Recent advancements in large scale text-to-image models have opened new
possibilities for guiding the creation of images through human-devised natural
language. However, while prior literature has primarily focused on the
generation of individual images, it is essential to consider the capability of
these models to ensure coherency within a sequence of images to fulfill the
demands of real-world applications such as storytelling. To address this, here
we present a novel neural pipeline for generating a coherent storybook from the
plain text of a story. Specifically, we leverage a combination of a pre-trained
Large Language Model and a text-guided Latent Diffusion Model to generate
coherent images. While previous story synthesis frameworks typically require a
large-scale text-to-image model trained on expensive image-caption pairs to
maintain the coherency, we employ simple textual inversion techniques along
with detector-based semantic image editing which allows zero-shot generation of
the coherent storybook. Experimental results show that our proposed method
outperforms state-of-the-art image editing baselines.

本文提出了一种基于文本的神经管道，利用预训练的语言模型和文本引导的潜在扩散模型来生成一系列连贯的图像，实现连贯的故事书生成，并展示了无需昂贵的图像 - 标题对训练的文本转换技术的性能表现。

使用扩散模型从纯文本故事生成连贯的故事书的零射生成

Zero-shot Generation of Coherent Storybook from Plain Text Story using Diffusion Models

We introduce Stanza, an open-source Python natural language processing
toolkit supporting 66 human languages. Compared to existing widely used
toolkits, Stanza features a language-agnostic fully neural pipeline for text
analysis, including tokenization, multi-word token expansion, lemmatization,
part-of-speech and morphological feature tagging, dependency parsing, and named
entity recognition. We have trained Stanza on a total of 112 datasets,
including the Universal Dependencies treebanks and other multilingual corpora,
and show that the same neural architecture generalizes well and achieves
competitive performance on all languages tested. Additionally, Stanza includes
a native Python interface to the widely used Java Stanford CoreNLP software,
which further extends its functionality to cover other tasks such as
coreference resolution and relation extraction. Source code, documentation, and
pretrained models for 66 languages are available at
this https URL

Stanza 是一个支持 66 种人类语言的 Python 自然语言处理工具包，具有语言无关的完全神经管道，包括标记化，词形变化，词性和形态特征标记，依存分析和命名实体识别。