38 lines
1.6 KiB
Python
38 lines
1.6 KiB
Python
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#!/usr/bin/env python3
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"""
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Train a diffusion model for layout patch generation (skeleton).
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Planned: fine-tune Stable Diffusion (or Latent Diffusion) with optional ControlNet edge/skeleton conditions.
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Dependencies to consider: diffusers, transformers, accelerate, torch, torchvision, opencv-python.
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Current status: CLI skeleton and TODOs only.
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"""
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from __future__ import annotations
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import argparse
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def main() -> None:
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parser = argparse.ArgumentParser(description="Train diffusion model for layout patches (skeleton)")
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parser.add_argument("--data_dir", type=str, required=True, help="Prepared dataset root (images/ + conditions/)")
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parser.add_argument("--output_dir", type=str, required=True, help="Checkpoint output directory")
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parser.add_argument("--image_size", type=int, default=256)
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parser.add_argument("--batch_size", type=int, default=8)
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parser.add_argument("--lr", type=float, default=1e-4)
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parser.add_argument("--max_steps", type=int, default=100000)
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parser.add_argument("--use_controlnet", action="store_true", help="Train with ControlNet conditioning")
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parser.add_argument("--condition_types", type=str, nargs="*", default=["edge"], help="e.g., edge skeleton dist")
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args = parser.parse_args()
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# TODO: implement dataset/dataloader (images and optional conditions)
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# TODO: load base pipeline (Stable Diffusion or Latent Diffusion) and optionally ControlNet
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# TODO: set up optimizer, LR schedule, EMA, gradient accumulation, and run training loop
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# TODO: save periodic checkpoints to output_dir
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print("[TODO] Implement diffusion training loop and checkpoints.")
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if __name__ == "__main__":
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main()
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