313 lines
11 KiB
Python
313 lines
11 KiB
Python
"""
|
|
Batch Processing Script for Image Prompt Variations
|
|
Automates generation of multiple prompt variations from input templates
|
|
"""
|
|
|
|
import json
|
|
from pathlib import Path
|
|
from typing import List, Dict, Optional
|
|
from core.orchestrator import PromptOrchestrator
|
|
from core.modifiers import ModifierCategory
|
|
|
|
|
|
class BatchProcessor:
|
|
"""Handles batch processing of image prompts with various automation features"""
|
|
|
|
def __init__(self, output_dir: str = "output"):
|
|
"""
|
|
Initialize batch processor.
|
|
|
|
Args:
|
|
output_dir: Directory to save output files
|
|
"""
|
|
self.orchestrator = PromptOrchestrator()
|
|
self.output_dir = Path(output_dir)
|
|
self.output_dir.mkdir(exist_ok=True)
|
|
|
|
def process_single_prompt(self, base_prompt: str, config: Dict) -> List[Dict]:
|
|
"""
|
|
Process a single prompt with specified configuration.
|
|
|
|
Args:
|
|
base_prompt: The base prompt to vary
|
|
config: Configuration dict with keys:
|
|
- lighting_ids: List of modifier IDs (or "all", "dry", "wet")
|
|
- styles: List of style names to apply
|
|
- export_format: Output format (txt, json, csv)
|
|
|
|
Returns:
|
|
List of variation dictionaries
|
|
"""
|
|
# Determine which lighting modifiers to use
|
|
if config.get("lighting_ids") == "all":
|
|
modifier_ids = list(range(1, 21))
|
|
elif config.get("lighting_ids") == "dry":
|
|
dry_mods = self.orchestrator.get_modifiers_by_category(ModifierCategory.DRY)
|
|
modifier_ids = [m.id for m in dry_mods]
|
|
elif config.get("lighting_ids") == "wet":
|
|
wet_mods = self.orchestrator.get_modifiers_by_category(ModifierCategory.WET)
|
|
modifier_ids = [m.id for m in wet_mods]
|
|
else:
|
|
modifier_ids = config.get("lighting_ids", [])
|
|
|
|
# Generate variations
|
|
variations = self.orchestrator.generate_variation_batch(
|
|
base_prompt=base_prompt,
|
|
modifier_ids=modifier_ids if modifier_ids else None,
|
|
include_styles=config.get("styles", None)
|
|
)
|
|
|
|
return variations
|
|
|
|
def process_multiple_prompts(self, prompts: List[str], config: Dict,
|
|
output_filename: str = "batch_output") -> str:
|
|
"""
|
|
Process multiple base prompts and save results.
|
|
|
|
Args:
|
|
prompts: List of base prompts
|
|
config: Configuration dictionary
|
|
output_filename: Name for output file (without extension)
|
|
|
|
Returns:
|
|
Path to output file
|
|
"""
|
|
all_results = []
|
|
|
|
for idx, prompt in enumerate(prompts, 1):
|
|
variations = self.process_single_prompt(prompt, config)
|
|
all_results.append({
|
|
"base_prompt_id": idx,
|
|
"base_prompt": prompt,
|
|
"variations": variations
|
|
})
|
|
|
|
# Export results
|
|
export_format = config.get("export_format", "json")
|
|
output_path = self.output_dir / f"{output_filename}.{export_format}"
|
|
|
|
if export_format == "json":
|
|
with open(output_path, "w", encoding="utf-8") as f:
|
|
json.dump(all_results, f, indent=2)
|
|
elif export_format == "txt":
|
|
with open(output_path, "w", encoding="utf-8") as f:
|
|
for result in all_results:
|
|
f.write(f"\n{'=' * 80}\n")
|
|
f.write(f"BASE PROMPT #{result['base_prompt_id']}: {result['base_prompt']}\n")
|
|
f.write(f"{'=' * 80}\n\n")
|
|
for var in result['variations']:
|
|
f.write(f"[{var['id']}] {var['type'].upper()} - {var['category']}\n")
|
|
f.write(f" Description: {var['description']}\n")
|
|
f.write(f" Prompt: {var['prompt']}\n\n")
|
|
elif export_format == "csv":
|
|
with open(output_path, "w", encoding="utf-8") as f:
|
|
f.write("base_prompt_id,base_prompt,variation_id,type,category,description,prompt\n")
|
|
for result in all_results:
|
|
for var in result['variations']:
|
|
f.write(f"{result['base_prompt_id']},\"{result['base_prompt']}\",")
|
|
f.write(f"{var['id']},{var['type']},{var['category']},")
|
|
f.write(f"\"{var['description']}\",\"{var['prompt']}\"\n")
|
|
|
|
return str(output_path)
|
|
|
|
def create_preset_collection(self, base_prompt: str, preset: str) -> List[Dict]:
|
|
"""
|
|
Apply a preset collection of variations.
|
|
|
|
Presets:
|
|
- "full_day_cycle": All DRY modifiers (complete day cycle)
|
|
- "wet_night": All WET modifiers (dramatic night scenes)
|
|
- "key_moments": Selected dramatic lighting moments
|
|
- "commercial": Best modifiers for commercial photography
|
|
|
|
Args:
|
|
base_prompt: The base prompt
|
|
preset: Preset name
|
|
|
|
Returns:
|
|
List of variations
|
|
"""
|
|
preset_configs = {
|
|
"full_day_cycle": {
|
|
"lighting_ids": "dry",
|
|
"styles": None
|
|
},
|
|
"wet_night": {
|
|
"lighting_ids": "wet",
|
|
"styles": None
|
|
},
|
|
"key_moments": {
|
|
"lighting_ids": [1, 7, 8, 10, 18, 20], # Sunrise, golden hour, sunset, blue hours, wet night
|
|
"styles": None
|
|
},
|
|
"commercial": {
|
|
"lighting_ids": [3, 4, 7, 11], # Crisp lighting for product shots
|
|
"styles": None
|
|
},
|
|
"artistic": {
|
|
"lighting_ids": [7, 8, 9, 10],
|
|
"styles": ["impressionist", "minimalist", "art_deco"]
|
|
}
|
|
}
|
|
|
|
if preset not in preset_configs:
|
|
raise ValueError(f"Unknown preset: {preset}. Available: {list(preset_configs.keys())}")
|
|
|
|
return self.process_single_prompt(base_prompt, preset_configs[preset])
|
|
|
|
def generate_comparison_set(self, base_prompt: str,
|
|
modifier_pairs: List[tuple]) -> List[Dict]:
|
|
"""
|
|
Generate side-by-side comparison prompts.
|
|
|
|
Args:
|
|
base_prompt: Base prompt
|
|
modifier_pairs: List of (modifier_id_1, modifier_id_2) tuples
|
|
|
|
Returns:
|
|
List of comparison variation sets
|
|
"""
|
|
comparisons = []
|
|
|
|
for mod_a, mod_b in modifier_pairs:
|
|
prompt_a = self.orchestrator.apply_lighting_modifier(base_prompt, mod_a)
|
|
prompt_b = self.orchestrator.apply_lighting_modifier(base_prompt, mod_b)
|
|
|
|
comparisons.append({
|
|
"comparison_type": f"modifier_{mod_a}_vs_{mod_b}",
|
|
"variant_a": {
|
|
"modifier_id": mod_a,
|
|
"description": self.orchestrator.modifiers[mod_a].description,
|
|
"prompt": prompt_a
|
|
},
|
|
"variant_b": {
|
|
"modifier_id": mod_b,
|
|
"description": self.orchestrator.modifiers[mod_b].description,
|
|
"prompt": prompt_b
|
|
}
|
|
})
|
|
|
|
return comparisons
|
|
|
|
|
|
def example_workflows():
|
|
"""Demonstrate various workflow scenarios"""
|
|
processor = BatchProcessor(output_dir="prompt_variations")
|
|
|
|
print("BATCH PROCESSING WORKFLOWS\n")
|
|
print("=" * 80)
|
|
|
|
# Workflow 1: Single prompt with all lighting variations
|
|
print("\n1. FULL LIGHTING CYCLE GENERATION")
|
|
print("-" * 80)
|
|
base_prompt_1 = "Luxury sedan in urban environment, architectural background"
|
|
|
|
config_full = {
|
|
"lighting_ids": "all",
|
|
"export_format": "json"
|
|
}
|
|
|
|
output_1 = processor.process_multiple_prompts(
|
|
prompts=[base_prompt_1],
|
|
config=config_full,
|
|
output_filename="full_lighting_cycle"
|
|
)
|
|
print(f"✓ Generated full lighting cycle → {output_1}")
|
|
|
|
# Workflow 2: Multiple prompts with preset
|
|
print("\n2. KEY MOMENTS PRESET (Multiple Prompts)")
|
|
print("-" * 80)
|
|
base_prompts = [
|
|
"Sports car on mountain road, scenic vista",
|
|
"Classic convertible on beach, sunset backdrop",
|
|
"Electric SUV in modern city, glass buildings"
|
|
]
|
|
|
|
variations = []
|
|
for prompt in base_prompts:
|
|
vars = processor.create_preset_collection(prompt, "key_moments")
|
|
variations.extend(vars)
|
|
|
|
print(f"✓ Generated {len(variations)} variations across {len(base_prompts)} base prompts")
|
|
|
|
# Workflow 3: Style transformations
|
|
print("\n3. STYLE TRANSFORMATION SET")
|
|
print("-" * 80)
|
|
base_prompt_3 = "Vintage motorcycle in garage, mechanical details"
|
|
|
|
config_styles = {
|
|
"lighting_ids": [7], # Golden hour only
|
|
"styles": ["cyberpunk", "art_deco", "minimalist", "noir"],
|
|
"export_format": "txt"
|
|
}
|
|
|
|
output_3 = processor.process_multiple_prompts(
|
|
prompts=[base_prompt_3],
|
|
config=config_styles,
|
|
output_filename="style_variations"
|
|
)
|
|
print(f"✓ Generated style variations → {output_3}")
|
|
|
|
# Workflow 4: Comparison set
|
|
print("\n4. COMPARISON SET GENERATION")
|
|
print("-" * 80)
|
|
base_prompt_4 = "Modern architecture, geometric forms"
|
|
|
|
comparison_pairs = [
|
|
(4, 7), # Midday vs Golden Hour
|
|
(11, 18), # Night dry vs Night wet
|
|
(1, 8) # Sunrise vs Sunset
|
|
]
|
|
|
|
comparisons = processor.generate_comparison_set(base_prompt_4, comparison_pairs)
|
|
|
|
# Save comparisons
|
|
output_4 = processor.output_dir / "comparisons.json"
|
|
with open(output_4, "w", encoding="utf-8") as f:
|
|
json.dump(comparisons, f, indent=2)
|
|
|
|
print(f"✓ Generated {len(comparisons)} comparison sets → {output_4}")
|
|
|
|
# Workflow 5: Commercial photography set
|
|
print("\n5. COMMERCIAL PHOTOGRAPHY PRESET")
|
|
print("-" * 80)
|
|
base_prompt_5 = "Product shot of smartwatch, clean background"
|
|
|
|
commercial_vars = processor.create_preset_collection(base_prompt_5, "commercial")
|
|
print(f"✓ Generated {len(commercial_vars)} commercial-grade variations")
|
|
|
|
print("\n" + "=" * 80)
|
|
print("All workflows completed successfully!")
|
|
print(f"Output directory: {processor.output_dir}")
|
|
|
|
|
|
def quick_generation_example():
|
|
"""Quick example for immediate use"""
|
|
orchestrator = PromptOrchestrator()
|
|
|
|
# Your base prompt
|
|
base = "Ferrari on coastal highway at sunset"
|
|
|
|
print("\nQUICK GENERATION EXAMPLES")
|
|
print("=" * 80)
|
|
|
|
# Generate 5 quick variations
|
|
variations = [
|
|
orchestrator.apply_lighting_modifier(base, 1), # Sunrise
|
|
orchestrator.apply_lighting_modifier(base, 7), # Golden hour
|
|
orchestrator.apply_lighting_modifier(base, 10), # Blue hour
|
|
orchestrator.apply_lighting_modifier(base, 18), # Blue hour wet
|
|
orchestrator.apply_style_transformation(base, "cyberpunk")
|
|
]
|
|
|
|
for i, var in enumerate(variations, 1):
|
|
print(f"\n{i}. {var}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# Run example workflows
|
|
example_workflows()
|
|
|
|
# Quick generation
|
|
quick_generation_example()
|