This paper presents a novel framework termed Cut-and-Paste for real-word
semantic video editing under the guidance of text prompt and additional
reference image. While the text-driven video editing has demonstrated
remarkable ability to generate highly diverse videos following given text
prompts, the fine-grained semantic edits are hard to control by plain textual
prompt only in terms of object details and edited region, and cumbersome long
text descriptions are usually needed for the task. We therefore investigate
subject-driven video editing for more precise control of both edited regions
and background preservation, and fine-grained semantic generation. We achieve
this goal by introducing an reference image as supplementary input to the
text-driven video editing, which avoids racking your brain to come up with a
cumbersome text prompt describing the detailed appearance of the object. To
limit the editing area, we refer to a method of cross attention control in
image editing and successfully extend it to video editing by fusing the
attention map of adjacent frames, which strikes a balance between maintaining
video background and spatio-temporal consistency. Compared with current
methods, the whole process of our method is like ``cut" the source object to be
edited and then ``paste" the target object provided by reference image. We
demonstrate that our method performs favorably over prior arts for video
editing under the guidance of text prompt and extra reference image, as
measured by both quantitative and subjective evaluations.

本文提出了一种名为剪贴的创新框架，用于根据文本提示和额外参考图像进行现实语义视频编辑。

剪切和拼贴：主题驱动的视频编辑与注意力控制

Cut-and-Paste: Subject-Driven Video Editing with Attention Control

Image cartoonization has attracted significant interest in the field of image
generation. However, most of the existing image cartoonization techniques
require re-training models using images of cartoon style. In this paper, we
present CartoonDiff, a novel training-free sampling approach which generates
image cartoonization using diffusion transformer models. Specifically, we
decompose the reverse process of diffusion models into the semantic generation
phase and the detail generation phase. Furthermore, we implement the image
cartoonization process by normalizing high-frequency signal of the noisy image
in specific denoising steps. CartoonDiff doesn't require any additional
reference images, complex model designs, or the tedious adjustment of multiple
parameters. Extensive experimental results show the powerful ability of our
CartoonDiff. The project page is available at: this https URL

本文介绍了 CartoonDiff，一种基于扩散 Transformer 模型的无需训练的采样方法，用于生成图像卡通化效果。通过将扩散模型的逆向过程分解为语义生成和细节生成阶段，并在特定去噪步骤中对噪声图像的高频信号进行归一化，实现了图像卡通化过程。CartoonDiff 不需要任何额外的参考图像，复杂的模型设计或繁琐的参数调整。广泛的实验结果表明了 CartoonDiff 的强大能力。

Cartoondiff: 使用扩散转换模型进行无需训练的卡通图像生成

Cartoondiff: Training-free Cartoon Image Generation with Diffusion  Transformer Models

Architectural design is a highly complex practice that involves a wide
diversity of disciplines, technologies, proprietary design software, expertise,
and an almost infinite number of constraints, across a vast array of design
tasks. Enabling intuitive, accessible, and scalable design processes is an
important step towards performance-driven and sustainable design for all. To
that end, we introduce Architext, a novel semantic generation assistive tool.
Architext enables design generation with only natural language prompts, given
to large-scale Language Models, as input. We conduct a thorough quantitative
evaluation of Architext's downstream task performance, focusing on semantic
accuracy and diversity for a number of pre-trained language models ranging from
120 million to 6 billion parameters. Architext models are able to learn the
specific design task, generating valid residential layouts at a near 100% rate.
Accuracy shows great improvement when scaling the models, with the largest
model (GPT-J) yielding impressive accuracy ranging between 25% to over 80% for
different prompt categories. We open source the finetuned Architext models and
our synthetic dataset, hoping to inspire experimentation in this exciting area
of design research.

Architext 是一个新型的语义生成辅助工具，通过自然语言提示，利用大规模的语言模型进行设计生成，对不同大小的预训练语言模型进行语义准确性和多样性的评估，能在不同的场景下产生高质量的设计结果。