Combine code, mathematics

RABIULISLAMSP88

and various natural languages. Unify and facilitate large-scale training. So they created the "he concept block" corresponding to en. If en is translated as word understanding, he may be translated by us as "the picture block is used to train the r video model. In fact, the reason why the application of en in large language models is so successful is also due to the rnfrer architecture, which is paired with en. Therefore, r, as a video generation diffusion model, is different from the mainstream video generation diffusion model in that it adopts the rnfrer architecture. Mainstream video generation and diffusion models mostly


use the U-Ne architecture, which means that enI wins in the choice of experience and technical route. But everyone knows that the "successful password" of the rnfrer architecture has become mainstream in text and image generation. Why do others use enI Rich People Phone Number List without thinking of using it for video generation? This comes from another problem: the full attention mechanism in the rnfrer architecture Memory requirements will increase quadratically with the length of the input sequence, so the computational cost will be very, very high when processing micro-signals such as videos. In layman's terms, although the effect of using rnfrer will be good,





the computing resources required are also very scary. This is not very economical. Of course, although enI has obtained various financings, it is still not that wealthy, so they did not directly invest resources but thought of another way to solve the problem of high computing costs. Here we must first Introducing the concept of "len latent", it is a kind of "dimensionality reduction or compression" that aims to express the essence of information with less information. Let’s give an inappropriate but easy-to-understand example. It’s as if we can use a three-dimensional view to save and record the structure of a