Explore Wan2.1-T2V-14B

Provides the main natural-language instruction for the model. Include the subject, action, style, composition, and constraints that matter; clearer concrete details give the model more guidance about the intended result.

Describes unwanted subjects, styles, defects, or artifacts such as blur or watermarks. It steers generation away from those concepts but is not a guaranteed content filter.

Sets the vertical resolution of generated images or video frames in pixels. Larger values create more detail but require more memory and processing time; keep the height compatible with the model’s supported dimensions and desired aspect ratio.

Sets the horizontal resolution of generated images or video frames in pixels. Larger values create more detail but require more memory and processing time; combine it with height to choose the aspect ratio.

Controls the length of the generated video as a frame count. Duration depends on the playback frame rate, and more frames increase generation time and memory use; some pipelines accept only particular frame-count intervals.

Controls how many iterative denoising passes produce the final media. More steps can refine detail and consistency but increase generation time, with diminishing quality gains beyond the model’s useful range.

Sets the strength of classifier-free guidance toward the text prompt. Higher values usually improve literal prompt adherence but can reduce natural motion or create artifacts; lower values give the model more visual freedom.

Requests multiple video variations from one prompt in a single run. Each result uses additional computation and memory, and different seeds or random states allow the variations to differ.

Caps the tokenized prompt length accepted by the model’s text encoder. Text beyond the limit is truncated, so increasing it can preserve long descriptions when supported but uses more memory.

Initializes the pipeline’s random-number generator. Reusing the same seed, prompt, model, and settings usually reproduces the same output, while changing it creates a new variation.

Chooses the data representation returned by the pipeline. Image frames are convenient for viewing, NumPy arrays suit numerical processing, and latent tensors are compact intermediate data that still require decoding.

Chooses the structure of the pipeline result. Enabled returns an object with named properties that is easier to read; disabled returns a positional tuple that may suit older or lower-level integrations.