Generate images with Alibaba Qwen-Image-2.0-Pro via inference.sh CLI. Professional text rendering, fine-grained realism, enhanced semantic adherence. Ideal for posters, banners, and text-heavy designs. Triggers: qwen image pro, qwen-image-pro, qwen 2 pro, alibaba image pro, dashscope pro, professional text rendering
Generate images with Alibaba Qwen-Image-2.0-Pro via inference.sh CLI. Best for professional text rendering and complex designs.

Requires inference.sh CLI (
infsh). Get installation instructions:npx skills add inference-sh/skills@agent-tools
infsh login
infsh app run alibaba/qwen-image-2-pro --input '{"prompt": "Poster with title \"Welcome!\" in bold blue text"}'
infsh app run alibaba/qwen-image-2-pro --input '{
"prompt": "A futuristic cityscape at sunset with flying cars"
}'
infsh app run alibaba/qwen-image-2-pro --input '{
"prompt": "Healing-style hand-drawn poster featuring three puppies playing with a ball. The main title \"Come Play Ball!\" is prominently displayed at the top in bold, blue cartoon font. Below, the subtitle \"Join the Fun!\" appears in green font.",
"width": 1024,
"height": 1536,
"prompt_extend": false
}'
infsh app run alibaba/qwen-image-2-pro --input '{
"prompt": "Professional marketing banner for summer sale. Large text \"SUMMER SALE\" in white on gradient sunset background. \"50% OFF\" in yellow below. Clean, modern design.",
"width": 1920,
"height": 1080,
"prompt_extend": false,
"negative_prompt": "blurry text, distorted text, low quality"
}'
infsh app run alibaba/qwen-image-2-pro --input '{
"prompt": "Minimalist logo design for a coffee shop called \"Bean & Brew\"",
"num_images": 4
}'
infsh app run alibaba/qwen-image-2-pro --input '{
"prompt": "Make the person from Image 1 wear the outfit from Image 2",
"reference_images": [
{"uri": "https://example.com/person.jpg"},
{"uri": "https://example.com/outfit.jpg"}
],
"num_images": 2
}'
infsh app run alibaba/qwen-image-2-pro --input '{
"prompt": "Abstract geometric art in blue and gold",
"seed": 12345
}'
| Parameter | Type | Description |
|---|---|---|
prompt | string | Required. What to generate or edit (max 800 chars) |
reference_images | array | Input images for editing (1-3 images) |
num_images | integer | Number of images to generate (1-6) |
width | integer | Output width in pixels (512-2048) |
height | integer | Output height in pixels (512-2048) |
watermark | boolean | Add "Qwen-Image" watermark |
negative_prompt | string | Content to avoid (max 500 chars) |
prompt_extend | boolean | Enable prompt rewriting (default: true) |
seed | integer | Random seed for reproducibility (0-2147483647) |
Size constraint: Total pixels must be between 512×512 and 2048×2048.
| Field | Type | Description |
|---|---|---|
images | array | The generated or edited images (PNG format) |
output_meta | object | Metadata with dimensions and count |
For best text results with the Pro model:
"Title: \"Hello World!\""prompt_extend: false for precise control"blurry text, distorted text, low quality"Example prompt structure:
Poster with the title "GRAND OPENING" in large red serif font at the top center.
Below, the date "March 15, 2024" in smaller black text.
Background: elegant gold and white gradient.
Style: professional, clean, modern.
{
"negative_prompt": "low resolution, low quality, deformed limbs, deformed fingers, oversaturated, waxy, no facial details, overly smooth, AI-like, chaotic composition, blurry text, distorted text"
}
# 1. Generate sample input to see all options
infsh app sample alibaba/qwen-image-2-pro --save input.json
# 2. Edit the prompt
# 3. Run
infsh app run alibaba/qwen-image-2-pro --input input.json
from inferencesh import inference
client = inference()
# Text-heavy poster
result = client.run({
"app": "alibaba/qwen-image-2-pro",
"input": {
"prompt": "Poster with title \"Welcome!\" in bold blue text at top",
"width": 1024,
"height": 1536,
"prompt_extend": False
}
})
print(result["output"])
# Stream live updates
for update in client.run({
"app": "alibaba/qwen-image-2-pro",
"input": {
"prompt": "Professional product photography of a watch"
}
}, stream=True):
if update.get("progress"):
print(f"progress: {update['progress']}%")
if update.get("output"):
print(f"output: {update['output']}")
# Standard Qwen-Image (faster, general use)
npx skills add inference-sh/skills@qwen-image
# Full platform skill (all 150+ apps)
npx skills add inference-sh/skills@agent-tools
# All image generation models
npx skills add inference-sh/skills@ai-image-generation
Browse all image apps: infsh app list --category image