Seongjin_Nano-Banana

Nano-Banana PPT Slide Image Generator

Generate high-quality PPT slide images from JSON slide outlines using the Gemini Image Generation API (Nano Banana Pro - gemini-3-pro-image-preview).

Quick Reference (30 seconds)

Purpose: Generate PPT slide images from JSON outlines containing Nano-Banana optimized prompts.

Execution Command:

# Generate all slides
uv run python "{skill_scripts_dir}/Generate_Slides.py" "{json_file}" --output-dir "{output_dir}"

# Generate specific slides only (for recovery/retry)
uv run python "{skill_scripts_dir}/Generate_Slides.py" "{json_file}" --output-dir "{output_dir}" --slides 5
uv run python "{skill_scripts_dir}/Generate_Slides.py" "{json_file}" --output-dir "{output_dir}" --slides 5,7,9

Script Location: .claude/skills/Seongjin_Skill_Nano-Banana/Scripts/Generate_Slides.py

Prerequisites:

• Python dependencies: google-genai, python-dotenv, pillow
• API Key: .claude/skills/Seongjin_Skill_Nano-Banana/Scripts/.env with GOOGLE_API_KEY=your_key_here

Output: PNG images saved as slide_001.png, slide_002.png, etc.

Implementation Guide (5 minutes)

Basic Usage

Step 1: Ensure prerequisites are installed

pip install google-genai python-dotenv pillow

Step 2: Create .env file in skill Scripts directory

# Create at: .claude/skills/Seongjin_Skill_Nano-Banana/Scripts/.env
GOOGLE_API_KEY=your_google_api_key_here

Step 3: Execute the script

# Basic execution (output: {json_name}_images/)
uv run python "/path/to/Generate_Slides.py" "slides.json"

# With custom output directory
uv run python "/path/to/Generate_Slides.py" "slides.json" --output-dir "./output"

# With custom .env file path
uv run python "/path/to/Generate_Slides.py" "slides.json" --env-file "/path/to/.env"

# Generate specific slides only (for recovery/retry)
uv run python "/path/to/Generate_Slides.py" "slides.json" --slides 5           # Single slide
uv run python "/path/to/Generate_Slides.py" "slides.json" --slides 5,7,9       # Multiple slides
uv run python "/path/to/Generate_Slides.py" "slides.json" --slides 1-5         # Range
uv run python "/path/to/Generate_Slides.py" "slides.json" --slides 1-3,5,7-9   # Mixed

Input JSON Schema

The script expects a JSON file with the following structure:

{
  "source_file": "/path/to/source.md",
  "created_at": "2024-12-05T14:30:22",
  "total_slides": 10,
  "theme": "light_warm",
  "target_audience": "general",
  "reference_images": ["/path/to/global_ref.png"],
  "slides": [
    {
      "page": 1,
      "title": "Slide Title",
      "key_message": "Reading Deck detailed explanation (2-4 sentences)",
      "nano_banana_prompt": "Self-contained English instructions with Korean text",
      "reference_images": ["/path/to/slide_specific_ref.png"]
    }
  ]
}

Top-Level Field Descriptions:

• source_file: Original source file path
• reference_images: (Optional) Global reference images applied to ALL slides

Slide Field Descriptions:

• page: Slide number (starts from 1)
• title: Slide title (Korean, max 10 words)
• key_message: Reading Deck detailed explanation (Korean, 2-4 sentences)
• nano_banana_prompt: Self-contained image generation prompt (English instructions with quoted Korean text)
• reference_images: (Optional) Slide-specific reference images (combined with global references)

nano_banana_prompt 4-Layer Structure

All prompts must include these 4 layers:

Layer 1 - Scene Declaration: Slide format, text position, layout composition Layer 2 - Photography/Style: Background style, lighting, color mood, visual elements Layer 3 - Text Rendering: All Korean text in quotes for exact rendering Layer 4 - Technical Specs: 16:9 aspect ratio, 1920x1080 resolution, readability requirements

Script Configuration

Configurable constants in the script:

MODEL = "gemini-3-pro-image-preview"  # Gemini model
ASPECT_RATIO = "16:9"                  # Image aspect ratio
IMAGE_SIZE = "2K"                      # Image resolution
MAX_RETRIES = 50                       # Maximum retry attempts
RETRY_DELAY_SECONDS = 30               # Delay for quota/rate limit errors
MAX_WORKERS = 50                       # Maximum parallel workers

Output Example

============================================================
Starting PARALLEL image generation for 22 slides
Output directory: slides_images
Model: gemini-3-pro-image-preview
Max workers: 50
Max retries: 50
Retry delay: 30 seconds
============================================================

[Slide 01/22] Title Slide - Starting...
  [Attempt 1/50] Generating image...
[Slide 01] Success - Saved to slide_001.png
...

============================================================
Generation Complete!
============================================================
Total slides processed: 22
Successful: 22
Failed: 0
Total time: 163.4 seconds
Average time per slide: 7.4 seconds
============================================================

Advanced Implementation (10+ minutes)

Error Handling

The script implements sophisticated error handling:

Rate Limit / Quota Errors:

• Detection: Checks for "rate" or "quota" in error message
• Recovery: 30-second delay before retry
• Maximum: 50 retry attempts

Safety Filter Blocks:

• Detection: Checks for "blocked" or "safety" in error message
• Recovery: Skip the slide, log the error
• Impact: Slide will not be generated

Other Errors:

• Recovery: 5-second delay before retry
• Maximum: 50 retry attempts

Performance Optimization

Parallel Execution:

• Uses ThreadPoolExecutor with configurable max workers (default: 50)
• Processes multiple slides concurrently
• Results are collected as tasks complete

Average Performance Metrics:

• Generation time per slide: 7-10 seconds
• Throughput: 6-9 slides per minute (with parallelization)
• Success rate: 95%+ (with retry logic)

Reference Images

The script supports reference images to guide visual style and content generation.

Use Cases:

• Consistent character appearance across slides
• Brand asset integration (logos, icons)
• Style reference for visual consistency
• Photo-based slide generation

Reference Image Types:

1. Global Reference Images (top-level reference_images):

   • Applied to ALL slides in the presentation
   • Use for: brand assets, consistent characters, style guides

2. Slide-Specific Reference Images (per-slide reference_images):

   • Applied only to that specific slide
   • Combined with global references (slide-specific first, then global)
   • Use for: slide-specific photos, diagrams, charts

Example JSON with Reference Images:

{
  "source_file": "/path/to/source.md",
  "reference_images": ["/path/to/logo.png", "/path/to/style_guide.png"],
  "slides": [
    {
      "page": 1,
      "title": "Title Slide",
      "nano_banana_prompt": "Create a title slide incorporating the provided logo...",
      "reference_images": ["/path/to/hero_image.png"]
    },
    {
      "page": 2,
      "title": "Team Introduction",
      "nano_banana_prompt": "Create a team photo slide using the provided photos...",
      "reference_images": ["/path/to/person1.png", "/path/to/person2.png"]
    }
  ]
}

Output with Reference Images:

[Global Reference] Found 2 global reference image(s)
[Slide 01/10] Title Slide (refs: 3) - Starting...
  [Reference Image] Loaded: hero_image.png
  [Reference Image] Loaded: logo.png
  [Reference Image] Loaded: style_guide.png
  [Attempt 1/10] Generating image with 3 reference(s)...
[Slide 01] Success - Saved to slide_001.png

Best Practices:

• Keep reference images under 5 per slide for optimal performance
• Use high-quality images (minimum 512x512 pixels recommended)
• Ensure reference images are accessible (absolute paths recommended)
• Reference images are optional - omit the field if not needed

Integration Workflow

The complete PPT generation workflow:

Step 1: Source Analysis (PPT-Planner Agent)

• Analyzes markdown or PDF source files
• Generates JSON slide outline with nano_banana_prompt

Step 2: Image Generation (Nano-Banana Script)

• Reads JSON slide outline
• Generates PNG images for each slide

Step 3: Assembly (Manual or Automation)

• Combine generated images into final presentation

Programmatic Integration

from pathlib import Path
import subprocess

def generate_slides(json_path: str, output_dir: str = None) -> int:
    """Generate slides from JSON using Nano-Banana script."""
    script_path = "/path/to/Generate_Slides.py"
    cmd = ["uv", "run", "python", script_path, json_path]

    if output_dir:
        cmd.extend(["--output-dir", output_dir])

    result = subprocess.run(cmd, capture_output=True, text=True)
    return result.returncode

Related Resources

Related Agent:

• Seongjin_Agent_PPT-Planner: Generates JSON slide outlines from source files

Related Command:

• /ppt: Orchestrates the complete PPT generation workflow

Model Information:

• Model: gemini-3-pro-image-preview (Nano Banana Pro)
• Capabilities: Text rendering, high-quality 2K images, 16:9 aspect ratio

API Documentation:

• Google AI Studio: https://aistudio.google.com/
• Gemini API: https://ai.google.dev/

Works Well With

• Seongjin_Agent_PPT-Planner - Generates JSON slide outlines from markdown/PDF
• /ppt command - Orchestrates complete source-to-slides workflow

Troubleshooting

API Key Not Found:

• Ensure .env file exists at .claude/skills/Seongjin_Skill_Nano-Banana/Scripts/.env
• Verify GOOGLE_API_KEY is set correctly

Quota Exceeded:

• Wait for quota reset or reduce MAX_WORKERS
• Check Google AI Studio for quota status

Safety Filter Triggered:

• Review prompt content for problematic terms
• Simplify or rephrase the nano_banana_prompt
• Avoid controversial or explicit content

Image Generation Failed:

• Check network connectivity
• Verify API key is valid
• Review prompt structure follows 4-layer format

Partial Slide Recovery:

• Use --slides parameter to regenerate only failed slides
• Example: --slides 5,12,15 to regenerate slides 5, 12, and 15
• Supports single numbers, comma-separated lists, and ranges (e.g., 1-5, 1-3,5,7-9)