Revolutionizing Image Generation: Introducing DiT, the Transformer-Based Diffusion Model
In a groundbreaking development in the realm of artificial intelligence, researchers have unveiled DiT (Diffusion Transformers), an innovative diffusion model that combines the power of the Transformer architecture with the principles of denoising diffusion probabilistic models (DDPMs). Spearheaded by William Peebles, the head of research at Sora, and Assistant Professor Xie Senning from New York University, DiT is poised to reshape the landscape of image generation.
Diffusion models, a class of generative models, have gained significant attention for their ability to create new samples by gradually removing noise from data. The core innovation of DiT lies in its substitution of traditional convolutional neural networks (such as U-Net) with the Transformer architecture, allowing for the efficient handling of image latent representations. This shift has been made possible by the growing popularity of OpenAI’s video generation model, Sora, which is built on the technical foundations of DiT.
In the DiT framework, images are initially compressed into smaller latent representations through an autoencoder, typically a variational autoencoder (VAE). By training the diffusion model in this latent space, computational demands are reduced, particularly when dealing with high-resolution images. The Transformer’s self-attention mechanism then processes these latent representations, enabling the model to capture long-range dependencies in images, thereby generating high-quality visuals.
For those interested in exploring DiT further, official resources are available. The project’s homepage can be found at https://www.wpeebles.com/DiT, and the research paper is accessible on Arxiv at https://arxiv.org/pdf/2212.09748.pdf. The code for DiT is open-source and hosted on GitHub at https://github.com/facebookresearch/DiT, while a Hugging Face space dedicated to the model is located at https://huggingface.co/spaces/wpeebles/DiT. A live demonstration can be experienced on Replicate at https://replicate.com/arielreplicate/scalable_diffusion_with_transformers, and a Google Colab notebook for running DiT is available at http://colab.research.google.com/github/facebookresearch/DiT/blob/main/run_DiT.ipynb.
The technical workings of DiT involve several key steps:
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Data Preparation: Input images are first encoded into a lower-dimensional latent space using a pre-trained VAE. This latent representation condenses, for instance, a 256×256×3 RGB image into a 32×32×4 vector.
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Patchification: The latent representation is then divided into smaller patches, which become individual tokens for the Transformer model. This process enables the model to process images incrementally.
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Token Embedding and Positional Encoding: Each patch undergoes a linear embedding transformation to a fixed-dimensional vector, and position encoding is added to enable the model to understand the spatial context of the patches.
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Transformer Blocks: The token sequence is processed through a series of Transformer blocks, each comprising self-attention layers, feedforward neural networks, and layer normalization. Researchers have experimented with variations, such as adaptive layer normalization (adaLN), cross-attention, and in-context conditioning to handle conditional information like time steps and class labels.
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Conditional Diffusion Process: During training, DiT learns the reverse diffusion process, restoring clean images from noisy data. This involves predicting noise statistics, like mean and covariance.
These advancements in image generation have far-reaching implications for fields like computer graphics, digital art, and potentially even medical imaging. By leveraging the Transformer architecture, DiT promises to enhance image quality, efficiency, and scalability, marking a significant stride in AI-generated content.
As AI continues to evolve, models like DiT demonstrate the potential for merging established techniques with novel architectures, pushing the boundaries of what is possible in image generation. With its focus on Transformer-based diffusion, DiT is set to leave an indelible mark on the future of artificial intelligence and its applications in the creative and scientific domains.
【source】https://ai-bot.cn/dit/
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