# flux-krea-dev

> Flux Krea Dev is a text-to-image model built by Black Forest Labs in collaboration with Krea AI, designed to generate highly photorealistic images that avoid the common 'AI look' artifacts (plastic skin, overexposed lighting, synthetic textures). It emphasizes real texture, natural lighting, and aesthetic control.

## Overview

- **Endpoint**: `POST https://api.muapi.ai/api/v1/flux-krea-dev`
- **Model ID**: `flux-krea-dev`
- **Category**: text to image
- **Variant**: Krea Dev
- **Family**: flux
- **Cost**: 0.015 credits per call (some models compute cost dynamically based on params)

## API Usage

MuApi uses a **submit-then-poll** pattern: submit a job, get a `request_id`, then poll the predictions endpoint until `status` is `completed`. Optionally pass `?webhook=YOUR_URL` on the submit call to receive a POST callback when the job finishes (skip polling).

**Authentication**: send your MuApi key in the `x-api-key` header. Get one at https://muapi.ai/access-keys.

### 1. Submit a job

```http
POST https://api.muapi.ai/api/v1/flux-krea-dev
Content-Type: application/json
x-api-key: YOUR_API_KEY
```

**Minimum (required only):**

```json
{
  "prompt": "<prompt>"
}
```

**Full example (all params):**

```json
{
  "prompt": "<prompt>",
  "aspect_ratio": "1:1",
  "num_images": 1
}
```

**Response:**

```json
{
  "request_id": "abc123",
  "status": "processing"
}
```

### 2. Poll for the result

```http
GET https://api.muapi.ai/api/v1/predictions/{request_id}/result
x-api-key: YOUR_API_KEY
```

Possible `status` values: `queued`, `pending`, `processing`, `completed`, `failed`, `cancelled`. Poll every 2-5 seconds until terminal. When `completed`, the result URLs are in the `outputs` array.

**Example response when `completed`:**

```json
{
  "id": "abc123",
  "status": "completed",
  "outputs": [
    "https://cdn.muapi.ai/.../output.png"
  ],
  "urls": {
    "get": "https://api.muapi.ai/api/v1/predictions/abc123/result"
  },
  "created_at": "2026-05-08T12:34:56Z",
  "has_nsfw_contents": []
}
```

### cURL

```bash
# 1. Submit
REQUEST_ID=$(curl -s -X POST https://api.muapi.ai/api/v1/flux-krea-dev \
  -H "x-api-key: $MUAPI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"prompt":"<prompt>"}' | jq -r .request_id)

# 2. Poll until completed
while :; do
  RESP=$(curl -s https://api.muapi.ai/api/v1/predictions/$REQUEST_ID/result -H "x-api-key: $MUAPI_API_KEY")
  STATUS=$(echo "$RESP" | jq -r .status)
  [ "$STATUS" = "completed" ] && echo "$RESP" | jq .outputs && break
  [ "$STATUS" = "failed" ] && echo "$RESP" && exit 1
  sleep 3
done
```

### Python

```python
import os, time, requests

API = "https://api.muapi.ai/api/v1"
headers = {"x-api-key": os.environ["MUAPI_API_KEY"]}

r = requests.post(f"{API}/flux-krea-dev", headers=headers, json={"prompt":"<prompt>"})
request_id = r.json()["request_id"]

while True:
    res = requests.get(f"{API}/predictions/{request_id}/result", headers=headers).json()
    if res["status"] == "completed":
        print(res["outputs"]); break
    if res["status"] == "failed":
        raise RuntimeError(res.get("error")); 
    time.sleep(3)
```

## Input Schema

The API accepts the following input parameters:

- **`prompt`** (`string`, _required_):
  Text prompt describing the image. The length of the prompt must be between 2 and 3000 characters.

- **`aspect_ratio`** (`string`, _optional_):
  Aspect ratio of the output image.
  - Default: `"1:1"`
  - Options: `"16:9"`, `"9:16"`, `"1:1"`, `"4:3"`, `"3:4"`, `"3:2"`, `"2:3"`, `"21:9"`, `"9:21"`

- **`num_images`** (`int`, _optional_):
  Number of images generated in single request. Each number will charge separately
  - Default: `1`

## Output Schema

The polling endpoint returns the following fields:

- **`id`** (`string`): The request ID.
- **`status`** (`string`): One of `queued`, `pending`, `processing`, `completed`, `failed`, `cancelled`.
- **`outputs`** (`array`): URLs to generated images/videos/audio. Empty until `status` is `completed`.
- **`urls.get`** (`string`): Self-link to re-fetch this prediction.
- **`error`** (`string` | `null`): Error message if `status` is `failed`.
- **`created_at`** (`string`): ISO-8601 timestamp of when the request was created.
- **`has_nsfw_contents`** (`array of boolean`): Per-output NSFW detection flags.

## Webhooks (optional)

Append `?webhook=https://your-server/path` to the submit URL. When the job reaches a terminal state, MuApi will POST the same shape as the polling response to your URL — no polling needed.

## Agent Integration

MuApi ships an MCP server and CLI so agents (Claude Code, Cursor, custom) can call this endpoint without writing HTTP code:

```bash
# Install the CLI
npm install -g muapi-cli

# Authenticate once
muapi auth login

# Expose all MuApi models as MCP tools to your agent
muapi mcp serve
```

The MCP server exposes tools that wrap submit + poll for every model, including `flux-krea-dev`. See `muapi --help` for category-specific shortcuts (`muapi image generate`, `muapi video from-image`, etc.).

## Related Models
- [Dev LoRA](https://muapi.ai/playground/flux-dev-lora)
- [Pulid Image to Image](https://muapi.ai/playground/flux-pulid)
- [Schnell](https://muapi.ai/playground/flux-schnell)
- [Redux Image to Image](https://muapi.ai/playground/flux-redux)
- [Flux LoRA Trainer](https://muapi.ai/playground/flux-lora-trainer)
- [Dev](https://muapi.ai/playground/flux-dev)
- [Krea Dev](https://muapi.ai/playground/flux-krea-dev)
- [FLUX.1 [dev] Style LoRA Trainer](https://muapi.ai/playground/flux-1-dev-style-lora-trainer)
- [FLUX.2 [klein] 4B Style LoRA Trainer](https://muapi.ai/playground/flux-2-klein-4b-style-lora-trainer)
- [FLUX.2 [klein] 9B Style LoRA Trainer](https://muapi.ai/playground/flux-2-klein-9b-style-lora-trainer)
- [FLUX.1 [dev] Inference with Trained LoRA](https://muapi.ai/playground/flux-1-dev-style-lora-inference)

## Resources
- [Playground Page](https://muapi.ai/playground/flux-krea-dev)
- [API Reference](https://muapi.ai/playground/flux-krea-dev?tab=2)
- [Global llms.txt](https://muapi.ai/llms.txt)