qwen-image-2-pro

Imported from halt-catch-fire/mutiny (tools/image/qwen-image-2-pro/SKILL.md). Install upstream with npx skills add halt-catch-fire/mutiny --skill qwen-image-2-pro. Copyright stays with the author.

Install the belt CLI skill: npx skills add belt-sh/cli

Qwen-Image Pro - Professional Image Generation

Generate images with Alibaba Qwen-Image-2.0-Pro via inference.sh CLI. Best for professional text rendering and complex designs.

Qwen-Image-2.0-Pro

Quick Start

Requires inference.sh CLI (belt). Install instructions

belt login

belt app run alibaba/qwen-image-2-pro --input '{"prompt": "Poster with title \"Welcome!\" in bold blue text"}'

Pro Model Capabilities

• Professional Text Rendering: Multi-line and paragraph-level text with fine-grained detail
• Fine-grained Realism: Better textures and photorealistic scenes
• Stronger Semantic Adherence: More accurately follows complex prompts
• Complex Designs: Ideal for text + image combinations

Examples

Basic Text-to-Image

belt app run alibaba/qwen-image-2-pro --input '{
  "prompt": "A futuristic cityscape at sunset with flying cars"
}'

Text-Heavy Poster

belt 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
}'

Marketing Banner

belt 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"
}'

Multiple Variations

belt app run alibaba/qwen-image-2-pro --input '{
  "prompt": "Minimalist logo design for a coffee shop called \"Bean & Brew\"",
  "num_images": 4
}'

Image Editing (Style Transfer)

belt 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
}'

Reproducible Generation

belt app run alibaba/qwen-image-2-pro --input '{
  "prompt": "Abstract geometric art in blue and gold",
  "seed": 12345
}'

Input Options

Size constraint: Total pixels must be between 512×512 and 2048×2048.

Output

Text Rendering Tips

For best text results with the Pro model:

1. Use quotes around exact text: "Title: \"Hello World!\""
2. Specify font details: color, style, size, position
3. Disable prompt_extend: Set prompt_extend: false for precise control
4. Use negative prompts: "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.

Recommended Negative Prompt

{
  "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"
}

Sample Workflow

# 1. Generate sample input to see all options
belt app sample alibaba/qwen-image-2-pro --save input.json

# 2. Edit the prompt
# 3. Run
belt app run alibaba/qwen-image-2-pro --input input.json

Python SDK

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']}")

Related Skills

# Standard Qwen-Image (faster, general use)
npx skills add inference-sh/skills@qwen-image

# Full platform skill (all apps)
npx skills add inference-sh/skills@infsh-cli

# All image generation models
npx skills add inference-sh/skills@ai-image-generation

Browse all image apps: belt app list --category image

Documentation

• Running Apps - How to run apps via CLI
• Streaming Results - Real-time progress updates
• File Handling - Working with images