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Running on Zero
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e793773 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | """Generate every trajectory for one release example case."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import Any
from scope.config import SCOPE_MODEL_ID, InferenceConfig
from scope.inference import _prepare_device, generate
from scope.weights import load_pipeline, resolve_model_dir
def _select_case_examples(manifest_path: Path, case_id: str) -> list[dict[str, Any]]:
manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
case = next((item for item in manifest["cases"] if item["id"] == case_id), None)
if case is None:
raise KeyError(f"Unknown case: {case_id}")
root = manifest_path.parent
return [
{
**case,
"first_frame": root / case["first_frame"],
"pose": root / trajectory["pose"],
"trajectory_id": trajectory["id"],
}
for trajectory in case["trajectories"]
]
def _output_path(output_dir: Path, example: dict[str, Any]) -> Path:
return output_dir / f"{example['id']}__{example['trajectory_id']}.mp4"
def main() -> None:
parser = argparse.ArgumentParser(
description="Generate every trajectory for one case with a single model load"
)
parser.add_argument("--manifest", type=Path, default=Path("examples/manifest.json"))
parser.add_argument("--case", required=True)
parser.add_argument(
"--output_dir", "--output-dir", dest="output_dir", type=Path, default=Path("outputs")
)
parser.add_argument("--model_path", "--scope-model", dest="model_path", default=SCOPE_MODEL_ID)
parser.add_argument("--cache_dir", "--cache-dir", dest="cache_dir", type=Path, default=None)
parser.add_argument(
"--negative_prompt",
"--negative-prompt",
dest="negative_prompt",
type=Path,
default=Path("configs/negative_prompt.txt"),
)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument(
"--vram_limit_gb", "--vram-limit-gb", dest="vram_limit_gb", type=float, default=None
)
parser.add_argument("--overwrite", action="store_true")
args = parser.parse_args()
examples = _select_case_examples(args.manifest, args.case)
pending = [
example
for example in examples
if args.overwrite or not _output_path(args.output_dir, example).is_file()
]
print(f"Selected {len(examples)} trajectories for {args.case}; {len(pending)} pending.")
if not pending:
return
config = InferenceConfig(seed=args.seed)
model_dir = resolve_model_dir(args.model_path, args.cache_dir)
pipe = load_pipeline(model_dir, config)
print("Loaded the complete SCoPE model once for this case.")
device = _prepare_device(pipe, args.vram_limit_gb)
negative_prompt = args.negative_prompt.read_text(encoding="utf-8").strip()
for index, example in enumerate(pending, start=1):
output = _output_path(args.output_dir, example)
temporary = output.with_name(f".{output.stem}.partial{output.suffix}")
print(f"[{index}/{len(pending)}] Generating {example['trajectory_id']}")
generate(pipe, example, temporary, config, negative_prompt, device)
temporary.replace(output)
print(f"Saved {output}")
if __name__ == "__main__":
main()
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