Open-Sora vs. OpenAI’s Sora: A Comparison

Author(s): Meng Li

Originally published on Towards AI.

Distinguishing OpenAI’s Sora

Recently, I began exploring the open-source video generation project, Open-Sora.

The core idea behind Open-Sora is to democratize advanced video generation technology through open-source means, making it accessible to the general public.

Moreover, it offers streamlined and user-friendly tools and content.

As a result, the complexity of video production has been significantly reduced.

For us, the average users, this is indeed great news.

Additionally, Open-Sora has introduced several innovations in model details.

As for how to use Open-Sora, don’t worry, let’s take our time to explore it together.

https://arxiv.org/pdf/2212.09748.pdf

Recently, when I was analyzing Stable Diffusion 3, I encountered the Diffusion Transformer (DiT) architecture.

This architecture introduces a learnable flow for both image and text tokens, enabling the bidirectional flow of text and image information.

Stable Diffusion 3 further improved this architecture, allowing it to generate new images more flexibly based on text and image information.

https://hpc-ai.com/blog/open-sora-v1.0

Did you know? Open-Sora is also based on the DiT architecture.

The creators started with an open-source text-to-image model, PixArt-α, and added a temporal attention layer to it.

Just like that, a model capable of generating videos was born!

The entire model architecture is intuitive, including a pre-trained VAE, a text encoder, and the model named STDiT.

During the training phase, the pre-trained VAE encoder is responsible for compressing… Read the full blog for free on Medium.

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Published via Towards AI