@Magncsans: When we write AI video prompts nowadays, we usually structure them, like: global settings, character and material bindi…

When we write AI video prompts nowadays, we usually structure them, like: global settings, character and material binding, per-second timestamps, and negative prompts. It looks like it's already clearly divided, but when it comes to actual generation, the model still has to process a lot of information all at once. When all this information is crammed into a single generation, the model not only has to understand the visuals but also "guess" the spatial relationships based on the text. The more complex the information, the more likely it is to conflict with itself, and in the end, it's inevitably going to come down to luck. The role of the white model, while not 100% following our design, at least first demonstrates the composition, number of characters, positioning, movement paths, occlusion relationships, and camera paths. This way, character performances, materials, lighting and shadows, and special effects can still be controlled with prompts, but spatial relationships no longer rely entirely on text for the model to imagine on its own. So I think what the white model truly reduces isn't all randomness, but randomness in spatial relationships. But here's a problem: if friends who don't have much sense of cinematography make the white model, it's actually not as good as relying on the storyboard compositions generated randomly by the video model. For someone like me, who has a specific composition in mind for every shot, I'll use the white model for everything and "shoot it once" myself first. so: multiple characters in the same space—use white model strong cinematic sense with your own compositional aesthetics—use white model complex camera movements + multiple characters—use white model ordinary dialogue—pure text