tasks, capable of efficiently capturing dependencies in data and producing high-quality results. 4.1.2. Essence of DiT A Diffusion Transformer is a new type of diffusion model that combines the denoising diffusion probabilistic model (DDPM) with the Transformer architecture. 4.1.3. Core Idea of DiT The core idea of the Diffusion Transformer is to use the Transformer as the backbone network for the diffusion model, instead of traditional convolutional neural networks (such as U-Net), to handle the latent representations of images. 4.2. Workflow of DiTs By introducing noise and training a neural network to reverse the noise addition process, combined with the Transformer model, image or video generation and transformation are achieved. This process involves data preprocessing, noise introduction, model training, and the final image or video generation. 4.2.1. Data Preprocessing Convert the input image or video data into a format that can be processed by the model, such as dividing the image into fixed-size patches and then transforming these patches into feature vectors. 4.2.2. Noise Introduction Gradually introduce noise to the feature vectors after data preprocessing, forming a noise-increasing diffusion process. This process can be seen as a transformation from original data to noisy data. 4.2.3. Model Training Use the feature vectors with introduced noise as input to train the Diffusion Transformer model. The goal of the model is to learn how to reverse the noise addition process, i.e., to recover the original data from the noisy data. 4.2.4. Image or Video Generation After the model training is complete, new images or videos can be generated by inputting noisy data (or randomly generated noise) into the model, which