Generate images from text prompts

🤖/image/generate generates images from text prompts using AI.

Usage example

{
  "steps": {
    ":original": {
      "robot": "/upload/handle"
    },
    "inpainted": {
      "robot": "/image/generate",
      "use": [
        {
          "name": ":original",
          "as": "image"
        },
        {
          "name": ":original",
          "as": "mask"
        }
      ],
      "model": "google/nano-banana-pro",
      "prompt": "Replace the masked area with a breaching whale. Keep the rest of the image unchanged.",
      "format": "png"
    }
  }
}

Parameters

• interpolate

  boolean | Record<string, boolean>

  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

  Record<string, boolean> | boolean | Array<string>

  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 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.

• user_meta

  Record<string, any>(default: {})

  Adds custom metadata to each file emitted by this Robot without modifying the file’s contents.

  The values are merged with any existing user_meta carried by the input file. If both objects contain the same key, this Robot’s value takes precedence. Assembly Variables are supported, for example { "internal_file_id": "${file.id}" }.

• queue

  batch

  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

  boolean(default: false)

  Force a Robot to accept a file type it would have ignored.

  By default, Robots ignore files they are not familiar with. 🤖/video/encode, 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

  boolean | Array<meta | execute>(default: [])

  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

  string | Array<string> | Array<object> | object

  Specifies which Step(s) to use as input.

  For inpainting, provide both the source image and mask through use, typically with:

  {
    "use": [
      { "name": ":original", "as": "image" },
      { "name": ":original", "as": "mask" }
    ]
  }
  

  Best practice:

  • Tag source and mask inputs explicitly using as (or semantic upload field names)
  • Keep the prompt focused on what should change in the masked/transparent region
  • Leave the model/provider choice to the robot defaults unless you have a specific need

• model

  string

  The AI model to use. Defaults to google/nano-banana. Supported models include flux-1.1-pro-ultra, flux-schnell, recraft-v3, google/nano-banana, google/nano-banana-2, google/nano-banana-pro, openai/gpt-image-2, and stability-ai/stable-diffusion-inpainting. The legacy alias gpt-image-2 is also accepted for backwards compatibility.

• prompt — required

  string

  Prompt describing the desired image. For inpainting, describe what should appear in the masked/transparent region and that the rest should stay unchanged.

• format

  jpeg | jpg | png | gif | webp | svg

  Output format. Defaults depend on model: png for Google models and openai/gpt-image-2, svg for recraft-v3, jpeg for others. Google models currently return PNG only.

• seed

  string | number

  Seed for the random number generator.

• aspect_ratio

  string

  Requested output aspect ratio. For Google models, width/height can also be used and orientation is derived automatically when aspect_ratio is omitted.

• height

  string | number

  Requested output height in pixels (mainly used by Google image models and openai/gpt-image-2).

• width

  string | number

  Requested output width in pixels (mainly used by Google image models and openai/gpt-image-2).

• style

  string

  Style of the generated image.

• num_outputs

  string | number

  Number of output variants to generate (1-10).

• provider

  string(default: "auto")

  Chooses the best provider based on your request.

Demos

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