| """Command-line inference for StatePlay.""" |
| from __future__ import annotations |
|
|
| import argparse |
| from pathlib import Path |
|
|
| import torch |
| from PIL import Image |
|
|
| from . import NEG_PROMPT, StatePlayPipeline |
| from .utils import save_mp4 |
|
|
|
|
| def build_parser() -> argparse.ArgumentParser: |
| parser = argparse.ArgumentParser(description="Generate an SF3 rollout with StatePlay") |
| parser.add_argument("--checkpoint", required=True, help="StatePlay .safetensors checkpoint") |
| parser.add_argument("--base-model", required=True, help="Directory containing the Wan base assets") |
| parser.add_argument("--image", required=True, help="First-frame PNG/JPG") |
| parser.add_argument("--actions", required=True, help="Action/state parquet") |
| prompt = parser.add_mutually_exclusive_group() |
| prompt.add_argument("--prompt", help="Positive prompt text") |
| prompt.add_argument("--prompt-file", help="UTF-8 text file containing the positive prompt") |
| parser.add_argument("--output", default="output.mp4", help="Generated MP4 path") |
| parser.add_argument("--state-output", default=None, help="Predicted, ground-truth, and error state text path") |
| parser.add_argument("--steps", type=int, default=30) |
| parser.add_argument("--cfg", type=float, default=5.0) |
| parser.add_argument("--state-cfg", type=float, default=1.0) |
| parser.add_argument("--action-cfg", type=float, default=1.0) |
| parser.add_argument("--seed", type=int, default=2) |
| parser.add_argument("--num-frames", type=int, default=101) |
| parser.add_argument("--state-sampling", choices=("interpolation", "avg", "end"), default="end") |
| parser.add_argument("--negative-prompt", default=NEG_PROMPT) |
| parser.add_argument("--device", default="cuda") |
| return parser |
|
|
|
|
| def main() -> None: |
| args = build_parser().parse_args() |
| if args.prompt_file: |
| positive_prompt = Path(args.prompt_file).read_text(encoding="utf-8").strip() |
| else: |
| positive_prompt = args.prompt |
|
|
| output = Path(args.output) |
| output.parent.mkdir(parents=True, exist_ok=True) |
| pipeline = StatePlayPipeline.from_pretrained( |
| base_model_dir=args.base_model, |
| checkpoint_path=args.checkpoint, |
| torch_dtype=torch.bfloat16, |
| ).to(args.device) |
| state_output = None |
| if not pipeline.uses_latent_state: |
| state_output = Path(args.state_output) if args.state_output else output.with_name(f"{output.stem}_state.txt") |
| state_output.parent.mkdir(parents=True, exist_ok=True) |
| result = pipeline( |
| image=Image.open(args.image), |
| actions_parquet=args.actions, |
| state_parquet=args.actions, |
| state_txt=str(state_output) if state_output is not None else None, |
| state_sampling=args.state_sampling, |
| prompt=positive_prompt, |
| negative_prompt=args.negative_prompt, |
| num_frames=args.num_frames, |
| num_inference_steps=args.steps, |
| cfg_scale=args.cfg, |
| state_cfg_scale=args.state_cfg, |
| action_cfg_scale=args.action_cfg, |
| seed=args.seed, |
| ) |
| save_mp4(result.frames[0], str(output), fps=20) |
| print(f"Saved video: {output.resolve()}") |
| if state_output is not None: |
| print(f"Saved state: {state_output.resolve()}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
