> ## Documentation Index
> Fetch the complete documentation index at: https://docs.oxen.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Qwen Image

> Text-to-image with strong text rendering

<CardGroup cols={1}>
  <Card title="Try Qwen Image in the Workbench" icon="flask" href="https://www.oxen.ai/ai/workbench?model=qwen-image">
    Run this model interactively, tune parameters, and compare outputs.
  </Card>
</CardGroup>

**Model ID:** `qwen-image`

Qwen/Qwen-Image is an image generation model that excels in **complex text rendering**—including for both alphabetic and logographic languages such as English and Chinese—and **precise image editing**.

It is particularly strong in producing images with high-fidelity embedded text, making it well-suited for tasks where maintaining the integrity and clarity of text within generated images is critical. The model also provides consistent and realistic editing capabilities, such as style transfer, object addition/removal, and detailed attribute editing, with improved preservation of identity for people and products.

Some other noteworthy features of Qwen/Qwen-Image include robust support for image understanding tasks—such as object detection, semantic segmentation, depth and edge estimation, novel view synthesis, and super-resolution—and native integration with ControlNet for enhanced conditioning and control of outputs.

| Metric             | Value      |
| ------------------ | ---------- |
| Parameter Count    | 20 billion |
| Mixture of Experts | No         |
| Context Length     | Unknown    |
| Multilingual       | Yes        |
| Quantized\*        | No         |

\**Quantization is specific to the inference provider and the model may be offered with different quantization levels by other providers.*

## Example request

<Tip>
  Use the [Workbench](https://www.oxen.ai/ai/workbench?model=qwen-image) as a request builder: configure parameters for this model in the UI, then open the **API** tab to copy the exact cURL or Python call.
</Tip>

<Tabs>
  <Tab title="Sync">
    See the [image generation reference](/inference-api/reference/image_generation) for more details.

    <Tabs>
      <Tab title="Minimal">
        <CodeGroup>
          ```bash cURL theme={null}
          curl -X POST https://hub.oxen.ai/api/ai/images/generate \
            -H "Content-Type: application/json" \
            -H "Authorization: Bearer $OXEN_API_KEY" \
            -d '{
            "model": "qwen-image",
            "prompt": "A beautiful landscape painting of a serene lake with mountains in the background"
          }'
          ```

          ```python Python theme={null}
          import os
          import requests

          response = requests.post(
              "https://hub.oxen.ai/api/ai/images/generate",
              headers={
                  "Content-Type": "application/json",
                  "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}",
              },
              json={
                  "model": "qwen-image",
                  "prompt": "A beautiful landscape painting of a serene lake with mountains in the background"
              },
          )
          response.raise_for_status()
          print(response.json())
          ```
        </CodeGroup>
      </Tab>

      <Tab title="All parameters">
        <CodeGroup>
          ```bash cURL theme={null}
          curl -X POST https://hub.oxen.ai/api/ai/images/generate \
            -H "Content-Type: application/json" \
            -H "Authorization: Bearer $OXEN_API_KEY" \
            -d '{
            "model": "qwen-image",
            "prompt": "A beautiful landscape painting of a serene lake with mountains in the background",
            "negative_prompt": " ",
            "aspect_ratio": "16:9",
            "image_size": "optimize_for_quality",
            "num_inference_steps": 30,
            "guidance": 3,
            "output_format": "jpg",
            "output_quality": 80,
            "disable_safety_checker": false
          }'
          ```

          ```python Python theme={null}
          import os
          import requests

          response = requests.post(
              "https://hub.oxen.ai/api/ai/images/generate",
              headers={
                  "Content-Type": "application/json",
                  "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}",
              },
              json={
                  "model": "qwen-image",
                  "prompt": "A beautiful landscape painting of a serene lake with mountains in the background",
                  "negative_prompt": " ",
                  "aspect_ratio": "16:9",
                  "image_size": "optimize_for_quality",
                  "num_inference_steps": 30,
                  "guidance": 3,
                  "output_format": "jpg",
                  "output_quality": 80,
                  "disable_safety_checker": false
              },
          )
          response.raise_for_status()
          print(response.json())
          ```
        </CodeGroup>
      </Tab>
    </Tabs>
  </Tab>

  <Tab title="Async">
    See the [async queue reference](/inference-api/reference/async_queue) for more details.

    <Tabs>
      <Tab title="Minimal">
        <CodeGroup>
          ```bash cURL theme={null}
          # Enqueue, capture the generation id.
          GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \
            -H "Content-Type: application/json" \
            -H "Authorization: Bearer $OXEN_API_KEY" \
            -d '{
            "model": "qwen-image",
            "prompt": "A beautiful landscape painting of a serene lake with mountains in the background"
          }' | jq -r '.generations[0].generation_id')

          # Poll until the generation reaches a terminal status.
          while true; do
            STATUS=$(curl -s -H "Authorization: Bearer $OXEN_API_KEY" \
              "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | jq -r '.status')
            echo "Status: $STATUS"
            case $STATUS in succeeded|failed|cancelled) break;; esac
            sleep 5
          done

          # Print the result.
          curl -s -H "Authorization: Bearer $OXEN_API_KEY" \
            "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | jq .
          ```

          ```python Python theme={null}
          import os
          import time
          import requests

          HEADERS = {
              "Content-Type": "application/json",
              "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}",
          }

          enqueue = requests.post(
              "https://hub.oxen.ai/api/ai/queue",
              headers=HEADERS,
              json={
                  "model": "qwen-image",
                  "prompt": "A beautiful landscape painting of a serene lake with mountains in the background"
              },
          )
          enqueue.raise_for_status()
          generation_id = enqueue.json()["generations"][0]["generation_id"]

          while True:
              data = requests.get(
                  f"https://hub.oxen.ai/api/ai/queue/{generation_id}",
                  headers=HEADERS,
              ).json()
              if data["status"] in {"succeeded", "failed", "cancelled"}:
                  break
              time.sleep(5)

          if data["status"] == "succeeded":
              print(f"Result: {data['result_url']}")
          else:
              print(f"Generation {data['status']}: {data.get('error_message')}")
          ```
        </CodeGroup>
      </Tab>

      <Tab title="All parameters">
        <CodeGroup>
          ```bash cURL theme={null}
          # Enqueue, capture the generation id.
          GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \
            -H "Content-Type: application/json" \
            -H "Authorization: Bearer $OXEN_API_KEY" \
            -d '{
            "model": "qwen-image",
            "prompt": "A beautiful landscape painting of a serene lake with mountains in the background",
            "negative_prompt": " ",
            "aspect_ratio": "16:9",
            "image_size": "optimize_for_quality",
            "num_inference_steps": 30,
            "guidance": 3,
            "output_format": "jpg",
            "output_quality": 80,
            "disable_safety_checker": false
          }' | jq -r '.generations[0].generation_id')

          # Poll until the generation reaches a terminal status.
          while true; do
            STATUS=$(curl -s -H "Authorization: Bearer $OXEN_API_KEY" \
              "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | jq -r '.status')
            echo "Status: $STATUS"
            case $STATUS in succeeded|failed|cancelled) break;; esac
            sleep 5
          done

          # Print the result.
          curl -s -H "Authorization: Bearer $OXEN_API_KEY" \
            "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | jq .
          ```

          ```python Python theme={null}
          import os
          import time
          import requests

          HEADERS = {
              "Content-Type": "application/json",
              "Authorization": f"Bearer {os.environ['OXEN_API_KEY']}",
          }

          enqueue = requests.post(
              "https://hub.oxen.ai/api/ai/queue",
              headers=HEADERS,
              json={
                  "model": "qwen-image",
                  "prompt": "A beautiful landscape painting of a serene lake with mountains in the background",
                  "negative_prompt": " ",
                  "aspect_ratio": "16:9",
                  "image_size": "optimize_for_quality",
                  "num_inference_steps": 30,
                  "guidance": 3,
                  "output_format": "jpg",
                  "output_quality": 80,
                  "disable_safety_checker": false
              },
          )
          enqueue.raise_for_status()
          generation_id = enqueue.json()["generations"][0]["generation_id"]

          while True:
              data = requests.get(
                  f"https://hub.oxen.ai/api/ai/queue/{generation_id}",
                  headers=HEADERS,
              ).json()
              if data["status"] in {"succeeded", "failed", "cancelled"}:
                  break
              time.sleep(5)

          if data["status"] == "succeeded":
              print(f"Result: {data['result_url']}")
          else:
              print(f"Generation {data['status']}: {data.get('error_message')}")
          ```
        </CodeGroup>
      </Tab>
    </Tabs>
  </Tab>

  <Tab title="Async with SSE">
    See the [async queue reference](/inference-api/reference/async_queue) for more details.

    <Tabs>
      <Tab title="Minimal">
        <CodeGroup>
          ```bash cURL theme={null}
          # Enqueue, capture the generation id.
          GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \
            -H "Content-Type: application/json" \
            -H "Authorization: Bearer $OXEN_API_KEY" \
            -d '{
            "model": "qwen-image",
            "prompt": "A beautiful landscape painting of a serene lake with mountains in the background"
          }' | jq -r '.generations[0].generation_id')

          # Stream the SSE channel, grab the data line that follows a
          # media_generation_completed event for our id, and pretty-print it.
          curl -sN -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/events \
            | awk -v id="$GEN_ID" '
              /^event: media_generation_completed$/ { expect=1; next }
              /^data: / && expect {
                payload = substr($0, 7)
                if (index(payload, "\"generation_id\":\"" id "\"")) { print payload; exit }
                expect = 0
              }
            ' | jq .
          ```

          ```python Python theme={null}
          import json
          import os
          import requests

          API_KEY = os.environ["OXEN_API_KEY"]
          AUTH = {"Authorization": f"Bearer {API_KEY}"}

          enqueue = requests.post(
              "https://hub.oxen.ai/api/ai/queue",
              headers={**AUTH, "Content-Type": "application/json"},
              json={
                  "model": "qwen-image",
                  "prompt": "A beautiful landscape painting of a serene lake with mountains in the background"
              },
          )
          enqueue.raise_for_status()
          generation_id = enqueue.json()["generations"][0]["generation_id"]

          with requests.get(
              "https://hub.oxen.ai/api/events",
              headers=AUTH,
              stream=True,
          ) as stream:
              event_name = None
              for line in stream.iter_lines(decode_unicode=True):
                  if line.startswith("event: "):
                      event_name = line.removeprefix("event: ")
                  elif line.startswith("data: ") and event_name == "media_generation_completed":
                      payload = json.loads(line.removeprefix("data: "))
                      if payload.get("generation_id") == generation_id:
                          print(payload)
                          break
          ```
        </CodeGroup>
      </Tab>

      <Tab title="All parameters">
        <CodeGroup>
          ```bash cURL theme={null}
          # Enqueue, capture the generation id.
          GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \
            -H "Content-Type: application/json" \
            -H "Authorization: Bearer $OXEN_API_KEY" \
            -d '{
            "model": "qwen-image",
            "prompt": "A beautiful landscape painting of a serene lake with mountains in the background",
            "negative_prompt": " ",
            "aspect_ratio": "16:9",
            "image_size": "optimize_for_quality",
            "num_inference_steps": 30,
            "guidance": 3,
            "output_format": "jpg",
            "output_quality": 80,
            "disable_safety_checker": false
          }' | jq -r '.generations[0].generation_id')

          # Stream the SSE channel, grab the data line that follows a
          # media_generation_completed event for our id, and pretty-print it.
          curl -sN -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/events \
            | awk -v id="$GEN_ID" '
              /^event: media_generation_completed$/ { expect=1; next }
              /^data: / && expect {
                payload = substr($0, 7)
                if (index(payload, "\"generation_id\":\"" id "\"")) { print payload; exit }
                expect = 0
              }
            ' | jq .
          ```

          ```python Python theme={null}
          import json
          import os
          import requests

          API_KEY = os.environ["OXEN_API_KEY"]
          AUTH = {"Authorization": f"Bearer {API_KEY}"}

          enqueue = requests.post(
              "https://hub.oxen.ai/api/ai/queue",
              headers={**AUTH, "Content-Type": "application/json"},
              json={
                  "model": "qwen-image",
                  "prompt": "A beautiful landscape painting of a serene lake with mountains in the background",
                  "negative_prompt": " ",
                  "aspect_ratio": "16:9",
                  "image_size": "optimize_for_quality",
                  "num_inference_steps": 30,
                  "guidance": 3,
                  "output_format": "jpg",
                  "output_quality": 80,
                  "disable_safety_checker": false
              },
          )
          enqueue.raise_for_status()
          generation_id = enqueue.json()["generations"][0]["generation_id"]

          with requests.get(
              "https://hub.oxen.ai/api/events",
              headers=AUTH,
              stream=True,
          ) as stream:
              event_name = None
              for line in stream.iter_lines(decode_unicode=True):
                  if line.startswith("event: "):
                      event_name = line.removeprefix("event: ")
                  elif line.startswith("data: ") and event_name == "media_generation_completed":
                      payload = json.loads(line.removeprefix("data: "))
                      if payload.get("generation_id") == generation_id:
                          print(payload)
                          break
          ```
        </CodeGroup>
      </Tab>
    </Tabs>
  </Tab>
</Tabs>

## Fetch model details

The [models endpoint](/inference-api/reference/models/overview) returns the full model object, including its `json_request_schema`.

```bash theme={null}
curl -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/ai/models/qwen-image
```

## Request parameters

### Required parameters

| Field    | Type     | Default                                                                              | Description                |
| -------- | -------- | ------------------------------------------------------------------------------------ | -------------------------- |
| `prompt` | `string` | `"A beautiful landscape painting of a serene lake with mountains in the background"` | Prompt for generated image |

### Optional parameters

| Field                    | Type      | Default                  | Description                                                                                                                                   |
| ------------------------ | --------- | ------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------- |
| `negative_prompt`        | `string`  | `" "`                    | Negative prompt for generated image                                                                                                           |
| `aspect_ratio`           | `string`  | `"16:9"`                 | Aspect ratio for the generated image One of: 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3.                                                             |
| `image_size`             | `string`  | `"optimize_for_quality"` | Image size for the generated image One of: optimize\_for\_quality, optimize\_for\_speed.                                                      |
| `num_inference_steps`    | `integer` | `30`                     | Number of denoising steps. Recommended range is 28-50, and lower number of steps produce lower quality outputs, faster. Range: 1 – 50.        |
| `guidance`               | `number`  | `3`                      | Guidance for generated image. Lower values can give more realistic images. Good values to try are 2, 2.5, 3 and 3.5 Range: 0 – 10.            |
| `seed`                   | `integer` | —                        | Random seed. Set for reproducible generation                                                                                                  |
| `output_format`          | `string`  | `"jpg"`                  | Format of the output images One of: webp, jpg, png.                                                                                           |
| `output_quality`         | `integer` | `80`                     | Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs Range: 0 – 100. |
| `disable_safety_checker` | `boolean` | `false`                  | Disable safety checker for generated images.                                                                                                  |