在本文中，我们提出了Nuwa-Infinity，这是无限视觉合成的生成模型，该模型被定义为生成任意尺寸的高分辨率图像或长期视频的任务。提出了一种自回旋的自回旋生成机制来处理这一可变大小的生成任务，其中全球补丁级自回归模型考虑了补丁之间的依赖性，以及局部令牌级自动回收式模型在每个贴片中的视觉令牌之间的依赖性。将附近的上下文池（NCP）引入已生成的与缓存相关的补丁，作为当前补丁生成的上下文，该贴片可以显着节省计算成本而不牺牲补丁级依赖性模型。任意方向控制器（ADC）用于确定不同视觉合成任务的合适生成订单，并学习订单感知的位置嵌入。与DALL-E，Imagen和Parti相比，NUWA-INFINITY可以生成具有任意大小的高分辨率图像，并支持长期视频的生成。与NUWA（也涵盖图像和视频）相比，NUWA-Infinity在分辨率和可变尺寸的生成方面具有出色的视觉合成功能。 github链接是https://github.com/microsoft/nuwa。主页链接是https://nuwa-infinity.microsoft.com。

本文介绍了一个名为N \“UWA的统一的多模式预训练模型，可以为各种可视化合成任务生成新的或操作现有的视觉数据（即，图像和视频）。涵盖语言，图像和视频同时设计了一个不同的场景，设计了一个3D变压器编码器 - 解码器框架，它不仅可以处理作为3D数据的视频，而且分别适应文本和图像作为1D和2D数据。还提出了附近的3D（3DNA）机制考虑视觉数据的性质并降低计算复杂性。我们在8个下游任务上评估n \“UWA。与几个强的基线相比，n \“uwa达到了最先进的结果，对图像生成，文本到视频生成，视频预测等。此外，它还显示出令人惊讶的良好零射起能力关于文本引导的图像和视频操作任务。项目repo是https://github.com/microsoft/nuwa。

Recently, vector quantized autoregressive (VQ-AR) models have shown remarkable results in text-to-image synthesis by equally predicting discrete image tokens from the top left to bottom right in the latent space. Although the simple generative process surprisingly works well, is this the best way to generate the image? For instance, human creation is more inclined to the outline-to-fine of an image, while VQ-AR models themselves do not consider any relative importance of each component. In this paper, we present a progressive denoising model for high-fidelity text-to-image image generation. The proposed method takes effect by creating new image tokens from coarse to fine based on the existing context in a parallel manner and this procedure is recursively applied until an image sequence is completed. The resulting coarse-to-fine hierarchy makes the image generation process intuitive and interpretable. Extensive experiments demonstrate that the progressive model produces significantly better results when compared with the previous VQ-AR method in FID score across a wide variety of categories and aspects. Moreover, the text-to-image generation time of traditional AR increases linearly with the output image resolution and hence is quite time-consuming even for normal-size images. In contrast, our approach allows achieving a better trade-off between generation quality and speed.

创建视频是为了表达情感，交换信息和分享经验。视频合成很长时间以来一直吸引了研究人员。尽管视觉合成的进步驱动了迅速的进展，但大多数现有研究都集中在提高框架的质量和之间的过渡上，而在生成更长的视频方面几乎没有取得进展。在本文中，我们提出了一种基于3D-VQGAN和Transformers的方法，以生成具有数千帧的视频。我们的评估表明，我们的模型在16架视频剪辑中培训了来自UCF-101，Sky TimeLapse和Taichi-HD数据集等标准基准测试片段，可以生成多样化，连贯和高质量的长视频。我们还展示了我们通过将时间信息与文本和音频结合在一起来生成有意义的长视频的方法的条件扩展。可以在https://songweige.github.io/projects/tats/index.html上找到视频和代码。

文本到图像合成的最新进展导致了较大的经过验证的变压器，具有出色的能力，可以从给定文本产生可视化。但是，这些模型不适合专门的任务，例如故事可视化，该任务要求代理商制作一系列图像，给定相应的字幕序列，形成叙述。此外，我们发现故事可视化任务无法适应新叙事中看不见的情节和角色的概括。因此，我们首先提出了故事延续的任务，其中生成的视觉故事是在源图像上进行的，从而可以更好地对具有新角色的叙述进行更好的概括。然后，我们使用特定于（a）顺序图像生成的任务特定模块和（b）从初始帧复制相关元素的任务特定模块来增强或“复古”文本对图像合成模型。然后，我们探讨了预训练模型的全模型芬太尼以及对参数适应的及时调整。我们在两个现有数据集（PororoSV和FlintStonessV）上评估了我们的方法storydall-e，并介绍了从视频吸引数据集收集的新数据集DIDEMOSV。我们还基于生成的对抗网络（GAN）开发了一个模型故事游戏，以进行故事的延续，并将其与StoryDall-E模型进行比较，以展示我们方法的优势。我们表明，我们的复古拟合方法优于基于GAN的模型，用于故事延续，并促进从源图像中复制视觉元素，从而改善了生成的视觉故事中的连续性。最后，我们的分析表明，经过审计的变压器努力理解包含几个角色的叙述。总体而言，我们的工作表明，可以验证的文本对图像合成模型可以适应复杂和低资源的任务，例如故事延续。

产生人类想要的声音效果是一个重要的话题。但是，在这一领域，很少有研究声音发电。在这项研究中，我们调查了以文本提示为条件的声音，并提出了一个新型的文本对生成框架，该框架由文本编码器组成，矢量量化了变异自动编码器（VQ-VAE），解码器和歌手。该框架首先使用解码器将从文本编码器提取的文本特征传递到借助VQ-VAE的MEL光谱图中，然后使用Vocoder将生成的MEL光谱图转换为波形。我们发现，解码器显着影响发电性能。因此，我们专注于在这项研究中设计一个好的解码器。我们从传统的自动回解码器开始，该解码器已被证明是以前的Sound Generation Works中的最先进方法。但是，AR解码器始终按顺序预测MEL-SPECTROGIN图令牌，这引入了单向偏见和错误问题的积累。此外，使用AR解码器，声音生成时间随着声音持续时间线性增加。为了克服AR解码器引入的缺点，我们提出了一个基于离散扩散模型的非自动回形解码器，称为DiffSound。具体而言，DIFFSOUND可以在一个步骤中预测所有MEL光谱图令牌，然后在下一步中完善预测的令牌，因此可以在几个步骤后获得最优于预测的结果。我们的实验表明，与AR解码器相比，我们提出的差异不仅产生更好的文本到单一生成结果，而且还具有更快的生成速度，例如MOS：3.56 \ textit {v.s} 2.786，并且生成速度为五个比AR解码器快的时间。

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github.io

We introduce the MAsked Generative VIdeo Transformer, MAGVIT, to tackle various video synthesis tasks with a single model. We introduce a 3D tokenizer to quantize a video into spatial-temporal visual tokens and propose an embedding method for masked video token modeling to facilitate multi-task learning. We conduct extensive experiments to demonstrate the quality, efficiency, and flexibility of MAGVIT. Our experiments show that (i) MAGVIT performs favorably against state-of-the-art approaches and establishes the best-published FVD on three video generation benchmarks, including the challenging Kinetics-600. (ii) MAGVIT outperforms existing methods in inference time by two orders of magnitude against diffusion models and by 60x against autoregressive models. (iii) A single MAGVIT model supports ten diverse generation tasks and generalizes across videos from different visual domains. The source code and trained models will be released to the public at https://magvit.cs.cmu.edu.

Large-scale diffusion-based generative models have led to breakthroughs in text-conditioned high-resolution image synthesis. Starting from random noise, such text-to-image diffusion models gradually synthesize images in an iterative fashion while conditioning on text prompts. We find that their synthesis behavior qualitatively changes throughout this process: Early in sampling, generation strongly relies on the text prompt to generate text-aligned content, while later, the text conditioning is almost entirely ignored. This suggests that sharing model parameters throughout the entire generation process may not be ideal. Therefore, in contrast to existing works, we propose to train an ensemble of text-to-image diffusion models specialized for different synthesis stages. To maintain training efficiency, we initially train a single model, which is then split into specialized models that are trained for the specific stages of the iterative generation process. Our ensemble of diffusion models, called eDiff-I, results in improved text alignment while maintaining the same inference computation cost and preserving high visual quality, outperforming previous large-scale text-to-image diffusion models on the standard benchmark. In addition, we train our model to exploit a variety of embeddings for conditioning, including the T5 text, CLIP text, and CLIP image embeddings. We show that these different embeddings lead to different behaviors. Notably, the CLIP image embedding allows an intuitive way of transferring the style of a reference image to the target text-to-image output. Lastly, we show a technique that enables eDiff-I's "paint-with-words" capability. A user can select the word in the input text and paint it in a canvas to control the output, which is very handy for crafting the desired image in mind. The project page is available at https://deepimagination.cc/eDiff-I/

我们介绍了文本到图像生成的矢量量化扩散（VQ-扩散）模型。该方法基于矢量量化变分性AutoEncoder（VQ-VAE），其潜像通过最近开发的去噪扩散概率（DDPM）的条件变体为基础。我们发现这种潜在空间方法非常适合于图像到图像生成任务，因为它不仅消除了具有现有方法的单向偏差，还允许我们结合掩模和更换的扩散策略，以避免积累错误，这是现有方法的严重问题。我们的实验表明，与具有类似数量的参数数量的传统自回归（AR）模型相比，VQ扩散产生明显更好的文本到图像生成结果。与以前的基于GAN的文本到图像方法相比，我们的VQ扩散可以通过大边缘处理更复杂的场景并提高合成的图像质量。最后，我们表明我们的方法中的图像生成计算可以通过Reparameter化进行高效。利用传统的AR方法，文本到图像生成时间随输出图像分辨率线性增加，因此即使对于正常尺寸图像也是相当耗时的。 VQ-扩散使我们能够在质量和速度之间实现更好的权衡。我们的实验表明，具有Reparameterization的VQ扩散模型比传统的AR方法快15倍，同时实现更好的图像质量。

用于图像文本生成任务的传统方法主要是分别解决自然双向生成任务，专注于设计任务特定的框架以提高所生成的样本的质量和保真度。最近，Vision-Language预训练模型大大提高了图像到文本生成任务的性能，但仍未开发出用于文本到图像综合任务的大规模预训练模型。在本文中，我们提出了一个具有变压器模型的双向图像文本生成的统一生成的预训练框架的Ernie-Vi​​lg。基于图像量化模型，我们将图像生成和文本生成标准为在文本/图像输入上调节的自回归生成任务。双向图像文本生成建模简化了视觉和语言的语义对齐。对于文本到图像生成过程，我们进一步提出了端到端的训练方法，共同学习视觉序列发生器和图像重建。为了探讨双向文本图像生成的大规模预培训景观，我们在大规模数据集中培训了100亿参数的Ernie-Vi​​lg模型，以145百万（中文）图像 - 文本对实现了达到的状态 - 文本到图像和图像到文本任务的最佳性能，以便在MS-Coco上获取7.9的FID，用于文本到图像合成以及用于图像标题的Coco-CN和AIC-ICC的最佳结果。

随着信息中的各种方式存在于现实世界中的各种方式，多式联信息之间的有效互动和融合在计算机视觉和深度学习研究中的多模式数据的创造和感知中起着关键作用。通过卓越的功率，在多式联运信息中建模互动，多式联运图像合成和编辑近年来已成为一个热门研究主题。与传统的视觉指导不同，提供明确的线索，多式联路指南在图像合成和编辑方面提供直观和灵活的手段。另一方面，该领域也面临着具有固有的模态差距的特征的几个挑战，高分辨率图像的合成，忠实的评估度量等。在本调查中，我们全面地阐述了最近多式联运图像综合的进展根据数据模型和模型架构编辑和制定分类。我们从图像合成和编辑中的不同类型的引导方式开始介绍。然后，我们描述了多模式图像综合和编辑方法，其具有详细的框架，包括生成的对抗网络（GAN），GaN反转，变压器和其他方法，例如NERF和扩散模型。其次是在多模式图像合成和编辑中广泛采用的基准数据集和相应的评估度量的综合描述，以及分析各个优点和限制的不同合成方法的详细比较。最后，我们为目前的研究挑战和未来的研究方向提供了深入了解。与本调查相关的项目可在HTTPS://github.com/fnzhan/mise上获得

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.

条件图像合成旨在根据文本描述，参考图像和图像块的形式创建图像，以保存的，以及它们的组合。在本文中，我们提出了一个新的两级架构M6-UFC，统一了任何数量的多模态控件。在M6-UFC中，各种控制信号和合成图像都均匀地表示为由变压器处理的离散令牌序列。与现有的两级自回归方式不同，如Dall-E和VQGAN，M6-UFC在第二阶段采用非自动发作生成（NAR），以增强合成图像的整体一致性，以支持保留指定的图像块，以及提高合成速度。此外，我们设计了一种逐步算法，其迭代地改善了非自动产生的图像，其中包括用于评估符合控制的符合和评估合成图像的保真度的两个估计器的帮助。在新收集的大型服装数据集M2C时装和面部数据集多模态Celeba-HQ上进行了广泛的实验验证了M6-UFC可以合成符合灵活的多模态控制的高保真图像。

随着视觉前训练的成功，我们目睹了最先进的方式，以多模式的理解和产生推动。但是，当前的预训练范式不能一次靶向所有模式（例如，文本生成和图像生成），或者需要多重设计良好的任务，从而显着限制可伸缩性。我们证明，可以通过文本和图像序列的前缀语言建模目标学习统一的模态模型。得益于简单但功能强大的预训练范式，我们提出的模型Davinci非常易于训练，可扩展到巨大的数据，并且可以适应跨模态（语言 /视觉 /视觉+语言）的各种下游任务（类型）（理解） / generation）和设置（例如，零射，微调，线性评估）具有单个统一体系结构。达文奇（Davinci）在26个理解 /发电任务的广泛范围内实现了竞争性能，并且在大多数任务上都超过了以前的统一视力语言模型，包括Imagenet分类（+1.6％），VQAV2（+1.4％）（+1.4％），可可标题生成（Bleu@@@@@ 4 +1.1％，苹果酒 +1.5％）和可可图像生成（ +0.9％，FID -1.0％），在可比的模型和数据量表处。此外，我们通过在异质和广泛的分布覆盖范围内报告不同尺度的量表上的性能，为将来的研究提供了明确的基准。我们的结果建立了新的，更强的基线，以便将来在不同的数据量表上进行比较，并阐明了更广泛地比较VLP模型的困难。

Evaluating and comparing text-to-image models is a challenging problem. Significant advances in the field have recently been made, piquing interest of various industrial sectors. As a consequence, a gold standard in the field should cover a variety of tasks and application contexts. In this paper a novel evaluation approach is experimented, on the basis of: (i) a curated data set, made by high-quality royalty-free image-text pairs, divided into ten categories; (ii) a quantitative metric, the CLIP-score, (iii) a human evaluation task to distinguish, for a given text, the real and the generated images. The proposed method has been applied to the most recent models, i.e., DALLE2, Latent Diffusion, Stable Diffusion, GLIDE and Craiyon. Early experimental results show that the accuracy of the human judgement is fully coherent with the CLIP-score. The dataset has been made available to the public.

我们介绍了自回归文本到图像（Parti）模型的途径，该模型生成高保真的影像图像并支持涉及复杂组成和世界知识的内容丰富的合成。 Parti将文本对图像生成视为类似于机器翻译的序列到序列建模问题，图像令牌的序列是目标输出，而不是其他语言的文本令牌。这种策略自然可以利用大型语言模型的先前工作，通过扩展数据和模型尺寸，能力和性能的持续进展。我们的方法很简单：首先，Parti使用基于变压器的图像令牌VIT-VQGAN将图像编码为离散令牌的序列。其次，我们通过将编码器二次变压器模型缩放到20B参数来实现一致的质量改进，其新的最新零弹药FID得分为7.23，而MS-Coco的FIDED得分为3.22。我们对本地化叙述以及党的详细分析（P2），这是1600多个英语提示的新的整体基准，证明了Parti在各种类别和难度方面的有效性。我们还探索并突出了我们的模型的局限性，以定义和体现关注重点领域以进一步改进。有关高分辨率图像，请参见https://parti.research.google/。

The image captioning task is typically realized by an auto-regressive method that decodes the text tokens one by one. We present a diffusion-based captioning model, dubbed the name DDCap, to allow more decoding flexibility. Unlike image generation, where the output is continuous and redundant with a fixed length, texts in image captions are categorical and short with varied lengths. Therefore, naively applying the discrete diffusion model to text decoding does not work well, as shown in our experiments. To address the performance gap, we propose several key techniques including best-first inference, concentrated attention mask, text length prediction, and image-free training. On COCO without additional caption pre-training, it achieves a CIDEr score of 117.8, which is +5.0 higher than the auto-regressive baseline with the same architecture in the controlled setting. It also performs +26.8 higher CIDEr score than the auto-regressive baseline (230.3 v.s.203.5) on a caption infilling task. With 4M vision-language pre-training images and the base-sized model, we reach a CIDEr score of 125.1 on COCO, which is competitive to the best well-developed auto-regressive frameworks. The code is available at https://github.com/buxiangzhiren/DDCap.

我们研究了联合视频和语言（VL）预培训，以实现跨模型学习和益处丰富的下游VL任务。现有的作品要么提取低质量的视频特征或学习有限的文本嵌入，但忽略了高分辨率视频和多样化的语义可以显着提高跨模型学习。在本文中，我们提出了一种新的高分辨率和多样化的视频 - 语言预训练模型（HD-VILA），用于许多可视任务。特别是，我们收集具有两个不同属性的大型数据集：1）第一个高分辨率数据集包括371.5k小时的720p视频，2）最多样化的数据集涵盖15个流行的YouTube类别。为了启用VL预培训，我们通过学习丰富的时空特征的混合变压器联合优化HD-VILA模型，以及多峰变压器，用于强制学习视频功能与多样化文本的交互。我们的预训练模式实现了新的最先进的导致10 VL了解任务和2个新颖的文本到视觉生成任务。例如，我们以零拍摄MSR-VTT文本到视频检索任务的相对增加38.5％R @ 1的相对增长，高分辨率数据集LSMDC为53.6％。学习的VL嵌入也有效地在文本到视觉操纵和超分辨率任务中产生视觉上令人愉悦和语义相关结果。

Video generation requires synthesizing consistent and persistent frames with dynamic content over time. This work investigates modeling the temporal relations for composing video with arbitrary length, from a few frames to even infinite, using generative adversarial networks (GANs). First, towards composing adjacent frames, we show that the alias-free operation for single image generation, together with adequately pre-learned knowledge, brings a smooth frame transition without compromising the per-frame quality. Second, by incorporating the temporal shift module (TSM), originally designed for video understanding, into the discriminator, we manage to advance the generator in synthesizing more consistent dynamics. Third, we develop a novel B-Spline based motion representation to ensure temporal smoothness to achieve infinite-length video generation. It can go beyond the frame number used in training. A low-rank temporal modulation is also proposed to alleviate repeating contents for long video generation. We evaluate our approach on various datasets and show substantial improvements over video generation baselines. Code and models will be publicly available at https://genforce.github.io/StyleSV.