Interior from Kling. What happens between the prompt and the video The Kling team told us how the generator teaches us to fulfill our director's wishes. Tutorial videos are edited almost like an editorial sheet. The article examines an 18-second video. The usual description confused a sharp cut with a smooth transition and missed that the line started before the hero appeared. If such details are regularly lost in training descriptions, it is more difficult for the generator to learn how to manipulate them. Each reference has its own role. Let's say you need to capture a face from the first image, a jacket from the second and only camera movement from the video. Kling is taught to explicitly link each reference to the desired part of the scene. Otherwise, “do it like here” easily becomes “at the same time, take the background and lighting away. Prompt Enhancer is trained on real generations. First, he is taught to rewrite queries in the language of those very detailed descriptions. Then, for one request, several versions of the prompt are created, videos are created from them, and the result is evaluated. An interesting detail from the team: after that, the result became more stable, but the most expressive options suffered. If the judge is penalizing objects, it's easier to choose a camera movement that the model is already good at. The referee must also be able to watch the video. If he sees too few frames, he might miss how the hero's arm breaks for a split second. Kling has two assessors: one explains where and what went wrong, the other quickly gives marks on movement, image and response to the request. And one more detail about what's under the hood. The visual and language parts of Kling-MLLM were ported from the Kwan family 3.5-3.8 and added audio processing. We are talking about a system that helps you understand and evaluate videos. The video itself was created by a separate model. Really geeky: The era of pure diffusion transformer (DiT) scaling for pixel enhancement is over. Today, the bottleneck of video creation has shifted from processing to semantic control and multimodal understanding.

Interior from Kling. What happens between the prompt and the video The Kling team told us how the generator teaches us to fulfill our director's wishes. Tutorial videos are edited almost like an editorial sheet. The article examines an 18-second video. The usual description confused a sharp cut with a smooth transition and missed that the line started before the hero appeared. If such details are regularly lost in training descriptions, it is more difficult for the generator to learn how to manipulate them. Each reference has its own role. Let's say you need to capture a face from the first image, a jacket from the second and only camera movement from the video. Kling is taught to explicitly link each reference to the desired part of the scene. Otherwise, “do it like here” easily becomes “at the same time, take the background and lighting away. Prompt Enhancer is trained on real generations. First, he is taught to rewrite queries in the language of those very detailed descriptions. Then, for one request, several versions of the prompt are created, videos are created from them, and the result is evaluated. An interesting detail from the team: after that, the result became more stable, but the most expressive options suffered. If the judge is penalizing objects, it's easier to choose a camera movement that the model is already good at. The referee must also be able to watch the video. If he sees too few frames, he might miss how the hero's arm breaks for a split second. Kling has two assessors: one explains where and what went wrong, the other quickly gives marks on movement, image and response to the request. And one more detail about what's under the hood. The visual and language parts of Kling-MLLM were ported from the Kwan family 3.5-3.8 and added audio processing. We are talking about a system that helps you understand and evaluate videos. The video itself was created by a separate model. Really geeky: The era of pure diffusion transformer (DiT) scaling for pixel enhancement is over. Today, the bottleneck of video creation has shifted from processing to semantic control and multimodal understanding.