From a16d0b8301e790d9eacebeaad7101ba812dfd492 Mon Sep 17 00:00:00 2001
From: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date: Thu, 9 Oct 2025 21:31:37 +1100
Subject: [PATCH] tests(mm): refactor model identification tests

Overhaul of model identification (probing) tests. Previously we didn't
test the correctness of probing except in a few narrow cases - now we
do.

See tests/model_identification/README.md for a detailed overview of the
new test setup. It includes instructions for adding a new test case. In
brief:

- Download the model you want to add as a test case
- Run a script against it to generate the test model files
- Fill in the expected model type/format/base/etc in the generated test
metadata JSON file

Included test cases:
- All starter models
- A handful of other models that I had installed
- Models present in the previous test cases as smoke tests, now also
tested for correctness
---
 .gitattributes                                |   1 +
 scripts/strip_models.py                       | 134 ---------
 .../model_install/test_model_install.py       |   5 +-
 .../model_records/test_model_records_sql.py   |  93 ++++--
 tests/backend/ip_adapter/test_ip_adapter.py   |   2 +-
 .../model_manager/model_manager_fixtures.py   |  37 ++-
 .../util/test_hf_model_select.py              |   2 +-
 tests/conftest.py                             |  26 --
 tests/model_identification/README.md          | 149 ++++++++++
 .../__init__.py}                              |   0
 tests/model_identification/strip_model.py     | 112 +++++++
 .../stripped_model_on_disk.py                 |  83 ++++++
 .../__test_metadata__.json                    |   3 +
 .../config.json                               |   3 +
 .../diffusion_pytorch_model.safetensors       |   3 +
 .../diffusion_pytorch_model_twins.safetensors |   3 +
 .../__test_metadata__.json                    |   3 +
 .../peft_adapter_model.safetensors            |   0
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 .../EasyNegative.safetensors                  |   3 +
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 .../test_identification.py                    | 109 +++++++
 tests/test_model_probe.py                     | 276 ------------------
 .../minimal_config_model.json                 |   3 -
 .../sd-1/main/dreamshaper-8-inpainting/README |   1 -
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 .../vae/sd-vae-ft-mse/config.json             |  29 --
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 401 files changed, 1393 insertions(+), 849 deletions(-)
 delete mode 100644 scripts/strip_models.py
 create mode 100644 tests/model_identification/README.md
 rename tests/{test_model_probe/sd-1/main/dreamshaper-8-inpainting/unet/diffusion_pytorch_model.fp16.safetensors => model_identification/__init__.py} (100%)
 create mode 100644 tests/model_identification/strip_model.py
 create mode 100644 tests/model_identification/stripped_model_on_disk.py
 create mode 100644 tests/model_identification/stripped_models/01b5565e-133c-4e9b-afdf-c8b028fc3560/__test_metadata__.json
 create mode 100644 tests/model_identification/stripped_models/01b5565e-133c-4e9b-afdf-c8b028fc3560/config.json
 create mode 100644 tests/model_identification/stripped_models/01b5565e-133c-4e9b-afdf-c8b028fc3560/diffusion_pytorch_model.safetensors
 create mode 100644 tests/model_identification/stripped_models/01b5565e-133c-4e9b-afdf-c8b028fc3560/diffusion_pytorch_model_twins.safetensors
 create mode 100644 tests/model_identification/stripped_models/027d8059-e7f8-46c3-9aa7-90a0ad5cccbe/__test_metadata__.json
 rename tests/{test_model_probe/stripped_models => model_identification/stripped_models/027d8059-e7f8-46c3-9aa7-90a0ad5cccbe}/peft_adapter_model.safetensors (100%)
 create mode 100644 tests/model_identification/stripped_models/11431812-384a-4058-98c4-ac34c30e11be/__test_metadata__.json
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 create mode 100644 tests/model_identification/stripped_models/dad77dcb-9e55-4bb1-9739-9dd01f9eb232/tokenizer_2/spiece.model
 create mode 100644 tests/model_identification/stripped_models/dad77dcb-9e55-4bb1-9739-9dd01f9eb232/tokenizer_2/tokenizer.json
 create mode 100644 tests/model_identification/stripped_models/dad77dcb-9e55-4bb1-9739-9dd01f9eb232/tokenizer_2/tokenizer_config.json
 create mode 100644 tests/model_identification/stripped_models/dc79db49-7f38-4f54-b4f1-e7c521ded481/__test_metadata__.json
 rename tests/{test_model_probe/stripped_models/llava-onevision-qwen2-7b-si-hf => model_identification/stripped_models/dc79db49-7f38-4f54-b4f1-e7c521ded481}/added_tokens.json (100%)
 rename tests/{test_model_probe/stripped_models/llava-onevision-qwen2-7b-si-hf => model_identification/stripped_models/dc79db49-7f38-4f54-b4f1-e7c521ded481}/chat_template.json (100%)
 rename tests/{test_model_probe/stripped_models/llava-onevision-qwen2-7b-si-hf => model_identification/stripped_models/dc79db49-7f38-4f54-b4f1-e7c521ded481}/config.json (100%)
 rename tests/{test_model_probe/stripped_models/llava-onevision-qwen2-7b-si-hf => model_identification/stripped_models/dc79db49-7f38-4f54-b4f1-e7c521ded481}/generation_config.json (100%)
 rename tests/{test_model_probe/stripped_models/llava-onevision-qwen2-7b-si-hf => model_identification/stripped_models/dc79db49-7f38-4f54-b4f1-e7c521ded481}/merges.txt (100%)
 rename tests/{test_model_probe/stripped_models/llava-onevision-qwen2-7b-si-hf => model_identification/stripped_models/dc79db49-7f38-4f54-b4f1-e7c521ded481}/model-00001-of-00004.safetensors (100%)
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 rename tests/{test_model_probe/stripped_models/llava-onevision-qwen2-7b-si-hf => model_identification/stripped_models/dc79db49-7f38-4f54-b4f1-e7c521ded481}/processor_config.json (100%)
 rename tests/{test_model_probe/stripped_models/llava-onevision-qwen2-7b-si-hf => model_identification/stripped_models/dc79db49-7f38-4f54-b4f1-e7c521ded481}/special_tokens_map.json (100%)
 rename tests/{test_model_probe/stripped_models/llava-onevision-qwen2-7b-si-hf => model_identification/stripped_models/dc79db49-7f38-4f54-b4f1-e7c521ded481}/tokenizer.json (100%)
 rename tests/{test_model_probe/stripped_models/llava-onevision-qwen2-7b-si-hf => model_identification/stripped_models/dc79db49-7f38-4f54-b4f1-e7c521ded481}/tokenizer_config.json (100%)
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 rename tests/{test_model_probe/stripped_models/omi => model_identification/stripped_models/ff775f86-a9f6-4a4b-88b0-9343a2f49b6e}/sdxl_lora_full.safetensors (100%)
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diff --git a/.gitattributes b/.gitattributes
index df4754736a..6cf175e7c5 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -4,3 +4,4 @@
 * text=auto
 docker/** text eol=lf
 tests/test_model_probe/stripped_models/** filter=lfs diff=lfs merge=lfs -text
+tests/model_identification/stripped_models/** filter=lfs diff=lfs merge=lfs -text
diff --git a/scripts/strip_models.py b/scripts/strip_models.py
deleted file mode 100644
index 156dda0056..0000000000
--- a/scripts/strip_models.py
+++ /dev/null
@@ -1,134 +0,0 @@
-"""
-Usage:
-    strip_models.py <models_input_dir> <stripped_output_dir>
-
-    Strips tensor data from model state_dicts while preserving metadata.
-    Used to create lightweight models for testing model classification.
-
-Parameters:
-  <models_input_dir>     Directory containing original models.
-  <stripped_output_dir>  Directory where stripped models will be saved.
-
-Options:
-  -h, --help             Show this help message and exit
-"""
-
-import argparse
-import json
-import shutil
-import sys
-from pathlib import Path
-from typing import Optional
-
-import humanize
-import torch
-
-from invokeai.backend.model_manager.model_on_disk import ModelOnDisk, StateDict
-from invokeai.backend.model_manager.search import ModelSearch
-
-METADATA_KEY = "metadata_key_for_stripped_models"
-
-
-def strip(v):
-    match v:
-        case torch.Tensor():
-            return {"shape": v.shape, "dtype": str(v.dtype), "fakeTensor": True}
-        case dict():
-            return {k: strip(v) for k, v in v.items()}
-        case list() | tuple():
-            return [strip(x) for x in v]
-        case _:
-            return v
-
-
-STR_TO_DTYPE = {str(dtype): dtype for dtype in torch.__dict__.values() if isinstance(dtype, torch.dtype)}
-
-
-def dress(v):
-    match v:
-        case {"shape": shape, "dtype": dtype_str, "fakeTensor": True}:
-            dtype = STR_TO_DTYPE[dtype_str]
-            return torch.empty(shape, dtype=dtype)
-        case dict():
-            return {k: dress(v) for k, v in v.items()}
-        case list() | tuple():
-            return [dress(x) for x in v]
-        case _:
-            return v
-
-
-def load_stripped_model(path: Path, *args, **kwargs):
-    with open(path, "r") as f:
-        contents = json.load(f)
-        contents.pop(METADATA_KEY, None)
-    return dress(contents)
-
-
-class StrippedModelOnDisk(ModelOnDisk):
-    def load_state_dict(self, path: Optional[Path] = None) -> StateDict:
-        path = self.resolve_weight_file(path)
-        return load_stripped_model(path)
-
-    def metadata(self, path: Optional[Path] = None) -> dict[str, str]:
-        path = self.resolve_weight_file(path)
-        with open(path, "r") as f:
-            contents = json.load(f)
-        return contents.get(METADATA_KEY, {})
-
-
-def create_stripped_model(original_model_path: Path, stripped_model_path: Path) -> ModelOnDisk:
-    original = ModelOnDisk(original_model_path)
-    if original.path.is_file():
-        shutil.copy2(original.path, stripped_model_path)
-    else:
-        shutil.copytree(original.path, stripped_model_path, dirs_exist_ok=True)
-    stripped = ModelOnDisk(stripped_model_path)
-    print(f"Created clone of {original.name} at {stripped.path}")
-
-    for component_path in stripped.weight_files():
-        original_state_dict = stripped.load_state_dict(component_path)
-
-        stripped_state_dict = strip(original_state_dict)  # type: ignore
-        metadata = stripped.metadata()
-        contents = {**stripped_state_dict, METADATA_KEY: metadata}
-        with open(component_path, "w") as f:
-            json.dump(contents, f, indent=4)
-
-    before_size = humanize.naturalsize(original.size())
-    after_size = humanize.naturalsize(stripped.size())
-    print(f"{original.name} before: {before_size}, after: {after_size}")
-
-    return stripped
-
-
-def parse_arguments():
-    class Parser(argparse.ArgumentParser):
-        def error(self, reason):
-            raise ValueError(reason)
-
-    parser = Parser()
-    parser.add_argument("models_input_dir", type=Path)
-    parser.add_argument("stripped_output_dir", type=Path)
-
-    try:
-        args = parser.parse_args()
-    except ValueError as e:
-        print(f"Error: {e}", file=sys.stderr)
-        print(__doc__, file=sys.stderr)
-        sys.exit(2)
-
-    if not args.models_input_dir.exists():
-        parser.error(f"Error: Input models directory '{args.models_input_dir}' does not exist.")
-    if not args.models_input_dir.is_dir():
-        parser.error(f"Error: '{args.input_models_dir}' is not a directory.")
-
-    return args
-
-
-if __name__ == "__main__":
-    args = parse_arguments()
-    model_paths = sorted(ModelSearch().search(args.models_input_dir))
-
-    for path in model_paths:
-        stripped_path = args.stripped_output_dir / path.name
-        create_stripped_model(path, stripped_path)
diff --git a/tests/app/services/model_install/test_model_install.py b/tests/app/services/model_install/test_model_install.py
index 846cbc2906..f1b24aefd2 100644
--- a/tests/app/services/model_install/test_model_install.py
+++ b/tests/app/services/model_install/test_model_install.py
@@ -32,9 +32,8 @@ from invokeai.app.services.model_install.model_install_common import (
     URLModelSource,
 )
 from invokeai.app.services.model_records import ModelRecordChanges, UnknownModelException
-from invokeai.backend.model_manager.config import (
+from invokeai.backend.model_manager.taxonomy import (
     BaseModelType,
-    InvalidModelConfigException,
     ModelFormat,
     ModelRepoVariant,
     ModelType,
@@ -71,7 +70,7 @@ def test_registration_meta(mm2_installer: ModelInstallServiceBase, embedding_fil
 
 def test_registration_meta_override_fail(mm2_installer: ModelInstallServiceBase, embedding_file: Path) -> None:
     key = None
-    with pytest.raises((ValidationError, InvalidModelConfigException)):
+    with pytest.raises(ValidationError):
         key = mm2_installer.register_path(
             embedding_file, ModelRecordChanges(name="banana_sushi", type=ModelType("lora"))
         )
diff --git a/tests/app/services/model_records/test_model_records_sql.py b/tests/app/services/model_records/test_model_records_sql.py
index 41bbc2c024..df124b8115 100644
--- a/tests/app/services/model_records/test_model_records_sql.py
+++ b/tests/app/services/model_records/test_model_records_sql.py
@@ -16,15 +16,24 @@ from invokeai.app.services.model_records import (
     UnknownModelException,
 )
 from invokeai.app.services.model_records.model_records_base import ModelRecordChanges
-from invokeai.backend.model_manager import BaseModelType, ModelFormat, ModelType
-from invokeai.backend.model_manager.config import (
-    ControlAdapterDefaultSettings,
-    MainDiffusersConfig,
+from invokeai.backend.model_manager.configs.controlnet import ControlAdapterDefaultSettings
+from invokeai.backend.model_manager.configs.main import (
+    Main_Diffusers_SD1_Config,
+    Main_Diffusers_SD2_Config,
+    Main_Diffusers_SDXL_Config,
     MainModelDefaultSettings,
-    TI_File_Config,
-    VAEDiffusersConfig,
 )
-from invokeai.backend.model_manager.taxonomy import ModelSourceType
+from invokeai.backend.model_manager.configs.textual_inversion import TI_File_SD1_Config
+from invokeai.backend.model_manager.configs.vae import VAE_Diffusers_SD1_Config
+from invokeai.backend.model_manager.taxonomy import (
+    BaseModelType,
+    ModelFormat,
+    ModelRepoVariant,
+    ModelSourceType,
+    ModelType,
+    ModelVariantType,
+    SchedulerPredictionType,
+)
 from invokeai.backend.util.logging import InvokeAILogger
 from tests.fixtures.sqlite_database import create_mock_sqlite_database
 
@@ -40,8 +49,8 @@ def store(
     return ModelRecordServiceSQL(db, logger)
 
 
-def example_ti_config(key: Optional[str] = None) -> TI_File_Config:
-    config = TI_File_Config(
+def example_ti_config(key: Optional[str] = None) -> TI_File_SD1_Config:
+    config = TI_File_SD1_Config(
         source="test/source/",
         source_type=ModelSourceType.Path,
         path="/tmp/pokemon.bin",
@@ -61,7 +70,7 @@ def test_type(store: ModelRecordServiceBase):
     config = example_ti_config("key1")
     store.add_model(config)
     config1 = store.get_model("key1")
-    assert isinstance(config1, TI_File_Config)
+    assert isinstance(config1, TI_File_SD1_Config)
 
 
 def test_raises_on_violating_uniqueness(store: ModelRecordServiceBase):
@@ -122,7 +131,7 @@ def test_exists(store: ModelRecordServiceBase):
 
 
 def test_filter(store: ModelRecordServiceBase):
-    config1 = MainDiffusersConfig(
+    config1 = Main_Diffusers_SD1_Config(
         key="config1",
         path="/tmp/config1",
         name="config1",
@@ -132,8 +141,11 @@ def test_filter(store: ModelRecordServiceBase):
         file_size=1001,
         source="test/source",
         source_type=ModelSourceType.Path,
+        variant=ModelVariantType.Normal,
+        prediction_type=SchedulerPredictionType.Epsilon,
+        repo_variant=ModelRepoVariant.Default,
     )
-    config2 = MainDiffusersConfig(
+    config2 = Main_Diffusers_SD1_Config(
         key="config2",
         path="/tmp/config2",
         name="config2",
@@ -143,17 +155,21 @@ def test_filter(store: ModelRecordServiceBase):
         file_size=1002,
         source="test/source",
         source_type=ModelSourceType.Path,
+        variant=ModelVariantType.Normal,
+        prediction_type=SchedulerPredictionType.Epsilon,
+        repo_variant=ModelRepoVariant.Default,
     )
-    config3 = VAEDiffusersConfig(
+    config3 = VAE_Diffusers_SD1_Config(
         key="config3",
         path="/tmp/config3",
         name="config3",
-        base=BaseModelType("sd-2"),
+        base=BaseModelType.StableDiffusion1,
         type=ModelType.VAE,
         hash="CONFIG3HASH",
         file_size=1003,
         source="test/source",
         source_type=ModelSourceType.Path,
+        repo_variant=ModelRepoVariant.Default,
     )
     for c in config1, config2, config3:
         store.add_model(c)
@@ -176,7 +192,7 @@ def test_filter(store: ModelRecordServiceBase):
 
 
 def test_unique(store: ModelRecordServiceBase):
-    config1 = MainDiffusersConfig(
+    config1 = Main_Diffusers_SD1_Config(
         path="/tmp/config1",
         base=BaseModelType.StableDiffusion1,
         type=ModelType.Main,
@@ -185,28 +201,35 @@ def test_unique(store: ModelRecordServiceBase):
         file_size=1004,
         source="test/source/",
         source_type=ModelSourceType.Path,
+        variant=ModelVariantType.Normal,
+        prediction_type=SchedulerPredictionType.Epsilon,
+        repo_variant=ModelRepoVariant.Default,
     )
-    config2 = MainDiffusersConfig(
+    config2 = Main_Diffusers_SD2_Config(
         path="/tmp/config2",
-        base=BaseModelType("sd-2"),
+        base=BaseModelType.StableDiffusion2,
         type=ModelType.Main,
         name="nonuniquename",
         hash="CONFIG1HASH",
         file_size=1005,
         source="test/source/",
         source_type=ModelSourceType.Path,
+        variant=ModelVariantType.Normal,
+        prediction_type=SchedulerPredictionType.Epsilon,
+        repo_variant=ModelRepoVariant.Default,
     )
-    config3 = VAEDiffusersConfig(
+    config3 = VAE_Diffusers_SD1_Config(
         path="/tmp/config3",
-        base=BaseModelType("sd-2"),
+        base=BaseModelType.StableDiffusion1,
         type=ModelType.VAE,
         name="nonuniquename",
         hash="CONFIG1HASH",
         file_size=1006,
         source="test/source/",
         source_type=ModelSourceType.Path,
+        repo_variant=ModelRepoVariant.Default,
     )
-    config4 = MainDiffusersConfig(
+    config4 = Main_Diffusers_SD1_Config(
         path="/tmp/config4",
         base=BaseModelType.StableDiffusion1,
         type=ModelType.Main,
@@ -215,6 +238,9 @@ def test_unique(store: ModelRecordServiceBase):
         file_size=1007,
         source="test/source/",
         source_type=ModelSourceType.Path,
+        variant=ModelVariantType.Normal,
+        prediction_type=SchedulerPredictionType.Epsilon,
+        repo_variant=ModelRepoVariant.Default,
     )
     # config1, config2 and config3 are compatible because they have unique combos
     # of name, type and base
@@ -229,7 +255,7 @@ def test_unique(store: ModelRecordServiceBase):
 
 
 def test_filter_2(store: ModelRecordServiceBase):
-    config1 = MainDiffusersConfig(
+    config1 = Main_Diffusers_SD1_Config(
         path="/tmp/config1",
         name="config1",
         base=BaseModelType.StableDiffusion1,
@@ -238,8 +264,11 @@ def test_filter_2(store: ModelRecordServiceBase):
         file_size=1008,
         source="test/source/",
         source_type=ModelSourceType.Path,
+        variant=ModelVariantType.Normal,
+        prediction_type=SchedulerPredictionType.Epsilon,
+        repo_variant=ModelRepoVariant.Default,
     )
-    config2 = MainDiffusersConfig(
+    config2 = Main_Diffusers_SD1_Config(
         path="/tmp/config2",
         name="config2",
         base=BaseModelType.StableDiffusion1,
@@ -248,28 +277,37 @@ def test_filter_2(store: ModelRecordServiceBase):
         file_size=1009,
         source="test/source/",
         source_type=ModelSourceType.Path,
+        variant=ModelVariantType.Normal,
+        prediction_type=SchedulerPredictionType.Epsilon,
+        repo_variant=ModelRepoVariant.Default,
     )
-    config3 = MainDiffusersConfig(
+    config3 = Main_Diffusers_SD2_Config(
         path="/tmp/config3",
         name="dup_name1",
-        base=BaseModelType("sd-2"),
+        base=BaseModelType.StableDiffusion2,
         type=ModelType.Main,
         hash="CONFIG3HASH",
         file_size=1010,
         source="test/source/",
         source_type=ModelSourceType.Path,
+        variant=ModelVariantType.Normal,
+        prediction_type=SchedulerPredictionType.Epsilon,
+        repo_variant=ModelRepoVariant.Default,
     )
-    config4 = MainDiffusersConfig(
+    config4 = Main_Diffusers_SDXL_Config(
         path="/tmp/config4",
         name="dup_name1",
-        base=BaseModelType("sdxl"),
+        base=BaseModelType.StableDiffusionXL,
         type=ModelType.Main,
         hash="CONFIG3HASH",
         file_size=1011,
         source="test/source/",
         source_type=ModelSourceType.Path,
+        variant=ModelVariantType.Normal,
+        prediction_type=SchedulerPredictionType.Epsilon,
+        repo_variant=ModelRepoVariant.Default,
     )
-    config5 = VAEDiffusersConfig(
+    config5 = VAE_Diffusers_SD1_Config(
         path="/tmp/config5",
         name="dup_name1",
         base=BaseModelType.StableDiffusion1,
@@ -278,6 +316,7 @@ def test_filter_2(store: ModelRecordServiceBase):
         file_size=1012,
         source="test/source/",
         source_type=ModelSourceType.Path,
+        repo_variant=ModelRepoVariant.Default,
     )
     for c in config1, config2, config3, config4, config5:
         store.add_model(c)
diff --git a/tests/backend/ip_adapter/test_ip_adapter.py b/tests/backend/ip_adapter/test_ip_adapter.py
index 138de39888..87b696c534 100644
--- a/tests/backend/ip_adapter/test_ip_adapter.py
+++ b/tests/backend/ip_adapter/test_ip_adapter.py
@@ -1,7 +1,7 @@
 import pytest
 import torch
 
-from invokeai.backend.model_manager import BaseModelType, ModelType, SubModelType
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType, SubModelType
 from invokeai.backend.stable_diffusion.diffusion.unet_attention_patcher import UNetAttentionPatcher
 from invokeai.backend.util.test_utils import install_and_load_model
 
diff --git a/tests/backend/model_manager/model_manager_fixtures.py b/tests/backend/model_manager/model_manager_fixtures.py
index a1933d7eff..6ca4b77f0a 100644
--- a/tests/backend/model_manager/model_manager_fixtures.py
+++ b/tests/backend/model_manager/model_manager_fixtures.py
@@ -14,15 +14,19 @@ from invokeai.app.services.model_install import ModelInstallService, ModelInstal
 from invokeai.app.services.model_load import ModelLoadService, ModelLoadServiceBase
 from invokeai.app.services.model_manager import ModelManagerService, ModelManagerServiceBase
 from invokeai.app.services.model_records import ModelRecordServiceBase, ModelRecordServiceSQL
-from invokeai.backend.model_manager import BaseModelType, ModelFormat, ModelType, ModelVariantType
-from invokeai.backend.model_manager.config import (
-    LoRADiffusersConfig,
-    MainCheckpointConfig,
-    MainDiffusersConfig,
-    VAEDiffusersConfig,
-)
+from invokeai.backend.model_manager.configs.lora import LoRA_Diffusers_SD1_Config, LoRA_Diffusers_SDXL_Config
+from invokeai.backend.model_manager.configs.main import Main_Checkpoint_SD1_Config, Main_Diffusers_SDXL_Config
+from invokeai.backend.model_manager.configs.vae import VAE_Diffusers_SD1_Config
 from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
-from invokeai.backend.model_manager.taxonomy import ModelSourceType
+from invokeai.backend.model_manager.taxonomy import (
+    BaseModelType,
+    ModelFormat,
+    ModelRepoVariant,
+    ModelSourceType,
+    ModelType,
+    ModelVariantType,
+    SchedulerPredictionType,
+)
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 from tests.backend.model_manager.model_metadata.metadata_examples import (
@@ -132,19 +136,20 @@ def mm2_record_store(mm2_app_config: InvokeAIAppConfig) -> ModelRecordServiceBas
     db = create_mock_sqlite_database(mm2_app_config, logger)
     store = ModelRecordServiceSQL(db, logger)
     # add five simple config records to the database
-    config1 = VAEDiffusersConfig(
+    config1 = VAE_Diffusers_SD1_Config(
         key="test_config_1",
         path="/tmp/foo1",
         format=ModelFormat.Diffusers,
         name="test2",
-        base=BaseModelType.StableDiffusion2,
+        base=BaseModelType.StableDiffusion1,
         type=ModelType.VAE,
         hash="111222333444",
         file_size=4096,
         source="stabilityai/sdxl-vae",
         source_type=ModelSourceType.HFRepoID,
+        repo_variant=ModelRepoVariant.Default,
     )
-    config2 = MainCheckpointConfig(
+    config2 = Main_Checkpoint_SD1_Config(
         key="test_config_2",
         path="/tmp/foo2.ckpt",
         name="model1",
@@ -157,8 +162,9 @@ def mm2_record_store(mm2_app_config: InvokeAIAppConfig) -> ModelRecordServiceBas
         file_size=8192,
         source="https://civitai.com/models/206883/split",
         source_type=ModelSourceType.Url,
+        prediction_type=SchedulerPredictionType.Epsilon,
     )
-    config3 = MainDiffusersConfig(
+    config3 = Main_Diffusers_SDXL_Config(
         key="test_config_3",
         path="/tmp/foo3",
         format=ModelFormat.Diffusers,
@@ -170,8 +176,11 @@ def mm2_record_store(mm2_app_config: InvokeAIAppConfig) -> ModelRecordServiceBas
         source="author3/model3",
         description="This is test 3",
         source_type=ModelSourceType.HFRepoID,
+        variant=ModelVariantType.Normal,
+        prediction_type=SchedulerPredictionType.Epsilon,
+        repo_variant=ModelRepoVariant.Default,
     )
-    config4 = LoRADiffusersConfig(
+    config4 = LoRA_Diffusers_SDXL_Config(
         key="test_config_4",
         path="/tmp/foo4",
         format=ModelFormat.Diffusers,
@@ -183,7 +192,7 @@ def mm2_record_store(mm2_app_config: InvokeAIAppConfig) -> ModelRecordServiceBas
         source="author4/model4",
         source_type=ModelSourceType.HFRepoID,
     )
-    config5 = LoRADiffusersConfig(
+    config5 = LoRA_Diffusers_SD1_Config(
         key="test_config_5",
         path="/tmp/foo5",
         format=ModelFormat.Diffusers,
diff --git a/tests/backend/model_manager/util/test_hf_model_select.py b/tests/backend/model_manager/util/test_hf_model_select.py
index 9ef61bdcda..5530fa0c90 100644
--- a/tests/backend/model_manager/util/test_hf_model_select.py
+++ b/tests/backend/model_manager/util/test_hf_model_select.py
@@ -3,7 +3,7 @@ from typing import List
 
 import pytest
 
-from invokeai.backend.model_manager import ModelRepoVariant
+from invokeai.backend.model_manager.taxonomy import ModelRepoVariant
 from invokeai.backend.model_manager.util.select_hf_files import filter_files
 
 
diff --git a/tests/conftest.py b/tests/conftest.py
index 44a29043a1..d2835120e9 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -7,14 +7,9 @@
 import logging
 import shutil
 from pathlib import Path
-from types import SimpleNamespace
 
-import picklescan.scanner
 import pytest
-import safetensors.torch
-import torch
 
-import invokeai.backend.quantization.gguf.loaders as gguf_loaders
 from invokeai.app.services.board_image_records.board_image_records_sqlite import SqliteBoardImageRecordStorage
 from invokeai.app.services.board_records.board_records_sqlite import SqliteBoardRecordStorage
 from invokeai.app.services.bulk_download.bulk_download_default import BulkDownloadService
@@ -25,7 +20,6 @@ from invokeai.app.services.invocation_services import InvocationServices
 from invokeai.app.services.invocation_stats.invocation_stats_default import InvocationStatsService
 from invokeai.app.services.invoker import Invoker
 from invokeai.backend.util.logging import InvokeAILogger
-from scripts.strip_models import load_stripped_model
 from tests.backend.model_manager.model_manager_fixtures import *  # noqa: F403
 from tests.fixtures.sqlite_database import create_mock_sqlite_database  # noqa: F401
 from tests.test_nodes import TestEventService
@@ -82,23 +76,3 @@ def invokeai_root_dir(tmp_path_factory) -> Path:
     temp_dir: Path = tmp_path_factory.mktemp("data") / "invokeai_root"
     shutil.copytree(root_template, temp_dir)
     return temp_dir
-
-
-@pytest.fixture(scope="function")
-def override_model_loading(monkeypatch):
-    """The legacy model probe directly calls model loading functions (e.g. torch.load) and also performs file scanning
-     via picklescan.scanner.scan_file_path. This fixture replaces these functions with test-friendly versions for
-     model files that have been 'stripped' to reduce their size (see scripts/strip_models.py).
-
-    Ideally, model loading would be injected as a dependency (i.e. ModelOnDisk) - but to avoid modifying the legacy probe,
-    we monkeypatch as a temporary workaround until the legacy probe is fully deprecated.
-    """
-    monkeypatch.setattr(torch, "load", load_stripped_model)
-    monkeypatch.setattr(safetensors.torch, "load", load_stripped_model)
-    monkeypatch.setattr(safetensors.torch, "load_file", load_stripped_model)
-    monkeypatch.setattr(gguf_loaders, "gguf_sd_loader", load_stripped_model)
-
-    def fake_scan(*args, **kwargs):
-        return SimpleNamespace(infected_files=0, scan_err=None)
-
-    monkeypatch.setattr(picklescan.scanner, "scan_file_path", fake_scan)
diff --git a/tests/model_identification/README.md b/tests/model_identification/README.md
new file mode 100644
index 0000000000..d668cc88a6
--- /dev/null
+++ b/tests/model_identification/README.md
@@ -0,0 +1,149 @@
+# Model Probe (Identification) Testing
+
+Invoke's model Identification system is tested against example model files. Test cases are lightweight representations of real models which have been "stripped" of their tensor data.
+
+## Setup
+
+Test cases are stored with git lfs. You _must_ [install git lfs](https://git-lfs.com/) to pull down the test cases and add to them.
+
+```bash
+# Only need to do this once
+git lfs install
+# Pull the actual model files down - if you just do `git pull` you'll only get pointers
+git lfs pull
+```
+
+## Running the Tests
+
+To run the tests use:
+
+```bash
+pytest -v tests/test_model_probe/test_identification.py
+```
+
+## Stripped Model Files
+
+Invoke abstracts the loading of a model's state dict and metadata in a class called [`ModelOnDisk`](../invokeai/backend/model_manager/model_on_disk.py). This class loads real model weights. We use it to inspect models and identify them.
+
+For testing purposes, we create a stripped-down version of model weights that contain only the model structure and metadata for each key, without the actual tensor data. The state dict structure is typically all we need to identify models; the tensors themselves are not needed. This allows us to store test cases in the repo without adding many gigabytes of data.
+
+To see how this works, check out [`StrippedModelOnDisk`](./stripped_model_on_disk.py). This class includes logic to strip models and to load these stripped models for testing.
+
+### Some Models Cannot Be Stripped
+
+Certain models cannot be stripped because identification relies on inspecting the actual tensor data. We have to store the full model files for these test cases.
+
+> Currently, the only models that cannot be stripped are [`spandrel`](https://github.com/chaiNNer-org/spandrel/) image-to-image models. `spandrel` supports _many_ model architectures but doesn't provide a way to identify or assert support for a model by its state dict structure alone.
+>
+> To positively identify these models, we must attempt to load the model using spandrel. If it loads successfully, we assume it is a supported model. Therefore, we cannot strip these models and must store the full model files in the test cases. We only store one such model to keep the test suite size manageable.
+>
+> `StrippedModelOnDisk` will simply pass-through the "live" tensor data for these models when loading them to test.
+
+## Adding New Test Cases
+
+Run the [`strip_model.py`](./strip_model.py) script to create a new test case. For example:
+
+```bash
+python strip_model.py /path/to/your/model --output_dir ./stripped_models
+```
+
+It supports single-file models and multi-file models (e.g. diffusers-style models). The output will be a directory named with a UUID, containing the stripped model files and a dummy `__test_metadata__.json` file.
+
+Example output structure for a single-file model:
+
+```
+stripped_models/
+└── 19fd1a40-c5b7-4734-bd3a-6e0e948cce0b/
+    ├── __test_metadata__.json
+    └── Standard Reference (XLabs FLUX IP-Adapter v2).safetensors
+```
+
+This test metadata file should contain a single JSON dict and must be filled out manually with the expected identification results.
+
+### Structure of `__test_metadata__.json`
+
+This file contains a single JSON dict. Here's an example for a FLUX IP Adapter checkpoint:
+
+```json
+{
+  "source": "https://huggingface.co/XLabs-AI/flux-ip-adapter-v2/resolve/main/ip_adapter.safetensors",
+  "file_name": "Standard Reference (XLabs FLUX IP-Adapter v2).safetensors",
+  "expected_config_attrs": {
+    "type": "ip_adapter",
+    "format": "checkpoint",
+    "base": "flux"
+  }
+}
+```
+
+See the details below for each field.
+
+#### `"source"`
+
+A string indicating the source of the model (e.g. a Hugging Face repo ID or URL). This is not used for identification, but is useful for reference so we know where the model came from. Nothing will break if this field is missing or incorrect, but it is good practice to fill it out.
+
+- Example HF Repo ID: `"RunDiffusion/Juggernaut-XL-v9"`
+- Example URL: `"https://huggingface.co/XpucT/Deliberate/resolve/main/Deliberate_v5.safetensors"`
+
+### `"file_name"`
+
+If the model is a single file (e.g. a `.safetensors` file), this is the name of that file. The test suite will look for this file in the test case directory.
+
+If the model is multi-file (e.g. diffusers-style), omit this key or set it to a falsey value like `null` or an empty string.
+
+- Example: `"model.safetensors"`
+
+> The `strip_model.py` script will automatically fill this field in for single-file models.
+
+### `"expected_config_attrs"`
+
+This field is a dict of expected configuration attributes for the model. It is required for all test cases.
+
+It is used to verify that the model's configuration matches expectations. The keys and values in this dict depend on the specific model and its configuration.
+
+These attributes must be included, as they are the primary discriminators for models:
+
+- `"type"`: The type of the model. This is the value of the `ModelType` enum.
+- `"format"`: The format of the model files. This is the value of the `ModelFormat` enum.
+- `"base"`: The base model pipeline architecture associated with this model. Many models do not have an associated base. For these, use `"any"`. This is the value of the `BaseModelType` enum.
+
+Depending on the kind of model, these additional keys may be useful:
+
+- `"prediction_type"`: The prediction type used by the model. This is the value of the `SchedulerPredictionType` enum.
+- `"variant"`: The variant of the model, if applicable. This is the value of the `ModelVariantType` enum.
+
+To see all possible values for these enums, check out their definitions in [`invokeai/backend/model_manager/taxonomy.py`](../invokeai/backend/model_manager/taxonomy.py).
+
+For example, for a SD1.5 main (pipeline) inpainting model in diffusers format, you might have:
+
+```json
+{
+  "expected_config_attrs": {
+    "type": "main",
+    "format": "diffusers",
+    "base": "sd-1",
+    "prediction_type": "epsilon",
+    "variant": "inpaint"
+  }
+}
+```
+
+### `"notes"`
+
+This is an optional string field where you can add any notes or comments about the test case. It can be useful for providing context or explaining any special considerations.
+
+### `"override_fields"`
+
+In some rare cases, we may need to provide additional hints to the identification system to help it identify the model correctly.
+
+Currently, the only known case where we need extra information is to differentiate between single-file SD1.x, SD2.x and SDXL VAEs. These models have identical structures, so we need to provide a hint. Though it is far from ideal, we use simple string matching on the model's name to provide this hint.
+
+For example, when users install the `taesdxl` VAE from the HF repo `madebyollin/taesdxl`, the identification system will get the model name `taesdxl`. It sees "xl" in the name and infers that this is a SDXL VAE. To reproduce this in a test case, we add the following to `__test_metadata__.json`:
+
+```json
+{
+  "override_fields": {
+    "name": "taesdxl"
+  }
+}
+```
diff --git a/tests/test_model_probe/sd-1/main/dreamshaper-8-inpainting/unet/diffusion_pytorch_model.fp16.safetensors b/tests/model_identification/__init__.py
similarity index 100%
rename from tests/test_model_probe/sd-1/main/dreamshaper-8-inpainting/unet/diffusion_pytorch_model.fp16.safetensors
rename to tests/model_identification/__init__.py
diff --git a/tests/model_identification/strip_model.py b/tests/model_identification/strip_model.py
new file mode 100644
index 0000000000..8544845b3e
--- /dev/null
+++ b/tests/model_identification/strip_model.py
@@ -0,0 +1,112 @@
+"""
+Usage:
+    strip_model.py <model_path> <output_dir>
+
+    Strips tensor data from model state_dict while preserving metadata.
+    Used to create lightweight models for testing model classification.
+
+Parameters:
+  <model_path>     The path to the model to be stripped.
+  <output_dir>     Directory where stripped models will be saved (e.g. tests/test_model_probe/stripped_models)
+
+Options:
+  -h, --help       Show this help message and exit
+"""
+
+import argparse
+import json
+import shutil
+import sys
+from copy import deepcopy
+from pathlib import Path
+from typing import Any
+
+import humanize
+
+from invokeai.app.util.misc import uuid_string
+from invokeai.backend.model_manager.model_on_disk import ModelOnDisk
+from tests.model_identification.stripped_model_on_disk import StrippedModelOnDisk
+
+TEST_METADATA_FILENAME = "__test_metadata__.json"
+
+
+TEST_METADATA: dict[str, Any] = {
+    "source": "",
+    "file_name": "",
+    "override_fields": {},
+    "expected_config_attrs": {},
+    "notes": "",
+}
+
+
+def create_stripped_model(model_path: Path, output_dir: Path):
+    """Creates a stripped version of the model at model_path in output_dir. A test metadata file is also created."""
+
+    original_mod = ModelOnDisk(model_path)
+
+    # The stripped model will be stored in a new directory named with a UUID. This mirrors the application's
+    # normalized model storage file structure.
+    uuid = uuid_string()
+    stripped_model_dir = output_dir / uuid
+    stripped_model_dir.mkdir(parents=True, exist_ok=True)
+
+    test_metadata_content = deepcopy(TEST_METADATA)
+
+    if original_mod.path.is_file():
+        shutil.copy2(original_mod.path, stripped_model_dir / original_mod.path.name)
+        test_metadata_content["file_name"] = original_mod.path.name
+    else:
+        shutil.copytree(original_mod.path, stripped_model_dir, dirs_exist_ok=True)
+
+    stripped_mod = ModelOnDisk(stripped_model_dir)
+
+    print(f"Created clone of {original_mod.name} at {stripped_mod.path}")
+
+    for component_path in stripped_mod.weight_files():
+        original_state_dict = stripped_mod.load_state_dict(component_path)
+
+        stripped_state_dict = StrippedModelOnDisk.strip(original_state_dict)
+        metadata = stripped_mod.metadata()
+        contents = {**stripped_state_dict, StrippedModelOnDisk.METADATA_KEY: metadata}
+        component_path.write_text(json.dumps(contents, indent=2))
+
+    test_metadata_path = stripped_model_dir / TEST_METADATA_FILENAME
+    test_metadata_path.write_text(json.dumps(test_metadata_content, indent=2))
+
+    before_size = humanize.naturalsize(original_mod.size())
+    after_size = humanize.naturalsize(stripped_mod.size())
+
+    print(f"{original_mod.name} before: {before_size}, after: {after_size}")
+
+    return stripped_mod
+
+
+def parse_arguments():
+    class Parser(argparse.ArgumentParser):
+        def error(self, message: str):
+            raise ValueError(message)
+
+    parser = Parser()
+    parser.add_argument("model_path", type=Path)
+    parser.add_argument("output_dir", type=Path)
+
+    try:
+        args = parser.parse_args()
+    except ValueError as e:
+        print(f"Error: {e}", file=sys.stderr)
+        print(__doc__, file=sys.stderr)
+        sys.exit(2)
+
+    if not args.model_path.exists():
+        parser.error(f"Error: Input model path '{args.model_path}' does not exist.")
+
+    return args
+
+
+if __name__ == "__main__":
+    args = parse_arguments()
+
+    model_path = Path(args.model_path)
+    output_dir = Path(args.output_dir)
+
+    create_stripped_model(model_path, output_dir)
diff --git a/tests/model_identification/stripped_model_on_disk.py b/tests/model_identification/stripped_model_on_disk.py
new file mode 100644
index 0000000000..13d91e23e8
--- /dev/null
+++ b/tests/model_identification/stripped_model_on_disk.py
@@ -0,0 +1,83 @@
+import json
+from pathlib import Path
+from typing import Any, Optional
+
+import gguf
+import torch
+
+from invokeai.backend.model_manager.model_on_disk import ModelOnDisk, StateDict
+from invokeai.backend.quantization.gguf.ggml_tensor import GGMLTensor
+
+
+class StrippedModelOnDisk(ModelOnDisk):
+    METADATA_KEY = "metadata_key_for_stripped_models"
+    STR_TO_DTYPE = {str(dtype): dtype for dtype in torch.__dict__.values() if isinstance(dtype, torch.dtype)}
+
+    def load_state_dict(self, path: Optional[Path] = None) -> StateDict:
+        path = self.resolve_weight_file(path)
+        return self.load_stripped_model(path)
+
+    def metadata(self, path: Optional[Path] = None) -> dict[str, str]:
+        path = self.resolve_weight_file(path)
+        with open(path, "r") as f:
+            contents = json.load(f)
+        return contents.get(self.METADATA_KEY, {})
+
+    @classmethod
+    def strip(cls, v: Any):
+        match v:
+            case GGMLTensor():
+                # GGMLTensor needs special handling to preserve quantization metadata. It is a subclass of torch.Tensor,
+                # so we need to check for it before checking for torch.Tensor.
+                return {
+                    "quantized_data": cls.strip(v.quantized_data),
+                    "ggml_quantization_type": v._ggml_quantization_type.name,
+                    "tensor_shape": list(v.tensor_shape),
+                    "compute_dtype": str(v.compute_dtype),
+                    "fakeGGMLTensor": True,
+                }
+            case torch.Tensor():
+                return {"shape": v.shape, "dtype": str(v.dtype), "fakeTensor": True}
+            case dict():
+                return {k: cls.strip(v) for k, v in v.items()}
+            case list() | tuple():
+                return [cls.strip(x) for x in v]
+            case _:
+                return v
+
+    @classmethod
+    def dress(cls, v: Any):
+        match v:
+            case {
+                "quantized_data": quantized_data,
+                "ggml_quantization_type": qtype_name,
+                "tensor_shape": tensor_shape,
+                "compute_dtype": compute_dtype_str,
+                "fakeGGMLTensor": True,
+            }:
+                # Reconstruct the GGMLTensor from stripped data
+                qtype = gguf.GGMLQuantizationType[qtype_name]
+                compute_dtype = cls.STR_TO_DTYPE[compute_dtype_str]
+                dressed_quantized_data = cls.dress(quantized_data)
+                return GGMLTensor(
+                    data=dressed_quantized_data,
+                    ggml_quantization_type=qtype,
+                    tensor_shape=torch.Size(tensor_shape),
+                    compute_dtype=compute_dtype,
+                )
+            case {"shape": shape, "dtype": dtype_str, "fakeTensor": True}:
+                dtype = cls.STR_TO_DTYPE[dtype_str]
+                return torch.empty(shape, dtype=dtype)
+            case dict():
+                return {k: cls.dress(v) for k, v in v.items()}
+            case list() | tuple():
+                return [cls.dress(x) for x in v]
+            case _:
+                return v
+
+    @classmethod
+    def load_stripped_model(cls, path: Path, *args, **kwargs):
+        with open(path, "r") as f:
+            contents = json.load(f)
+            contents.pop(cls.METADATA_KEY, None)
+        return cls.dress(contents)
diff --git a/tests/model_identification/stripped_models/01b5565e-133c-4e9b-afdf-c8b028fc3560/__test_metadata__.json b/tests/model_identification/stripped_models/01b5565e-133c-4e9b-afdf-c8b028fc3560/__test_metadata__.json
new file mode 100644
index 0000000000..5f75cf6392
--- /dev/null
+++ b/tests/model_identification/stripped_models/01b5565e-133c-4e9b-afdf-c8b028fc3560/__test_metadata__.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 160
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diff --git a/tests/test_model_probe/stripped_models/omi/sdxl_lora_full.safetensors b/tests/model_identification/stripped_models/ff775f86-a9f6-4a4b-88b0-9343a2f49b6e/sdxl_lora_full.safetensors
similarity index 100%
rename from tests/test_model_probe/stripped_models/omi/sdxl_lora_full.safetensors
rename to tests/model_identification/stripped_models/ff775f86-a9f6-4a4b-88b0-9343a2f49b6e/sdxl_lora_full.safetensors
diff --git a/tests/model_identification/test_identification.py b/tests/model_identification/test_identification.py
new file mode 100644
index 0000000000..dbd377f2c1
--- /dev/null
+++ b/tests/model_identification/test_identification.py
@@ -0,0 +1,109 @@
+import json
+from dataclasses import dataclass
+from enum import Enum
+from pathlib import Path
+from pprint import pformat
+from typing import Any
+
+import pytest
+
+from invokeai.backend.model_manager.configs.controlnet import ControlAdapterDefaultSettings
+from invokeai.backend.model_manager.configs.factory import (
+    ModelConfigFactory,
+)
+from invokeai.backend.model_manager.configs.main import MainModelDefaultSettings
+from invokeai.backend.model_manager.taxonomy import (
+    BaseModelType,
+)
+from invokeai.backend.util.logging import InvokeAILogger
+from tests.model_identification.stripped_model_on_disk import StrippedModelOnDisk
+
+logger = InvokeAILogger.get_logger(__file__)
+
+
+@pytest.mark.parametrize(
+    "model_name,preprocessor",
+    [
+        ("some_canny_model", "canny_image_processor"),
+        ("some_depth_model", "depth_anything_image_processor"),
+        ("some_pose_model", "dw_openpose_image_processor"),
+        ("i like turtles", None),
+    ],
+)
+def test_controlnet_t2i_default_settings(model_name: str, preprocessor: str | None):
+    assert ControlAdapterDefaultSettings.from_model_name(model_name).preprocessor == preprocessor
+
+
+@pytest.mark.parametrize(
+    "base,attrs",
+    [
+        (BaseModelType.StableDiffusion1, {"width": 512, "height": 512}),
+        (BaseModelType.StableDiffusion2, {"width": 768, "height": 768}),
+        (BaseModelType.StableDiffusionXL, {"width": 1024, "height": 1024}),
+        (BaseModelType.StableDiffusionXLRefiner, None),
+        (BaseModelType.Any, None),
+    ],
+)
+def test_default_settings_main(base: BaseModelType, attrs: dict[str, Any] | None):
+    settings = MainModelDefaultSettings.from_base(base)
+    if attrs is None:
+        assert settings is None
+    else:
+        for key, value in attrs.items():
+            assert getattr(settings, key) == value
+
+
+@dataclass
+class ModelAttributeMismatch:
+    key: str
+    expected: Any
+    actual: Any
+
+    def __str__(self) -> str:
+        return f"{self.key} expected {self.expected}, got {self.actual}"
+
+
+def _get_model_paths(datadir: Path) -> list[Path]:
+    """Helper to collect model paths for parameterization."""
+    return [p for p in (datadir / "stripped_models").iterdir() if p.is_dir()]
+
+
+@pytest.mark.parametrize("model_path", _get_model_paths(Path(__file__).parent))
+def test_model_identification(model_path: Path):
+    """Verifies results from ModelConfigBase.classify are consistent with those from ModelProbe.probe.
+    The test paths are gathered from the 'test_model_probe' directory.
+    """
+    id = model_path.name
+    test_metadata_path = model_path / "__test_metadata__.json"
+    test_metadata = json.loads(test_metadata_path.read_text())
+
+    if file_name := test_metadata.get("file_name", ""):
+        model_path = model_path / file_name
+
+    mod = StrippedModelOnDisk(model_path)
+
+    override_fields = test_metadata.get("override_fields", None)
+
+    try:
+        result = ModelConfigFactory.from_model_on_disk(mod, override_fields, allow_unknown=False)
+    except Exception as e:
+        print(mod.path)
+        pytest.fail(f"{id}: Exception during model probing: {e}")
+
+    if result.config is None:
+        pytest.fail(f"{id}: no match, detailed results:\n{pformat(result.details)}")
+
+    config = result.config
+
+    mismatched_attrs: list[ModelAttributeMismatch] = []
+
+    for key, expected_value in test_metadata["expected_config_attrs"].items():
+        actual_value = getattr(config, key)
+        if isinstance(actual_value, Enum):
+            actual_value = actual_value.value
+        if actual_value != expected_value:
+            mismatched_attrs.append(ModelAttributeMismatch(key, expected_value, actual_value))
+
+    if mismatched_attrs:
+        msg = "; ".join(str(m) for m in mismatched_attrs)
+        pytest.fail(f"{id}: {msg}")
diff --git a/tests/test_model_probe.py b/tests/test_model_probe.py
deleted file mode 100644
index f1249e4dc1..0000000000
--- a/tests/test_model_probe.py
+++ /dev/null
@@ -1,276 +0,0 @@
-import abc
-import json
-from pathlib import Path
-from typing import Any, get_args
-
-import pydantic
-import pytest
-import torch
-from polyfactory.factories.pydantic_factory import ModelFactory
-from sympy.testing.pytest import slow
-from torch import tensor
-
-from invokeai.backend.model_manager import BaseModelType, ModelFormat, ModelRepoVariant, ModelType, ModelVariantType
-from invokeai.backend.model_manager.config import (
-    AnyModelConfig,
-    Config_Base,
-    InvalidModelConfigException,
-    MainDiffusersConfig,
-    ModelConfigFactory,
-    get_model_discriminator_value,
-)
-from invokeai.backend.model_manager.legacy_probe import (
-    CkptType,
-    ModelProbe,
-    VaeFolderProbe,
-    get_default_settings_control_adapters,
-    get_default_settings_main,
-)
-from invokeai.backend.model_manager.model_on_disk import ModelOnDisk
-from invokeai.backend.model_manager.search import ModelSearch
-from invokeai.backend.util.logging import InvokeAILogger
-from scripts.strip_models import StrippedModelOnDisk
-
-logger = InvokeAILogger.get_logger(__file__)
-
-
-@pytest.mark.parametrize(
-    "vae_path,expected_type",
-    [
-        ("sd-vae-ft-mse", BaseModelType.StableDiffusion1),
-        ("sdxl-vae", BaseModelType.StableDiffusionXL),
-        ("taesd", BaseModelType.StableDiffusion1),
-        ("taesdxl", BaseModelType.StableDiffusionXL),
-    ],
-)
-def test_get_base_type(vae_path: str, expected_type: BaseModelType, datadir: Path):
-    sd1_vae_path = datadir / "vae" / vae_path
-    probe = VaeFolderProbe(sd1_vae_path)
-    base_type = probe.get_base_type()
-    assert base_type == expected_type
-    repo_variant = probe.get_repo_variant()
-    assert repo_variant == ModelRepoVariant.Default
-
-
-def test_repo_variant(datadir: Path):
-    probe = VaeFolderProbe(datadir / "vae" / "taesdxl-fp16")
-    repo_variant = probe.get_repo_variant()
-    assert repo_variant == ModelRepoVariant.FP16
-
-
-def test_controlnet_t2i_default_settings():
-    assert get_default_settings_control_adapters("some_canny_model").preprocessor == "canny_image_processor"
-    assert get_default_settings_control_adapters("some_depth_model").preprocessor == "depth_anything_image_processor"
-    assert get_default_settings_control_adapters("some_pose_model").preprocessor == "dw_openpose_image_processor"
-    assert get_default_settings_control_adapters("i like turtles") is None
-
-
-def test_default_settings_main():
-    assert get_default_settings_main(BaseModelType.StableDiffusion1).width == 512
-    assert get_default_settings_main(BaseModelType.StableDiffusion1).height == 512
-    assert get_default_settings_main(BaseModelType.StableDiffusion2).width == 512
-    assert get_default_settings_main(BaseModelType.StableDiffusion2).height == 512
-    assert get_default_settings_main(BaseModelType.StableDiffusionXL).width == 1024
-    assert get_default_settings_main(BaseModelType.StableDiffusionXL).height == 1024
-    assert get_default_settings_main(BaseModelType.StableDiffusionXLRefiner) is None
-    assert get_default_settings_main(BaseModelType.Any) is None
-
-
-def test_probe_handles_state_dict_with_integer_keys(tmp_path: Path):
-    # This structure isn't supported by invoke, but we still need to handle it gracefully.
-    # See https://github.com/invoke-ai/InvokeAI/issues/6044
-    state_dict_with_integer_keys: CkptType = {
-        320: (
-            {
-                "linear1.weight": tensor([1.0]),
-                "linear1.bias": tensor([1.0]),
-                "linear2.weight": tensor([1.0]),
-                "linear2.bias": tensor([1.0]),
-            },
-            {
-                "linear1.weight": tensor([1.0]),
-                "linear1.bias": tensor([1.0]),
-                "linear2.weight": tensor([1.0]),
-                "linear2.bias": tensor([1.0]),
-            },
-        ),
-    }
-    sd_path = tmp_path / "sd.pt"
-    torch.save(state_dict_with_integer_keys, sd_path)
-    with pytest.raises(InvalidModelConfigException):
-        ModelProbe.get_model_type_from_checkpoint(sd_path, state_dict_with_integer_keys)
-
-
-def test_probe_sd1_diffusers_inpainting(datadir: Path):
-    config = ModelProbe.probe(datadir / "sd-1/main/dreamshaper-8-inpainting")
-    assert isinstance(config, MainDiffusersConfig)
-    assert config.base is BaseModelType.StableDiffusion1
-    assert config.variant is ModelVariantType.Inpaint
-    assert config.repo_variant is ModelRepoVariant.FP16
-
-
-class MinimalConfigExample(Config_Base):
-    type: ModelType = ModelType.Main
-    format: ModelFormat = ModelFormat.Checkpoint
-    fun_quote: str
-
-    @classmethod
-    def matches(cls, mod: ModelOnDisk, **overrides) -> bool:
-        return mod.path.suffix == ".json"
-
-    @classmethod
-    def parse(cls, mod: ModelOnDisk) -> dict[str, Any]:
-        with open(mod.path, "r") as f:
-            contents = json.load(f)
-
-        return {
-            "fun_quote": contents["quote"],
-            "base": BaseModelType.Any,
-        }
-
-
-def test_minimal_working_example(datadir: Path):
-    model_path = datadir / "minimal_config_model.json"
-    overrides = {"base": BaseModelType.StableDiffusion1}
-    config = ModelConfigFactory.from_model_on_disk(
-        mod=model_path,
-        overrides=overrides,
-    )
-
-    assert isinstance(config, MinimalConfigExample)
-    assert config.base == BaseModelType.StableDiffusion1
-    assert config.path == model_path.as_posix()
-    assert config.fun_quote == "Minimal working example of a ModelConfigBase subclass"
-
-
-@pytest.mark.xfail(reason="Known issue with 'helloyoung25d_V15j.safetensors'.", strict=True)
-def test_regression_against_model_probe(datadir: Path, override_model_loading):
-    """Verifies results from ModelConfigBase.classify are consistent with those from ModelProbe.probe.
-    The test paths are gathered from the 'test_model_probe' directory.
-    """
-    configs_with_tests = set()
-    model_paths = ModelSearch().search(datadir / "stripped_models")
-    fake_hash = "abcdefgh"  # skip hashing to make test quicker
-    fake_key = "123"  # fixed uuid for comparison
-
-    for path in model_paths:
-        legacy_config = new_config = None
-
-        try:
-            legacy_config = ModelProbe.probe(path, {"hash": fake_hash, "key": fake_key})
-        except InvalidModelConfigException:
-            pass
-
-        try:
-            stripped_mod = StrippedModelOnDisk(path)
-            new_config = ModelConfigFactory.from_model_on_disk(
-                mod=stripped_mod,
-                overrides={"hash": fake_hash, "key": fake_key},
-            )
-        except InvalidModelConfigException:
-            pass
-
-        if legacy_config and new_config:
-            assert type(legacy_config) is type(new_config)
-            assert legacy_config.model_dump_json() == new_config.model_dump_json()
-
-        elif legacy_config:
-            assert type(legacy_config) in Config_Base.USING_LEGACY_PROBE
-
-        elif new_config:
-            assert type(new_config) in Config_Base.USING_CLASSIFY_API
-
-        else:
-            raise ValueError(f"Both probe and classify failed to classify model at path {path}.")
-
-        config_type = type(legacy_config or new_config)
-        configs_with_tests.add(config_type)
-
-    untested_configs = Config_Base.all_config_classes() - configs_with_tests - {MinimalConfigExample}
-    logger.warning(f"Function test_regression_against_model_probe missing test case for: {untested_configs}")
-
-
-def create_fake_configs(config_cls, n):
-    factory_args = {
-        "__use_defaults__": True,
-        "__random_seed__": 1234,
-        "__check_model__": True,
-    }
-    factory = ModelFactory.create_factory(config_cls, **factory_args)
-    return [factory.build() for _ in range(n)]
-
-
-@slow
-def test_serialisation_roundtrip():
-    """After classification, models are serialised to json and stored in the database.
-    We need to ensure they are de-serialised into the original config with all relevant fields restored.
-    """
-    excluded = {MinimalConfigExample}
-    for config_cls in Config_Base.all_config_classes() - excluded:
-        trials_per_class = 50
-        configs_with_random_data = create_fake_configs(config_cls, trials_per_class)
-
-        for config in configs_with_random_data:
-            as_json = config.model_dump_json()
-            as_dict = json.loads(as_json)
-            reconstructed = ModelConfigFactory.make_config(as_dict)
-            assert isinstance(reconstructed, config_cls)
-            assert config.model_dump_json() == reconstructed.model_dump_json()
-
-
-def test_discriminator_tagging_for_config_instances():
-    """Verify that each ModelConfig instance is assigned the correct, unique Pydantic discriminator tag."""
-    excluded = {MinimalConfigExample}
-    config_classes = Config_Base.all_config_classes() - excluded
-
-    tags = {c.get_tag() for c in config_classes}
-    assert len(tags) == len(config_classes), "Each config should have its own unique tag"
-
-    for config_cls in config_classes:
-        expected_tag = config_cls.get_tag().tag
-
-        trials_per_class = 3
-        configs_with_random_data = create_fake_configs(config_cls, trials_per_class)
-
-        for config in configs_with_random_data:
-            assert get_model_discriminator_value(config) == expected_tag
-
-
-def test_inheritance_order():
-    """
-    Safeguard test to warn against incorrect inheritance order.
-
-    Config classes using multiple inheritance should inherit from ModelConfigBase last
-    to ensure that more specific fields take precedence over the generic defaults.
-
-    It may be worth rethinking our config taxonomy in the future, but in the meantime
-    this test can help prevent debugging effort.
-    """
-    for config_cls in Config_Base.all_config_classes():
-        excluded = {abc.ABC, pydantic.BaseModel, object}
-        inheritance_list = [cls for cls in config_cls.mro() if cls not in excluded]
-        assert inheritance_list[-1] is Config_Base
-
-
-def test_any_model_config_includes_all_config_classes():
-    """Safeguard test to ensure that AnyModelConfig includes all ModelConfigBase subclasses."""
-
-    union_type = get_args(AnyModelConfig)[0]
-
-    extracted = set()
-    for annotated_pair in get_args(union_type):
-        config_class, _ = get_args(annotated_pair)
-        extracted.add(config_class)
-
-    expected = set(Config_Base.all_config_classes()) - {MinimalConfigExample}
-    assert extracted == expected
-
-
-def test_config_uniquely_matches_model(datadir: Path):
-    model_paths = ModelSearch().search(datadir / "stripped_models")
-    for path in model_paths:
-        mod = StrippedModelOnDisk(path)
-        matches = {cls for cls in Config_Base.USING_CLASSIFY_API if cls.matches(mod)}
-        assert len(matches) <= 1, f"Model at path {path} matches multiple config classes: {matches}"
-        if not matches:
-            logger.warning(f"Model at path {path} does not match any config classes using classify API.")
diff --git a/tests/test_model_probe/minimal_config_model.json b/tests/test_model_probe/minimal_config_model.json
deleted file mode 100644
index 11c3963d33..0000000000
--- a/tests/test_model_probe/minimal_config_model.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-  "quote": "Minimal working example of a ModelConfigBase subclass"
-}
diff --git a/tests/test_model_probe/sd-1/main/dreamshaper-8-inpainting/README b/tests/test_model_probe/sd-1/main/dreamshaper-8-inpainting/README
deleted file mode 100644
index 15349b5f0a..0000000000
--- a/tests/test_model_probe/sd-1/main/dreamshaper-8-inpainting/README
+++ /dev/null
@@ -1 +0,0 @@
-This folder contains config files copied from [Lykon/dreamshaper-8-inpainting](https://huggingface.co/Lykon/dreamshaper-8-inpainting).
diff --git a/tests/test_model_probe/sd-1/main/dreamshaper-8-inpainting/model_index.json b/tests/test_model_probe/sd-1/main/dreamshaper-8-inpainting/model_index.json
deleted file mode 100644
index e4e983f7ba..0000000000
--- a/tests/test_model_probe/sd-1/main/dreamshaper-8-inpainting/model_index.json
+++ /dev/null
@@ -1,34 +0,0 @@
-{
-  "_class_name": "StableDiffusionInpaintPipeline",
-  "_diffusers_version": "0.21.0.dev0",
-  "_name_or_path": "lykon-models/dreamshaper-8-inpainting",
-  "feature_extractor": [
-    "transformers",
-    "CLIPFeatureExtractor"
-  ],
-  "requires_safety_checker": true,
-  "safety_checker": [
-    "stable_diffusion",
-    "StableDiffusionSafetyChecker"
-  ],
-  "scheduler": [
-    "diffusers",
-    "DEISMultistepScheduler"
-  ],
-  "text_encoder": [
-    "transformers",
-    "CLIPTextModel"
-  ],
-  "tokenizer": [
-    "transformers",
-    "CLIPTokenizer"
-  ],
-  "unet": [
-    "diffusers",
-    "UNet2DConditionModel"
-  ],
-  "vae": [
-    "diffusers",
-    "AutoencoderKL"
-  ]
-}
diff --git a/tests/test_model_probe/sd-1/main/dreamshaper-8-inpainting/scheduler/scheduler_config.json b/tests/test_model_probe/sd-1/main/dreamshaper-8-inpainting/scheduler/scheduler_config.json
deleted file mode 100644
index a63f334bd6..0000000000
--- a/tests/test_model_probe/sd-1/main/dreamshaper-8-inpainting/scheduler/scheduler_config.json
+++ /dev/null
@@ -1,23 +0,0 @@
-{
-  "_class_name": "DEISMultistepScheduler",
-  "_diffusers_version": "0.21.0.dev0",
-  "algorithm_type": "deis",
-  "beta_end": 0.012,
-  "beta_schedule": "scaled_linear",
-  "beta_start": 0.00085,
-  "clip_sample": false,
-  "dynamic_thresholding_ratio": 0.995,
-  "lower_order_final": true,
-  "num_train_timesteps": 1000,
-  "prediction_type": "epsilon",
-  "sample_max_value": 1.0,
-  "set_alpha_to_one": false,
-  "skip_prk_steps": true,
-  "solver_order": 2,
-  "solver_type": "logrho",
-  "steps_offset": 1,
-  "thresholding": false,
-  "timestep_spacing": "leading",
-  "trained_betas": null,
-  "use_karras_sigmas": false
-}
diff --git a/tests/test_model_probe/sd-1/main/dreamshaper-8-inpainting/unet/config.json b/tests/test_model_probe/sd-1/main/dreamshaper-8-inpainting/unet/config.json
deleted file mode 100644
index d9f3b21d92..0000000000
--- a/tests/test_model_probe/sd-1/main/dreamshaper-8-inpainting/unet/config.json
+++ /dev/null
@@ -1,66 +0,0 @@
-{
-  "_class_name": "UNet2DConditionModel",
-  "_diffusers_version": "0.21.0.dev0",
-  "_name_or_path": "/home/patrick/.cache/huggingface/hub/models--lykon-models--dreamshaper-8-inpainting/snapshots/15dcb9dec91a39ee498e3917c9ef6174b103862d/unet",
-  "act_fn": "silu",
-  "addition_embed_type": null,
-  "addition_embed_type_num_heads": 64,
-  "addition_time_embed_dim": null,
-  "attention_head_dim": 8,
-  "attention_type": "default",
-  "block_out_channels": [
-    320,
-    640,
-    1280,
-    1280
-  ],
-  "center_input_sample": false,
-  "class_embed_type": null,
-  "class_embeddings_concat": false,
-  "conv_in_kernel": 3,
-  "conv_out_kernel": 3,
-  "cross_attention_dim": 768,
-  "cross_attention_norm": null,
-  "down_block_types": [
-    "CrossAttnDownBlock2D",
-    "CrossAttnDownBlock2D",
-    "CrossAttnDownBlock2D",
-    "DownBlock2D"
-  ],
-  "downsample_padding": 1,
-  "dual_cross_attention": false,
-  "encoder_hid_dim": null,
-  "encoder_hid_dim_type": null,
-  "flip_sin_to_cos": true,
-  "freq_shift": 0,
-  "in_channels": 9,
-  "layers_per_block": 2,
-  "mid_block_only_cross_attention": null,
-  "mid_block_scale_factor": 1,
-  "mid_block_type": "UNetMidBlock2DCrossAttn",
-  "norm_eps": 1e-05,
-  "norm_num_groups": 32,
-  "num_attention_heads": null,
-  "num_class_embeds": null,
-  "only_cross_attention": false,
-  "out_channels": 4,
-  "projection_class_embeddings_input_dim": null,
-  "resnet_out_scale_factor": 1.0,
-  "resnet_skip_time_act": false,
-  "resnet_time_scale_shift": "default",
-  "sample_size": 64,
-  "time_cond_proj_dim": null,
-  "time_embedding_act_fn": null,
-  "time_embedding_dim": null,
-  "time_embedding_type": "positional",
-  "timestep_post_act": null,
-  "transformer_layers_per_block": 1,
-  "up_block_types": [
-    "UpBlock2D",
-    "CrossAttnUpBlock2D",
-    "CrossAttnUpBlock2D",
-    "CrossAttnUpBlock2D"
-  ],
-  "upcast_attention": null,
-  "use_linear_projection": false
-}
diff --git a/tests/test_model_probe/stripped_models/Alien Style.safetensors b/tests/test_model_probe/stripped_models/Alien Style.safetensors
deleted file mode 100644
index 349a03cdd0..0000000000
--- a/tests/test_model_probe/stripped_models/Alien Style.safetensors	
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:23afd7036e869f12021e6da9c17d60f22b6fcc35c5b0bdadbec28d57b115f732
-size 582621
diff --git a/tests/test_model_probe/stripped_models/EasyNegative.safetensors b/tests/test_model_probe/stripped_models/EasyNegative.safetensors
deleted file mode 100644
index 0f9fd38080..0000000000
--- a/tests/test_model_probe/stripped_models/EasyNegative.safetensors
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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-{
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-{
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-{
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-{
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diff --git a/tests/test_model_probe/vae/taesdxl-fp16/diffusion_pytorch_model.fp16.safetensors b/tests/test_model_probe/vae/taesdxl-fp16/diffusion_pytorch_model.fp16.safetensors
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-{
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