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.opencode/skills/ai-artist/scripts/generate.py
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371
.opencode/skills/ai-artist/scripts/generate.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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AI Artist Generate - Nano Banana image generation with 3 creative modes
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Uses 129 actual prompts from awesome-nano-banana-pro-prompts collection.
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Usage:
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python generate.py "<concept>" --output <path.png> [options]
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Modes:
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--mode search : Find best matching prompt (default)
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--mode creative : Remix elements from multiple prompts
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--mode wild : AI-enhanced out-of-the-box interpretation
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--mode all : Generate all 3 variations
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"""
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import argparse
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import sys
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import os
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import re
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import random
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from pathlib import Path
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# Add parent for core imports
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sys.path.insert(0, str(Path(__file__).parent))
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from core import search
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# Gemini API setup
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CLAUDE_ROOT = Path.home() / '.claude'
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sys.path.insert(0, str(CLAUDE_ROOT / 'scripts'))
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PROJECT_CLAUDE = Path(__file__).parent.parent.parent.parent
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sys.path.insert(0, str(PROJECT_CLAUDE / 'scripts'))
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try:
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from resolve_env import resolve_env
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CENTRALIZED_RESOLVER = True
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except ImportError:
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CENTRALIZED_RESOLVER = False
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try:
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from dotenv import load_dotenv
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load_dotenv(Path.home() / '.claude' / '.env')
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load_dotenv(Path.home() / '.claude' / 'skills' / '.env')
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except ImportError:
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pass
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try:
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from google import genai
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from google.genai import types
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GENAI_AVAILABLE = True
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except ImportError:
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GENAI_AVAILABLE = False
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# ============ CONFIGURATION ============
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NANO_BANANA_MODELS = {
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"flash2": "gemini-3.1-flash-image-preview", # Nano Banana 2 (new default)
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"flash": "gemini-2.5-flash-image",
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"pro": "gemini-3-pro-image-preview",
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}
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DEFAULT_MODEL = "flash2"
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ASPECT_RATIOS = ["1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "21:9"]
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def get_api_key() -> str:
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"""Get Gemini API key from environment."""
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if CENTRALIZED_RESOLVER:
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return resolve_env('GEMINI_API_KEY', skill='ai-multimodal')
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return os.getenv('GEMINI_API_KEY')
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def adapt_prompt(template_prompt: str, concept: str, **kwargs) -> str:
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"""Adapt a template prompt to the user's concept.
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Intelligently replaces variables and adapts the prompt while keeping
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the original structure and Nano Banana narrative style.
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"""
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prompt = template_prompt
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# Replace common variable patterns
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replacements = {
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# Raycast-style arguments
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r'\{argument name="[^"]*" default="[^"]*"\}': concept,
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r'\{argument name=[^}]+\}': concept,
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# Bracket variables
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r'\[insert [^\]]+\]': concept,
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r'\[subject\]': concept,
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r'\[concept\]': concept,
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r'\[topic\]': concept,
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r'\[product\]': concept,
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r'\[scene\]': concept,
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r'\[description\]': concept,
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# Generic placeholders
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r'\{[^}]+\}': lambda m: kwargs.get(m.group(0)[1:-1], concept),
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}
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for pattern, replacement in replacements.items():
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if callable(replacement):
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prompt = re.sub(pattern, replacement, prompt, flags=re.IGNORECASE)
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else:
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prompt = re.sub(pattern, replacement, prompt, flags=re.IGNORECASE)
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# Ensure negative constraints exist (Nano Banana style)
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if "NEVER" not in prompt and "DO NOT" not in prompt:
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prompt += " NEVER add watermarks or unwanted text. DO NOT include labels."
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return prompt
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def mode_search(concept: str, verbose: bool = False) -> tuple[str, dict]:
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"""Mode 1: Find best matching prompt from awesome collection."""
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result = search(concept, "awesome", 1)
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if result.get("count", 0) > 0:
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match = result["results"][0]
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prompt = adapt_prompt(match["prompt"], concept)
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if verbose:
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print(f" [SEARCH] Matched: {match['title'][:60]}...")
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print(f" Author: {match.get('author', 'Unknown')}")
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return prompt, {"mode": "search", "match": match}
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# Fallback to basic prompt
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prompt = f"A professional image of {concept}. High quality, detailed. Professional photography. NEVER add watermarks."
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return prompt, {"mode": "search", "match": None}
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def mode_creative(concept: str, verbose: bool = False) -> tuple[str, dict]:
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"""Mode 2: Creative remix - combine elements from multiple prompts."""
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# Get top 3 matches
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result = search(concept, "awesome", 3)
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matches = result.get("results", [])
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if len(matches) < 2:
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return mode_search(concept, verbose)
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# Extract key elements from each prompt
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elements = []
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for m in matches:
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prompt = m.get("prompt", "")
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# Extract style descriptions, lighting, composition hints
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if "style" in prompt.lower() or "lighting" in prompt.lower():
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elements.append(prompt[:200])
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if verbose:
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print(f" [CREATIVE] Remixing {len(matches)} prompts:")
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for m in matches:
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print(f" - {m['title'][:50]}...")
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# Build creative remix
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base = matches[0]["prompt"]
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style_hints = []
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# Extract style from second match
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if len(matches) > 1:
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m2 = matches[1]["prompt"]
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style_match = re.search(r'(style[^.]+\.)', m2, re.IGNORECASE)
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if style_match:
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style_hints.append(style_match.group(1))
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# Extract lighting/mood from third match
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if len(matches) > 2:
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m3 = matches[2]["prompt"]
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light_match = re.search(r'(lighting[^.]+\.)', m3, re.IGNORECASE)
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if light_match:
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style_hints.append(light_match.group(1))
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# Adapt and enhance
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prompt = adapt_prompt(base, concept)
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if style_hints:
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prompt += " " + " ".join(style_hints)
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return prompt, {"mode": "creative", "matches": [m["title"] for m in matches]}
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def mode_wild(concept: str, verbose: bool = False) -> tuple[str, dict]:
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"""Mode 3: Wild/Out-of-the-box - AI-enhanced creative interpretation."""
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result = search(concept, "awesome", 5)
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matches = result.get("results", [])
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# Creative transformations
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transformations = [
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"reimagined as a Japanese Ukiyo-e woodblock print with Prussian blue and vermilion",
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"transformed into a premium liquid glass Bento grid infographic",
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"captured as a vintage 1800s patent document with technical drawings",
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"rendered as a surreal dreamscape with volumetric god rays",
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"depicted in cyberpunk neon aesthetic with holographic elements",
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"illustrated as a hand-drawn chalkboard explanation",
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"visualized as an isometric 3D diorama with miniature figures",
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"presented as a cinematic movie poster with dramatic lighting",
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"created as a vaporwave aesthetic with glitch effects and Roman statues",
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"designed as a premium Apple-style product showcase",
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]
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# Pick random transformation
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transform = random.choice(transformations)
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if matches:
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# Use structure from a random match but apply wild transformation
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base = random.choice(matches)
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prompt = f"{concept}, {transform}. "
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# Extract any technical camera/quality settings from matched prompt
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tech_match = re.search(r'(\d+mm lens|f/[\d.]+|Canon|Nikon|professional photography)', base["prompt"])
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if tech_match:
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prompt += f"Shot with {tech_match.group(1)}. "
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if verbose:
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print(f" [WILD] Transform: {transform}")
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print(f" Based on: {base['title'][:50]}...")
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else:
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prompt = f"{concept}, {transform}. Professional quality."
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prompt += " NEVER add watermarks. DO NOT include unwanted text."
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return prompt, {"mode": "wild", "transformation": transform}
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def generate_image(
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prompt: str,
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output_path: str,
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model: str = DEFAULT_MODEL,
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aspect_ratio: str = "1:1",
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size: str = "2K",
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verbose: bool = False
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) -> dict:
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"""Generate image using Nano Banana (Gemini image models)."""
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if not GENAI_AVAILABLE:
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return {"status": "error", "error": "google-genai not installed. Run: pip install google-genai"}
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api_key = get_api_key()
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if not api_key:
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return {"status": "error", "error": "GEMINI_API_KEY not found"}
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model_id = NANO_BANANA_MODELS.get(model, model)
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if verbose:
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print(f"\n[Nano Banana Generation]")
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print(f" Model: {model_id}")
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print(f" Aspect: {aspect_ratio}")
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print(f" Prompt: {prompt[:100]}...")
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try:
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client = genai.Client(api_key=api_key)
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# Build config
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image_config_args = {'aspect_ratio': aspect_ratio}
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if 'pro' in model_id.lower() and size:
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image_config_args['image_size'] = size
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config = types.GenerateContentConfig(
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response_modalities=['IMAGE'],
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image_config=types.ImageConfig(**image_config_args)
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)
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response = client.models.generate_content(
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model=model_id,
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contents=[prompt],
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config=config
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)
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output_file = Path(output_path)
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output_file.parent.mkdir(parents=True, exist_ok=True)
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if hasattr(response, 'candidates') and response.candidates:
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for part in response.candidates[0].content.parts:
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if part.inline_data:
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with open(output_file, 'wb') as f:
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f.write(part.inline_data.data)
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if verbose:
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print(f" Generated: {output_file}")
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return {"status": "success", "output": str(output_file), "model": model_id}
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return {"status": "error", "error": "No image in response"}
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except Exception as e:
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return {"status": "error", "error": str(e)}
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def main():
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parser = argparse.ArgumentParser(
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description="AI Artist Generate - Nano Banana with 3 creative modes",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Modes:
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search : Find best matching prompt from 129 curated prompts (default)
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creative : Remix elements from multiple matching prompts
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wild : AI-enhanced out-of-the-box creative interpretation
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all : Generate all 3 variations
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Examples:
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# Search mode (default)
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python generate.py "tech conference banner" -o banner.png
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# Creative remix
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python generate.py "AI workshop" -o workshop.png --mode creative
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# Wild/experimental
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python generate.py "product showcase" -o product.png --mode wild
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# Generate all 3 variations
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python generate.py "futuristic city" -o city.png --mode all
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"""
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)
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parser.add_argument("concept", help="Core concept/subject to generate")
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parser.add_argument("--output", "-o", required=True, help="Output image path")
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parser.add_argument("--mode", "-m", choices=["search", "creative", "wild", "all"],
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default="search", help="Generation mode")
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parser.add_argument("--model", choices=list(NANO_BANANA_MODELS.keys()),
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default=DEFAULT_MODEL, help="Model: flash2 (default, Nano Banana 2), flash, or pro")
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parser.add_argument("--aspect-ratio", "-ar", choices=ASPECT_RATIOS, default="1:1")
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parser.add_argument("--size", choices=["1K", "2K", "4K"], default="2K")
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parser.add_argument("--verbose", "-v", action="store_true")
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parser.add_argument("--show-prompt", action="store_true", help="Print generated prompt")
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parser.add_argument("--dry-run", action="store_true", help="Build prompt without generating")
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args = parser.parse_args()
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if args.verbose:
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print(f"[Concept: {args.concept}]")
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# Determine modes to run
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modes = ["search", "creative", "wild"] if args.mode == "all" else [args.mode]
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for mode in modes:
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if args.verbose or len(modes) > 1:
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print(f"\n{'='*50}")
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print(f"[Mode: {mode.upper()}]")
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# Build prompt based on mode
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if mode == "search":
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prompt, meta = mode_search(args.concept, args.verbose)
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elif mode == "creative":
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prompt, meta = mode_creative(args.concept, args.verbose)
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elif mode == "wild":
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prompt, meta = mode_wild(args.concept, args.verbose)
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if args.show_prompt or args.verbose:
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print(f"\n[Prompt]\n{prompt}\n")
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if args.dry_run:
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print("[Dry run - no generation]")
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continue
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# Generate output path for mode
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output_path = args.output
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if len(modes) > 1:
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base = Path(args.output)
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output_path = str(base.parent / f"{base.stem}-{mode}{base.suffix}")
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result = generate_image(
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prompt=prompt,
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output_path=output_path,
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model=args.model,
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aspect_ratio=args.aspect_ratio,
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size=args.size,
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verbose=args.verbose
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)
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if result["status"] == "success":
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print(f"✓ Generated: {result['output']}")
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else:
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print(f"✗ Error: {result['error']}")
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if __name__ == "__main__":
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main()
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