OpenAI Sora and More – OpenAI Weekly Updates

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Unveiling Sora: OpenAI’s Groundbreaking Video Generation Model as a World Simulator

In a groundbreaking leap towards building general-purpose simulators of the physical world, OpenAI has introduced Sora, a large-scale, text-conditional diffusion model designed for video generation. This ingenious approach represents a significant advance in generative models, showcasing Sora’s ability to produce high-fidelity videos and images of variable durations, resolutions, and aspect ratios.

Sora – A Generalist Model for Visual Data

While previous works have explored generative modeling of video data using various methods, Sora distinguishes itself as a generalist model capable of generating diverse visual content. Unlike models limited to specific video types or fixed sizes, Sora can seamlessly create videos and images spanning different durations, aspect ratios, and resolutions, producing up to a full minute of high-definition video.

Transforming Visual Data into Patches

Taking inspiration from large language models (LLMs) that leverage tokens to unify diverse text modalities, Sora introduces the concept of visual patches. These patches serve as an adequate representation for training generative models on a wide range of videos and images. Sora turns videos into patches by compressing them into a lower-dimensional latent space, subsequently decomposing the representation into spacetime patches.

Scalable Video Compression and Patch Extraction

To achieve this, Sora employs a video compression network that reduces the dimensionality of visual data, both temporally and spatially. The resulting compressed latent space allows for efficient generation and reconstruction of videos. Spacetime latent patches are then extracted from the compressed video, serving as transformer tokens for the diffusion model.

Scaling Transformers for Video Generation

As a diffusion transformer, Sora is trained to predict the original “clean” patches from noisy input patches, showcasing its effectiveness in scaling for video generation. The model’s scalability is demonstrated by comparing video samples as training compute increases, highlighting improved sample quality.

Variable Durations, Resolutions, and Aspect Ratios

Unlike past approaches that resized or trimmed videos to a standard size, Sora is trained on data at its native size, offering several benefits. The model exhibits sampling flexibility, allowing it to generate videos of varying resolutions, including widescreen and vertical formats. Training on native aspect ratios enhances composition and framing, resulting in improved video quality.

Language Understanding and Prompting Capabilities

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To train text-to-video generation systems, Sora employs re-captioning techniques and leverages GPT to turn short user prompts into detailed captions. This enables Sora to generate high-quality videos that accurately follow user prompts. Notably, the model can be prompted with various inputs, such as pre-existing images or videos, expanding its capabilities for diverse tasks.

Extending Generated Videos and Video-to-Video Editing

Sora excels in extending videos backward or forward in time, creating seamless infinite loops. Video-to-video editing capabilities are demonstrated through the SDEdit technique, enabling Sora to transform styles and environments of input videos zero-shot.

Image Generation and Simulation Capabilities

Sora showcases its versatility by generating high-resolution images and arranging patches of Gaussian noise in a spatial grid. The model also exhibits interesting emergent simulation capabilities, simulating aspects of people, animals, and environments from the physical world. These include 3D consistency, long-range coherence, object permanence, interaction with the world, and simulation of digital worlds.

The Promise of Scaling Video Models

The emergent capabilities demonstrated by Sora suggest that the continued scaling of video models is a promising path toward developing highly capable simulators of the physical and digital world. Sora’s ability to simulate actions, maintain object permanence, and even simulate digital worlds indicates its potential for diverse applications across various domains.

Sora’s Impact on Video Generation and Simulation

OpenAI’s Sora marks a significant milestone in developing video generation models, showcasing unprecedented capabilities in generating diverse visual content and simulating aspects of the physical world. As the field continues to evolve, Sora’s scalability, flexibility, and emergent properties position it as a powerful tool for advancing the state-of-the-art in generative models and world simulation.

OpenAI Soars to $80 Billion Valuation in Thrive Capital Deal

In a recent strategic move, OpenAI, the renowned artificial intelligence company founded in 2015, has reportedly secured a valuation of over $80 billion through a deal with venture capital firm Thrive Capital. This valuation marks a significant leap, nearly tripling from just nine months ago when the company closed a $300 million share sale, achieving a valuation of approximately $27 billion.

Key Highlights of the Deal

According to reports from the New York Times, the latest deal involves OpenAI engaging in an existing share sale through a “tender offer” led by Thrive Capital. This arrangement enables employees to cash out their shares in the AI giant. While specific details remain undisclosed, the move indicates OpenAI’s commitment to creating liquidity for its workforce.

This development positions OpenAI as the third-highest-valued tech startup globally, trailing behind ByteDance, the parent company of TikTok ($225 billion), and SpaceX, led by Elon Musk ($150 billion), according to data from CB Insights.

Background and Previous Valuations

Founded by Elon Musk, Sam Altman, and others, OpenAI has been a focal point for significant investments since its inception. Microsoft, in particular, has been a significant investor, injecting $13 billion into the company. Despite this substantial investment, Microsoft clarified that it doesn’t hold ownership in OpenAI but is entitled to a share of profit distributions.

In April of the previous year, notable investors, including Thrive Capital, Sequoia Capital, Andreessen Horowitz, and K2 Global, collectively acquired new shares in OpenAI, driving its valuation to around $27 billion—the recent deal with Thrive Capital further cements OpenAI’s financial standing in the tech industry.

Path to $80 Billion Valuation

Discussions surrounding a potential deal of this magnitude surfaced last year, with reports suggesting a share sale that could value OpenAI at up to $90 billion, as the Wall Street Journal detailed. While the final valuation landed slightly below this estimate, the $80 billion valuation remains a substantial achievement for OpenAI.

As for OpenAI’s future plans, CEO Sam Altman, briefly ousted from the company last year and subsequently reinstated following internal upheaval, has emphasized that the company has no plans to go public shortly.

OpenAI’s journey from its foundation in 2015 to the current $80 billion valuation reflects the company’s continuous growth, strategic partnerships, and commitment to advancing artificial intelligence technologies.

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