# Generate videos from text prompts

Robot: `/video/generate`

🤖/video/generate creates videos from text prompts using AI models.

Stage: ga

## Usage example

Generate a video from a text prompt:

```json
{
  "steps": {
    "generated_video": {
      "robot": "/video/generate",
      "prompt": "A slow cinematic drone shot of ocean cliffs at sunrise, realistic lighting.",
      "duration": 5,
      "aspect_ratio": "16:9",
      "format": "mp4"
    }
  }
}
```

## Parameters

* `interpolate`: Controls whether Assembly Variables are interpolated for individual instruction fields.

  By default, most Robot instruction fields interpolate Assembly Variables. Set this to `false` to treat every instruction field as literal text, or set an individual field path to `false` to treat only that field as literal text. For Robot-specific fields that are literal by default, set this to `true` or set that field path to `true` to opt back into interpolation.

  Use field names such as `path`, or dotted paths such as `ffmpeg.vf` for nested objects.

* `output_meta`: Allows you to specify a set of metadata that is more expensive on CPU power to calculate, and thus is disabled by default to keep your Assemblies processing fast.

  For images, you can add `"has_transparency": true` in this object to extract if the image contains transparent parts and `"dominant_colors": true` to extract an array of hexadecimal color codes from the image.

  For images, you can also add `"blurhash": true` to extract a [BlurHash](https://blurha.sh) string — a compact representation of a placeholder for the image, useful for showing a blurred preview while the full image loads.

  For videos, you can add the `"colorspace": true` parameter to extract the colorspace of the output video.

  For videos, you can also add `"interlaced": true` to detect whether the video is interlaced. This combines the cheap ffprobe `field_order` flag with a bounded `idet` sampling pass over the first frames of the source, exposing `interlaced`, `field_order`, and a diagnostic `interlace_detection` object under `file.meta`. This is computationally expensive and billed accordingly.

  For audio, you can add `"mean_volume": true` to get a single value representing the mean average volume of the audio file.

  You can also set this to `false` to skip metadata extraction and speed up transcoding.

* `result`

* `queue`: Setting the queue to 'batch', manually downgrades the priority of jobs for this step to avoid consuming Priority job slots for jobs that don't need zero queue waiting times

* `force_accept`: Force a Robot to accept a file type it would have ignored.

  By default, Robots ignore files they are not familiar with.
  [🤖/video/encode](/docs/robots/video-encode.md), for
  example, will happily ignore input images.

  With the `force_accept` parameter set to `true`, you can force Robots to accept all files thrown at them.
  This will typically lead to errors and should only be used for debugging or combatting edge cases.

* `ignore_errors`: Ignore errors during specific phases of processing.

  Setting this to `["meta"]` will cause the Robot to ignore errors during metadata extraction.

  Setting this to `["execute"]` will cause the Robot to ignore errors during the main execution phase.

  Setting this to `true` is equivalent to `["meta", "execute"]` and will ignore errors in both phases.

* `use`: Specifies which Step(s) to use as input.

  * You can pick any names for Steps except `":original"` (reserved for user uploads handled by Transloadit)
  * You can provide several Steps as input with arrays:
    ```json
    {
      "use": [
        ":original",
        "encoded",
        "resized"
      ]
    }
    ```
  * You can also tag input Steps with `as` to pass semantic intent to robots:
    ```json
    {
      "use": [
        {
          "name": ":original",
          "as": "image"
        },
        {
          "name": ":original",
          "as": "mask"
        }
      ]
    }
    ```

  > [!Tip]
  > That's likely all you need to know about `use`, but you can view [Advanced use cases](/docs/topics/use-parameter.md).

* `model`: The AI model to use for video generation. Defaults to minimax/video-01.

* `prompt`: The prompt describing the desired video content.

* `format`: Format of the generated video.

* `seed`: Seed for the random number generator.

* `aspect_ratio`: Aspect ratio of the generated video.

* `height`: Height of the generated video.

* `width`: Width of the generated video.

* `style`: Style of the generated video.

* `num_outputs`: Number of video variants to generate.

* `duration`: Duration of the generated video in seconds.

* `fps`: Frames per second of the output video.

* `motion_amount`: Controls the intensity of motion in the generated video.

* `camera_motion`: Camera movement type (e.g., pan-left, pan-right, zoom-in, zoom-out, orbit, static, dolly, crane).

* `negative_prompt`: Describes what should be avoided in the generated video.

* `reference_strength`: How closely the output should follow the reference input (0.0 - 1.0).

* `provider`: Chooses the best provider based on your request.