Image generation has been successfully cast as an autoregressive sequence generation or transformation problem. Recent work has shown that self-attention is an effective way of modeling textual sequences. In this work, we generalize a recently proposed model architecture based on self-attention, the Transformer, to a sequence modeling formulation of image generation with a tractable likelihood. By restricting the selfattention mechanism to attend to local neighborhoods we significantly increase the size of images the model can process in practice, despite maintaining significantly larger receptive fields per layer than typical convolutional neural networks. While conceptually simple, our generative models significantly outperform the current state of the art in image generation on ImageNet, improving the best published negative log-likelihood on ImageNet from 3.83 to 3.77. We also present results on image super-resolution with a large magnification ratio, applying an encoder-decoder configuration of our architecture. In a human evaluation study, we find that images generated by our super-resolution model fool human observers three times more often than the previous state of the art.

This work explores conditional image generation with a new image density model based on the PixelCNN architecture. The model can be conditioned on any vector, including descriptive labels or tags, or latent embeddings created by other networks. When conditioned on class labels from the ImageNet database, the model is able to generate diverse, realistic scenes representing distinct animals, objects, landscapes and structures. When conditioned on an embedding produced by a convolutional network given a single image of an unseen face, it generates a variety of new portraits of the same person with different facial expressions, poses and lighting conditions. We also show that conditional PixelCNN can serve as a powerful decoder in an image autoencoder. Additionally, the gated convolutional layers in the proposed model improve the log-likelihood of PixelCNN to match the state-ofthe-art performance of PixelRNN on ImageNet, with greatly reduced computational cost.

Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel values and encodes the complete set of dependencies in the image. Architectural novelties include fast twodimensional recurrent layers and an effective use of residual connections in deep recurrent networks. We achieve log-likelihood scores on natural images that are considerably better than the previous state of the art. Our main results also provide benchmarks on the diverse ImageNet dataset. Samples generated from the model appear crisp, varied and globally coherent.

Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions. This makes them expressive, but also computationally infeasible for long sequences, such as high-resolution images. We demonstrate how combining the effectiveness of the inductive bias of CNNs with the expressivity of transformers enables them to model and thereby synthesize high-resolution images. We show how to (i) use CNNs to learn a contextrich vocabulary of image constituents, and in turn (ii) utilize transformers to efficiently model their composition within high-resolution images. Our approach is readily applied to conditional synthesis tasks, where both non-spatial information, such as object classes, and spatial information, such as segmentations, can control the generated image. In particular, we present the first results on semanticallyguided synthesis of megapixel images with transformers and obtain the state of the art among autoregressive models on class-conditional ImageNet. Code and pretrained models can be found at https://git.io/JnyvK.

现实世界中的数据是高维的：即使在压缩后，书籍，图像或音乐表演也很容易包含数十万个元素。但是，最常用的自回归模型，变压器非常昂贵，以缩放捕获这种远程结构所需的输入和层数。我们开发了感知者AR，这是一种自回归的模态 - 不合骨架构，它使用交叉注意力将远程输入映射到少数潜在的潜在，同时还可以维护端到端的因果关系掩盖。感知器AR可以直接进行十万个令牌，从而实现了实用的长篇小写密度估计，而无需手工制作的稀疏模式或记忆机制。当对图像或音乐进行培训时，感知器AR会生成具有清晰长期连贯性和结构的输出。我们的架构还获得了长期基准测试的最新可能性，包括64 x 64个Imagenet图像和PG-19书籍。

基于注意的模型，由变压器举例说明，可以有效地模拟长距离依赖性，而是遭受自我注意操作的二次复杂性，使得基于生成的对抗网络（GAN）的高分辨率图像生成使得它们难以采用。在本文中，我们向变压器推出了两个关键成分来解决这一挑战。首先，在生成过程的低分辨率阶段，用所提出的多轴阻塞自我关注取代了标准的全球自我关注，这允许有效地混合本地和全球关注。其次，在高分辨率阶段，我们降低了自我关注，同时只保持多层的感知让人想起隐含的神经功能。为了进一步提高性能，我们基于横向引入额外的自我调制组件。结果模型表示为命中，具有关于图像尺寸的几乎线性的计算复杂度，从而直接缩放到合成高清晰度图像。我们在实验中展示了所提出的命中，实现最先进的FID得分31.87和2.95在无条件的ImageNet上，分别具有合理的吞吐量的128美元和256美元\ times 256美元。我们认为，拟议的命中是全球发电机的一个重要里程碑，完全没有卷积。

Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Among their salient benefits, Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent networks e.g., Long short-term memory (LSTM). Different from convolutional networks, Transformers require minimal inductive biases for their design and are naturally suited as set-functions. Furthermore, the straightforward design of Transformers allows processing multiple modalities (e.g., images, videos, text and speech) using similar processing blocks and demonstrates excellent scalability to very large capacity networks and huge datasets. These strengths have led to exciting progress on a number of vision tasks using Transformer networks. This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline. We start with an introduction to fundamental concepts behind the success of Transformers i.e., self-attention, large-scale pre-training, and bidirectional feature encoding. We then cover extensive applications of transformers in vision including popular recognition tasks (e.g., image classification, object detection, action recognition, and segmentation), generative modeling, multi-modal tasks (e.g., visual-question answering, visual reasoning, and visual grounding), video processing (e.g., activity recognition, video forecasting), low-level vision (e.g., image super-resolution, image enhancement, and colorization) and 3D analysis (e.g., point cloud classification and segmentation). We compare the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value. Finally, we provide an analysis on open research directions and possible future works. We hope this effort will ignite further interest in the community to solve current challenges towards the application of transformer models in computer vision.

生成建模研究的持续趋势是将样本分辨率推高更高，同时减少培训和采样的计算要求。我们的目标是通过技术的组合进一步推动这一趋势 - 每个组件代表当前效率在各自领域的顶峰。其中包括载体定量的GAN（VQ-GAN），该模型具有高水平的损耗 - 但感知上微不足道的压缩模型；沙漏变形金刚，一个高度可扩展的自我注意力模型；和逐步未胶片的denoising自动编码器（Sundae），一种非自动化（NAR）文本生成模型。出乎意料的是，当应用于多维数据时，我们的方法突出了沙漏变压器的原始公式中的弱点。鉴于此，我们建议对重采样机制进行修改，该机制适用于将分层变压器应用于多维数据的任何任务。此外，我们证明了圣代表到长序列长度的可伸缩性 - 比先前的工作长四倍。我们提出的框架秤达到高分辨率（$ 1024 \ times 1024 $），并迅速火车（2-4天）。至关重要的是，训练有素的模型在消费级GPU（GTX 1080TI）上大约2秒内生产多样化和现实的百像样品。通常，该框架是灵活的：支持任意数量的采样步骤，示例自动插入，自我纠正功能，有条件的生成和NAR公式，以允许任意介绍掩护。我们在FFHQ256上获得10.56的FID得分 - 仅在100个采样步骤中以不到一半的采样步骤接近原始VQ -GAN，而FFHQ1024的FFHQ1024和21.85。

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. * Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.† Work performed while at Google Brain.‡ Work performed while at Google Research.

DeNoising扩散模型代表了计算机视觉中最新的主题，在生成建模领域表现出了显着的结果。扩散模型是一个基于两个阶段的深层生成模型，一个正向扩散阶段和反向扩散阶段。在正向扩散阶段，通过添加高斯噪声，输入数据在几个步骤中逐渐受到干扰。在反向阶段，模型的任务是通过学习逐步逆转扩散过程来恢复原始输入数据。尽管已知的计算负担，即由于采样过程中涉及的步骤数量，扩散模型对生成样品的质量和多样性得到了广泛赞赏。在这项调查中，我们对视觉中应用的denoising扩散模型的文章进行了全面综述，包括该领域的理论和实际贡献。首先，我们识别并介绍了三个通用扩散建模框架，这些框架基于扩散概率模型，噪声调节得分网络和随机微分方程。我们进一步讨论了扩散模型与其他深层生成模型之间的关系，包括变异自动编码器，生成对抗网络，基于能量的模型，自回归模型和正常流量。然后，我们介绍了计算机视觉中应用的扩散模型的多角度分类。最后，我们说明了扩散模型的当前局限性，并设想了一些有趣的未来研究方向。

We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and decoder networks, making our model an attractive candidate for applications where the encoding and/or decoding speed is critical. Additionally, VQ-VAE requires sampling an autoregressive model only in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images. We demonstrate that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity.

神经场通过将坐标输入映射到采样值来模型信号。从视觉，图形到生物学和天文学的许多领域，它们正成为越来越重要的主链体系结构。在本文中，我们探讨了这些网络中常见的调理机制之间的差异，这是将神经场从信号的记忆转移到概括的基本要素，其中共同建模了位于歧管上的一组信号。特别是，我们对这些机制的缩放行为感兴趣，以对日益高维的调理变量感兴趣。正如我们在实验中显示的那样，高维条件是建模复杂数据分布的关键，因此，确定哪种体系结构在处理此类问题时最能实现哪种选择。为此，我们运行了使用串联，超网络和基于注意力的调理策略对2D，3D和4D信号进行建模的实验，这是文献中尚未进行的必要但费力的努力。我们发现，基于注意力的条件在各种环境中的其他方法都优于其他方法。

利用深度学习的最新进展，文本到图像生成模型目前具有吸引公众关注的优点。其中两个模型Dall-E 2和Imagen已经证明，可以从图像的简单文本描述中生成高度逼真的图像。基于一种称为扩散模型的新型图像生成方法，文本对图像模型可以生产许多不同类型的高分辨率图像，其中人类想象力是唯一的极限。但是，这些模型需要大量的计算资源来训练，并处理从互联网收集的大量数据集。此外，代码库和模型均未发布。因此，它可以防止AI社区尝试这些尖端模型，从而使其结果复制变得复杂，即使不是不可能。在本文中，我们的目标是首先回顾这些模型使用的不同方法和技术，然后提出我们自己的文本模型模型实施。高度基于DALL-E 2，我们引入了一些轻微的修改，以应对所引起的高计算成本。因此，我们有机会进行实验，以了解这些模型的能力，尤其是在低资源制度中。特别是，我们提供了比Dall-e 2的作者（包括消融研究）更深入的分析。此外，扩散模型使用所谓的指导方法来帮助生成过程。我们引入了一种新的指导方法，该方法可以与其他指导方法一起使用，以提高图像质量。最后，我们的模型产生的图像质量相当好，而不必维持最先进的文本对图像模型的重大培训成本。

通过将图像形成过程分解成逐个申请的去噪自身额，扩散模型（DMS）实现了最先进的合成导致图像数据和超越。另外，它们的配方允许引导机构来控制图像生成过程而不会再刷新。然而，由于这些模型通常在像素空间中直接操作，因此强大的DMS的优化通常消耗数百个GPU天，并且由于顺序评估，推理是昂贵的。为了在保留其质量和灵活性的同时启用有限计算资源的DM培训，我们将它们应用于强大的佩带自动化器的潜在空间。与以前的工作相比，这种代表上的培训扩散模型允许第一次达到复杂性降低和细节保存之间的近乎最佳点，极大地提高了视觉保真度。通过将跨关注层引入模型架构中，我们将扩散模型转化为强大而柔性的发电机，以进行诸如文本或边界盒和高分辨率合成的通用调节输入，以卷积方式变得可以实现。我们的潜在扩散模型（LDMS）实现了一种新的技术状态，可在各种任务中进行图像修复和高竞争性能，包括无条件图像生成，语义场景合成和超级分辨率，同时与基于像素的DMS相比显着降低计算要求。代码可在https://github.com/compvis/lattent-diffusion获得。

桥接全球上下文交互正确对大面具的高保真图像完成非常重要。先前的方法通过深或大的接收领域（RF）卷积无法逃离附近互动的主导地位，这可能是劣等的。在本文中，我们建议将图像完成视为无缝的序列到序列预测任务，并部署变压器以直接捕获编码器中的远程依赖性。至关重要，我们使用具有小而非重叠的RF的限制性CNN，用于加权令牌表示，这允许变压器明确地模拟所有层中的相同重要性，而在使用较大的RF时，没有隐含地混淆邻居令牌。为了改善可见区域之间的外观一致性，引入了一种新的注意力层（aal）以更好地利用远方相关的高频功能。总体而言，与若干数据集上的最先进方法相比，大量实验表现出卓越的性能。

最近对变形金刚的爆炸利益提出了他们成为计算机视觉任务的强大“通用”模型的潜力，例如分类，检测和分割。虽然这些尝试主要研究歧视模型，但我们探索变压器，更加臭名昭着的难以愿景任务，例如生成的对抗网络（GANS）。我们的目标是通过仅使用纯的变压器的架构，开展一项完全没有卷曲的GAN的试点研究。我们的Vanilla GaN架构被称为Cransgan，包括一个基于内存友好的变换器的发电机，逐渐增加了特征分辨率，并且相应地是多尺度鉴别器来捕获同时语义上下文和低级纹理。在他们之上，我们介绍了新的网格自我关注模块，以便进一步缓解记忆瓶颈，以便扩展到高分辨率的发电。我们还开发了一个独特的培训配方，包括一系列技术，可以减轻转发的培训不稳定问题，例如数据增强，修改的归一化和相对位置编码。与使用卷积骨架的当前最先进的GAN相比，我们最好的建筑达到了竞争力的表现。具体而言，转发在STL-10上设置10.43和18.28的最新的最新成立得分为18.28，表现优于样式。当涉及更高分辨率（例如256 x 256）的生成任务时，例如Celeba-HQ和Lsun-Church，Rancorgan继续生产具有高保真度和令人印象深刻的纹理细节的不同视觉示例。此外，我们通过可视化培训动力学，深入了解基于变压器的生成模型，了解他们的行为如何与卷积的行为。代码可在https://github.com/vita-group/transgan中获得。

Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we find that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and low-data classification. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full fine-tuning, matching the top supervised pretrained models. We are also competitive with self-supervised benchmarks on ImageNet when substituting pixels for a VQVAE encoding, achieving 69.0% top-1 accuracy on a linear probe of our features.

Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as machine translation, with the added advantage that they concurrently process all inputs in the sequence, leading to easy parallelization and faster training times. Despite these successes, however, popular feed-forward sequence models like the Transformer fail to generalize in many simple tasks that recurrent models handle with ease, e.g. copying strings or even simple logical inference when the string or formula lengths exceed those observed at training time. We propose the Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses these issues. UTs combine the parallelizability and global receptive field of feed-forward sequence models like the Transformer with the recurrent inductive bias of RNNs. We also add a dynamic per-position halting mechanism and find that it improves accuracy on several tasks. In contrast to the standard Transformer, under certain assumptions UTs can be shown to be Turing-complete. Our experiments show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state of the art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset. * Equal contribution, alphabetically by last name. † Work performed while at Google Brain.

我描述了使用规定规则作为替代物的训练流模型的技巧，以最大程度地发出可能性。此技巧的实用性限制在非条件模型中，但是该方法的扩展应用于数据和条件信息的最大可能性分布的最大可能性，可用于训练复杂的\ textit \ textit {条件{条件}流模型。与以前的方法不同，此方法非常简单：它不需要明确了解条件分布，辅助网络或其他特定体系结构，或者不需要超出最大可能性的其他损失项，并且可以保留潜在空间和数据空间之间的对应关系。所得模型具有非条件流模型的所有属性，对意外输入具有鲁棒性，并且可以预测在给定输入上的解决方案的分布。它们具有预测代表性的保证，并且是解决高度不确定问题的自然和强大方法。我在易于可视化的玩具问题上演示了这些属性，然后使用该方法成功生成类条件图像并通过超分辨率重建高度退化的图像。

Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. We extend the space of such models using real-valued non-volume preserving (real NVP) transformations, a set of powerful, stably invertible, and learnable transformations, resulting in an unsupervised learning algorithm with exact log-likelihood computation, exact and efficient sampling, exact and efficient inference of latent variables, and an interpretable latent space. We demonstrate its ability to model natural images on four datasets through sampling, log-likelihood evaluation, and latent variable manipulations.