Pocess was not easy to control
practiced, he often went crazy and output some stuff that no one could understand. At the same time, his improvement process is essentially a constant imitation of previous works, so he still lacks creativity, resulting in a potentially low ceiling. The diffusion model is a diligent and intelligent painter. He is not a mechanical imitation. Instead, he learned the relationship between the connotation of the image and the image while studying a large number of previous paintings. He roughly knows what "beauty" in the image should be. It was more like he was thinking about what a certain "style" of such an image should look like. He was a more promising painter than N. In other words, enI chose the diffusion model as a paradigm to create the Vincent video model, which is a good start at the moment. It chose a potential painter to cultivate.Then another question arises. Because everyone knows the superiority of the diffusion model. In addition to enI, there are many friends who are also doing diffusion models. Why does enI look more amazing? Because enI has such a thinking. I once worked on a large language Rich People Phone Number List model. I have achieved very good results and achieved such great success. Is it possible for me to use this experience to achieve a new success? The answer is yes. enI believes that its previous success in large language models is due to the fact that en can be translated into tokens, tokens, and tokens, which will make it easier to understand some en. It can elegantly
https://lh7-us.googleusercontent.com/xJGSWm0GJmuD8UflI0HbccuhTSnL7dCCMwKd6R8gc5FyhQhC4L1lPWDqwhkn9p-GLDUIiWHOmrCBdlT5733oWVF24XKTi2QJ9IG1n6vTi3xExcWioz659liKWyZG15hPRATnaXOEAjgSxsRTghj1wqA
combine code, mathematics, 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
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