Diffusion models have quickly become the go-to paradigm for generative modelling of perceptual signals (such as images and sound) through iterative refinement. Their success hinges on the fact that the underlying physical phenomena are continuous. For inherently discrete and categorical data such as language, various diffusion-inspired alternatives have been proposed. However, the continuous nature of diffusion models conveys many benefits, and in this work we endeavour to preserve it. We propose CDCD, a framework for modelling categorical data with diffusion models that are continuous both in time and input space. We demonstrate its efficacy on several language modelling tasks.

Can continuous diffusion models bring the same performance breakthrough on natural language they did for image generation? To circumvent the discrete nature of text data, we can simply project tokens in a continuous space of embeddings, as is standard in language modeling. We propose Self-conditioned Embedding Diffusion, a continuous diffusion mechanism that operates on token embeddings and allows to learn flexible and scalable diffusion models for both conditional and unconditional text generation. Through qualitative and quantitative evaluation, we show that our text diffusion models generate samples comparable with those produced by standard autoregressive language models - while being in theory more efficient on accelerator hardware at inference time. Our work paves the way for scaling up diffusion models for text, similarly to autoregressive models, and for improving performance with recent refinements to continuous diffusion.

在本文中，我们提出了一种新的生成模型，逐步逐步的去噪AutoEncoder（Sundae），不依赖于自回归模型。类似地与去噪扩散技术，在从随机输入开始并从随机输入开始并每次直到收敛改善它们时，日出施加Sundae。我们提出了一个简单的新改进运算符，它比扩散方法更少迭代，同时在定性地在自然语言数据集上产生更好的样本。Sundae在WMT'14英语到德语翻译任务上实现最先进的结果（非自回归方法），在巨大清洁的常见爬网数据集和Python代码的数据集上对无条件语言建模的良好定性结果来自GitHub。通过在模板中填充任意空白模式，Sundae的非自动增加性质开辟了超出左右提示的可能性。

Diffusion models have achieved state-of-the-art synthesis quality on visual and audio tasks, and recent works adapt them to textual data by diffusing on the embedding space. But the difference between the continuous data space and the embedding space raises challenges to the diffusion model, which have not been carefully explored. In this paper, we conduct systematic studies and analyze the challenges threefold. Firstly, the data distribution is learnable for embeddings, which may lead to the collapse of the loss function. Secondly, as the norm of embedding varies between popular and rare words, adding the same noise scale will lead to sub-optimal results. In addition, we find that noises sampled from a standard Gaussian distribution may distract the diffusion process. To solve the above challenges, we propose Difformer, a denoising diffusion probabilistic model based on Transformer, which consists of three techniques including utilizing an anchor loss function, a layer normalization module for embeddings, and a norm factor to the Gaussian noise. All techniques are complementary to each other and critical to boosting the model performance together. Experiments are conducted on benchmark datasets over two seminal text generation tasks including machine translation and text summarization. The results show that Difformer significantly outperforms the embedding diffusion baselines, while achieving competitive results with strong autoregressive baselines.

Transfer learning, where a model is first pre-trained on a data-rich task before being finetuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.

We present DiffusionBERT, a new generative masked language model based on discrete diffusion models. Diffusion models and many pre-trained language models have a shared training objective, i.e., denoising, making it possible to combine the two powerful models and enjoy the best of both worlds. On the one hand, diffusion models offer a promising training strategy that helps improve the generation quality. On the other hand, pre-trained denoising language models (e.g., BERT) can be used as a good initialization that accelerates convergence. We explore training BERT to learn the reverse process of a discrete diffusion process with an absorbing state and elucidate several designs to improve it. First, we propose a new noise schedule for the forward diffusion process that controls the degree of noise added at each step based on the information of each token. Second, we investigate several designs of incorporating the time step into BERT. Experiments on unconditional text generation demonstrate that DiffusionBERT achieves significant improvement over existing diffusion models for text (e.g., D3PM and Diffusion-LM) and previous generative masked language models in terms of perplexity and BLEU score.

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。

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。

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。

Score-based modeling through stochastic differential equations (SDEs) has provided a new perspective on diffusion models, and demonstrated superior performance on continuous data. However, the gradient of the log-likelihood function, i.e., the score function, is not properly defined for discrete spaces. This makes it non-trivial to adapt \textcolor{\cdiff}{the score-based modeling} to categorical data. In this paper, we extend diffusion models to discrete variables by introducing a stochastic jump process where the reverse process denoises via a continuous-time Markov chain. This formulation admits an analytical simulation during backward sampling. To learn the reverse process, we extend score matching to general categorical data and show that an unbiased estimator can be obtained via simple matching of the conditional marginal distributions. We demonstrate the effectiveness of the proposed method on a set of synthetic and real-world music and image benchmarks.

Diffusion model, a new generative modelling paradigm, has achieved great success in image, audio, and video generation. However, considering the discrete categorical nature of text, it is not trivial to extend continuous diffusion models to natural language, and text diffusion models are less studied. Sequence-to-sequence text generation is one of the essential natural language processing topics. In this work, we apply diffusion models to approach sequence-to-sequence text generation, and explore whether the superiority generation performance of diffusion model can transfer to natural language domain. We propose SeqDiffuSeq, a text diffusion model for sequence-to-sequence generation. SeqDiffuSeq uses an encoder-decoder Transformers architecture to model denoising function. In order to improve generation quality, SeqDiffuSeq combines the self-conditioning technique and a newly proposed adaptive noise schedule technique. The adaptive noise schedule has the difficulty of denoising evenly distributed across time steps, and considers exclusive noise schedules for tokens at different positional order. Experiment results illustrate the good performance on sequence-to-sequence generation in terms of text quality and inference time.

扩散模型是一类深入生成模型，在具有密集理论建立的各种任务上显示出令人印象深刻的结果。尽管与其他最先进的模型相比，扩散模型的样本合成质量和多样性令人印象深刻，但它们仍然遭受了昂贵的抽样程序和次优可能的估计。最近的研究表明，对提高扩散模型的性能的热情非常热情。在本文中，我们对扩散模型的现有变体进行了首次全面综述。具体而言，我们提供了扩散模型的第一个分类法，并将它们分类为三种类型，即采样加速增强，可能性最大化的增强和数据将来增强。我们还详细介绍了其他五个生成模型（即变异自动编码器，生成对抗网络，正常流量，自动回归模型和基于能量的模型），并阐明扩散模型与这些生成模型之间的连接。然后，我们对扩散模型的应用进行彻底研究，包括计算机视觉，自然语言处理，波形信号处理，多模式建模，分子图生成，时间序列建模和对抗性纯化。此外，我们提出了与这种生成模型的发展有关的新观点。

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.

Diffusion models have achieved great success in modeling continuous data modalities such as images, audio, and video, but have seen limited use in discrete domains such as language. Recent attempts to adapt diffusion to language have presented diffusion as an alternative to autoregressive language generation. We instead view diffusion as a complementary method that can augment the generative capabilities of existing pre-trained language models. We demonstrate that continuous diffusion models can be learned in the latent space of a pre-trained encoder-decoder model, enabling us to sample continuous latent representations that can be decoded into natural language with the pre-trained decoder. We show that our latent diffusion models are more effective at sampling novel text from data distributions than a strong autoregressive baseline and also enable controllable generation.

We introduce M-VADER: a diffusion model (DM) for image generation where the output can be specified using arbitrary combinations of images and text. We show how M-VADER enables the generation of images specified using combinations of image and text, and combinations of multiple images. Previously, a number of successful DM image generation algorithms have been introduced that make it possible to specify the output image using a text prompt. Inspired by the success of those models, and led by the notion that language was already developed to describe the elements of visual contexts that humans find most important, we introduce an embedding model closely related to a vision-language model. Specifically, we introduce the embedding model S-MAGMA: a 13 billion parameter multimodal decoder combining components from an autoregressive vision-language model MAGMA and biases finetuned for semantic search.

基于扩散的生成模型已经证明了感知上令人印象深刻的合成能力，但是它们也可以是基于可能性的模型吗？我们以肯定的方式回答了这一点，并介绍了一个基于扩散的生成模型家族，该模型可以在标准图像密度估计基准上获得最先进的可能性。与其他基于扩散的模型不同，我们的方法允许与其他模型的其余部分共同对噪声时间表进行有效优化。我们表明，根据扩散数据的信噪比，变异下限（VLB）简化为非常短的表达，从而改善了我们对该模型类别的理论理解。使用这种见解，我们证明了文献中提出的几个模型之间的等效性。此外，我们表明连续时间VLB在噪声方面不变，除了其端点处的信噪比。这使我们能够学习一个噪声时间表，以最大程度地减少所得VLB估计器的差异，从而更快地优化。将这些进步与建筑改进相结合，我们获得了图像密度估计基准的最先进的可能性，超过了多年来主导这些基准测试的自回旋模型，通常优化了很多年。此外，我们展示了如何将模型用作BITS背包压缩方案的一部分，并展示了接近理论最佳的无损压缩率。代码可在https://github.com/google-research/vdm上找到。

我们表明，在将直接转换应用到数据集之后，自回归语言模型可以学会填充文本，这简单地将文本的跨度从文档的中间移动到了其末尾。虽然近年来这种数据增强引起了人们的极大兴趣，但我们提供了广泛的证据，表明以这种方式转换的数据很大一部分并不会损害原始的左右生成能力，这是通过困惑和抽样评估来衡量的广泛的尺度。鉴于培训模型对中间的有用性，简单性和效率（FIM），我们建议默认情况下使用FIM培训未来的自回归语言模型。为此，我们在关键的超参数上运行一系列消融，例如数据转换频率，转换的结构以及选择填充跨度的方法。我们使用这些消融来规定强大的默认设置和最佳实践来训练FIM模型。我们发布了最佳的填充模型，该模型在API中培训了最佳实践，并发布了我们的填充基准，以帮助未来的研究。

非自动进取的生成变压器最近表现出令人印象深刻的图像产生性能，并且比自动回归对应物更快。但是，从视觉令牌的真实关节分布中进行的最佳并行采样仍然是一个开放的挑战。在本文中，我们介绍了代币批评，这是一种辅助模型，用于指导非自动性生成变压器的采样。鉴于掩盖和重建的真实图像，对代币批判性模型进行了训练，以区分哪种视觉令牌属于原始图像，哪些是由生成变压器采样的。在非自动回归迭代采样过程中，令牌批评者用于选择要接受的代币以及拒绝和重新取样的代币。再加上最先进的生成变压器令牌 - 批判性可显着提高其性能，并且在挑战性的课堂条件化成像生成中，就产生的图像质量和多样性之间的权衡取舍了最近的扩散模型和gan 。

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。

Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive loglikelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code at https://github.com/ openai/improved-diffusion.