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https://github.com/invoke-ai/InvokeAI
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chore: ruff lint
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@ -42,7 +42,7 @@ async def list_model_records(
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"""Get a list of models."""
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record_store = ApiDependencies.invoker.services.model_records
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if base_models and len(base_models) > 0:
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models_raw = list()
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models_raw = []
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for base_model in base_models:
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models_raw.extend(
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[x.model_dump() for x in record_store.search_by_attr(base_model=base_model, model_type=model_type)]
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@ -49,7 +49,7 @@ class FastModelHash(object):
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def _hash_dir(cls, model_location: Union[str, Path]) -> str:
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components: Dict[str, str] = {}
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for root, dirs, files in os.walk(model_location):
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for root, _dirs, files in os.walk(model_location):
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for file in files:
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# only tally tensor files because diffusers config files change slightly
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# depending on how the model was downloaded/converted.
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@ -61,6 +61,6 @@ class FastModelHash(object):
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# hash all the model hashes together, using alphabetic file order
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md5 = hashlib.md5()
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for path, fast_hash in sorted(components.items()):
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for _path, fast_hash in sorted(components.items()):
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md5.update(fast_hash.encode("utf-8"))
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return md5.hexdigest()
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@ -52,16 +52,16 @@ def test_type(store: ModelRecordServiceBase):
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def test_add(store: ModelRecordServiceBase):
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raw = dict(
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path="/tmp/foo.ckpt",
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name="model1",
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base=BaseModelType("sd-1"),
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type="main",
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config="/tmp/foo.yaml",
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variant="normal",
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format="checkpoint",
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original_hash="111222333444",
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)
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raw = {
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"path": "/tmp/foo.ckpt",
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"name": "model1",
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"base": BaseModelType("sd-1"),
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"type": "main",
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"config": "/tmp/foo.yaml",
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"variant": "normal",
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"format": "checkpoint",
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"original_hash": "111222333444",
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}
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store.add_model("key1", raw)
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config1 = store.get_model("key1")
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assert config1 is not None
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