Recent advancements in large generative models and real-time neural rendering
using point-based techniques pave the way for a future of widespread visual
data distribution through sharing synthesized 3D assets. However, while
standardized methods for embedding proprietary or copyright information, either
overtly or subtly, exist for conventional visual content such as images and
videos, this issue remains unexplored for emerging generative 3D formats like
Gaussian Splatting. We present GaussianStego, a method for embedding
steganographic information in the rendering of generated 3D assets. Our
approach employs an optimization framework that enables the accurate extraction
of hidden information from images rendered using Gaussian assets derived from
large models, while maintaining their original visual quality. We conduct
preliminary evaluations of our method across several potential deployment
scenarios and discuss issues identified through analysis. GaussianStego
represents an initial exploration into the novel challenge of embedding
customizable, imperceptible, and recoverable information within the renders
produced by current 3D generative models, while ensuring minimal impact on the
rendered content's quality.

通过使用高斯细分图渲染生成的 3D 资产，将自定义、难以察觉和可恢复的信息嵌入当前 3D 生成模型生成的渲染内容中，从而实现在广泛分布的视觉数据共享中嵌入隐写信息的初步探索。

高斯隐写：生成 3D 高斯喷点的通用隐写流程

GaussianStego: A Generalizable Stenography Pipeline for Generative 3D  Gaussians Splatting

Large Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno
are trained on a huge amount of language corpus, images, videos, and audio that
are extremely diverse from numerous domains. This training paradigm over
diverse well-curated data lies at the heart of generating creative and sensible
content. However, all previous graph generative models (e.g., GraphRNN, MDVAE,
MoFlow, GDSS, and DiGress) have been trained only on one dataset each time,
which cannot replicate the revolutionary success achieved by LGMs in other
fields. To remedy this crucial gap, we propose a new class of graph generative
model called Large Graph Generative Model (LGGM) that is trained on a large
corpus of graphs (over 5000 graphs) from 13 different domains. We empirically
demonstrate that the pre-trained LGGM has superior zero-shot generative
capability to existing graph generative models. Furthermore, our pre-trained
LGGM can be easily fine-tuned with graphs from target domains and demonstrate
even better performance than those directly trained from scratch, behaving as a
solid starting point for real-world customization. Inspired by Stable
Diffusion, we further equip LGGM with the capability to generate graphs given
text prompts (Text-to-Graph), such as the description of the network name and
domain (i.e., "The power-1138-bus graph represents a network of buses in a
power distribution system."), and network statistics (i.e., "The graph has a
low average degree, suitable for modeling social media interactions."). This
Text-to-Graph capability integrates the extensive world knowledge in the
underlying language model, offering users fine-grained control of the generated
graphs. We release the code, the model checkpoint, and the datasets at
this https URL

我们提出了一种名为大图生成模型（LGGM）的新型图生成模型，该模型在来自 13 个不同领域的大量图形语料库（超过 5000 个图形）上进行训练，并且在零样本生成能力方面优于现有的图生成模型。此外，我们的预训练 LGGM 可以轻松地通过来自目标领域的图形进行微调，并展现出比从头开始训练的模型更好的性能，作为实际定制的坚实起点。该模型还具备了根据文本提示生成图形的功能（文本到图形），该功能将底层语言模型中的广泛世界知识整合在一起，为用户提供对生成图形的细粒度控制。我们在下面的链接上发布了代码、模型检查点和数据集。

大规模生成图模型

Large Generative Graph Models

With the fast growth of parameter size, it becomes increasingly challenging
to deploy large generative models as they typically require large GPU memory
consumption and massive computation. Unstructured model pruning has been a
common approach to reduce both GPU memory footprint and the overall computation
while retaining good model accuracy. However, the existing solutions do not
provide a highly-efficient support for handling unstructured sparsity on modern
GPUs, especially on the highly-structured Tensor Core hardware. Therefore, we
propose Flash-LLM for enabling low-cost and highly-efficient large generative
model inference with the sophisticated support of unstructured sparsity on
high-performance but highly restrictive Tensor Cores. Based on our key
observation that the main bottleneck of generative model inference is the
several skinny matrix multiplications for which Tensor Cores would be
significantly under-utilized due to low computational intensity, we propose a
general Load-as-Sparse and Compute-as-Dense methodology for unstructured sparse
matrix multiplication. The basic insight is to address the significant memory
bandwidth bottleneck while tolerating redundant computations that are not
critical for end-to-end performance on Tensor Cores. Based on this, we design
an effective software framework for Tensor Core based unstructured SpMM,
leveraging on-chip resources for efficient sparse data extraction and
computation/memory-access overlapping. At SpMM kernel level, Flash-LLM
significantly outperforms the state-of-the-art library, i.e., Sputnik and
SparTA by an average of 2.9x and 1.5x, respectively. At end-to-end framework
level on OPT-30B/66B/175B models, for tokens per GPU-second, Flash-LLM achieves
up to 3.8x and 3.6x improvement over DeepSpeed and FasterTransformer,
respectively, with significantly lower inference cost.

Flash-LLM 是一种针对大型生成模型的低成本高效大规模推断框架，通过优化稀疏矩阵乘法，在高性能 Tensor Cores 上实现了显著的性能提升。