We first obtain patch features and image embedding of the style image from CLIP, then we select the patches sharing less similarity with text prompt as texture guidance, and use a global projection module to transform it into global style descriptions. The global projection module is trained with a contrastive dataset constructed by model illusion through contrastive learning. The style information is then injected into the model through the decoupled cross-attention. The motion adapter and gray tile ControlNet are used to enhance dynamic quality and enable content control respectively.