Generative models have enabled intuitive image creation and manipulation
using natural language. In particular, diffusion models have recently shown
remarkable results for natural image editing. In this work, we propose to apply
diffusion techniques to edit textures, a specific class of images that are an
essential part of 3D content creation pipelines. We analyze existing editing
methods and show that they are not directly applicable to textures, since their
common underlying approach, manipulating attention maps, is unsuitable for the
texture domain. To address this, we propose a novel approach that instead
manipulates CLIP image embeddings to condition the diffusion generation. We
define editing directions using simple text prompts (e.g., "aged wood" to "new
wood") and map these to CLIP image embedding space using a texture prior, with
a sampling-based approach that gives us identity-preserving directions in CLIP
space. To further improve identity preservation, we project these directions to
a CLIP subspace that minimizes identity variations resulting from entangled
texture attributes. Our editing pipeline facilitates the creation of arbitrary
sliders using natural language prompts only, with no ground-truth annotated
data necessary.

应用扩散技术编辑纹理，通过操作 CLIP 图像嵌入来调节扩散生成，无需标注数据即可用自然语言提示进行编辑。

TexSliders: 基於 CLIP 空間的擴散式紋理編輯

TexSliders: Diffusion-Based Texture Editing in CLIP Space

Typically, autonomous driving adopts a modular design, which divides the full
stack into perception, prediction, planning and control parts. Though
interpretable, such modular design tends to introduce a substantial amount of
redundancy. Recently, multimodal large language models (MLLM) and diffusion
techniques have demonstrated their superior performance on comprehension and
generation ability. In this paper, we first introduce the concept of
interleaved vision-action pair, which unifies the format of visual features and
control signals. Based on the vision-action pairs, we construct a general world
model based on MLLM and diffusion model for autonomous driving, termed
ADriver-I. It takes the vision-action pairs as inputs and autoregressively
predicts the control signal of the current frame. The generated control signals
together with the historical vision-action pairs are further conditioned to
predict the future frames. With the predicted next frame, ADriver-I performs
further control signal prediction. Such a process can be repeated infinite
times, ADriver-I achieves autonomous driving in the world created by itself.
Extensive experiments are conducted on nuScenes and our large-scale private
datasets. ADriver-I shows impressive performance compared to several
constructed baselines. We hope our ADriver-I can provide some new insights for
future autonomous driving and embodied intelligence.

基于多模态大语言模型和扩散技术，我们提出了一种自主驾驶世界模型 ADriver-I，该模型以交织的视觉 - 动作对为基础，能够预测当前帧的控制信号，并使用历史的视觉 - 动作对和生成的控制信号来预测未来的帧，通过无限反馈循环，ADriver-I 实现了自主驾驶。我们通过在 nuScenes 和大规模私有数据集上进行广泛实验，证明了 ADriver-I 在性能上的卓越表现，希望该模型能为未来自主驾驶和具身智能提供新的洞见。

ADriver-I: 无人驾驶的通用世界模型

ADriver-I: A General World Model for Autonomous Driving

Batik, a unique blend of art and craftsmanship, is a distinct artistic and
technological creation for Indonesian society. Research on batik motifs is
primarily focused on classification. However, further studies may extend to the
synthesis of batik patterns. Generative Adversarial Networks (GANs) have been
an important deep learning model for generating synthetic data, but often face
challenges in the stability and consistency of results. This research focuses
on the use of StyleGAN2-Ada and Diffusion techniques to produce realistic and
high-quality synthetic batik patterns. StyleGAN2-Ada is a variation of the GAN
model that separates the style and content aspects in an image, whereas
diffusion techniques introduce random noise into the data. In the context of
batik, StyleGAN2-Ada and Diffusion are used to produce realistic synthetic
batik patterns. This study also made adjustments to the model architecture and
used a well-curated batik dataset. The main goal is to assist batik designers
or craftsmen in producing unique and quality batik motifs with efficient
production time and costs. Based on qualitative and quantitative evaluations,
the results show that the model tested is capable of producing authentic and
quality batik patterns, with finer details and rich artistic variations. The
dataset and code can be accessed
here:this https URL

本研究使用 StyleGAN2-Ada 和扩散技术来产生逼真和高质量的合成巴蒂克纹样，旨在协助巴蒂克设计师或工匠以高效的生产时间和成本制作独特且高品质的巴蒂克纹样。基于定性和定量的评估结果显示，所测试的模型能够产生真实、高质量、细节更丰富、艺术变化更多样的巴蒂克纹样。