Style transfer is a technique that applies the visual characteristics of one image or artistic style to the content of another, separating the "what" of an image from the "how it looks" and recombining them. A photograph of a city street might be rendered in the brushstroke style of Van Gogh, or a video clip might be transformed to adopt the color palette and texture of a specific film stock, with the underlying content preserved but the surface visual treatment replaced.

The technique originated with neural style transfer algorithms that used convolutional neural networks to separate and recombine content and style representations, allowing arbitrary artistic styles to be applied to photographs. In contemporary AI generation, style transfer concepts are applied more broadly through techniques like image-to-image generation, style conditioning via reference images, LoRA fine-tuning on specific artistic styles, and prompt-based style specification. These approaches give creators flexible tools for applying consistent visual treatments across generated content, matching the aesthetic of existing footage, or translating realistic content into stylized visual languages for specific creative effects.

When using style transfer approaches in AI generation, providing a strong reference image that clearly represents the target style, combined with precise style vocabulary in the text prompt, produces more reliable results than either approach alone. Describing both the stylistic qualities to adopt and referencing visual examples that embody them gives the model the clearest possible signal about the intended visual treatment.