扩散概率模型（DPMS）在竞争对手GANS的图像生成中取得了显着的质量。但与GAN不同，DPMS使用一组缺乏语义含义的一组潜在变量，并且不能作为其他任务的有用表示。本文探讨了使用DPMS进行表示学习的可能性，并寻求通过自动编码提取输入图像的有意义和可解码的表示。我们的主要思想是使用可学习的编码器来发现高级语义，以及DPM作为用于建模剩余随机变化的解码器。我们的方法可以将任何图像编码为两部分潜在的代码，其中第一部分是语义有意义和线性的，第二部分捕获随机细节，允许接近精确的重建。这种功能使当前箔基于GaN的方法的挑战性应用，例如实际图像上的属性操作。我们还表明，这两级编码可提高去噪效率，自然地涉及各种下游任务，包括几次射击条件采样。

Diffusion Probabilistic Models (DPMs) have shown a powerful capacity of generating high-quality image samples. Recently, diffusion autoencoders (Diff-AE) have been proposed to explore DPMs for representation learning via autoencoding. Their key idea is to jointly train an encoder for discovering meaningful representations from images and a conditional DPM as the decoder for reconstructing images. Considering that training DPMs from scratch will take a long time and there have existed numerous pre-trained DPMs, we propose \textbf{P}re-trained \textbf{D}PM \textbf{A}uto\textbf{E}ncoding (\textbf{PDAE}), a general method to adapt existing pre-trained DPMs to the decoders for image reconstruction, with better training efficiency and performance than Diff-AE. Specifically, we find that the reason that pre-trained DPMs fail to reconstruct an image from its latent variables is due to the information loss of forward process, which causes a gap between their predicted posterior mean and the true one. From this perspective, the classifier-guided sampling method can be explained as computing an extra mean shift to fill the gap, reconstructing the lost class information in samples. These imply that the gap corresponds to the lost information of the image, and we can reconstruct the image by filling the gap. Drawing inspiration from this, we employ a trainable model to predict a mean shift according to encoded representation and train it to fill as much gap as possible, in this way, the encoder is forced to learn as much information as possible from images to help the filling. By reusing a part of network of pre-trained DPMs and redesigning the weighting scheme of diffusion loss, PDAE can learn meaningful representations from images efficiently. Extensive experiments demonstrate the effectiveness, efficiency and flexibility of PDAE.

扩散概率模型已被证明在几个竞争性图像综合基准上产生最先进的结果，但缺乏低维，可解释的潜在空间，并且在一代中慢慢。另一方面，变形AutoEncoders（VAES）通常可以访问低维潜空间，但表现出差的样品质量。尽管最近的进步，VAE通常需要潜在代码的高维层次结构来产生高质量样本。我们呈现DiffUsevae，一种新的生成框架，它在扩散模型框架内集成了VAE，并利用这一点以设计用于扩散模型的新型条件参数化。我们表明所得模型可以在采样效率方面提高无条件扩散模型，同时还配备了具有低维VAE的扩散模型推断潜码。此外，我们表明所提出的模型可以产生高分辨率样本，并展示与标准基准上的最先进模型相当的合成质量。最后，我们表明所提出的方法可用于可控制的图像合成，并且还展示了图像超分辨率和去噪等下游任务的开箱即用功能。为了重现性，我们的源代码将公开可用于\ url {https://github.com/kpandey008/diffusevae}。

Inspired by the impressive performance of recent face image editing methods, several studies have been naturally proposed to extend these methods to the face video editing task. One of the main challenges here is temporal consistency among edited frames, which is still unresolved. To this end, we propose a novel face video editing framework based on diffusion autoencoders that can successfully extract the decomposed features - for the first time as a face video editing model - of identity and motion from a given video. This modeling allows us to edit the video by simply manipulating the temporally invariant feature to the desired direction for the consistency. Another unique strength of our model is that, since our model is based on diffusion models, it can satisfy both reconstruction and edit capabilities at the same time, and is robust to corner cases in wild face videos (e.g. occluded faces) unlike the existing GAN-based methods.

过去十年已经开发了各种各样的深度生成模型。然而，这些模型通常同时努力解决三个关键要求，包括：高样本质量，模式覆盖和快速采样。我们称之为这些要求所征收的挑战是生成的学习Trielemma，因为现有模型经常为他人交易其中一些。特别是，去噪扩散模型表明了令人印象深刻的样本质量和多样性，但它们昂贵的采样尚未允许它们在许多现实世界应用中应用。在本文中，我们认为这些模型中的缓慢采样基本上归因于去噪步骤中的高斯假设，这些假设仅针对小型尺寸的尺寸。为了使得具有大步骤的去噪，从而减少去噪步骤的总数，我们建议使用复杂的多模态分布来模拟去噪分布。我们引入了去噪扩散生成的对抗网络（去噪扩散GANS），其使用多模式条件GaN模拟每个去噪步骤。通过广泛的评估，我们表明去噪扩散GAN获得原始扩散模型的样本质量和多样性，而在CIFAR-10数据集中是2000 $ \时代。与传统的GAN相比，我们的模型表现出更好的模式覆盖和样本多样性。据我们所知，去噪扩散GaN是第一模型，可在扩散模型中降低采样成本，以便允许它们廉价地应用于现实世界应用。项目页面和代码：https://nvlabs.github.io/denoising-diffusion-gan

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。

生成照片 - 现实图像，语义编辑和表示学习是高分辨率生成模型的许多潜在应用中的一些。最近在GAN的进展将它们建立为这些任务的绝佳选择。但是，由于它们不提供推理模型，因此使用GaN潜在空间无法在实际图像上完成诸如分类的图像编辑或下游任务。尽管培训了训练推理模型或设计了一种迭代方法来颠覆训练有素的发生器，但之前的方法是数据集（例如人类脸部图像）和架构（例如样式）。这些方法是非延伸到新型数据集或架构的。我们提出了一般框架，该框架是不可知的架构和数据集。我们的主要识别是，通过培训推断和生成模型在一起，我们允许它们彼此适应并收敛到更好的质量模型。我们的\ textbf {invang}，可逆GaN的简短，成功将真实图像嵌入到高质量的生成模型的潜在空间。这使我们能够执行图像修复，合并，插值和在线数据增强。我们展示了广泛的定性和定量实验。

We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for fidelity using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128×128, 4.59 on ImageNet 256×256, and 7.72 on ImageNet 512×512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.94 on ImageNet 256×256 and 3.85 on ImageNet 512×512. We release our code at https://github.com/openai/guided-diffusion.

作为生成部件作为自回归模型的向量量化变形式自动化器（VQ-VAE）的集成在图像生成上产生了高质量的结果。但是，自回归模型将严格遵循采样阶段的逐步扫描顺序。这导致现有的VQ系列模型几乎不会逃避缺乏全球信息的陷阱。连续域中的去噪扩散概率模型（DDPM）显示了捕获全局背景的能力，同时产生高质量图像。在离散状态空间中，一些作品已经证明了执行文本生成和低分辨率图像生成的可能性。我们认为，在VQ-VAE的富含内容的离散视觉码本的帮助下，离散扩散模型还可以利用全局上下文产生高保真图像，这补偿了沿像素空间的经典自回归模型的缺陷。同时，离散VAE与扩散模型的集成解决了传统的自回归模型的缺点是超大的，以及在生成图像时需要在采样过程中的过度时间的扩散模型。结果发现所生成的图像的质量严重依赖于离散的视觉码本。广泛的实验表明，所提出的矢量量化离散扩散模型（VQ-DDM）能够实现与低复杂性的顶层方法的相当性能。它还展示了在没有额外培训的图像修复任务方面与自回归模型量化的其他矢量突出的优势。

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 present a novel image inversion framework and a training pipeline to achieve high-fidelity image inversion with high-quality attribute editing. Inverting real images into StyleGAN's latent space is an extensively studied problem, yet the trade-off between the image reconstruction fidelity and image editing quality remains an open challenge. The low-rate latent spaces are limited in their expressiveness power for high-fidelity reconstruction. On the other hand, high-rate latent spaces result in degradation in editing quality. In this work, to achieve high-fidelity inversion, we learn residual features in higher latent codes that lower latent codes were not able to encode. This enables preserving image details in reconstruction. To achieve high-quality editing, we learn how to transform the residual features for adapting to manipulations in latent codes. We train the framework to extract residual features and transform them via a novel architecture pipeline and cycle consistency losses. We run extensive experiments and compare our method with state-of-the-art inversion methods. Qualitative metrics and visual comparisons show significant improvements. Code: https://github.com/hamzapehlivan/StyleRes

自由格式介绍是在任意二进制掩码指定的区域中向图像中添加新内容的任务。大多数现有方法训练了一定的面具分布，这将其概括能力限制为看不见的掩模类型。此外，通过像素和知觉损失的训练通常会导致对缺失区域的简单质地扩展，而不是语义上有意义的一代。在这项工作中，我们提出重新启动：基于deno的扩散概率模型（DDPM）的内部介入方法，甚至适用于极端掩模。我们采用预定的无条件DDPM作为生成先验。为了调节生成过程，我们仅通过使用给定的图像信息对未掩盖的区域进行采样来改变反向扩散迭代。由于该技术不会修改或调节原始DDPM网络本身，因此该模型可为任何填充形式产生高质量和不同的输出图像。我们使用标准面具和极端口罩验证面部和通用图像的方法。重新粉刷优于最先进的自动回归，而GAN的方法至少在六个面具分布中进行了五个。 github存储库：git.io/repaint

最近，GaN反演方法与对比语言 - 图像预先绘制（CLIP）相结合，可以通过文本提示引导零拍摄图像操作。然而，由于GaN反转能力有限，它们对不同实物的不同实物的应用仍然困难。具体地，这些方法通常在与训练数据相比，改变对象标识或产生不需要的图像伪影的比较与新颖姿势，视图和高度可变内容重建具有新颖姿势，视图和高度可变内容的困难。为了减轻这些问题并实现真实图像的忠实操纵，我们提出了一种新的方法，Dumbused Clip，其使用扩散模型执行文本驱动的图像操纵。基于近期扩散模型的完整反转能力和高质量的图像生成功率，即使在看不见的域之间也成功地执行零拍摄图像操作。此外，我们提出了一种新颖的噪声组合方法，允许简单的多属性操作。与现有基线相比，广泛的实验和人类评估确认了我们的方法的稳健和卓越的操纵性能。

We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at https://github.com/hojonathanho/diffusion.

去噪扩散概率模型（DDPMS）在没有对抗性训练的情况下实现了高质量的图像生成，但它们需要模拟Markov链以产生样品的许多步骤。为了加速采样，我们呈现去噪扩散隐式模型（DDIM），更有效的迭代类隐式概率模型，具有与DDPM相同的培训过程。在DDPMS中，生成过程被定义为Markovian扩散过程的反向。我们构建一类导致相同的训练目标的非马尔可瓦夫扩散过程，但其反向过程可能会更快地采样。我们经验证明，与DDPM相比，DDIM可以生产高质量的样本10倍以上$ 50 \时间$ 50 \倍。允许我们缩小对样本质量的计算，并可以直接执行语义有意义的图像插值潜在的空间。

Recent work has shown that a variety of semantics emerge in the latent space of Generative Adversarial Networks (GANs) when being trained to synthesize images. However, it is difficult to use these learned semantics for real image editing. A common practice of feeding a real image to a trained GAN generator is to invert it back to a latent code. However, existing inversion methods typically focus on reconstructing the target image by pixel values yet fail to land the inverted code in the semantic domain of the original latent space. As a result, the reconstructed image cannot well support semantic editing through varying the inverted code. To solve this problem, we propose an in-domain GAN inversion approach, which not only faithfully reconstructs the input image but also ensures the inverted code to be semantically meaningful for editing. We first learn a novel domain-guided encoder to project a given image to the native latent space of GANs. We then propose domain-regularized optimization by involving the encoder as a regularizer to fine-tune the code produced by the encoder and better recover the target image. Extensive experiments suggest that our inversion method achieves satisfying real image reconstruction and more importantly facilitates various image editing tasks, significantly outperforming start-of-the-arts. 1

Our goal with this survey is to provide an overview of the state of the art deep learning technologies for face generation and editing. We will cover popular latest architectures and discuss key ideas that make them work, such as inversion, latent representation, loss functions, training procedures, editing methods, and cross domain style transfer. We particularly focus on GAN-based architectures that have culminated in the StyleGAN approaches, which allow generation of high-quality face images and offer rich interfaces for controllable semantics editing and preserving photo quality. We aim to provide an entry point into the field for readers that have basic knowledge about the field of deep learning and are looking for an accessible introduction and overview.

Although Generative Adversarial Networks (GANs) have made significant progress in face synthesis, there lacks enough understanding of what GANs have learned in the latent representation to map a random code to a photo-realistic image. In this work, we propose a framework called InterFaceGAN to interpret the disentangled face representation learned by the state-of-the-art GAN models and study the properties of the facial semantics encoded in the latent space. We first find that GANs learn various semantics in some linear subspaces of the latent space. After identifying these subspaces, we can realistically manipulate the corresponding facial attributes without retraining the model. We then conduct a detailed study on the correlation between different semantics and manage to better disentangle them via subspace projection, resulting in more precise control of the attribute manipulation. Besides manipulating the gender, age, expression, and presence of eyeglasses, we can even alter the face pose and fix the artifacts accidentally made by GANs. Furthermore, we perform an in-depth face identity analysis and a layer-wise analysis to evaluate the editing results quantitatively. Finally, we apply our approach to real face editing by employing GAN inversion approaches and explicitly training feed-forward models based on the synthetic data established by InterFaceGAN. Extensive experimental results suggest that learning to synthesize faces spontaneously brings a disentangled and controllable face representation.

潜在矢量生成模型的潜在空间中数据点的不同编码可能会导致数据背后的不同解释因素的效率或多或少有效且分开的特征。最近，许多作品都致力于探索特定模型的潜在空间，主要集中在研究特征如何分离以及如何在可见空间中产生所需数据变化的轨迹变化。在这项工作中，我们解决了比较不同模型的潜在空间的更一般问题，寻找它们之间的转换。我们将调查局限于人脸数据歧管的熟悉且在很大程度上研究的生成模型案例。本文报道的令人惊讶的初步结果是（前提是（前提是模型尚未被教导或明确地想象以不同的方式采取行动）简单的线性映射足以从潜在空间传递到另一个信息，同时保留大多数信息。

扩散模型是一类新的生成模型，在依靠固体概率原理的同时，标志着高质量图像生成中的里程碑。这使他们成为神经图像压缩的有前途的候选模型。本文概述了基于有条件扩散模型的端到端优化框架。除了扩散过程固有的潜在变量外，该模型还引入了额外的“ content”潜在变量，以调节降解过程。解码后，扩散过程有条件地生成/重建祖先采样。我们的实验表明，这种方法的表现优于表现最佳的传统图像编解码器之一（BPG）和一个在两个压缩基准上的神经编解码器，我们将重点放在速率感知权衡方面。定性地，我们的方法显示出比经典方法更少的减压工件。