Name: InstantID API: Identity-Preserving Portrait Generator
Brand: magicapi
SKU: api-magicapi-realistic-image-generation-zsxkib-instant-id
Availability: InStock
Author: API Market

Shashank Agarwal

# 📸 Identity-Preserving Portrait Generator API (zsxkib InstantID)

## Overview
InstantID makes realistic images of real people from a single face photo. Send a face `image` and a text `prompt`; the model generates new images of that person in the style and scene you describe, optionally following the pose of a `pose_image`. It runs on SDXL, and you can pick from 17 base checkpoints with `sdxl_weights`.

Each request returns a list of image URLs, one per `num_outputs`.

## Features
- **One face photo is enough**: the face in `image` sets the identity of the person in the result.
- **Pose control**: pass a `pose_image` and keep `enable_pose_controlnet` on (default) to follow its pose; `pose_strength` sets how closely (0 to 1).
- **Extra ControlNets**: enable Canny (`enable_canny_controlnet`, `canny_strength`) and depth (`enable_depth_controlnet`, `depth_strength`) guidance.
- **Identity and detail strength**: `controlnet_conditioning_scale` (IdentityNet strength, for fidelity) and `ip_adapter_scale` (image adapter strength, for detail), each 0 to 1.5.
- **17 base checkpoints**: `sdxl_weights` from `stable-diffusion-xl-base-1.0` (default) to photographic, anime and fast models such as `RealVisXL_V4.0_Lightning`.
- **Fast mode**: `enable_lcm` uses Latent Consistency Models for fewer steps (`lcm_num_inference_steps` 1 to 10), trading some quality for speed; it works best on close-up portraits.
- **Output**: 1 to 8 images per request in `webp` (default), `jpg` or `png`.

## Use Cases
1. **Profile pictures and avatars**: create stylized portraits of a person from one photo.
2. **Photo styles and looks**: show the same person as an analog film photo, a painting or an anime character.
3. **Posed portraits**: generate a person in the pose of a reference image for storyboards or mockups.
4. **Personalized content**: create consistent images of the same person across a series.

## Visual Examples
### Example Input
Face image (`image`):

![Face photo sent as image in the InstantID example request](https://blog.api.market/wp-content/uploads/2025/12/professional-headshot-api-market.webp)

Pose image (`pose_image`):

![Pose reference sent as pose_image in the InstantID example request](https://blog.api.market/wp-content/uploads/2026/10/instant-id-input-api-market-1.jpeg)

Prompt: "analog film photo of a woman. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality"

Settings: `sdxl_weights` `protovision-xl-high-fidel`, `scheduler` `EulerDiscreteScheduler`, `num_inference_steps` `30`, `guidance_scale` `5`, `ip_adapter_scale` `0.8`, `controlnet_conditioning_scale` `0.8`, `enable_pose_controlnet` `true` with `pose_strength` `0.4`, Canny and depth ControlNets off, `enable_lcm` `false`, `enhance_nonface_region` `true`, `num_outputs` `1`, `output_format` `webp`, `output_quality` `80`, plus the negative prompt shown in the request below.

### Example Output
![Analog film style portrait generated by InstantID from the example face and pose](https://blog.api.market/wp-content/uploads/2026/10/instant-id-output-zr36e9vb-api-market.webp)

## Input Parameters
| Field | Type | Required | Default | Allowed values | Description |
| --- | --- | --- | --- | --- | --- |
| `image` | string (URL) | Yes |  |  | Input face image |
| `pose_image` | string (URL) | No | none |  | Optional reference pose image |
| `prompt` | string | No | `a person` |  | Input prompt |
| `negative_prompt` | string | No | `""` |  | Input negative prompt |
| `sdxl_weights` | string | No | `stable-diffusion-xl-base-1.0` | `stable-diffusion-xl-base-1.0`, `juggernaut-xl-v8`, `afrodite-xl-v2`, `albedobase-xl-20`, `albedobase-xl-v13`, `animagine-xl-30`, `anime-art-diffusion-xl`, `anime-illust-diffusion-xl`, `dreamshaper-xl`, `dynavision-xl-v0610`, `guofeng4-xl`, `nightvision-xl-0791`, `omnigen-xl`, `pony-diffusion-v6-xl`, `protovision-xl-high-fidel`, `RealVisXL_V3.0_Turbo`, `RealVisXL_V4.0_Lightning` | Base weights to use |
| `face_detection_input_width` | integer | No | `640` | 640 to 4096 | Width of the input image for face detection |
| `face_detection_input_height` | integer | No | `640` | 640 to 4096 | Height of the input image for face detection |
| `scheduler` | string | No | `EulerDiscreteScheduler` | `DEISMultistepScheduler`, `HeunDiscreteScheduler`, `EulerDiscreteScheduler`, `DPMSolverMultistepScheduler`, `DPMSolverMultistepScheduler-Karras`, `DPMSolverMultistepScheduler-Karras-SDE` | Scheduler |
| `num_inference_steps` | integer | No | `30` | 1 to 500 | Number of denoising steps |
| `guidance_scale` | number | No | `7.5` | 1 to 50 | Scale for classifier-free guidance |
| `ip_adapter_scale` | number | No | `0.8` | 0 to 1.5 | Scale for image adapter strength (for detail) |
| `controlnet_conditioning_scale` | number | No | `0.8` | 0 to 1.5 | Scale for IdentityNet strength (for fidelity) |
| `enable_pose_controlnet` | boolean | No | `true` |  | Enable the Openpose ControlNet; when false, `pose_strength` is ignored |
| `pose_strength` | number | No | `0.4` | 0 to 1 | Openpose ControlNet strength, effective only if `enable_pose_controlnet` is true |
| `enable_canny_controlnet` | boolean | No | `false` |  | Enable the Canny ControlNet; when false, `canny_strength` is ignored |
| `canny_strength` | number | No | `0.3` | 0 to 1 | Canny ControlNet strength, effective only if `enable_canny_controlnet` is true |
| `enable_depth_controlnet` | boolean | No | `false` |  | Enable the Depth ControlNet; when false, `depth_strength` is ignored |
| `depth_strength` | number | No | `0.5` | 0 to 1 | Depth ControlNet strength, effective only if `enable_depth_controlnet` is true |
| `enable_lcm` | boolean | No | `false` |  | Enable fast inference with LCM (Latent Consistency Models). Speeds up inference at some cost in image quality; performs better with close-up portrait face images |
| `lcm_num_inference_steps` | integer | No | `5` | 1 to 10 | Only used when `enable_lcm` is true. Number of denoising steps when using LCM |
| `lcm_guidance_scale` | number | No | `1.5` | 1 to 20 | Only used when `enable_lcm` is true. Scale for classifier-free guidance when using LCM |
| `enhance_nonface_region` | boolean | No | `true` |  | Enhance non-face region |
| `output_format` | string | No | `webp` | `webp`, `jpg`, `png` | Format of the output images |
| `output_quality` | integer | No | `80` | 0 to 100 | Quality of the output images. 100 is best quality, 0 is lowest |
| `seed` | integer | No | none |  | Random seed. Leave blank to randomize the seed |
| `num_outputs` | integer | No | `1` | 1 to 8 | Number of images to output |
| `disable_safety_checker` | boolean | No | `false` |  | Disable safety checker for generated images |

## Pricing
- **Pay As You Go (PAYG) pricing**: you only pay for what you use.
- **Price**: $0.034 per API unit.
- **1 API unit = 1 successful request with up to 31 seconds of processing time.** A longer request counts one more unit per started 31 seconds of processing, so more `num_outputs`, more `num_inference_steps` or extra ControlNets can cost more.
- The example request above took about 13 seconds of processing, so it was billed as 1 unit and costs $0.034.
- **Failed or cancelled predictions are not billed.**
- **GET requests to check prediction status** are free and don't count toward API unit usage.
- **No monthly subscription fee.**

## API Documentation

### Base URL
All API requests go to: `https://prod.api.market/api/v1/magicapi/realistic-image-generation-zsxkib-instant-id`

### Authentication
Send your api.market API key in the `x-api-market-key` header:
```
x-api-market-key: YOUR_API_MARKET_KEY
```

### Asynchronous Processing
**IMPORTANT**: This API is asynchronous. The flow is:
1. Send a POST request to `/predictions`. A successful request returns **201 Created** with a prediction `id` and a `status` of `starting` or `processing`.
2. Poll GET `/predictions/{id}` with that ID.
3. Keep polling while `status` is `starting` or `processing`.
4. Stop when `status` is `succeeded` (read `output`), `failed` or `canceled` (read `error`).

A polling interval of 1 second and an overall timeout of 600 seconds (10 minutes) work well.

### Endpoints

#### Create a Prediction
```bash
curl -X POST "https://prod.api.market/api/v1/magicapi/realistic-image-generation-zsxkib-instant-id/predictions" \
  -H "Content-Type: application/json" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY" \
  -d '{
    "version": "2e4785a4d80dadf580077b2244c8d7c05d8e3faac04a04c02d8e099dd2876789",
    "input": {
      "image": "https://blog.api.market/wp-content/uploads/2025/12/professional-headshot-api-market.webp",
      "prompt": "analog film photo of a woman. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality",
      "scheduler": "EulerDiscreteScheduler",
      "enable_lcm": false,
      "pose_image": "https://blog.api.market/wp-content/uploads/2026/10/instant-id-input-api-market-1.jpeg",
      "num_outputs": 1,
      "sdxl_weights": "protovision-xl-high-fidel",
      "output_format": "webp",
      "pose_strength": 0.4,
      "canny_strength": 0.3,
      "depth_strength": 0.5,
      "guidance_scale": 5,
      "output_quality": 80,
      "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured (lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch,deformed, mutated, cross-eyed, ugly, disfigured",
      "ip_adapter_scale": 0.8,
      "lcm_guidance_scale": 1.5,
      "num_inference_steps": 30,
      "enable_pose_controlnet": true,
      "enhance_nonface_region": true,
      "enable_canny_controlnet": false,
      "enable_depth_controlnet": false,
      "lcm_num_inference_steps": 5,
      "face_detection_input_width": 640,
      "face_detection_input_height": 640,
      "controlnet_conditioning_scale": 0.8
    }
  }'
```

Response (201 Created):
```json
{
  "id": "zr36e9vbv5rmy0d1cczr2c8dzc",
  "version": "2e4785a4d80dadf580077b2244c8d7c05d8e3faac04a04c02d8e099dd2876789",
  "status": "starting",
  "created_at": "2026-10-22T10:54:23.447Z"
}
```

#### Check Prediction Status
```bash
curl -X GET "https://prod.api.market/api/v1/magicapi/realistic-image-generation-zsxkib-instant-id/predictions/{id}" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY"
```

Response when the prediction has finished (200 OK):
```json
{
  "id": "zr36e9vbv5rmy0d1cczr2c8dzc",
  "status": "succeeded",
  "output": [
    "https://blog.api.market/wp-content/uploads/2026/10/instant-id-output-zr36e9vb-api-market.webp"
  ],
  "error": null,
  "created_at": "2026-10-22T10:54:23.447Z",
  "started_at": "2026-10-22T10:56:30.138Z",
  "completed_at": "2026-10-22T10:56:43.500Z"
}
```

`output` is a list of image URLs, one per `num_outputs`.

### Status Codes
- **201 Created**: prediction created (POST `/predictions`). This is the normal success response.
- **200 OK**: prediction details returned (GET `/predictions/{id}`).
- **400 Bad Request**: invalid input, for example a missing `image` or an `sdxl_weights` value that is not in the list.
- **401 Unauthorized**: missing or invalid API key.
- **404 Not Found**: no prediction with that ID.
- **429 Too Many Requests**: rate limit exceeded.
- **500 Internal Server Error** or **503 Service Unavailable**: temporary server problem; retry later.

## Rate Limits
All plans allow 10 requests per second. Requests above that return `429 Too Many Requests`; back off before retrying and honour any `Retry-After` header. Polling a prediction with GET is free but still counts toward the per-second limit, so poll every 1 to 5 seconds rather than in a tight loop.

## Error Handling
- Check the HTTP status code of every request and read the response body for the error message.
- A prediction can also end with `status` set to `failed`; the `error` field then explains why. Failed predictions are not billed.
- Retry 429, 500 and 503 responses with exponential backoff. Fix the input before retrying a 400.

## Implementation Guide
### Example Code

#### JavaScript
```javascript
const apiKey = "YOUR_API_MARKET_KEY";
const baseUrl = "https://prod.api.market/api/v1/magicapi/realistic-image-generation-zsxkib-instant-id";

async function createAndWaitForPrediction(input) {
  const response = await fetch(`${baseUrl}/predictions`, {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "x-api-market-key": apiKey
    },
    body: JSON.stringify({ version: "2e4785a4d80dadf580077b2244c8d7c05d8e3faac04a04c02d8e099dd2876789", input })
  });

  if (response.status !== 201) {
    throw new Error(`API error: ${response.status} ${await response.text()}`);
  }

  const prediction = await response.json();
  return pollForResults(prediction.id);
}

const delay = (ms) => new Promise((resolve) => setTimeout(resolve, ms));

async function pollForResults(predictionId) {
  const maxTimeout = 600000;
  const pollInterval = 1000;
  const startTime = Date.now();

  while (Date.now() - startTime < maxTimeout) {
    const response = await fetch(`${baseUrl}/predictions/${predictionId}`, {
      headers: { "x-api-market-key": apiKey }
    });

    if (!response.ok) {
      throw new Error(`API error: ${response.status} ${await response.text()}`);
    }

    const prediction = await response.json();

    if (prediction.status === "succeeded") {
      return prediction.output;
    }

    if (prediction.status === "failed" || prediction.status === "canceled") {
      throw new Error(`Prediction ${prediction.status}: ${prediction.error || "Unknown error"}`);
    }

    await delay(pollInterval);
  }

  throw new Error("Prediction timed out after 10 minutes");
}

createAndWaitForPrediction({
  image: "https://blog.api.market/wp-content/uploads/2025/12/professional-headshot-api-market.webp",
  prompt: "analog film photo of a woman. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality",
  scheduler: "EulerDiscreteScheduler",
  enable_lcm: false,
  pose_image: "https://blog.api.market/wp-content/uploads/2026/10/instant-id-input-api-market-1.jpeg",
  num_outputs: 1,
  sdxl_weights: "protovision-xl-high-fidel",
  output_format: "webp",
  pose_strength: 0.4,
  canny_strength: 0.3,
  depth_strength: 0.5,
  guidance_scale: 5,
  output_quality: 80,
  negative_prompt: "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured (lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch,deformed, mutated, cross-eyed, ugly, disfigured",
  ip_adapter_scale: 0.8,
  lcm_guidance_scale: 1.5,
  num_inference_steps: 30,
  enable_pose_controlnet: true,
  enhance_nonface_region: true,
  enable_canny_controlnet: false,
  enable_depth_controlnet: false,
  lcm_num_inference_steps: 5,
  face_detection_input_width: 640,
  face_detection_input_height: 640,
  controlnet_conditioning_scale: 0.8
}).then((output) => console.log(output));
```

#### Python
```python
import time
import requests

API_KEY = "YOUR_API_MARKET_KEY"
BASE_URL = "https://prod.api.market/api/v1/magicapi/realistic-image-generation-zsxkib-instant-id"


def create_and_wait_for_prediction(input_data):
    headers = {"Content-Type": "application/json", "x-api-market-key": API_KEY}
    response = requests.post(
        f"{BASE_URL}/predictions",
        headers=headers,
        json={"version": "2e4785a4d80dadf580077b2244c8d7c05d8e3faac04a04c02d8e099dd2876789", "input": input_data},
    )

    if response.status_code != 201:
        raise Exception(f"API error: {response.status_code} {response.text}")

    return poll_for_results(response.json()["id"])


def poll_for_results(prediction_id):
    max_timeout = 600
    poll_interval = 1
    headers = {"x-api-market-key": API_KEY}
    start_time = time.time()

    while time.time() - start_time < max_timeout:
        response = requests.get(f"{BASE_URL}/predictions/{prediction_id}", headers=headers)

        if response.status_code != 200:
            raise Exception(f"API error: {response.status_code} {response.text}")

        prediction = response.json()

        if prediction["status"] == "succeeded":
            return prediction["output"]

        if prediction["status"] in ("failed", "canceled"):
            raise Exception(f"Prediction {prediction['status']}: {prediction.get('error') or 'Unknown error'}")

        time.sleep(poll_interval)

    raise Exception("Prediction timed out after 10 minutes")


print(create_and_wait_for_prediction({
    "image": "https://blog.api.market/wp-content/uploads/2025/12/professional-headshot-api-market.webp",
    "prompt": "analog film photo of a woman. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality",
    "scheduler": "EulerDiscreteScheduler",
    "enable_lcm": False,
    "pose_image": "https://blog.api.market/wp-content/uploads/2026/10/instant-id-input-api-market-1.jpeg",
    "num_outputs": 1,
    "sdxl_weights": "protovision-xl-high-fidel",
    "output_format": "webp",
    "pose_strength": 0.4,
    "canny_strength": 0.3,
    "depth_strength": 0.5,
    "guidance_scale": 5,
    "output_quality": 80,
    "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured (lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch,deformed, mutated, cross-eyed, ugly, disfigured",
    "ip_adapter_scale": 0.8,
    "lcm_guidance_scale": 1.5,
    "num_inference_steps": 30,
    "enable_pose_controlnet": True,
    "enhance_nonface_region": True,
    "enable_canny_controlnet": False,
    "enable_depth_controlnet": False,
    "lcm_num_inference_steps": 5,
    "face_detection_input_width": 640,
    "face_detection_input_height": 640,
    "controlnet_conditioning_scale": 0.8,
}))
```

## FAQ
- **What kind of face photo works best?**
  - A clear photo with one visible face. The model detects the face in `image`; raise `face_detection_input_width` and `face_detection_input_height` (up to 4096) for large photos where the face is small.
- **How do I make the result look more like the person?**
  - Raise `controlnet_conditioning_scale` (IdentityNet strength, for fidelity) and `ip_adapter_scale` (for detail), each up to 1.5.
- **Do I need a pose image?**
  - No. `pose_image` is optional. With one, the Openpose ControlNet (on by default) follows its pose at `pose_strength`.
- **How much does one request cost?**
  - $0.034 for each request with up to 31 seconds of processing. The example took about 13 seconds.
- **Is the 201 response from POST an error?**
  - No. 201 Created means the prediction was accepted. Use the returned `id` to poll GET `/predictions/{id}` until `status` is `succeeded`, `failed` or `canceled`.
- **What if my prediction fails?**
  - Failed predictions are not billed. Read the `error` field, adjust the input and try again.

## Contact Us
If you have any questions, reach out to us through the chat on the bottom right of the page. You can also email our support team at hello@magicapi.com.


Create realistic or stylized images of a person from a single face photo with InstantID on SDXL, with pose, Canny and depth control.