[Misc] Build canonical image task prompts (#6049)

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>
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Hongsheng Liu authored and GitHub committed 2026-08-12 08:30:34 +08:00
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# Text-To-Image
This example demonstrates how to deploy Qwen-Image model for online image generation service using vLLM-Omni.
This example demonstrates online text-to-image generation with vLLM-Omni. The
startup script and Python clients default to Qwen-Image, but `MODEL` can select
any supported text-to-image model. The existing curl helper remains a concrete
Qwen-Image request through the standard Images API.
## Start Server
@@ -242,8 +245,8 @@ count, use `size` and `n` rather than `height`, `width`, or
| `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 |
| `num_inference_steps` | int | model | Number of denoising steps |
| `true_cfg_scale` | float | model | True CFG scale when supported |
| `seed` | int | None | Random seed (reproducible) |
| `negative_prompt` | str | None | Negative prompt |
| `num_outputs_per_prompt` | int | 1 | Number of images to generate |