#!/usr/bin/env python3 """ THE BAKER — Deck Visual Generator Generate visuals for pitch deck slides using the trained LoRA model. Usage: source /home/workspaces/cultguard-agents/.devenv/state/venv/bin/activate python generate_deck_visual.py \ --prompt "the baker film style, barakat estate interior, grandeur with decay" \ --output assets/decks/visuals/estate.png \ --lora outputs/lora_weights.safetensors """ import argparse import logging from pathlib import Path import torch from PIL import Image logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def load_pipeline(base_model: str, lora_path: str | None = None): """Load Flux pipeline with optional LoRA weights.""" from diffusers import FluxPipeline logger.info(f"Loading Flux pipeline from {base_model}...") pipeline = FluxPipeline.from_pretrained( base_model, torch_dtype=torch.float16, ).to("cuda") if lora_path: logger.info(f"Loading LoRA weights from {lora_path}...") pipeline.load_lora_weights(lora_path) # Enable optimizations pipeline.enable_model_cpu_offload() pipeline.enable_vae_slicing() pipeline.enable_attention_slicing() return pipeline def generate_visual( pipeline, prompt: str, output_path: str, negative_prompt: str = "", width: int = 1024, height: int = 1024, steps: int = 28, guidance: float = 3.5, seed: int | None = None, ) -> Image.Image: """Generate a single visual.""" logger.info(f"Generating: {prompt[:80]}...") generator = None if seed is not None: generator = torch.Generator("cuda").manual_seed(seed) image = pipeline( prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=steps, guidance_scale=guidance, height=height, width=width, generator=generator, ).images[0] # Ensure output directory exists output_file = Path(output_path) output_file.parent.mkdir(parents=True, exist_ok=True) image.save(output_file, format="PNG", quality=95) logger.info(f"Saved: {output_file}") return image def generate_deck_batch( pipeline, output_dir: str, visuals: list[dict], ) -> list[Image.Image]: """Generate multiple deck visuals.""" output_path = Path(output_dir) output_path.mkdir(parents=True, exist_ok=True) images = [] for i, visual in enumerate(visuals): logger.info(f"Generating visual {i+1}/{len(visuals)}: {visual['name']}") output_file = output_path / f"{visual['name']}.png" image = generate_visual( pipeline=pipeline, prompt=visual["prompt"], output_path=str(output_file), negative_prompt=visual.get("negative_prompt", ""), width=visual.get("width", 1024), height=visual.get("height", 1024), steps=visual.get("steps", 28), guidance=visual.get("guidance", 3.5), seed=visual.get("seed"), ) images.append(image) return images # Preset deck visuals based on Cannes deck slides DECK_VISUALS = [ { "name": "01_cover", "prompt": "the baker film style, title card THE BAKER, deep black background #121212, gold accent #B78A3C, elegant typography, grandeur with decay, cinematic, pitch deck title slide", "width": 1024, "height": 576, # 16:9 for deck }, { "name": "02_the_bargain", "prompt": "the baker film style, barakat estate interior, baroque catholic weight with phoenician memory, grand room showing decay and wealth, chiaroscuro lighting, dust motes in light shafts, cinematic, grandeur with decay", }, { "name": "03_the_story", "prompt": "the baker film style, family dinner table, multiple generations, food abundance, underlying tension, chiaroscuro lighting, baroque setting, cinematic, grandeur with decay", }, { "name": "04_fault_line", "prompt": "the baker film style, carved wooden family crest half-covered in shadow, bullet casing and communion wafer resting beside it on marble, symbolic, chiaroscuro lighting, cinematic, grandeur with decay", }, { "name": "05_opening_wound", "prompt": "the baker film style, port of byblos at dawn, fishing caique eleftheria leaving, lebanon burning in background, blood and exile, crucifix sinking into sea, cinematic, grandeur with decay", }, { "name": "06_fredric", "prompt": "the baker film style, fredric barakat portrait, aging drug lord patriarch, physically diminished, surrounded by photographs pills guns priests and ghosts, chiaroscuro lighting, baroque interior, cinematic, grandeur with decay", }, { "name": "07_magda", "prompt": "the baker film style, magda barakat portrait, second wife matriarch, care as authority grief as power, black clothing, judging expression, chiaroscuro lighting, cinematic, grandeur with decay", }, { "name": "08_billy", "prompt": "the baker film style, billy barakat portrait, reckless son, violence as identity, demanding empire, remote contempt, dim lighting, club background, cinematic, grandeur with decay", }, { "name": "09_communion", "prompt": "the baker film style, maronite catholic church interior, holy communion ceremony, family in formal attire, candles and incense, religious iconography, chiaroscuro lighting, white dress communion girl, cinematic, grandeur with decay", }, { "name": "10_lebanon", "prompt": "the baker film style, lebanese cemetery on slopes, two elaborate caskets descending, matriarch in black brocade silk, bishop blessing, israeli fighter jet in sky, cinematic, grandeur with decay", }, { "name": "11_theme", "prompt": "the baker film style, bread and blood on family table, symbolic, grandeur with decay, chiaroscuro lighting, cinematic composition", }, { "name": "12_audience", "prompt": "the baker film style, family gathering, diaspora viewers, recognition and tension, warm lighting, cinematic, grandeur with decay", }, { "name": "13_closing", "prompt": "the baker film style, cemetery background, black and gold, THE END text space, cinematic, deck visual, grandeur with decay", }, ] def main(): parser = argparse.ArgumentParser(description="Generate THE BAKER deck visuals") parser.add_argument("--prompt", type=str, help="Custom prompt for single image generation") parser.add_argument("--output", type=str, default="output.png", help="Output file path") parser.add_argument("--lora", type=str, help="Path to trained LoRA weights (.safetensors)") parser.add_argument("--base-model", type=str, default="black-forest-labs/FLUX.2-dev", help="Base model path") parser.add_argument("--batch", action="store_true", help="Generate all preset deck visuals") parser.add_argument("--output-dir", type=str, default="assets/decks/visuals", help="Output directory for batch generation") parser.add_argument("--width", type=int, default=1024, help="Image width") parser.add_argument("--height", type=int, default=1024, help="Image height") parser.add_argument("--steps", type=int, default=28, help="Inference steps") parser.add_argument("--guidance", type=float, default=3.5, help="Guidance scale") parser.add_argument("--seed", type=int, default=None, help="Random seed") args = parser.parse_args() # Check CUDA if not torch.cuda.is_available(): logger.error("CUDA not available. GPU required.") return # Load pipeline pipeline = load_pipeline(args.base_model, args.lora) if args.batch: # Generate all preset visuals logger.info(f"Generating {len(DECK_VISUALS)} deck visuals...") generate_deck_batch(pipeline, args.output_dir, DECK_VISUALS) logger.info(f"Batch complete! Visuals saved to {args.output_dir}/") else: # Generate single image if not args.prompt: logger.error("--prompt required for single image generation") return generate_visual( pipeline=pipeline, prompt=args.prompt, output_path=args.output, width=args.width, height=args.height, steps=args.steps, guidance=args.guidance, seed=args.seed, ) logger.info("Generation complete!") if __name__ == "__main__": main()