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# Text-To-Image
Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/text_to_image>.
This example demonstrates how to deploy Qwen-Image model for online image generation service using vLLM-Omni.
## Start Server
### Basic Start
```bash
vllm serve Qwen/Qwen-Image --omni --port 8091
```
!!! note
If you encounter Out-of-Memory (OOM) issues or have limited GPU memory, you can enable VAE slicing and tiling to reduce memory usage, --vae-use-slicing --vae-use-tiling
### Start with Parameters
Or use the startup script:
```bash
bash run_server.sh
```
## API Calls
### Method 1: Using curl
```bash
# Basic text-to-image generation
bash run_curl_text_to_image.sh
# Or execute directly
curl -s http://localhost:8091/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"messages": [
{"role": "user", "content": "A beautiful landscape painting"}
],
"extra_body": {
"height": 1024,
"width": 1024,
"num_inference_steps": 50,
"true_cfg_scale": 4.0,
"seed": 42
}
}' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > output.png
```
### Method 2: Using Python Client
```bash
python openai_chat_client.py --prompt "A beautiful landscape painting" --output output.png
```
### Method 3: Using Gradio Demo
```bash
python gradio_demo.py
# Visit http://localhost:7860
```
## Request Format
### Simple Text Generation
```json
{
"messages": [
{"role": "user", "content": "A beautiful landscape painting"}
]
}
```
### Generation with Parameters
Use `extra_body` to pass generation parameters:
```json
{
"messages": [
{"role": "user", "content": "A beautiful landscape painting"}
],
"extra_body": {
"height": 1024,
"width": 1024,
"num_inference_steps": 50,
"true_cfg_scale": 4.0,
"seed": 42
}
}
```
### Multimodal Input (Text + Structured Content)
```json
{
"messages": [
{
"role": "user",
"content": [
{"type": "text", "text": "A beautiful landscape painting"}
]
}
]
}
```
## Generation Parameters (extra_body)
| Parameter | Type | Default | Description |
| ------------------------ | ----- | ------- | ------------------------------ |
| `height` | int | None | Image height in pixels |
| `width` | int | None | Image width in pixels |
| `size` | str | None | Image size (e.g., "1024x1024") |
| `num_inference_steps` | int | 50 | Number of denoising steps |
| `true_cfg_scale` | float | 4.0 | Qwen-Image CFG scale |
| `seed` | int | None | Random seed (reproducible) |
| `negative_prompt` | str | None | Negative prompt |
| `num_outputs_per_prompt` | int | 1 | Number of images to generate |
| `--cfg-parallel-size`. | int | 1 | Number of GPUs for CFG parallelism |
## Response Format
```json
{
"id": "chatcmpl-xxx",
"created": 1234567890,
"model": "Qwen/Qwen-Image",
"choices": [{
"index": 0,
"message": {
"role": "assistant",
"content": [{
"type": "image_url",
"image_url": {
"url": "data:image/png;base64,..."
}
}]
},
"finish_reason": "stop"
}],
"usage": {...}
}
```
## Extract Image
```bash
# Extract base64 from response and decode to image
cat response.json | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > output.png
```
## File Description
| File | Description |
| --------------------------- | ---------------------------- |
| `run_server.sh` | Server startup script |
| `run_curl_text_to_image.sh` | curl example |
| `openai_chat_client.py` | Python client |
| `gradio_demo.py` | Gradio interactive interface |
## Example materials
??? abstract "gradio_demo.py"
``````py
--8<-- "examples/online_serving/text_to_image/gradio_demo.py"
``````
??? abstract "openai_chat_client.py"
``````py
--8<-- "examples/online_serving/text_to_image/openai_chat_client.py"
``````
??? abstract "run_curl_text_to_image.sh"
``````sh
--8<-- "examples/online_serving/text_to_image/run_curl_text_to_image.sh"
``````
??? abstract "run_server.sh"
``````sh
--8<-- "examples/online_serving/text_to_image/run_server.sh"
``````