image-generation

Image Generation Skill

Overview

This skill generates high-quality images using structured prompts and a Python script. The workflow includes creating JSON-formatted prompts and executing image generation with optional reference images.

Core Capabilities

• Create structured JSON prompts for AIGC image generation
• Support multiple reference images for style/composition guidance
• Generate images through automated Python script execution
• Handle various image generation scenarios (character design, scenes, products, etc.)

Workflow

Step 1: Understand Requirements

When a user requests image generation, identify:

• Subject/content: What should be in the image
• Style preferences: Art style, mood, color palette
• Technical specs: Aspect ratio, composition, lighting
• Reference images: Any images to guide generation
• You don't need to check the folder under /mnt/user-data

Step 2: Create Structured Prompt

Generate a structured JSON file in /mnt/user-data/workspace/ with naming pattern: {descriptive-name}.json

Step 3: Execute Generation

Call the Python script:

python /mnt/skills/public/image-generation/scripts/generate.py \
  --prompt-file /mnt/user-data/workspace/prompt-file.json \
  --reference-images /path/to/ref1.jpg /path/to/ref2.png \
  --output-file /mnt/user-data/outputs/generated-image.jpg
  --aspect-ratio 16:9

Parameters:

• --prompt-file: Absolute path to JSON prompt file (required)
• --reference-images: Absolute paths to reference images (optional, space-separated)
• --output-file: Absolute path to output image file (required)
• --aspect-ratio: Aspect ratio of the generated image (optional, default: 16:9)

[!NOTE] Do NOT read the python file, just call it with the parameters.

Character Generation Example

User request: "Create a Tokyo street style woman character in 1990s"

Create prompt file: /mnt/user-data/workspace/asian-woman.json

Execute generation:

With reference images:

Common Scenarios

Use different JSON schemas for different scenarios.

Character Design:

• Physical attributes (gender, age, ethnicity, body type)
• Facial features and expressions
• Clothing and accessories
• Historical era or setting
• Pose and context

Scene Generation:

• Environment description
• Time of day, weather
• Mood and atmosphere
• Focal points and composition

Product Visualization:

• Product details and materials
• Lighting setup
• Background and context
• Presentation angle

Specific Templates

Read the following template file only when matching the user request.

• Doraemon Comic

Output Handling

After generation:

• Images are typically saved in /mnt/user-data/outputs/
• Share generated images with user using present_files tool
• Provide brief description of the generation result
• Offer to iterate if adjustments needed

Tips: Enhancing Generation with Reference Images

For scenarios where visual accuracy is critical, use the image_search tool first to find reference images before generation.

Recommended scenarios for using image_search tool:

• Character/Portrait Generation: Search for similar poses, expressions, or styles to guide facial features and body proportions
• Specific Objects or Products: Find reference images of real objects to ensure accurate representation
• Architectural or Environmental Scenes: Search for location references to capture authentic details
• Fashion and Clothing: Find style references to ensure accurate garment details and styling

Example workflow:

1. Call the image_search tool to find suitable reference images:

   image_search(query="Japanese woman street photography 1990s", size="Large")
2. Download the returned image URLs to local files
3. Use the downloaded images as --reference-images parameter in the generation script

This approach significantly improves generation quality by providing the model with concrete visual guidance rather than relying solely on text descriptions.

Providers (Gemini / MiniMax)

This skill auto-selects the provider by environment variables (no CLI change):

• GEMINI_API_KEY set → use Gemini (default, unchanged).
• Only MINIMAX_API_KEY set → use MiniMax (/v1/image_generation, model image-01).
• Force one explicitly with IMAGE_GENERATION_PROVIDER=gemini|minimax.

MiniMax optional overrides: MINIMAX_API_HOST (default https://api.minimaxi.com), MINIMAX_IMAGE_MODEL (default image-01). Reference images are sent as the MiniMax subject_reference character image. The CLI and --prompt-file / --reference-images / --output-file / --aspect-ratio arguments are identical for both providers.

MiniMax prompt handling (provider-internal). Authoring is provider-agnostic — write the same structured JSON regardless of which provider is active. MiniMax image-01 consumes a single text string, so the MiniMax path itself sends only the JSON prompt field (the other fields such as style / composition / negative_prompt apply to the Gemini path) and enables prompt_optimizer so MiniMax expands it server-side. MiniMax caps that prompt at 1500 characters; if the prompt field is longer, the script returns an error instead of calling the API. The Gemini path receives the full structured JSON.

Notes

• Always use English for prompts regardless of user's language
• JSON format ensures structured, parsable prompts
• Reference images enhance generation quality significantly
• Iterative refinement is normal for optimal results
• For character generation, include the detailed character object plus a consolidated prompt field