> ## Documentation Index
> Fetch the complete documentation index at: https://docs.livepeer.org/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Pipelines

> Full reference for the batch AI pipelines on the Livepeer network. Request shapes, response formats, per-pipeline parameters, and operational notes.

export const TableCell = ({children, align = "left", header = false, style = {}, className = "", ...rest}) => {
  const Component = header ? "th" : "td";
  return <Component className={className} style={{
    padding: "0.75rem 1rem",
    textAlign: align,
    border: header ? "none" : "1px solid var(--lp-color-border-default)",
    ...style
  }} {...rest}>
      {children}
    </Component>;
};

export const TableRow = ({children, header = false, hover = false, style = {}, className = "", ...rest}) => {
  const rowId = `table-row-${Math.random().toString(36).substr(2, 9)}`;
  return <>
      {hover && <style>{`
          #${rowId}:hover {
            background-color: var(--lp-color-bg-card);
          }
        `}</style>}
      <tr id={rowId} className={className} style={{
    ...header && ({
      backgroundColor: "var(--lp-color-accent-strong)",
      color: "var(--lp-color-on-accent)",
      fontWeight: "bold"
    }),
    ...style
  }} {...rest}>
        {children}
      </tr>
    </>;
};

export const StyledTable = ({children, variant = "default", style = {}, className = "", ...rest}) => {
  const wrapperVariants = {
    default: {
      border: "1px solid var(--lp-color-border-default)",
      backgroundColor: "var(--lp-color-bg-card)",
      overflow: "hidden"
    },
    bordered: {
      border: "2px solid var(--lp-color-accent)",
      backgroundColor: "var(--lp-color-bg-page)",
      overflow: "hidden"
    },
    minimal: {
      border: "none",
      backgroundColor: "transparent",
      overflow: "visible"
    }
  };
  return <div data-docs-styled-table-shell className={className} style={{
    width: "100%",
    padding: 0,
    margin: 0,
    ...wrapperVariants[variant],
    ...style
  }} {...rest}>
      <table data-docs-styled-table style={{
    width: "100%",
    borderCollapse: "collapse",
    borderSpacing: 0,
    margin: 0,
    backgroundColor: "transparent"
  }}>
        {children}
      </table>
    </div>;
};

export const CenteredContainer = ({children, maxWidth = "800px", padding = "0", preset = "default", width = "", minWidth = "", marginRight = "", marginBottom = "", textAlign = "", style = {}, className = "", ...rest}) => {
  const presets = {
    default: {},
    fitContent: {
      width: "fit-content",
      minWidth: "fit-content"
    },
    readable70: {
      width: "70%",
      minWidth: "fit-content"
    },
    readable80: {
      width: "80%",
      minWidth: "fit-content"
    },
    readable90: {
      width: "90%"
    },
    wide900: {
      maxWidth: "900px"
    }
  };
  const presetStyle = presets[preset] || presets.default;
  return <div className={className} style={{
    maxWidth: presetStyle.maxWidth || maxWidth,
    margin: "0 auto",
    padding: padding,
    ...presetStyle.width ? {
      width: presetStyle.width
    } : {},
    ...presetStyle.minWidth ? {
      minWidth: presetStyle.minWidth
    } : {},
    ...width ? {
      width
    } : {},
    ...minWidth ? {
      minWidth
    } : {},
    ...marginRight ? {
      marginRight
    } : {},
    ...marginBottom ? {
      marginBottom
    } : {},
    ...textAlign ? {
      textAlign
    } : {},
    ...style
  }} {...rest}>
      {children}
    </div>;
};

export const CustomDivider = ({color = "var(--lp-color-border-default)", middleText = "", spacing = "default", style = {}, className = "", ...rest}) => {
  const spacingPresets = {
    default: {
      margin: "24px 0"
    },
    overlap: {
      margin: "-1rem 0 -1rem 0"
    },
    tight: {
      margin: "0 0 -1rem 0"
    },
    section: {
      margin: "0 0 -2rem 0"
    },
    sectionOverlap: {
      margin: "-1rem 0 -2rem 0"
    },
    deepOverlap: {
      margin: "-1rem 0 -1.5rem 0"
    }
  };
  const spacingStyle = spacingPresets[spacing] || spacingPresets.default;
  return <div role="separator" aria-orientation="horizontal" className={className} style={{
    display: "flex",
    alignItems: "center",
    ...spacingStyle,
    fontSize: style?.fontSize || "16px",
    height: "fit-content",
    ...style
  }} {...rest}>
      <span style={{
    marginRight: "var(--lp-spacing-px-8)",
    opacity: 0.2
  }}>
        <Icon icon="/snippets/assets/logos/Livepeer-Logo-Symbol-Theme.svg" />
      </span>
      <div style={{
    flex: 1,
    height: "1px",
    background: "var(--lp-color-border-default)",
    opacity: 0.4
  }}></div>
      {middleText && <>
          <Icon icon="circle" size={2} />
          <span style={{
    margin: "0 8px",
    fontWeight: "bold",
    color: color,
    opacity: 0.7
  }}>
            {middleText}
          </span>
          <Icon icon="circle" size={2} />
        </>}
      <div style={{
    flex: 1,
    height: "1px",
    background: "var(--lp-color-border-default)",
    opacity: 0.4
  }}></div>
      <span style={{
    marginLeft: "var(--lp-spacing-px-8)",
    opacity: 0.2
  }}>
        <span style={{
    display: "inline-block",
    transform: "scaleX(-1)"
  }}>
          <Icon icon="/snippets/assets/logos/Livepeer-Logo-Symbol-Theme.svg" />
        </span>
      </span>
    </div>;
};

export const LinkArrow = ({href, label, description, newline = true, borderColor, className = '', style = {}, ...rest}) => {
  const linkArrowStyle = {
    display: 'inline-flex',
    alignItems: 'center',
    justifyContent: 'center',
    gap: "var(--lp-spacing-1)",
    width: 'fit-content',
    ...borderColor && ({
      borderColor
    })
  };
  return <span className={className} style={style} {...rest}>
      {newline && <br />}
      <span style={linkArrowStyle}>
        <a href={href} target="_blank" rel="noopener noreferrer">
          {label}
        </a>
        <Icon icon="arrow-up-right" size={14} color="var(--lp-color-accent)" />
      </span>
      {description && description}
      {description && <div style={{
    height: "var(--lp-spacing-3)"
  }} />}
    </span>;
};

<CenteredContainer preset="readable90">
  <Tip>Every batch AI pipeline uses the same HTTP POST pattern. Swap the endpoint path and the body fields; the auth, error handling, and response envelope stay identical.</Tip>
</CenteredContainer>

***

The Livepeer AI Gateway exposes nine batch pipelines and one LLM pipeline through HTTP POST endpoints. Each pipeline accepts a JSON request body keyed by `model_id` and pipeline-specific fields, and returns a JSON response with the result. Real-time video AI (`live-video-to-video`) runs through the trickle protocol and is covered separately in the [real-time AI overview](/v2/developers/build/ai-and-agents/realtime-ai/overview).

For warm models, VRAM requirements, and architecture support per pipeline, see [model support](/v2/developers/build/ai-and-agents/model-support). For SDK wrappers, see [AI SDKs](/v2/developers/build/ai-and-agents/ai-sdks-overview).

<CustomDivider />

## Shared conventions

**Base URL:** Any Livepeer Gateway endpoint. The community Gateway at `https://dream-gateway.livepeer.cloud` accepts unauthenticated requests for development.

**Authentication:** Bearer token when the Gateway requires it. The community Gateway does not require a token.

**Request format:** `POST /<pipeline-endpoint>` with `Content-Type: application/json`.

**`model_id` field:** Every pipeline accepts a `model_id` field specifying the Hugging Face model ID (or Ollama model ID for LLM). Omitting `model_id` uses the pipeline's default warm model.

**Error responses:** `400` for malformed requests, `422` for validation errors (invalid model\_id, missing required fields), `500` for inference failures. Error bodies include a `detail` field with the failure reason.

**Cold model latency:** If no Orchestrator has the requested model warm in GPU memory, the first request triggers a model load (30 seconds to 5 minutes depending on model size). Subsequent requests to the same model on the same Orchestrator are immediate.

<CustomDivider />

## Pipeline reference

<AccordionGroup>
  <Accordion title="text-to-image" icon="image">
    Generate images from text prompts using diffusion models (SDXL, SD 1.5, Flux).

    ```bash icon="terminal" theme={"theme":{"light":"github-light","dark":"dark-plus"}}
    curl -X POST https://dream-gateway.livepeer.cloud/text-to-image \
      -H "Content-Type: application/json" \
      -d '{
        "model_id": "SG161222/RealVisXL_V4.0_Lightning",
        "prompt": "a glowing neural network in a dark room",
        "width": 1024,
        "height": 1024,
        "guidance_scale": 7.5,
        "num_inference_steps": 8,
        "seed": 42
      }'
    ```

    | Field                   | Type    | Required | Description                                                          |
    | ----------------------- | ------- | -------- | -------------------------------------------------------------------- |
    | `model_id`              | string  | No       | Hugging Face model ID. Default: `SG161222/RealVisXL_V4.0_Lightning`  |
    | `prompt`                | string  | Yes      | Text prompt for generation                                           |
    | `negative_prompt`       | string  | No       | Terms to avoid in generation                                         |
    | `width`                 | integer | No       | Output width in pixels (default: 1024)                               |
    | `height`                | integer | No       | Output height in pixels (default: 1024)                              |
    | `guidance_scale`        | number  | No       | Classifier-free guidance scale (default: 7.5)                        |
    | `num_inference_steps`   | integer | No       | Denoising steps (default depends on model; Lightning models use 4-8) |
    | `seed`                  | integer | No       | Random seed for reproducibility                                      |
    | `num_images_per_prompt` | integer | No       | Number of images to generate (default: 1)                            |
    | `safety_check`          | boolean | No       | Run NSFW safety filter (default: true)                               |

    **Response:** JSON object with `images` array. Each image is a `{ url, seed }` object.
  </Accordion>

  <Accordion title="image-to-image" icon="image">
    Transform images using style transfer, enhancement, or img2img diffusion.

    ```bash icon="terminal" theme={"theme":{"light":"github-light","dark":"dark-plus"}}
    curl -X POST https://dream-gateway.livepeer.cloud/image-to-image \
      -F "model_id=timbrooks/instruct-pix2pix" \
      -F "prompt=make it look like a watercolour painting" \
      -F "image=@input.png" \
      -F "strength=0.8"
    ```

    | Field                 | Type    | Required | Description                                                      |
    | --------------------- | ------- | -------- | ---------------------------------------------------------------- |
    | `model_id`            | string  | No       | Default: `timbrooks/instruct-pix2pix`                            |
    | `image`               | file    | Yes      | Input image (multipart form upload)                              |
    | `prompt`              | string  | Yes      | Transformation instruction                                       |
    | `strength`            | number  | No       | How much to transform (0.0 = no change, 1.0 = full regeneration) |
    | `guidance_scale`      | number  | No       | Guidance scale (default: 7.5)                                    |
    | `num_inference_steps` | integer | No       | Denoising steps                                                  |
    | `seed`                | integer | No       | Random seed                                                      |
    | `safety_check`        | boolean | No       | NSFW filter (default: true)                                      |

    **Response:** JSON with `images` array, same format as text-to-image.

    <Note>image-to-image uses `multipart/form-data`, not `application/json`. The image is uploaded as a file field.</Note>
  </Accordion>

  <Accordion title="image-to-video" icon="film">
    Animate a still image into a short video clip using Stable Video Diffusion.

    ```bash icon="terminal" theme={"theme":{"light":"github-light","dark":"dark-plus"}}
    curl -X POST https://dream-gateway.livepeer.cloud/image-to-video \
      -F "model_id=stabilityai/stable-video-diffusion-img2vid-xt" \
      -F "image=@input.png" \
      -F "fps=6" \
      -F "motion_bucket_id=127"
    ```

    | Field              | Type    | Required | Description                                              |
    | ------------------ | ------- | -------- | -------------------------------------------------------- |
    | `model_id`         | string  | No       | Default: `stabilityai/stable-video-diffusion-img2vid-xt` |
    | `image`            | file    | Yes      | Input image (multipart form upload)                      |
    | `fps`              | integer | No       | Output frames per second (default: 6)                    |
    | `motion_bucket_id` | integer | No       | Motion intensity (0-255; default: 127)                   |
    | `seed`             | integer | No       | Random seed                                              |
    | `safety_check`     | boolean | No       | NSFW filter (default: true)                              |

    **Response:** JSON with `frames` array containing frame URLs, or a video URL.

    <Note>SVD outputs 14-25 frames at 576x1024 resolution. Text prompts are not used; the image is the sole conditioning input.</Note>
  </Accordion>

  <Accordion title="image-to-text" icon="text">
    Generate captions or descriptions for images using BLIP or vision-language models.

    ```bash icon="terminal" theme={"theme":{"light":"github-light","dark":"dark-plus"}}
    curl -X POST https://dream-gateway.livepeer.cloud/image-to-text \
      -F "model_id=Salesforce/blip-image-captioning-large" \
      -F "image=@photo.jpg"
    ```

    | Field      | Type   | Required | Description                                       |
    | ---------- | ------ | -------- | ------------------------------------------------- |
    | `model_id` | string | No       | Default: `Salesforce/blip-image-captioning-large` |
    | `image`    | file   | Yes      | Input image (multipart form upload)               |
    | `prompt`   | string | No       | Optional prompt to guide caption content          |

    **Response:** JSON with `text` field containing the generated caption.
  </Accordion>

  <Accordion title="audio-to-text" icon="microphone">
    Transcribe audio to text with per-chunk timestamps using Whisper.

    ```bash icon="terminal" theme={"theme":{"light":"github-light","dark":"dark-plus"}}
    curl -X POST https://dream-gateway.livepeer.cloud/audio-to-text \
      -F "model_id=openai/whisper-large-v3" \
      -F "audio=@recording.mp3"
    ```

    | Field      | Type   | Required | Description                                             |
    | ---------- | ------ | -------- | ------------------------------------------------------- |
    | `model_id` | string | No       | Default: `openai/whisper-large-v3`                      |
    | `audio`    | file   | Yes      | Audio file (mp4, webm, mp3, flac, wav, m4a). Max 50 MB. |

    **Response:** JSON with `text` (full transcript) and `chunks` array (per-segment timestamps and text).
  </Accordion>

  <Accordion title="text-to-speech" icon="volume">
    Generate natural speech from text using Parler-TTS.

    ```bash icon="terminal" theme={"theme":{"light":"github-light","dark":"dark-plus"}}
    curl -X POST https://dream-gateway.livepeer.cloud/text-to-speech \
      -H "Content-Type: application/json" \
      -d '{
        "model_id": "parler-tts/parler-tts-large-v1",
        "text": "Livepeer is a decentralised video infrastructure network.",
        "description": "A female speaker with a warm, clear voice and moderate pace."
      }'
    ```

    | Field         | Type   | Required | Description                                                    |
    | ------------- | ------ | -------- | -------------------------------------------------------------- |
    | `model_id`    | string | No       | Default: `parler-tts/parler-tts-large-v1`                      |
    | `text`        | string | Yes      | Text to synthesise. Max \~600 characters; chunk longer text.   |
    | `description` | string | No       | Voice characteristics (speaker identity, style, audio quality) |

    **Response:** JSON with `audio` object containing a URL to the generated audio file.

    <Note>Requires a pipeline-specific AI Runner container. Not all Orchestrators have this pipeline active.</Note>
  </Accordion>

  <Accordion title="upscale" icon="expand">
    Upscale low-resolution images using the SD x4-Upscaler (4x super-resolution).

    ```bash icon="terminal" theme={"theme":{"light":"github-light","dark":"dark-plus"}}
    curl -X POST https://dream-gateway.livepeer.cloud/upscale \
      -F "model_id=stabilityai/stable-diffusion-x4-upscaler" \
      -F "image=@lowres.png" \
      -F "prompt=high quality, sharp details"
    ```

    | Field          | Type    | Required | Description                                         |
    | -------------- | ------- | -------- | --------------------------------------------------- |
    | `model_id`     | string  | No       | Default: `stabilityai/stable-diffusion-x4-upscaler` |
    | `image`        | file    | Yes      | Input image (multipart form upload)                 |
    | `prompt`       | string  | No       | Optional quality guidance prompt                    |
    | `seed`         | integer | No       | Random seed                                         |
    | `safety_check` | boolean | No       | NSFW filter (default: true)                         |

    **Response:** JSON with `images` array, same format as text-to-image.
  </Accordion>

  <Accordion title="segment-anything-2" icon="scissors">
    Promptable visual segmentation for images using SAM 2 (Meta AI).

    ```bash icon="terminal" theme={"theme":{"light":"github-light","dark":"dark-plus"}}
    curl -X POST https://dream-gateway.livepeer.cloud/segment-anything-2 \
      -F "model_id=facebook/sam2-hiera-large" \
      -F "image=@photo.jpg" \
      -F 'point_coords=[[500,375]]' \
      -F 'point_labels=[1]'
    ```

    | Field          | Type   | Required | Description                                        |
    | -------------- | ------ | -------- | -------------------------------------------------- |
    | `model_id`     | string | No       | Default: `facebook/sam2-hiera-large`               |
    | `image`        | file   | Yes      | Input image                                        |
    | `point_coords` | array  | No       | Point prompts as `[[x,y], ...]`                    |
    | `point_labels` | array  | No       | Labels for points (1 = foreground, 0 = background) |
    | `box`          | array  | No       | Bounding box prompt `[x1, y1, x2, y2]`             |

    **Response:** JSON with `masks`, `scores`, and `logits` arrays.
  </Accordion>

  <Accordion title="llm" icon="message-bot">
    OpenAI-compatible chat completions using Ollama-based runner.

    ```bash icon="terminal" theme={"theme":{"light":"github-light","dark":"dark-plus"}}
    curl -X POST https://dream-gateway.livepeer.cloud/llm \
      -H "Content-Type: application/json" \
      -d '{
        "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
        "messages": [
          {"role": "user", "content": "Explain Livepeer in one sentence."}
        ]
      }'
    ```

    | Field         | Type    | Required | Description                                      |
    | ------------- | ------- | -------- | ------------------------------------------------ |
    | `model`       | string  | Yes      | Ollama-compatible model ID                       |
    | `messages`    | array   | Yes      | OpenAI-format message array (`role` + `content`) |
    | `max_tokens`  | integer | No       | Maximum output tokens                            |
    | `temperature` | number  | No       | Sampling temperature (0.0-2.0)                   |
    | `stream`      | boolean | No       | Stream response tokens (SSE)                     |

    **Response:** OpenAI-compatible chat completion object with `choices[0].message.content`.

    <Note>The LLM pipeline is in beta. The request format follows the OpenAI `/v1/chat/completions` shape. Supported models include Meta-Llama-3.1-8B-Instruct (warm, 8 GB VRAM), Mistral-7B-Instruct-v0.3, Gemma-2-9b-it, and Qwen2.5-7B-Instruct.</Note>
  </Accordion>
</AccordionGroup>

<CustomDivider />

## Operational notes

**Multipart vs JSON.** Pipelines that accept file uploads (image-to-image, image-to-video, image-to-text, audio-to-text, upscale, segment-anything-2) use `multipart/form-data`. Pipelines that accept only text input (text-to-image, text-to-speech, LLM) use `application/json`.

**Gateway selection.** The community Gateway routes to whichever Orchestrator in the Active Set has the requested model warm. For production, operate a self-hosted Gateway with `-maxPricePerUnit` to control costs, or use a Gateway provider with an API key.

**`safety_check` filter.** Enabled by default on image-generating pipelines. Set to `false` to disable. The filter runs on the Orchestrator side; disabling it does not affect content moderation policies that the Gateway operator may enforce.

The [AI quickstart](/v2/developers/build/ai-and-agents/ai-jobs-direct-quickstart) walks through the first inference call end-to-end with error handling.