The paper discusses the potential of large vision-language models as objects of interest for empirical cultural studies. Focusing on the comparative analysis of outputs from two popular text-to-image synthesis models, DALL-E 2 and Stable Diffusion, the paper tries to tackle the pros and cons of striving towards culturally agnostic vs. culturally specific AI models. The paper discusses several examples of memorization and bias in generated outputs which showcase the trade-off between risk mitigation and cultural specificity, as well as the overall impossibility of developing culturally agnostic models.

Machine learning models are now able to convert user-written text descriptions into naturalistic images. These models are available to anyone online and are being used to generate millions of images a day. We investigate these models and find that they amplify dangerous and complex stereotypes. Moreover, we find that the amplified stereotypes are difficult to predict and not easily mitigated by users or model owners. The extent to which these image-generation models perpetuate and amplify stereotypes and their mass deployment is cause for serious concern.

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.

Nine language-vision AI models trained on web scrapes with the Contrastive Language-Image Pretraining (CLIP) objective are evaluated for evidence of a bias studied by psychologists: the sexual objectification of girls and women, which occurs when a person's human characteristics are disregarded and the person is treated as a body or a collection of body parts. A first experiment uses standardized images of women from the Sexual OBjectification and EMotion Database, and finds that, commensurate with prior research in psychology, human characteristics are disassociated from images of objectified women: the model's recognition of emotional state is mediated by whether the subject is fully or partially clothed. Embedding association tests (EATs) return significant effect sizes for both anger (d >.8) and sadness (d >.5). A second experiment measures the effect in a representative application: an automatic image captioner (Antarctic Captions) includes words denoting emotion less than 50% as often for images of partially clothed women than for images of fully clothed women. A third experiment finds that images of female professionals (scientists, doctors, executives) are likely to be associated with sexual descriptions relative to images of male professionals. A fourth experiment shows that a prompt of "a [age] year old girl" generates sexualized images (as determined by an NSFW classifier) up to 73% of the time for VQGAN-CLIP (age 17), and up to 40% of the time for Stable Diffusion (ages 14 and 18); the corresponding rate for boys never surpasses 9%. The evidence indicates that language-vision AI models trained on automatically collected web scrapes learn biases of sexual objectification, which propagate to downstream applications.

文本指导的图像生成模型，例如DALL-E 2和稳定的扩散，最近受到了学术界和公众的关注。这些模型提供了文本描述，能够生成描绘各种概念和样式的高质量图像。但是，此类模型接受了大量公共数据的培训，并从其培训数据中隐含地学习关系，这些数据并不明显。我们证明，可以通过简单地用视觉上类似的非拉丁字符替换文本描述中的单个字符来触发并注入生成的图像中的常见多模型模型，这些偏见可以被触发并注入生成的图像。这些所谓的同符文更换使恶意用户或服务提供商能够诱导偏见到生成的图像中，甚至使整个一代流程变得无用。我们实际上说明了对DALL-E 2和稳定扩散的这种攻击，例如文本引导的图像生成模型，并进一步表明夹子的行为也相似。我们的结果进一步表明，经过多语言数据训练的文本编码器提供了一种减轻同符替代效果的方法。

Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the internet, they also suffer, as we demonstrate, from degenerated and biased human behavior. In turn, they may even reinforce such biases. To help combat these undesired side effects, we present safe latent diffusion (SLD). Specifically, to measure the inappropriate degeneration due to unfiltered and imbalanced training sets, we establish a novel image generation test bed-inappropriate image prompts (I2P)-containing dedicated, real-world image-to-text prompts covering concepts such as nudity and violence. As our exhaustive empirical evaluation demonstrates, the introduced SLD removes and suppresses inappropriate image parts during the diffusion process, with no additional training required and no adverse effect on overall image quality or text alignment.

数字艺术合成在多媒体社区中受到越来越多的关注，因为有效地与公众参与了艺术。当前的数字艺术合成方法通常使用单模式输入作为指导，从而限制了模型的表现力和生成结果的多样性。为了解决这个问题，我们提出了多模式引导的艺术品扩散（MGAD）模型，该模型是一种基于扩散的数字艺术品生成方法，它利用多模式提示作为控制无分类器扩散模型的指导。此外，对比度语言图像预处理（剪辑）模型用于统一文本和图像模式。关于生成的数字艺术绘画质量和数量的广泛实验结果证实了扩散模型和多模式指导的组合有效性。代码可从https://github.com/haha-lisa/mgad-multimodal-guided-artwork-diffusion获得。

文本对图像模型提供了前所未有的自由，可以通过自然语言指导创作。然而，尚不清楚如何行使这种自由以生成特定独特概念，修改其外观或以新角色和新颖场景构成它们的图像。换句话说，我们问：我们如何使用语言指导的模型将猫变成绘画，或者想象基于我们喜欢的玩具的新产品？在这里，我们提出了一种简单的方法，可以允许这种创造性自由。我们仅使用3-5个用户提供的概念（例如对象或样式）的图像，我们学会通过在冷冻文本到图像模型的嵌入空间中通过新的“单词”表示它。这些“单词”可以组成自然语言句子，以直观的方式指导个性化的创作。值得注意的是，我们发现有证据表明单词嵌入足以捕获独特而多样的概念。我们将我们的方法比较了各种基线，并证明它可以更忠实地描绘出一系列应用程序和任务的概念。我们的代码，数据和新单词将在以下网址提供：https：//textual-inversion.github.io

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。

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/。

关于文本到图像生成的研究在产生多样化和照片现实的图像方面取得了重大进展，这是由在大规模图像文本数据上训练的扩散和自动回归模型驱动的。尽管最先进的模型可以产生共同实体的高质量图像，但它们通常很难产生不常见的实体的图像，例如“ chortai（dog）”或“ picarones（食物）”。为了解决此问题，我们介绍了检索型的文本对图像生成器（Re-Imagen），这是一种生成模型，它使用检索到的信息来产生高保真和忠实的图像，即使对于稀有或看不见的实体也是如此。给定文本提示，重新构造访问外部多模式知识库以检索相关（图像，文本）对，并将它们用作引用来生成图像。通过此检索步骤，重新构造的知识是对上述实体的高级语义和低级视觉细节的了解，从而提高了其在产生实体视觉外观的准确性。我们在包含（图像，文本，检索）的构造数据集上训练Re-Imagen，以教导该模型在文本提示和检索上扎根。此外，我们制定了一种新的抽样策略，以使文本和检索条件的无分类指南交流，以平衡文本和检索对齐。 Re-Imagen在两个图像生成基准上获得了新的SOTA FID结果，例如Coco（IE，FID = 5.25）和Wikiimage（即FID = 5.82），而无需微调。为了进一步评估该模型的功能，我们介绍了EntityDrawBench，这是一种新的基准测试，可评估从多个视觉域的各种实体的图像生成，从频繁到稀有。人类对EntityDrawBench的评估表明，Re-Imagen与照片现实主义中最好的先前模型相同，但具有明显的忠诚，尤其是在较不频繁的实体上。

大型文本对图像模型在AI的演变中取得了显着的飞跃，从而使图像从给定的文本提示中实现了高质量和多样化的图像合成。但是，这些模型缺乏在给定的参考集中模仿受试者的外观，并在不同情况下合成它们的新颖性。在这项工作中，我们提出了一种新的方法，用于“个性化”文本图像扩散模型（将它们专门针对用户的需求）。仅作为一个主题的几张图像给出，我们将验证的文本对图像模型（图像，尽管我们的方法不限于特定模型），以便它学会了将唯一标识符与该特定主题结合。一旦将受试者嵌入模型的输出域中，就可以使用唯一标识符来合成主题的完全新颖的光真逼真的图像在不同场景中的上下文化。通过利用具有新的自动构基特异性的先前保存损失的语义先验嵌入到模型中，我们的技术可以在参考图像中未出现的不同场景，姿势，视图和照明条件中合成主题。我们将技术应用于几个以前无用的任务，包括主题重新定义，文本指导的视图合成，外观修改和艺术渲染（所有这些都保留了主题的关键特征）。项目页面：https：//dreambooth.github.io/

Recent works on diffusion models have demonstrated a strong capability for conditioning image generation, e.g., text-guided image synthesis. Such success inspires many efforts trying to use large-scale pre-trained diffusion models for tackling a challenging problem--real image editing. Works conducted in this area learn a unique textual token corresponding to several images containing the same object. However, under many circumstances, only one image is available, such as the painting of the Girl with a Pearl Earring. Using existing works on fine-tuning the pre-trained diffusion models with a single image causes severe overfitting issues. The information leakage from the pre-trained diffusion models makes editing can not keep the same content as the given image while creating new features depicted by the language guidance. This work aims to address the problem of single-image editing. We propose a novel model-based guidance built upon the classifier-free guidance so that the knowledge from the model trained on a single image can be distilled into the pre-trained diffusion model, enabling content creation even with one given image. Additionally, we propose a patch-based fine-tuning that can effectively help the model generate images of arbitrary resolution. We provide extensive experiments to validate the design choices of our approach and show promising editing capabilities, including changing style, content addition, and object manipulation. The code is available for research purposes at https://github.com/zhang-zx/SINE.git .

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

Neural image classifiers are known to undergo severe performance degradation when exposed to input that exhibits covariate-shift with respect to the training distribution. Successful hand-crafted augmentation pipelines aim at either approximating the expected test domain conditions or to perturb the features that are specific to the training environment. The development of effective pipelines is typically cumbersome, and produce transformations whose impact on the classifier performance are hard to understand and control. In this paper, we show that recent Text-to-Image (T2I) generators' ability to simulate image interventions via natural-language prompts can be leveraged to train more robust models, offering a more interpretable and controllable alternative to traditional augmentation methods. We find that a variety of prompting mechanisms are effective for producing synthetic training data sufficient to achieve state-of-the-art performance in widely-adopted domain-generalization benchmarks and reduce classifiers' dependency on spurious features. Our work suggests that further progress in T2I generation and a tighter integration with other research fields may represent a significant step towards the development of more robust machine learning systems.

Recent 3D generative models have achieved remarkable performance in synthesizing high resolution photorealistic images with view consistency and detailed 3D shapes, but training them for diverse domains is challenging since it requires massive training images and their camera distribution information. Text-guided domain adaptation methods have shown impressive performance on converting the 2D generative model on one domain into the models on other domains with different styles by leveraging the CLIP (Contrastive Language-Image Pre-training), rather than collecting massive datasets for those domains. However, one drawback of them is that the sample diversity in the original generative model is not well-preserved in the domain-adapted generative models due to the deterministic nature of the CLIP text encoder. Text-guided domain adaptation will be even more challenging for 3D generative models not only because of catastrophic diversity loss, but also because of inferior text-image correspondence and poor image quality. Here we propose DATID-3D, a domain adaptation method tailored for 3D generative models using text-to-image diffusion models that can synthesize diverse images per text prompt without collecting additional images and camera information for the target domain. Unlike 3D extensions of prior text-guided domain adaptation methods, our novel pipeline was able to fine-tune the state-of-the-art 3D generator of the source domain to synthesize high resolution, multi-view consistent images in text-guided targeted domains without additional data, outperforming the existing text-guided domain adaptation methods in diversity and text-image correspondence. Furthermore, we propose and demonstrate diverse 3D image manipulations such as one-shot instance-selected adaptation and single-view manipulated 3D reconstruction to fully enjoy diversity in text.

最近已被证明扩散模型产生高质量的合成图像，尤其是与指导技术配对，以促进忠诚的多样性。我们探索文本条件图像综合问题的扩散模型，并比较了两种不同的指导策略：剪辑指导和自由分类指导。我们发现后者是人类评估者的优选，用于光敏和标题相似度，并且通常产生光素质拟种样品。使用自由分类指导的35亿参数文本条件扩散模型的样本由人类评估者对来自Dall-E的人的人们青睐，即使后者使用昂贵的剪辑重新划分。此外，我们发现我们的模型可以进行微调，以执行图像修复，从而实现强大的文本驱动的图像编辑。我们在过滤的数据集中培训较小的模型，并在https://github.com/openai/glide-text2im释放代码和权重。

Recent large-scale image generation models such as Stable Diffusion have exhibited an impressive ability to generate fairly realistic images starting from a very simple text prompt. Could such models render real images obsolete for training image prediction models? In this paper, we answer part of this provocative question by questioning the need for real images when training models for ImageNet classification. More precisely, provided only with the class names that have been used to build the dataset, we explore the ability of Stable Diffusion to generate synthetic clones of ImageNet and measure how useful they are for training classification models from scratch. We show that with minimal and class-agnostic prompt engineering those ImageNet clones we denote as ImageNet-SD are able to close a large part of the gap between models produced by synthetic images and models trained with real images for the several standard classification benchmarks that we consider in this study. More importantly, we show that models trained on synthetic images exhibit strong generalization properties and perform on par with models trained on real data.

Large-scale diffusion models have achieved state-of-the-art results on text-to-image synthesis (T2I) tasks. Despite their ability to generate high-quality yet creative images, we observe that attribution-binding and compositional capabilities are still considered major challenging issues, especially when involving multiple objects. In this work, we improve the compositional skills of T2I models, specifically more accurate attribute binding and better image compositions. To do this, we incorporate linguistic structures with the diffusion guidance process based on the controllable properties of manipulating cross-attention layers in diffusion-based T2I models. We observe that keys and values in cross-attention layers have strong semantic meanings associated with object layouts and content. Therefore, we can better preserve the compositional semantics in the generated image by manipulating the cross-attention representations based on linguistic insights. Built upon Stable Diffusion, a SOTA T2I model, our structured cross-attention design is efficient that requires no additional training samples. We achieve better compositional skills in qualitative and quantitative results, leading to a 5-8% advantage in head-to-head user comparison studies. Lastly, we conduct an in-depth analysis to reveal potential causes of incorrect image compositions and justify the properties of cross-attention layers in the generation process.

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/