But you don’t want any picture – you usually want with a text prompt, with the image specified. And thus the dispersion model is paired with another model – such as a large language model (LLM) is trained to match the photos with the explanation of the text – which leads every stage of the cleaning process, and leads the dispersion model to the images that the larger language model immediately understands a good match.
On one side: This LLM is not pulling out links outside the thin air between text and photos. Most text -to -image and text -to -video models are trained today on big data sets, including billions of texts and photos or text and video pairs of pairs and scratching from the Internet (a exercise that many creators are very unhappy). This means that what you receive from such models is an ascension in the world because it is represented online, which is distorted by prejudice (and pornography).
It is easy to imagine working models working with photos. But the technique can be used with a variety of data, including audio and video. To prepare the movie clips, a blowing model has to clear the continuous frames of a video – a series of photos instead of just one icon.
What is an avocabous model?
It all takes a large amount of computers (Read: Energy). This is why most of the driving models used for video generation use a technique called auxiliary. Instead of taking action on raw data – millions of pixels in each video frame – the model works in what is known as Ivakat Space, in which the video frame (and text prompt) is compressed in the mathematics code that takes the only essential features of the data and throws the rest.
A similar thing happens when you stream a video on the Internet: A video server is sent to your screen in compressed format to reach you faster, and when it arrives, your computer or TV will convert it into a video able to see it again.