post-caption-inferences
Generate descriptive captions for images using AI, with options to control detail level, randomness, and model-specific training.
Instructions
Caption image(s)
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| dryRun | No | ||
| ensureIPCleared | No | Whether we try to ensure IP removal for new prompt generation. | |
| images | Yes | ||
| seed | No | If specified, the API will make a best effort to produce the same results, such that repeated requests with the same `seed` and parameters should return the same outputs. Must be used along with the same parameters including prompt, model's state, etc.. | |
| unwantedSequences | No | ||
| modelId | No | When provided, the model will follow the model's training images and examples' prompt to generate the captions. | |
| temperature | No | The sampling temperature to use. Higher values like `0.8` will make the output more random, while lower values like `0.2` will make it more focused and deterministic. We generally recommend altering this or `topP` but not both. | |
| assetIds | No | ||
| topP | No | An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So `0.1` means only the tokens comprising the top `10%` probability mass are considered. We generally recommend altering this or `temperature` but not both. | |
| detailsLevel | No | The details level used to generate the captions. When a modelId is provided and examples are available, the details level is ignored. |