Name: SDXL Multi-ControlNet LoRA API: Guided Image Generator
Brand: magicapi
SKU: api-magicapi-multi-controlnet-image-editing-api-fofr-sdxl-multi-controlnet-lora
Availability: InStock
Author: API Market

Shashank Agarwal

# 🎨 Multi-ControlNet Image Generator API (fofr SDXL Multi-ControlNet LoRA)

## Overview
SDXL Multi-ControlNet LoRA generates images with Stable Diffusion XL guided by up to three ControlNets. Each ControlNet takes a control image (for example an edge map, depth map or pose source) and keeps the generated image close to its structure while the `prompt` sets the content and style.

It also supports img2img (send an `image`) and inpainting (send an `image` and a `mask`). Each request returns a list of image URLs: the processed control image for each active ControlNet, followed by the generated images.

## Features
- **Up to three ControlNets**: `controlnet_1` to `controlnet_3`, each with its own image, conditioning scale, start and end.
- **ControlNet types**: `edge_canny`, `illusion`, `depth_leres`, `depth_midas`, `soft_edge_pidi`, `soft_edge_hed`, `lineart`, `lineart_anime`, `openpose`, or `none`.
- **img2img and inpainting**: send `image`, and a `mask` where white areas are repainted and black areas are kept.
- **Sizing**: set `width` and `height`, or size the output from the input image, a control image or the mask with `sizing_strategy`.
- **1 to 4 images per request** with `num_outputs`.
- **Refiner**: `base_image_refiner` adds a refining pass; `refine_steps` sets its steps.
- **Generation controls**: `scheduler`, `num_inference_steps`, `guidance_scale`, `prompt_strength`, `negative_prompt` and `seed`.

## Use Cases
1. **Keep a composition**: generate new images that follow the edges, depth or pose of a reference.
2. **Product and scene variations**: restyle a scene while keeping its layout.
3. **Inpainting**: repaint a masked area of an existing image from a prompt.
4. **Pose-guided characters**: use `openpose` on a photo to keep a person's pose in a new image.

## Visual Examples
### Example Input
Prompt: "A photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm"

Negative prompt: "rainbow"

ControlNet 1: `soft_edge_hed` with conditioning scale `0.8` on this control image (ControlNets 2 and 3 off):

![SDXL Multi-ControlNet example control image](https://blog.api.market/wp-content/uploads/2026/10/sdxl-multi-controlnet-lora-input-api-market.png)

768 x 768, `num_outputs` `1`, `K_EULER` scheduler, 30 steps, guidance scale 7.5, no refiner, watermark off.

### Example Output
The first image is the processed soft-edge control image, the second is the generated image.

![SDXL Multi-ControlNet output 1: the soft edge map extracted from the control image](https://blog.api.market/wp-content/uploads/2026/10/sdxl-multi-controlnet-lora-output-1-h5eyhp9a-api-market.png)

![SDXL Multi-ControlNet output 2: a golden astronaut riding a unicorn statue in a museum, following the control image's edges](https://blog.api.market/wp-content/uploads/2026/10/sdxl-multi-controlnet-lora-output-2-h5eyhp9a-api-market.png)

## Input Parameters
| Field | Type | Required | Default | Allowed values | Description |
| --- | --- | --- | --- | --- | --- |
| `prompt` | string | No | `An astronaut riding a rainbow unicorn` |  | Input prompt |
| `negative_prompt` | string | No | `""` |  | Negative prompt |
| `image` | string (URL) | No | none |  | Input image URL for img2img or inpaint mode |
| `mask` | string (URL) | No | none |  | Mask image URL for inpaint mode. Black areas are preserved, white areas are inpainted. |
| `width` | integer | No | `768` |  | Width of output image |
| `height` | integer | No | `768` |  | Height of output image |
| `sizing_strategy` | string | No | `width_height` | `width_height`, `input_image`, `controlnet_1_image`, `controlnet_2_image`, `controlnet_3_image`, `mask_image` | How to size the output: use width and height, or match the input image, a controlnet image or the mask image |
| `num_outputs` | integer | No | `1` | `1` to `4` | Number of images to output |
| `scheduler` | string | No | `K_EULER` | `DDIM`, `DPMSolverMultistep`, `HeunDiscrete`, `KarrasDPM`, `K_EULER_ANCESTRAL`, `K_EULER`, `PNDM` | 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 |
| `prompt_strength` | number | No | `0.8` | `0` to `1` | Prompt strength when using img2img or inpaint. 1.0 fully replaces the information in the image |
| `seed` | integer | No | none |  | Random seed. Leave blank to randomize the seed |
| `refine` | string | No | `no_refiner` | `no_refiner`, `base_image_refiner` | Which refine style to use |
| `refine_steps` | integer | No | none |  | For base_image_refiner, the number of steps to refine, defaults to num_inference_steps |
| `apply_watermark` | boolean | No | `true` |  | Applies a watermark so generated images can be identified downstream. Set to false to turn it off. |
| `lora_scale` | number | No | `0.6` | `0` to `1` | LoRA additive scale. Only applicable on trained models. |
| `lora_weights` | string | No | none |  | LoRA weights to use. Leave blank to use the default weights. |
| `disable_safety_checker` | boolean | No | `false` |  | Disable the safety checker for generated images |
| `controlnet_1` | string | No | `none` | `none`, `edge_canny`, `illusion`, `depth_leres`, `depth_midas`, `soft_edge_pidi`, `soft_edge_hed`, `lineart`, `lineart_anime`, `openpose` | ControlNet type applied to controlnet_1_image |
| `controlnet_1_image` | string (URL) | No | none |  | Control image for the first ControlNet (URL) |
| `controlnet_1_conditioning_scale` | number | No | `0.75` | `0` to `4` | How strong the controlnet conditioning is |
| `controlnet_1_start` | number | No | `0` | `0` to `1` | When controlnet conditioning starts |
| `controlnet_1_end` | number | No | `1` | `0` to `1` | When controlnet conditioning ends |
| `controlnet_2` | string | No | `none` | `none`, `edge_canny`, `illusion`, `depth_leres`, `depth_midas`, `soft_edge_pidi`, `soft_edge_hed`, `lineart`, `lineart_anime`, `openpose` | ControlNet type applied to controlnet_2_image |
| `controlnet_2_image` | string (URL) | No | none |  | Control image for the second ControlNet (URL) |
| `controlnet_2_conditioning_scale` | number | No | `0.75` | `0` to `4` | How strong the controlnet conditioning is |
| `controlnet_2_start` | number | No | `0` | `0` to `1` | When controlnet conditioning starts |
| `controlnet_2_end` | number | No | `1` | `0` to `1` | When controlnet conditioning ends |
| `controlnet_3` | string | No | `none` | `none`, `edge_canny`, `illusion`, `depth_leres`, `depth_midas`, `soft_edge_pidi`, `soft_edge_hed`, `lineart`, `lineart_anime`, `openpose` | ControlNet type applied to controlnet_3_image |
| `controlnet_3_image` | string (URL) | No | none |  | Control image for the third ControlNet (URL) |
| `controlnet_3_conditioning_scale` | number | No | `0.75` | `0` to `4` | How strong the controlnet conditioning is |
| `controlnet_3_start` | number | No | `0` | `0` to `1` | When controlnet conditioning starts |
| `controlnet_3_end` | number | No | `1` | `0` to `1` | When controlnet conditioning ends |

## Pricing
- **Pay As You Go (PAYG) pricing**: you only pay for what you use.
- **Price**: $0.0078 per API unit.
- **1 API unit = 1 request with up to 7 seconds of processing.** A request that takes longer uses one unit per started block of 7 seconds (for example, 20 seconds of processing uses 3 units, $0.0234).
- More steps, more outputs, larger sizes and the refiner take longer and can use more units.
- **Example cost**: the example request took 11.5 seconds of processing, which is 2 units, $0.0156.
- **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/multi-controlnet-image-editing-api-fofr-sdxl-multi-controlnet-lora`

### 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/multi-controlnet-image-editing-api-fofr-sdxl-multi-controlnet-lora/predictions" \
  -H "Content-Type: application/json" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY" \
  -d '{
      "version": "89eb212b3d1366a83e949c12a4b45dfe6b6b313b594cb8268e864931ac9ffb16",
      "input": {
        "width": 768,
        "height": 768,
        "prompt": "A photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "num_outputs": 1,
        "controlnet_1": "soft_edge_hed",
        "controlnet_2": "none",
        "controlnet_3": "none",
        "guidance_scale": 7.5,
        "apply_watermark": false,
        "negative_prompt": "rainbow",
        "prompt_strength": 0.8,
        "sizing_strategy": "width_height",
        "controlnet_1_end": 1,
        "controlnet_2_end": 1,
        "controlnet_3_end": 1,
        "controlnet_1_image": "https://blog.api.market/wp-content/uploads/2026/10/sdxl-multi-controlnet-lora-input-api-market.png",
        "controlnet_1_start": 0,
        "controlnet_2_start": 0,
        "controlnet_3_start": 0,
        "num_inference_steps": 30,
        "controlnet_1_conditioning_scale": 0.8,
        "controlnet_2_conditioning_scale": 0.8,
        "controlnet_3_conditioning_scale": 0.75
      }
    }'
```

Response (201 Created):
```json
{
  "id": "h5eyhp9apnrmw0d1d1ftkcwksg",
  "version": "89eb212b3d1366a83e949c12a4b45dfe6b6b313b594cb8268e864931ac9ffb16",
  "status": "starting",
  "created_at": "2026-10-23T10:47:09.929Z"
}
```

#### Check Prediction Status
```bash
curl -X GET "https://prod.api.market/api/v1/magicapi/multi-controlnet-image-editing-api-fofr-sdxl-multi-controlnet-lora/predictions/{id}" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY"
```

Response when the prediction has finished (200 OK):
```json
{
  "id": "h5eyhp9apnrmw0d1d1ftkcwksg",
  "status": "succeeded",
  "output": [
    "https://blog.api.market/wp-content/uploads/2026/10/sdxl-multi-controlnet-lora-output-1-h5eyhp9a-api-market.png",
    "https://blog.api.market/wp-content/uploads/2026/10/sdxl-multi-controlnet-lora-output-2-h5eyhp9a-api-market.png"
  ],
  "error": null,
  "created_at": "2026-10-23T10:47:09.929Z",
  "started_at": "2026-10-23T10:48:24.542Z",
  "completed_at": "2026-10-23T10:48:36.080Z"
}
```

`output` is a list of image URLs: first the processed control image of each active ControlNet, then the generated images (`num_outputs` of them).

### 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 `num_outputs` above 4 or a `scheduler` 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/multi-controlnet-image-editing-api-fofr-sdxl-multi-controlnet-lora";

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: "89eb212b3d1366a83e949c12a4b45dfe6b6b313b594cb8268e864931ac9ffb16", 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({
    "width": 768,
    "height": 768,
    "prompt": "A photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "num_outputs": 1,
    "controlnet_1": "soft_edge_hed",
    "controlnet_2": "none",
    "controlnet_3": "none",
    "guidance_scale": 7.5,
    "apply_watermark": false,
    "negative_prompt": "rainbow",
    "prompt_strength": 0.8,
    "sizing_strategy": "width_height",
    "controlnet_1_end": 1,
    "controlnet_2_end": 1,
    "controlnet_3_end": 1,
    "controlnet_1_image": "https://blog.api.market/wp-content/uploads/2026/10/sdxl-multi-controlnet-lora-input-api-market.png",
    "controlnet_1_start": 0,
    "controlnet_2_start": 0,
    "controlnet_3_start": 0,
    "num_inference_steps": 30,
    "controlnet_1_conditioning_scale": 0.8,
    "controlnet_2_conditioning_scale": 0.8,
    "controlnet_3_conditioning_scale": 0.75
  }).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/multi-controlnet-image-editing-api-fofr-sdxl-multi-controlnet-lora"


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": "89eb212b3d1366a83e949c12a4b45dfe6b6b313b594cb8268e864931ac9ffb16", "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({
    "width": 768,
    "height": 768,
    "prompt": "A photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "num_outputs": 1,
    "controlnet_1": "soft_edge_hed",
    "controlnet_2": "none",
    "controlnet_3": "none",
    "guidance_scale": 7.5,
    "apply_watermark": False,
    "negative_prompt": "rainbow",
    "prompt_strength": 0.8,
    "sizing_strategy": "width_height",
    "controlnet_1_end": 1,
    "controlnet_2_end": 1,
    "controlnet_3_end": 1,
    "controlnet_1_image": "https://blog.api.market/wp-content/uploads/2026/10/sdxl-multi-controlnet-lora-input-api-market.png",
    "controlnet_1_start": 0,
    "controlnet_2_start": 0,
    "controlnet_3_start": 0,
    "num_inference_steps": 30,
    "controlnet_1_conditioning_scale": 0.8,
    "controlnet_2_conditioning_scale": 0.8,
    "controlnet_3_conditioning_scale": 0.75
}))
```

## FAQ
- **Why did I get two images when num_outputs is 1?**
  - The list starts with the processed control image of each active ControlNet. The generated images follow it.
- **How do I use more than one ControlNet?**
  - Set `controlnet_2` (and `controlnet_3`) to a type other than `none` and send its image in `controlnet_2_image` (or `controlnet_3_image`). Each has its own conditioning scale, start and end.
- **How do I inpaint?**
  - Send the source `image` and a `mask`. White areas of the mask are repainted from the prompt; black areas are kept. `prompt_strength` sets how much changes.
- **Can I set the output size from my control image?**
  - Yes. Set `sizing_strategy` to `controlnet_1_image` (or another image) instead of `width_height`.
- **What does one request cost?**
  - One unit ($0.0078) covers up to 7 seconds of processing. The example took 11.5 seconds, so it used 2 units, $0.0156.

## 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.


Generate Stable Diffusion XL images that follow edge, depth, line art or pose control images, with img2img and inpainting. Pay as you go, no monthly fee.