##
Text to Video

Generate an video based on a given text prompt.

> For more details about the `text-to-video` task, check out its dedicated page! You will find examples and related materials.

###
Recommended models

- tencent/HunyuanVideo: A strong model for consistent video generation.
- Lightricks/LTX-Video: A text-to-video model with high fidelity motion and strong prompt adherence.
- Lightricks/LTX-Video-0.9.8-13B-distilled: Very fast model for video generation.

Explore all available models and find the one that suits you best here.

###
Using the API

Language

Provider

Settings

    import os
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        provider="fal-ai",
        api_key=os.environ["HF_TOKEN"],
    )

    video = client.text_to_video(
        "A young man walking on the street",
        model="Wan-AI/Wan2.2-T2V-A14B",
    )

###
API specification

####
Request

Payload |  |
--- | --- | ---
inputs* | string | The input text data (sometimes called “prompt”)
parameters | object |
num_frames | number | The num_frames parameter determines how many video frames are generated.
guidance_scale | number | A higher guidance scale value encourages the model to generate videos closely linked to the text prompt, but values too high may cause saturation and other artifacts.
negative_prompt | string[] | One or several prompt to guide what NOT to include in video generation.
num_inference_steps | integer | The number of denoising steps. More denoising steps usually lead to a higher quality video at the expense of slower inference.
seed | integer | Seed for the random number generator.

Headers |  |
--- | --- | ---
authorization | string | Authentication header in the form `'Bearer: hf_****'` when `hf_****` is a personal user access token with “Inference Providers” permission. You can generate one from your settings page.

####
Response

Body |  |
--- | --- | ---
video | unknown | The generated video returned as raw bytes in the payload.

Update on GitHub