Name: Flux History API: Oil Painting Image Generator
Brand: magicapi
SKU: api-magicapi-oil-painting-image-generator-umrabdulllah-flux-history
Availability: InStock
Author: API Market

Shashank Agarwal

# 🖌️ Oil Painting Image Generator API (umrabdulllah Flux History)

## Overview
Flux History is a LoRA from umrabdulllah for the FLUX.1 image model. Its developer describes it as generating oil painting images.

Send a text `prompt` to generate images. Add an `image` for image-to-image, or an `image` plus a `mask` for inpainting. Each request returns a list of 1 to 4 image URLs (`num_outputs`). The example prompt starts with the token `opaint`.

## Features
- **Oil painting look**: describe the subject, palette and brushwork in the prompt, as in the example below.
- **Two base models**: `model` `dev` (default, about 28 steps) or `schnell` (about 4 steps).
- **Image-to-image and inpainting**: send `image`, and optionally `mask`, with `prompt_strength` to control how much of the input is replaced.
- **Aspect ratios**: `1:1` (default), `16:9`, `21:9`, `3:2`, `2:3`, `4:5`, `5:4`, `3:4`, `4:3`, `9:16`, `9:21`, or `custom` with `width` and `height` (256 to 1440).
- **Up to 4 images per request** with `num_outputs`.
- **Extra LoRA**: stack a second LoRA with `extra_lora` and `extra_lora_scale`.
- **Output formats**: `webp` (default), `jpg` or `png`, with `output_quality` 0 to 100.
- **Fast mode**: `go_fast` runs an fp8-quantized model for speed.

## Use Cases
1. **Historical scenes**: painted illustrations for history articles, lessons and videos.
2. **Book and album covers**: oil painting style artwork in portrait or landscape ratios.
3. **Repainting photos**: send a photo as `image` with a painting prompt to redraw it as an oil painting.
4. **Variations**: generate up to 4 images per request and pick the best one.

## Visual Examples
### Example Input
Prompt: "opaintFrench Impressionist oil study in the style of Claude Monet, broken amphora of coins spilling onto cracked floor beside an empty treasury chest, palette knife marks with paint built up in layers, thick visible brushstrokes throughout, muted palette of clay brown ash gray and tarnished brass, oil paint on linen not art, stale air and echoes, atmospheric haze with soft diffused light"

Settings: `model` `dev`, `aspect_ratio` `16:9`, `megapixels` `1`, `num_outputs` 1, `guidance_scale` 3, `num_inference_steps` 28, `prompt_strength` 0.8, `lora_scale` 1, `extra_lora_scale` 1, `go_fast` `false`, `output_format` `webp`, `output_quality` 80.

### Example Output
![Flux History output: an oil painting of a broken amphora of coins beside an empty treasury chest](https://blog.api.market/wp-content/uploads/2026/10/flux-history-output-7y9frjka-api-market.webp)

## Input Parameters
| Field | Type | Required | Default | Allowed values | Description |
| --- | --- | --- | --- | --- | --- |
| `prompt` | string | Yes |  |  | Prompt for the generated image. Including the trigger word used in training makes the trained style more likely to appear |
| `image` | string (URL) | No | none |  | Input image URL (jpeg, png, gif or webp) for image-to-image or inpainting mode. If provided, `aspect_ratio`, `width` and `height` are ignored |
| `mask` | string (URL) | No | none |  | Mask image URL for inpainting mode. If provided, `aspect_ratio`, `width` and `height` are ignored |
| `aspect_ratio` | string | No | `1:1` | `1:1`, `16:9`, `21:9`, `3:2`, `2:3`, `4:5`, `5:4`, `3:4`, `4:3`, `9:16`, `9:21`, `custom` | Aspect ratio of the generated image. `custom` uses `width` and `height` and runs in bf16 mode |
| `height` | integer | No | none | 256 to 1440 | Height of the generated image. Only used when `aspect_ratio` is `custom`. Rounded to the nearest multiple of 16. Not compatible with fast generation |
| `width` | integer | No | none | 256 to 1440 | Width of the generated image. Only used when `aspect_ratio` is `custom`. Rounded to the nearest multiple of 16. Not compatible with fast generation |
| `prompt_strength` | number | No | `0.8` | 0 to 1 | Prompt strength for image-to-image. 1.0 fully replaces the information in the input image |
| `model` | string | No | `dev` | `dev`, `schnell` | Model to run. `dev` works best with about 28 inference steps, `schnell` needs only 4 |
| `num_outputs` | integer | No | `1` | 1 to 4 | Number of images to generate |
| `num_inference_steps` | integer | No | `28` | 1 to 50 | Number of denoising steps. More steps can give more detailed images but take longer |
| `guidance_scale` | number | No | `3` | 0 to 10 | Guidance scale for the diffusion process. Lower values can give more realistic images; 2, 2.5, 3 and 3.5 are good values to try |
| `seed` | integer | No | none |  | Random seed. Set it for reproducible generation |
| `output_format` | string | No | `webp` | `webp`, `jpg`, `png` | Format of the output images |
| `output_quality` | integer | No | `80` | 0 to 100 | Quality when saving the output images. 100 is best quality. Not used for `png` |
| `disable_safety_checker` | boolean | No | `false` |  | Disable the safety checker for generated images |
| `go_fast` | boolean | No | `false` |  | Run faster with a model optimized for speed (currently fp8 quantized); disable to run in original bf16 |
| `megapixels` | string | No | `1` | `1`, `0.25` | Approximate number of megapixels of the generated image |
| `lora_scale` | number | No | `1` | -1 to 3 | How strongly the main LoRA is applied. Values between 0 and 1 usually work best; with `go_fast` a 1.5x multiplier is applied |
| `extra_lora` | string | No | none |  | Extra LoRA weights to load: a Hugging Face model URL, a CivitAI model URL or a direct `.safetensors` file URL |
| `extra_lora_scale` | number | No | `1` | -1 to 3 | How strongly the extra LoRA is applied. Values between 0 and 1 usually work best; with `go_fast` a 1.5x multiplier is applied |

## Pricing
- **Pay As You Go (PAYG) pricing**: you only pay for what you use.
- **Price**: $0.017 per API unit.
- **1 API unit = 1 successful request with up to 10 seconds of processing.** A request that runs longer is billed one more unit for each started 10-second block (for example, 15 seconds of processing is 2 units, $0.034). More images (`num_outputs`), more steps or larger images take longer and can use more units.
- The example request above (one 16:9 image, 28 steps) took 10.2 seconds of processing, which is 2 units, so it cost $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/oil-painting-image-generator-umrabdulllah-flux-history`

### 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/oil-painting-image-generator-umrabdulllah-flux-history/predictions" \
  -H "Content-Type: application/json" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY" \
  -d '{
    "version": "bcd6783b08eea26142c7679b025735947bc83b2c13ce01832b8f61432f6bfa24",
    "input": {
      "model": "dev",
      "prompt": "opaintFrench Impressionist oil study in the style of Claude Monet, broken amphora of coins spilling onto cracked floor beside an empty treasury chest, palette knife marks with paint built up in layers, thick visible brushstrokes throughout, muted palette of clay brown ash gray and tarnished brass, oil paint on linen not art, stale air and echoes, atmospheric haze with soft diffused light",
      "go_fast": false,
      "lora_scale": 1,
      "megapixels": "1",
      "num_outputs": 1,
      "aspect_ratio": "16:9",
      "output_format": "webp",
      "guidance_scale": 3,
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }'
```

Response (201 Created):
```json
{
  "id": "7y9frjkakhrmy0d1d8wsdf0qbr",
  "version": "bcd6783b08eea26142c7679b025735947bc83b2c13ce01832b8f61432f6bfa24",
  "status": "starting",
  "created_at": "2026-10-23T19:25:10.360Z"
}
```

#### Check Prediction Status
```bash
curl -X GET "https://prod.api.market/api/v1/magicapi/oil-painting-image-generator-umrabdulllah-flux-history/predictions/{id}" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY"
```

Response when the prediction has finished (200 OK):
```json
{
  "id": "7y9frjkakhrmy0d1d8wsdf0qbr",
  "status": "succeeded",
  "output": [
    "https://blog.api.market/wp-content/uploads/2026/10/flux-history-output-7y9frjka-api-market.webp"
  ],
  "error": null,
  "created_at": "2026-10-23T19:25:10.360Z",
  "started_at": "2026-10-23T19:25:11.360Z",
  "completed_at": "2026-10-23T19:25:20.360Z"
}
```

`output` is an array of image URLs, one per generated image (`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 `prompt` or a `num_outputs` above 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/oil-painting-image-generator-umrabdulllah-flux-history";

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: "bcd6783b08eea26142c7679b025735947bc83b2c13ce01832b8f61432f6bfa24", 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({
  "model": "dev",
  "prompt": "opaintFrench Impressionist oil study in the style of Claude Monet, broken amphora of coins spilling onto cracked floor beside an empty treasury chest, palette knife marks with paint built up in layers, thick visible brushstrokes throughout, muted palette of clay brown ash gray and tarnished brass, oil paint on linen not art, stale air and echoes, atmospheric haze with soft diffused light",
  "go_fast": false,
  "lora_scale": 1,
  "megapixels": "1",
  "num_outputs": 1,
  "aspect_ratio": "16:9",
  "output_format": "webp",
  "guidance_scale": 3,
  "output_quality": 80,
  "prompt_strength": 0.8,
  "extra_lora_scale": 1,
  "num_inference_steps": 28
}).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/oil-painting-image-generator-umrabdulllah-flux-history"


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": "bcd6783b08eea26142c7679b025735947bc83b2c13ce01832b8f61432f6bfa24", "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({
    "model": "dev",
    "prompt": "opaintFrench Impressionist oil study in the style of Claude Monet, broken amphora of coins spilling onto cracked floor beside an empty treasury chest, palette knife marks with paint built up in layers, thick visible brushstrokes throughout, muted palette of clay brown ash gray and tarnished brass, oil paint on linen not art, stale air and echoes, atmospheric haze with soft diffused light",
    "go_fast": False,
    "lora_scale": 1,
    "megapixels": "1",
    "num_outputs": 1,
    "aspect_ratio": "16:9",
    "output_format": "webp",
    "guidance_scale": 3,
    "output_quality": 80,
    "prompt_strength": 0.8,
    "extra_lora_scale": 1,
    "num_inference_steps": 28
}))
```

## FAQ
- **How do I get the oil painting look?**
  - Describe the painting in the prompt: subject, palette, brushwork and lighting. The example prompt starts with `opaint` and describes thick brushstrokes and a muted palette.
- **Should I use `dev` or `schnell`?**
  - `dev` (default) works best with about 28 steps. `schnell` needs only about 4 steps, so it finishes faster.
- **How many images does one request return?**
  - Between 1 and 4, set by `num_outputs`. `output` is always an array of URLs.
- **How is a request billed?**
  - By processing time: $0.017 covers up to 10 seconds, and each further started 10-second block adds one unit.
- **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.


Generate oil painting images from text with the Flux History LoRA, plus image-to-image and inpainting. $0.017 per request up to 10 seconds of processing.