63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
import argparse
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import asyncio
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import logging
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import time
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from pathlib import Path
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from datetime import datetime
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from dotenv import load_dotenv
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from generate_image_local import text_to_image_invoke_ai
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from generate_model_local import image_to_3d_api
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load_dotenv()
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def get_timestamp():
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return datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
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def setup_logger(base_folder, timestamp):
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log_dir = base_folder / Path("logs")
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log_dir.mkdir(parents=True, exist_ok=True)
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logging.basicConfig(
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filename=log_dir / f"{timestamp}.log",
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level=logging.INFO,
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force=True
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)
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async def main():
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parser = argparse.ArgumentParser(description="Text to 3D model pipeline")
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parser.add_argument("--prompt", type=str, required=True, help="User text prompt")
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args = parser.parse_args()
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input_prompt = args.prompt
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print(f"Input prompt: {input_prompt}")
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image_generation_prompt = input_prompt + ", single object, plain neutral background, high-resolution, sharp details, soft studio lighting, front and side visible, realistic style, true-to-scale"
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pipeline_folder = Path(__file__).resolve().parent
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timestamp = get_timestamp()
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setup_logger(pipeline_folder, timestamp)
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time_checkpoint = time.time()
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image_path = pipeline_folder / "images" / f"{timestamp}.jpg"
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# TODO: use Invoke AI or Cloudflare, depending on env var
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#text_to_image_cloudflare(image_generation_prompt, image_path)
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await text_to_image_invoke_ai(image_generation_prompt, image_path)
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image_generation_time = time.time() - time_checkpoint
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time_checkpoint = time.time()
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logging.info(f"Image generation time: {round(image_generation_time, 1)} s")
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print(f"Generated image file: {image_path}")
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model_path = pipeline_folder / "models" / timestamp
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model_file = image_to_3d_api(image_path, model_path)
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model_generation_time = time.time() - time_checkpoint
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logging.info(f"Model generation time: {round(model_generation_time, 1)} s")
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print(f"Generated 3D model file: {model_file}")
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if __name__ == "__main__":
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asyncio.run(main())
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