ModelScope Image Generation

Generate AI images through ModelScope API with natural language support in Chinese and English.

Quick Start

Generate an image directly:

python "${SKILL_ROOT}/scripts/image-gen.py" --prompt "A golden cat sitting on velvet"

Or with Chinese:

python "${SKILL_ROOT}/scripts/image-gen.py" --prompt "一只金色的猫坐在天鹅绒垫子上"

Natural Language Triggers

This skill automatically activates when users say:

Chinese:

• "生成[描述]" - Generate [description]
• "画一个[描述]" - Draw a [description]
• "创建一张[描述]图片" - Create a [description] image
• "帮我画..." - Help me draw...
• "AI绘画..." - AI art...

English:

• "Generate [description]"
• "Draw a [description]"
• "Create a [description] image"
• "Make a picture of..."
• "Create an image of..."

Configuration

Setup API Token

Before generating images, configure your ModelScope API token:

1. Create config directory: ~/.modelscope-image-gen/
2. Create modelscope-image-gen.local.md:

---
api_key: your_modelscope_api_token_here
default_model: Tongyi-MAI/Z-Image-Turbo
default_width: 1024
default_height: 1024
poll_timeout: 300
output_dir: ./generated-images/
---

Getting API Token

Visit ModelScope to get your API token.

Usage

Single Image Generation

python "${SKILL_ROOT}/scripts/image-gen.py" \
  --prompt "A sunset over mountains" \
  --output ./generated-images/

Batch Generation

Create a prompts file (one per line):

python "${SKILL_ROOT}/scripts/image-gen.py" \
  --batch examples/batch-prompts.txt \
  --output ./generated-images/

Custom Parameters

python "${SKILL_ROOT}/scripts/image-gen.py" \
  --prompt "Futuristic city at night" \
  --model Tongyi-MAI/Z-Image-Turbo \
  --width 1024 \
  --height 1024 \
  --filename city-night \
  --timeout 300

Multiple Images

python "${SKILL_ROOT}/scripts/image-gen.py" \
  --prompt "Abstract art" \
  --count 5 \
  --output ./generated-images/

Script Parameters

How It Works

Async Task Flow

1. Submit Request: POST to /v1/images/generations with async mode
2. Poll Status: GET /v1/tasks/{task_id} until completion
3. Download: Retrieve image from result URL

Error Handling

Examples

See examples/batch-prompts.txt for sample prompts to use with batch generation.

Best Practices

1. Validate prompts before submission to avoid wasted API calls
2. Use batch mode for multiple generations to optimize throughput
3. Set appropriate timeouts based on prompt complexity
4. Save images immediately after successful generation
5. Handle rate limits with proper retry strategies