Tag Archives: Nvidia
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution video generation, a particularly resource-intensive task. We first pre-train an LDM on images only; then, we turn the image generator into a video generator by introducing a temporal dimension to the latent space diffusion model and fine-tuning on encoded image sequences, i.e., videos. Similarly, we temporally align diffusion model upsamplers, turning them into temporally consistent video super resolution models. We focus on two relevant real-world applications: Simulation of in-the-wild driving data and creative content creation with text-to-video modeling. In particular, we validate our Video LDM on real driving videos of resolution 512 x 1024, achieving state-of-the-art performance. Furthermore, our approach can easily leverage off-the-shelf pre-trained image LDMs, as we only need to train a temporal alignment model in that case. Doing so, we turn the publicly available, state-of-the-art text-to-image LDM Stable Diffusion into an efficient and expressive text-to-video model with resolution up to 1280 x 2048. We show that the temporal layers trained in this way generalize to different fine-tuned text-to-image LDMs. Utilizing this property, we show the first results for personalized text-to-video generation, opening exciting directions for future content creation. Read More
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AI Developers Stymied by Server Shortage at AWS, Microsoft, Google
Startups and other companies trying to capitalize on the artificial intelligence boom sparked by OpenAI are running into a problem: They can’t find enough specialized computers to make their own AI software.
A spike in demand for server chips that can train and run machine-learning software has caused a shortage, prompting major cloud-server providers including Amazon Web Services, Microsoft, Google and Oracle to limit their availability for customers, according to interviews with the cloud companies and their customers. Some customers have reported monthslong wait times to rent the hardware. Read More
Nvidia predicts AI models one million times more powerful than ChatGPT within 10 years
A million here, times a million there. Pretty soon you’re talking about big numbers. So Nvidia claims for its AI accelerating hardware in terms of the performance boost it has delivered over the last decade and will deliver again over the next 10 years.
The result, if Nvidia is correct, will be a new industry of AI factories across the world and gigantic breakthroughs in AI processing power. It also means, ostensibly, AI models one million times more powerful than existing examples, including ChatGPT, in AI processing terms at least. Read More
IBM says it’s been running ‘AI supercomputer’ since May but chose now to tell the world
Cloud-native Vela specializes in developing and training large-scale AI models – in-house only, though
IBM is the latest tech giant to unveil its own “AI supercomputer,” this one composed of a bunch of virtual machines running within IBM Cloud.
The system known as Vela, which the company claims has been online since May last year, is touted as IBM’s first AI-optimized, cloud-native supercomputer, created with the aim of developing and training large-scale AI models.
Before anyone rushes off to sign up for access, IBM stated that the platform is currently reserved for use by the IBM Research community. In fact, Vela has become the company’s “go-to environment” for researchers creating advanced AI capabilities since May 2022, including work on foundation models, it said. Read More
NVIDIA Omniverse Shines in a New Light with Magic3D
NVIDIA’s new text-to-3D synthesis model Magic3D creates high-quality 3D mesh models better than Google’s DreamFusion
Earlier this month, NVIDIA announced that it would be enabling the beta release of Omniverse, a platform where developers and creators can build Metaverse applications. In this way, the company has aligned its future along the metaverse vision, with the new platform allowing its users to create “digital twins” to simulate the real world.
One such step towards the realisation of such a dream that would help users to render a high-resolution 3D model for any 2D image input, or textual prompt, is Magic3D. Recently released by NVIDIA researchers, Magic3D is a text-to-3D synthesis model that creates high-quality 3D mesh models.
The model is a response to Google’s DreamFusion, in which the team used a pre-trained text-to-image diffusion model, circumventing the impossibility of having large-scale labelled 3D datasets, to optimise Neural Radiance Fields (NeRF). Read More
NVIDIA, Oracle CEOs in Fireside Chat Light Pathways to Enterprise AI
Speeding adoption of enterprise AI and accelerated computing, Oracle CEO Safra Catz and NVIDIA founder and CEO Jensen Huang discussed their companies’ expanding collaboration in a fireside chat live streamed today from Oracle CloudWorld in Las Vegas.
Oracle and NVIDIA announced plans to bring NVIDIA’s full accelerated computing stack to Oracle Cloud Infrastructure (OCI). It includes NVIDIA AI Enterprise, NVIDIA RAPIDS for Apache Spark and NVIDIA Clara for healthcare.
In addition, OCI will deploy tens of thousands more NVIDIA GPUs to its cloud service, including A100 and upcoming H100 accelerators. Read More
The AI Scaling Hypothesis
How far will this go?
The past decade of progress in AI can largely be summed up by one word: scale. The era of deep learning that started around 2010 has witnessed a continued increase in the size of state of the art models. This has only accelerated over the past several years, leading many to believe in the “AI Scaling Hypothesis”: the idea that more computational resources and training data may be the best answer to achieving the AI field’s long term goals. This article will provide an overview of what the scaling hypothesis is, what we know about scaling laws, and the latest results achieved by scaling. Read More
NVIDIA Launches Large Language Model Cloud Services to Advance AI and Digital Biology
NVIDIA NeMo LLM Service Helps Developers Customize Massive Language Models; NVIDIA BioNeMo Service Helps Researchers Generate and Predict Molecules, Proteins, DNA
NVIDIA today announced two new large language model cloud AI services — the NVIDIA NeMo Large Language Model Service and the NVIDIA BioNeMo LLM Service — that enable developers to easily adapt LLMs and deploy customized AI applications for content generation, text summarization, chatbots, code development, as well as protein structure and biomolecular property predictions, and more.
The NeMo LLM Service allows developers to rapidly tailor a number of pretrained foundation models using a training method called prompt learning on NVIDIA-managed infrastructure. The NVIDIA BioNeMo Service is a cloud application programming interface (API) that expands LLM use cases beyond language and into scientific applications to accelerate drug discovery for pharma and biotech companies. Read More
NVIDIA announces new Omniverse Avatar Cloud Engine (ACE) for building virtual assistants and digital humans
NVIDIA has today announced NVIDIA Omniverse Avatar Cloud Engine (ACE), a suite of cloud-native AI models and services that make it easier to build and customize lifelike virtual assistants and digital humans.
NVIDIA stated that by bringing these models and services to the cloud, ACE will enable businesses of any size to instantly access the massive computing power needed to create and deploy assistants and avatars that understand multiple languages, respond to speech prompts, interact with the environment and make intelligent recommendations. Read More
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