He is a. To do this, the network first reduces the dimensionality of the video to a latent space and then generates the video using the compressed video. This can reduce input information and effectively reduce the computational pressure caused by the architecture. In this way, most of the problems will be solved. has successfully integrated video models into its large language model paradigm with great success in the past, making almost any operation and semi-hard to consider the effect well. In addition, the selection of training routes is also slightly different. They chose “original size and duration training” instead of the industry-common method of “cutting videos into preset standard rich list sizes and durations before training”.

Such training brings many benefits: 

 I gave three examples. They performed Luxembourg Mobile Number List model comparison training on cropped-size videos and original-size videos. On the left is the video generated after training the model on the cropped video. On the right is the video generated after the model was trained on the original size video. In addition, Wensheng Video can better understand the user’s intention, thereby achieving better generation results.

 I also added some creativity to the model

First, training such a text-to-speech video model requires a lot of video footage containing text descriptions, so I used my own – function to add high-quality text descriptions. Basically only use necessary content from the article, and remember to follow the grammar for citations. Select a quote template, click the button, type in the search box, and select the item box when it appears.