feat(mm): rename "blake3" to "blake3_multi"

Just make it clearer which is which.

docs/features/CONFIGURATION.md

@@ -122,18 +122,18 @@ The provided token will be added as a `Bearer` token to the network requests to
 Models are hashed during installation, providing a stable identifier for models across all platforms. Hashing is a one-time operation.
 
 ```yaml
-hashing_algorithm: blake3_single
+hashing_algorithm: blake3_single # default value
 ```
 
 You might want to change this setting, depending on your system:
 
 - `blake3_single` (default): Single-threaded - best for spinning HDDs, still OK for SSDs
-- `blake3`: Parallelized, memory-mapped implementation - best for SSDs, terrible for spinning disks
+- `blake3_multi`: Parallelized, memory-mapped implementation - best for SSDs, terrible for spinning disks
 - `random`: Skip hashing entirely - fastest but of course no hash
 
 During the first startup after upgrading to v4, all of your models will be hashed. This can take a few minutes.
 
-Most common algorithms are supported, like `md5`, `sha256`, and `sha512`. These are typically much, much slower than `blake3`.
+Most common algorithms are supported, like `md5`, `sha256`, and `sha512`. These are typically much, much slower than either of the BLAKE3 variants.
 
 #### Path Settings
 

invokeai/app/services/config/config_default.py

@@ -115,7 +115,7 @@ class InvokeAIAppConfig(BaseSettings):
         allow_nodes: List of nodes to allow. Omit to allow all.
         deny_nodes: List of nodes to deny. Omit to deny none.
         node_cache_size: How many cached nodes to keep in memory.
-        hashing_algorithm: Model hashing algorthim for model installs. 'blake3' is best for SSDs. 'blake3_single' is best for spinning disk HDDs. 'random' disables hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models. Alternatively, any other hashlib algorithm is accepted, though these are not nearly as performant as blake3.<br>Valid values: `md5`, `sha1`, `sha224`, `sha256`, `sha384`, `sha512`, `blake2b`, `blake2s`, `sha3_224`, `sha3_256`, `sha3_384`, `sha3_512`, `shake_128`, `shake_256`, `blake3`, `blake3_single`, `random`
+        hashing_algorithm: Model hashing algorthim for model installs. 'blake3_multi' is best for SSDs. 'blake3_single' is best for spinning disk HDDs. 'random' disables hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models. Alternatively, any other hashlib algorithm is accepted, though these are not nearly as performant as blake3.<br>Valid values: `blake3_multi`, `blake3_single`, `random`, `md5`, `sha1`, `sha224`, `sha256`, `sha384`, `sha512`, `blake2b`, `blake2s`, `sha3_224`, `sha3_256`, `sha3_384`, `sha3_512`, `shake_128`, `shake_256`
         remote_api_tokens: List of regular expression and token pairs used when downloading models from URLs. The download URL is tested against the regex, and if it matches, the token is provided in as a Bearer token.
     """
 
@@ -191,7 +191,7 @@ class InvokeAIAppConfig(BaseSettings):
     node_cache_size:                int = Field(default=512,                description="How many cached nodes to keep in memory.")
 
     # MODEL INSTALL
-    hashing_algorithm: HASHING_ALGORITHMS = Field(default="blake3_single",  description="Model hashing algorthim for model installs. 'blake3' is best for SSDs. 'blake3_single' is best for spinning disk HDDs. 'random' disables hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models. Alternatively, any other hashlib algorithm is accepted, though these are not nearly as performant as blake3.")
+    hashing_algorithm: HASHING_ALGORITHMS = Field(default="blake3_single",  description="Model hashing algorthim for model installs. 'blake3_multi' is best for SSDs. 'blake3_single' is best for spinning disk HDDs. 'random' disables hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models. Alternatively, any other hashlib algorithm is accepted, though these are not nearly as performant as blake3.")
     remote_api_tokens: Optional[list[URLRegexTokenPair]] = Field(default=None, description="List of regular expression and token pairs used when downloading models from URLs. The download URL is tested against the regex, and if it matches, the token is provided in as a Bearer token.")
 
     # fmt: on

invokeai/backend/model_hash/model_hash.py

@@ -11,6 +11,9 @@
 from invokeai.app.util.misc import uuid_string
 
 HASHING_ALGORITHMS = Literal[
+    "blake3_multi",
+    "blake3_single",
+    "random",
     "md5",
     "sha1",
     "sha224",
@@ -25,9 +28,6 @@
     "sha3_512",
     "shake_128",
     "shake_256",
-    "blake3",
-    "blake3_single",
-    "random",
 ]
 MODEL_FILE_EXTENSIONS = (".ckpt", ".safetensors", ".bin", ".pt", ".pth")
 
@@ -64,7 +64,7 @@ def __init__(
         self, algorithm: HASHING_ALGORITHMS = "blake3_single", file_filter: Optional[Callable[[str], bool]] = None
     ) -> None:
         self.algorithm: HASHING_ALGORITHMS = algorithm
-        if algorithm == "blake3":
+        if algorithm == "blake3_multi":
             self._hash_file = self._blake3
         elif algorithm == "blake3_single":
             self._hash_file = self._blake3_single
@@ -226,4 +226,4 @@ def _default_file_filter(file_path: str) -> bool:
     def _get_prefix(algorithm: HASHING_ALGORITHMS) -> str:
         """Return the prefix for the given algorithm, e.g. \"blake3:\" or \"md5:\"."""
         # blake3_single is a single-threaded version of blake3, prefix should still be "blake3:"
-        return "blake3:" if algorithm == "blake3_single" else f"{algorithm}:"
+        return "blake3:" if algorithm == "blake3_single" or algorithm == "blake3_multi" else f"{algorithm}:"

tests/test_model_hash.py

@@ -16,7 +16,7 @@
         "sha512",
         "sha512:c4a10476b21e00042f638ad5755c561d91f2bb599d3504d25409495e1c7eda94543332a1a90fbb4efdaf9ee462c33e0336b5eae4acfb1fa0b186af452dd67dc6",
     ),
-    ("blake3", "blake3:ce3f0c5f3c05d119f4a5dcaf209b50d3149046a0d3a9adee9fed4c83cad6b4d0"),
+    ("blake3_multi", "blake3:ce3f0c5f3c05d119f4a5dcaf209b50d3149046a0d3a9adee9fed4c83cad6b4d0"),
     ("blake3_single", "blake3:ce3f0c5f3c05d119f4a5dcaf209b50d3149046a0d3a9adee9fed4c83cad6b4d0"),
 ]
 
@@ -29,7 +29,7 @@ def test_model_hash_hashes_file(tmp_path: Path, algorithm: HASHING_ALGORITHMS, e
     assert hash_ == expected_hash
 
 
-@pytest.mark.parametrize("algorithm", ["md5", "sha1", "sha256", "sha512", "blake3", "blake3_single"])
+@pytest.mark.parametrize("algorithm", ["md5", "sha1", "sha256", "sha512", "blake3_multi", "blake3_single"])
 def test_model_hash_hashes_dir(tmp_path: Path, algorithm: HASHING_ALGORITHMS):
     model_hash = ModelHash(algorithm)
     files = [Path(tmp_path, f"{i}.bin") for i in range(5)]
@@ -58,7 +58,7 @@ def test_model_hash_hashes_dir(tmp_path: Path, algorithm: HASHING_ALGORITHMS):
         ("sha1", "sha1:"),
         ("sha256", "sha256:"),
         ("sha512", "sha512:"),
-        ("blake3", "blake3:"),
+        ("blake3_multi", "blake3:"),
         ("blake3_single", "blake3:"),
     ],
 )
@@ -67,7 +67,7 @@ def test_model_hash_gets_prefix(algorithm: HASHING_ALGORITHMS, expected_prefix:
 
 
 def test_model_hash_blake3_matches_blake3_single(tmp_path: Path):
-    model_hash = ModelHash("blake3")
+    model_hash = ModelHash("blake3_multi")
     model_hash_simple = ModelHash("blake3_single")
 
     file = tmp_path / "test.bin"