The Sora architecture involves chunking up images or videos into patches to train the model to operate on these patches instead of full images. These patches act as atomic ingredients of the image that are mapped into latent space for processing. This approach marks a shift from traditional image-level network architectures to diffusion transformers. The comparison between different models like Sora, Valley free, and stable diffusion highlights the challenge of diminishing returns for companies specialized in this area. As models advance in handling text well, the focus now shifts to finer details like drawing hands accurately. While stable diffusion three is still in the testing phase, the competition in image processing appears to be intensifying with newer architectures like Sora and Gemini entering the scene.

Get the Snipd
podcast app

Unlock the knowledge in podcasts with the podcast player of the future.
App store bannerPlay store banner

AI-powered
podcast player

Listen to all your favourite podcasts with AI-powered features

Discover
highlights

Listen to the best highlights from the podcasts you love and dive into the full episode

Save any
moment

Hear something you like? Tap your headphones to save it with AI-generated key takeaways

Share
& Export

Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more

AI-powered
podcast player

Listen to all your favourite podcasts with AI-powered features

Discover
highlights

Listen to the best highlights from the podcasts you love and dive into the full episode