Name: ControlNet Deliberate API: Edge-Guided Image Generator
Brand: magicapi
SKU: api-magicapi-modify-images-edge-detection-deliberate-philz1337x-controlnet-deliberate
Availability: InStock
Author: API Market

Shashank Agarwal

# ✏️ Edge-Guided Image Generator API (philz1337x ControlNet Deliberate)

## Overview
ControlNet Deliberate modifies images with Canny edge detection and the Deliberate model. It detects the edges of your input image and generates new images that follow those edges while matching your text `prompt`, so the composition and outlines stay and the content, style and details change.

Each request returns a list of image URLs: the detected edge map followed by the generated images.

## Features
- **Edge-guided generation**: Canny edges from `image` steer the layout of the new image. Tune them with `low_threshold` and `high_threshold` (1 to 255) and `detect_resolution` (128 to 1024).
- **Prompt controls**: `prompt` describes the result, `a_prompt` is appended to it, and `n_prompt` lists what to avoid.
- **ControlNet strength**: `weight` (0.01 to 2) sets how strongly the edges guide the result.
- **Output size**: `image_resolution` of `256`, `512` (default) or `768`.
- **One or four images**: `num_samples` of `1` (default) or `4`.
- **Sampling controls**: `ddim_steps`, `scale` (classifier-free guidance, 0.1 to 30), `eta` and `seed`.

## Use Cases
1. **Restyling photos**: keep the pose and outline of a portrait and change the setting, styling or look.
2. **Sketch to image**: turn line drawings or sketches into detailed images.
3. **Product and concept variations**: create several looks for the same composition.
4. **Consistent layouts**: generate many images that share the same structure for a series or campaign.

## Visual Examples
### Example Input
Image:

![Portrait photo sent to the ControlNet Deliberate example request](https://blog.api.market/wp-content/uploads/2026/10/controlnet-deliberate-input-api-market.jpg)

Prompt: "RAW photo, portrait photo of 30 y.o woman queen, pale skin, slim body, (high detailed skin:1.2), background ocean, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3"

Settings: `num_samples` `1`, `image_resolution` `512`, `weight` `0.6`, `low_threshold` `100`, `high_threshold` `200`, `detect_resolution` `512`, `ddim_steps` `20`, `scale` `9`, `seed` `-67`, `a_prompt` "best quality, extremely detailed", `n_prompt` "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality".

### Example Output
The response listed two images. First, the detected edge map:

![Canny edge map detected from the example portrait](https://blog.api.market/wp-content/uploads/2026/10/controlnet-deliberate-output-1-f2p7s16c-api-market-1.png)

Then the generated image:

![Image generated by ControlNet Deliberate from the example edges and prompt](https://blog.api.market/wp-content/uploads/2026/10/controlnet-deliberate-output-2-f2p7s16c-api-market-1.png)

## Input Parameters
| Field | Type | Required | Default | Allowed values | Description |
| --- | --- | --- | --- | --- | --- |
| `image` | string (URL) | Yes |  |  | Input image |
| `prompt` | string | Yes |  |  | Prompt for the model |
| `num_samples` | string | No | `1` | `1`, `4` | Number of samples (higher values may run out of memory) |
| `image_resolution` | string | No | `512` | `256`, `512`, `768` | Image resolution to be generated |
| `weight` | number | No | `1` | 0.01 to 2 | Weight of ControlNet |
| `low_threshold` | integer | No | `100` | 1 to 255 | Canny line detection low threshold |
| `high_threshold` | integer | No | `200` | 1 to 255 | Canny line detection high threshold |
| `ddim_steps` | integer | No | `20` |  | Steps |
| `scale` | number | No | `9` | 0.1 to 30 | Scale for classifier-free guidance |
| `seed` | integer | No | none |  | Seed |
| `eta` | number | No | `0` |  | Controls the amount of noise added to the input data during the denoising diffusion process. Higher value means more noise |
| `a_prompt` | string | No | `best quality, extremely detailed` |  | Additional text appended to the prompt |
| `n_prompt` | string | No | `longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality` |  | Negative prompt |
| `detect_resolution` | integer | No | `512` | 128 to 1024 | Resolution at which edge detection is applied |

## Pricing
- **Pay As You Go (PAYG) pricing**: you only pay for what you use.
- **Price**: $0.036 per API unit.
- **1 API unit = 1 successful request with up to 23 seconds of processing time.** A longer request counts one more unit per started 23 seconds of processing, so `num_samples` `4`, `image_resolution` `768` or many `ddim_steps` can cost more.
- The example request above took about 7 seconds of processing, so it was billed as 1 unit and costs $0.036.
- **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/modify-images-edge-detection-deliberate-philz1337x-controlnet-deliberate`

### 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/modify-images-edge-detection-deliberate-philz1337x-controlnet-deliberate/predictions" \
  -H "Content-Type: application/json" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY" \
  -d '{
    "version": "57d86bd78018d138449fda45bfcafb8b10888379a600034cc2c7186faab98c66",
    "input": {
      "seed": -67,
      "image": "https://blog.api.market/wp-content/uploads/2026/10/controlnet-deliberate-input-api-market.jpg",
      "scale": 9,
      "prompt": "RAW photo, portrait photo of 30 y.o woman queen, pale skin, slim body, (high detailed skin:1.2), background ocean, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3",
      "weight": 0.6,
      "a_prompt": "best quality, extremely detailed",
      "n_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
      "ddim_steps": 20,
      "num_samples": "1",
      "low_threshold": 100,
      "high_threshold": 200,
      "image_resolution": "512",
      "detect_resolution": 512
    }
  }'
```

Response (201 Created):
```json
{
  "id": "f2p7s16cahrne0d1ccvr6xh03r",
  "version": "57d86bd78018d138449fda45bfcafb8b10888379a600034cc2c7186faab98c66",
  "status": "starting",
  "created_at": "2026-10-22T10:46:03.803Z"
}
```

#### Check Prediction Status
```bash
curl -X GET "https://prod.api.market/api/v1/magicapi/modify-images-edge-detection-deliberate-philz1337x-controlnet-deliberate/predictions/{id}" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY"
```

Response when the prediction has finished (200 OK):
```json
{
  "id": "f2p7s16cahrne0d1ccvr6xh03r",
  "status": "succeeded",
  "output": [
    "https://blog.api.market/wp-content/uploads/2026/10/controlnet-deliberate-output-1-f2p7s16c-api-market-1.png",
    "https://blog.api.market/wp-content/uploads/2026/10/controlnet-deliberate-output-2-f2p7s16c-api-market-1.png"
  ],
  "error": null,
  "created_at": "2026-10-22T10:46:03.803Z",
  "started_at": "2026-10-22T10:48:12.568Z",
  "completed_at": "2026-10-22T10:48:19.383Z"
}
```

`output` is a list of image URLs. The first URL is the detected edge map; the generated images follow, one per sample (`num_samples` `1` gives two URLs in total, `4` gives five).

### 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 `prompt`, or a `num_samples` other than `1` or `4`.
- **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/modify-images-edge-detection-deliberate-philz1337x-controlnet-deliberate";

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: "57d86bd78018d138449fda45bfcafb8b10888379a600034cc2c7186faab98c66", 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({
  seed: -67,
  image: "https://blog.api.market/wp-content/uploads/2026/10/controlnet-deliberate-input-api-market.jpg",
  scale: 9,
  prompt: "RAW photo, portrait photo of 30 y.o woman queen, pale skin, slim body, (high detailed skin:1.2), background ocean, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3",
  weight: 0.6,
  a_prompt: "best quality, extremely detailed",
  n_prompt: "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
  ddim_steps: 20,
  num_samples: "1",
  low_threshold: 100,
  high_threshold: 200,
  image_resolution: "512",
  detect_resolution: 512
}).then((output) => {
  const [edgeMap, ...images] = output;
  console.log("Edge map:", edgeMap);
  console.log("Generated images:", images);
});
```

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

API_KEY = "YOUR_API_MARKET_KEY"
BASE_URL = "https://prod.api.market/api/v1/magicapi/modify-images-edge-detection-deliberate-philz1337x-controlnet-deliberate"


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": "57d86bd78018d138449fda45bfcafb8b10888379a600034cc2c7186faab98c66", "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")


output = create_and_wait_for_prediction({
    "seed": -67,
    "image": "https://blog.api.market/wp-content/uploads/2026/10/controlnet-deliberate-input-api-market.jpg",
    "scale": 9,
    "prompt": "RAW photo, portrait photo of 30 y.o woman queen, pale skin, slim body, (high detailed skin:1.2), background ocean, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3",
    "weight": 0.6,
    "a_prompt": "best quality, extremely detailed",
    "n_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
    "ddim_steps": 20,
    "num_samples": "1",
    "low_threshold": 100,
    "high_threshold": 200,
    "image_resolution": "512",
    "detect_resolution": 512,
})
edge_map, *images = output
print("Edge map:", edge_map)
print("Generated images:", images)
```

## FAQ
- **Why do I get one more image than I asked for?**
  - The first URL in `output` is the Canny edge map the model detected from your image. The generated images follow it.
- **How do I keep more or less of the original outline?**
  - Raise `weight` to follow the edges more closely, lower it to give the prompt more freedom. Lower `low_threshold` and `high_threshold` to detect more edges, raise them to keep only strong ones.
- **Why are `num_samples` and `image_resolution` strings?**
  - They are fixed choices: send `"1"` or `"4"` for `num_samples` and `"256"`, `"512"` or `"768"` for `image_resolution`.
- **How much does one request cost?**
  - $0.036 for each request with up to 23 seconds of processing. The example took about 7 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.


Turn a photo or sketch into new images that keep its outlines, using Canny edge ControlNet with the Deliberate model. $0.036 per request, pay as you go.