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tests(mm): refactor model identification tests

Browse Source 
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
This commit is contained in:
psychedelicious
2025-10-09 21:31:37 +11:00
parent 1373b440c3
commit 4334cf733a
401 changed files with 1393 additions and 849 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
.gitattributes vendored
View File

@@ -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

134
scripts/strip_models.py
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@@ -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)

5
tests/app/services/model_install/test_model_install.py
View File

@@ -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"))
)

93
tests/app/services/model_records/test_model_records_sql.py
View File

@@ -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)

2
tests/backend/ip_adapter/test_ip_adapter.py
View File

@@ -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

37
tests/backend/model_manager/model_manager_fixtures.py
View File

@@ -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,

2
tests/backend/model_manager/util/test_hf_model_select.py
View File

@@ -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

26
tests/conftest.py
View File

@@ -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)

149
tests/model_identification/README.md Normal file
View File

@@ -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"
}
}
```

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"""
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)

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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)

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