# HunyuanImage 3.0 Instruct

> Generate high-resolution images from a text prompt with the instruction-tuned HunyuanImage 3.0 checkpoint, built for following long, complex, multi-part descriptions closely.

## Overview

- **Endpoint**: `https://queue.modelrunner.run/tencent/hunyuan-image/v3/instruct/text-to-image`
- **Model ID**: `tencent/hunyuan-image/v3/instruct/text-to-image`
- **Category**: text-to-image
- **Kind**: inference
- **Tags**: hunyuan-image, hunyuan, tencent, instruct, text-to-image, image-generation

## Pricing

- **Price**: $0.09 per megapixel

## Request Lifecycle

This model runs on the ModelRunner **asynchronous queue API** — a single POST does not return the output. Every call requires an `Authorization: Key $MODEL_RUNNER_KEY` header. Run three steps:

1. **Submit** — `POST https://queue.modelrunner.run/tencent/hunyuan-image/v3/instruct/text-to-image` with a JSON body. The response carries request handles only (no output yet):

```json
{
  "status": "IN_QUEUE",
  "request_id": "<21-char id>",
  "status_url": "https://queue.modelrunner.run/tencent/hunyuan-image/v3/instruct/text-to-image/requests/<request_id>/status",
  "response_url": "https://queue.modelrunner.run/tencent/hunyuan-image/v3/instruct/text-to-image/requests/<request_id>",
  "cancel_url": "https://queue.modelrunner.run/tencent/hunyuan-image/v3/instruct/text-to-image/requests/<request_id>/cancel"
}
```

2. **Poll status** — `GET <status_url>` until `status` is `COMPLETED`. Possible values are `IN_QUEUE`, `IN_PROGRESS`, `COMPLETED`, `FAILED`, `CANCELLED`. A `FAILED` request responds with HTTP 400 and an `error` field.

3. **Read result** — `GET <response_url>`. Returns the finished request, including the generated `output`:

```json
{ "id": "<request_id>", "status": "COMPLETED", "output": ..., "input": ... }
```

The JavaScript and Python SDKs below perform steps 2–3 for you. In any language without an SDK (Swift, Go, Kotlin, etc.) you must implement the polling loop and the final result fetch yourself — see the cURL example for the full flow.

### Input Schema

- **`seed`** (`integer | null`, _optional_):
  Random seed for reproducible results. The same seed and prompt produce the same image; leave unset for a random seed.

- **`prompt`** (`string`, _required_):
  The text prompt describing the image you want to generate.

- **`image_size`** (`ImageSize | ImageSizeEnum`, _optional_):
  The size of the generated image. Use a preset, an explicit width/height object, or 'auto' to let the model choose.
  - Default: `"auto"`
  - Options: `"auto"`, `"square_hd"`, `"square"`, `"portrait_4_3"`, `"portrait_16_9"`, `"landscape_4_3"`, `"landscape_16_9"`

- **`num_images`** (`integer`, _optional_):
  The number of images to generate. Each generated image is billed.
  - Default: `1`
  - Range: `1` to `4`

- **`output_format`** (`OutputFormatEnum`, _optional_):
  The format of the generated image.
  - Default: `"png"`
  - Options: `"jpeg"`, `"png"`

- **`guidance_scale`** (`number`, _optional_):
  The CFG scale. Higher values increase adherence to the prompt.
  - Default: `3.5`
  - Range: `"-inf"` to `20`

- **`enable_safety_checker`** (`boolean`, _optional_):
  If set to true, the safety checker will be enabled.
  - Default: `true`

- **`enable_prompt_expansion`** (`boolean`, _optional_):
  Whether to enable prompt expansion. This uses a large language model to expand the prompt with additional details while maintaining the original meaning. Enabled by default; disable it to have your exact wording honored.
  - Default: `true`

### Output Schema

_No `Output` schema properties are available._

## Default Example

**Input**

```json
{
  "prompt": "A biomechanical clocktower rising from a foggy Victorian harbor, brass gears fused with living vines, three fishermen in oilskin coats hauling nets in the foreground under a bruised violet dawn sky, steam curling from copper chimneys, hyper-detailed engraving style",
  "image_size": "portrait_16_9",
  "num_images": 1,
  "output_format": "png",
  "guidance_scale": 3.5,
  "enable_safety_checker": true,
  "enable_prompt_expansion": true
}
```

**Output**

```json
[
  "https://media.modelrunner.ai/hEFgyRhCJlMA5dQ9opIGG.png"
]
```
## Usage Examples

### cURL

The queue API is asynchronous: submit the request, poll `status_url` until it is `COMPLETED`, then read the result from `response_url`. Requires `jq`.

```bash
# 1. Submit the request (returns request handles, not the output)
SUBMIT=$(curl --silent --request POST \
  --url https://queue.modelrunner.run/tencent/hunyuan-image/v3/instruct/text-to-image \
  --header "Authorization: Key $MODEL_RUNNER_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "prompt": "A biomechanical clocktower rising from a foggy Victorian harbor, brass gears fused with living vines, three fishermen in oilskin coats hauling nets in the foreground under a bruised violet dawn sky, steam curling from copper chimneys, hyper-detailed engraving style",
  "image_size": "portrait_16_9",
  "num_images": 1,
  "output_format": "png",
  "guidance_scale": 3.5,
  "enable_safety_checker": true,
  "enable_prompt_expansion": true
}')

STATUS_URL=$(echo "$SUBMIT" | jq -r '.status_url')
RESPONSE_URL=$(echo "$SUBMIT" | jq -r '.response_url')

# 2. Poll until the request leaves the queue / in-progress state
while true; do
  STATUS=$(curl --silent --url "$STATUS_URL" \
    --header "Authorization: Key $MODEL_RUNNER_KEY" | jq -r '.status')
  echo "Status: $STATUS"
  case "$STATUS" in
    COMPLETED) break ;;
    FAILED|CANCELLED) echo "Request $STATUS"; exit 1 ;;
  esac
  sleep 1
done

# 3. Read the finished request, including the generated output
curl --silent --url "$RESPONSE_URL" \
  --header "Authorization: Key $MODEL_RUNNER_KEY"
```

### JavaScript

```javascript
import { modelrunner } from "@modelrunner/client";

const result = await modelrunner.subscribe("tencent/hunyuan-image/v3/instruct/text-to-image", {
  input: {
  "prompt": "A biomechanical clocktower rising from a foggy Victorian harbor, brass gears fused with living vines, three fishermen in oilskin coats hauling nets in the foreground under a bruised violet dawn sky, steam curling from copper chimneys, hyper-detailed engraving style",
  "image_size": "portrait_16_9",
  "num_images": 1,
  "output_format": "png",
  "guidance_scale": 3.5,
  "enable_safety_checker": true,
  "enable_prompt_expansion": true
}
});

console.log(result.data);
```

### Python

```python
import asyncio
import modelrunner_ai

async def main():
    response = await modelrunner_ai.submit_async(
        "tencent/hunyuan-image/v3/instruct/text-to-image",
        arguments={
  "prompt": "A biomechanical clocktower rising from a foggy Victorian harbor, brass gears fused with living vines, three fishermen in oilskin coats hauling nets in the foreground under a bruised violet dawn sky, steam curling from copper chimneys, hyper-detailed engraving style",
  "image_size": "portrait_16_9",
  "num_images": 1,
  "output_format": "png",
  "guidance_scale": 3.5,
  "enable_safety_checker": true,
  "enable_prompt_expansion": true
}
    )
    result = await response.get()
    print(result["output"])

asyncio.run(main())
```

## Additional Resources

- [Playground](https://modelrunner.ai/models/tencent/hunyuan-image/v3/instruct/text-to-image)
- [OpenAPI Schema](https://modelrunner.ai/models/tencent/hunyuan-image/v3/instruct/text-to-image/openapi.json)
- [LLM Instructions](https://modelrunner.ai/models/tencent/hunyuan-image/v3/instruct/text-to-image/llms.txt)