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https://github.com/invoke-ai/InvokeAI
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eb6e6548ed
BLAKE3 has poor performance on spinning disks when parallelized. See https://github.com/BLAKE3-team/BLAKE3/issues/31 - Replace `skip_model_hash` setting with `hashing_algorithm`. Any algorithm we support is accepted. - Add `random` algorithm: hashes a UUID with BLAKE3 to create a random "hash". Equivalent to the previous skip functionality. - Add `blake3_single` algorithm: hashes on a single thread using BLAKE3, fixes the aforementioned performance issue - Update model probe to accept the algorithm to hash with as an optional arg, defaulting to `blake3` - Update all calls of the probe to use the app's configured hashing algorithm - Update an external script that probes models - Update tests - Move ModelHash into its own module to avoid circuclar import issues
34 lines
902 B
Python
Executable File
34 lines
902 B
Python
Executable File
#!/bin/env python
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"""Little command-line utility for probing a model on disk."""
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import argparse
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from pathlib import Path
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from typing import get_args
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from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS
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from invokeai.backend.model_manager import InvalidModelConfigException, ModelProbe
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algos = ", ".join(set(get_args(HASHING_ALGORITHMS)))
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parser = argparse.ArgumentParser(description="Probe model type")
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parser.add_argument(
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"model_path",
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type=Path,
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nargs="+",
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)
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parser.add_argument(
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"--hash_algo",
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type=str,
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default="blake3",
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help=f"Hashing algorithm to use (default: blake3), one of: {algos}",
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)
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args = parser.parse_args()
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for path in args.model_path:
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try:
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info = ModelProbe.probe(path, hash_algo=args.hash_algo)
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print(f"{path}:{info.model_dump_json(indent=4)}")
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except InvalidModelConfigException as exc:
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print(exc)
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