# Qwen Image 2.1

> Qwen Image 2.1 API endpoint


## Overview

- **Endpoint**: `https://fal.run/fal-ai/qwen-image-2.1`
- **Model ID**: `fal-ai/qwen-image-2.1`
- **Category**: unknown
- **Kind**: inference


## Pricing

- **Price**: $0.02 per megapixels

For more details, see [fal.ai pricing](https://fal.ai/pricing).

## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:


- **`prompt`** (`string`, _required_):
  The prompt describing the image to generate.
  - Examples: "A capybara reading a book by candlelight, oil painting"

- **`prompt_expander`** (`PromptExpanderEnum`, _optional_):
  Prompt expansion mode. Quality enhances the prompt; none uses it unchanged. Default value: `"quality"`
  - Default: `"quality"`
  - Options: `"none"`, `"quality"`

- **`image_size`** (`ImageSize | Enum`, _optional_):
  Output size, rounded to multiples of 32 pixels. Default value: `square_hd`
  - Default: `"square_hd"`
  - One of: ImageSize | Enum

- **`guidance_scale`** (`float`, _optional_):
  Classifier-free guidance strength. The model defaults to no guidance. Default value: `1`
  - Default: `1`
  - Range: `1` to `10`

- **`negative_prompt`** (`string`, _optional_):
  Content to avoid; used only when guidance_scale is greater than 1. Default value: `""`
  - Default: `""`

- **`seed`** (`integer`, _optional_):
  Seed for reproducibility.

- **`num_images`** (`integer`, _optional_):
  Number of images to generate sequentially. Default value: `1`
  - Default: `1`
  - Range: `1` to `4`

- **`enable_safety_checker`** (`boolean`, _optional_):
  Whether to check generated images for unsafe content. Default value: `true`
  - Default: `true`

- **`output_format`** (`OutputFormatEnum`, _optional_):
  Output format. PNG and WebP preserve transparency; JPEG uses white. Default value: `"png"`
  - Default: `"png"`
  - Options: `"png"`, `"jpeg"`, `"webp"`

- **`sync_mode`** (`boolean`, _optional_):
  Return images as data URIs instead of CDN URLs.
  - Default: `false`



**Required Parameters Example**:

```json
{
  "prompt": "A capybara reading a book by candlelight, oil painting"
}
```

**Full Example**:

```json
{
  "prompt": "A capybara reading a book by candlelight, oil painting",
  "prompt_expander": "quality",
  "image_size": "square_hd",
  "guidance_scale": 1,
  "num_images": 1,
  "enable_safety_checker": true,
  "output_format": "png"
}
```


### Output Schema

The API returns the following output format:

- **`images`** (`list<Image>`, _required_):
  The generated image files info.
  - Array of Image

- **`timings`** (`Timings`, _required_)

- **`seed`** (`integer`, _required_):
  Seed of the generated Image. It will be the same value of the one passed in the
  input or the randomly generated that was used in case none was passed.

- **`has_nsfw_concepts`** (`list<boolean>`, _required_):
  Whether the generated images contain NSFW concepts.
  - Array of boolean

- **`prompt`** (`string`, _required_):
  The prompt used for generating the image.



**Example Response**:

```json
{
  "images": [
    {
      "url": "",
      "content_type": "image/jpeg"
    }
  ],
  "prompt": ""
}
```


## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://fal.run/fal-ai/qwen-image-2.1 \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
     "prompt": "A capybara reading a book by candlelight, oil painting"
   }'
```

### Python

Ensure you have the Python client installed:

```bash
pip install fal-client
```

Then use the API client to make requests:

```python
import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "fal-ai/qwen-image-2.1",
    arguments={
        "prompt": "A capybara reading a book by candlelight, oil painting"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)
```

### JavaScript

Ensure you have the JavaScript client installed:

```bash
npm install --save @fal-ai/client
```

Then use the API client to make requests:

```javascript
import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/qwen-image-2.1", {
  input: {
    prompt: "A capybara reading a book by candlelight, oil painting"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);
```


## Deploy your own model

To run your own weights, a fine-tune or a custom pipeline on the GPUs that serve this API, deploy it on fal Serverless. GPUs from H100 (80GB) to B300 (288GB), billed per second; an H100 is $4.50/h list, as low as $2.49/h. Prices, GPU specs and deploy steps: [fal Serverless](https://fal.ai/serverless.md).

## Additional Resources

### Documentation

- [Model Playground](https://fal.ai/models/fal-ai/qwen-image-2.1)
- [API Documentation](https://fal.ai/models/fal-ai/qwen-image-2.1/api)
- [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=fal-ai/qwen-image-2.1)

### fal.ai Platform

- [Platform Documentation](https://fal.ai/docs/documentation)
- [Python Client](https://fal.ai/docs/api-reference/client-libraries/python)
- [JavaScript Client](https://fal.ai/docs/api-reference/client-libraries/javascript)

### Other agent-readable surfaces

This file covers one model. To find anything else:

- [Platform overview](https://fal.ai/llms.txt): Entry points and representative endpoint IDs
- [Documentation index](https://fal.ai/docs/llms.txt): Every documentation page
- [Full documentation text](https://fal.ai/docs/llms-full.txt): The whole documentation inlined
- [Pricing](https://fal.ai/pricing.md): Model API and Serverless GPU prices
- [fal Serverless](https://fal.ai/serverless.md): Deploy your own models: GPU prices, specs and steps
- Any other model: `https://fal.ai/models/<endpoint-id>/llms.txt`