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Tidy names and locations of modules

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- Rename old "model_management" directory to "model_management_OLD" in order to catch
  dangling references to original model manager.
- Caught and fixed most dangling references (still checking)
- Rename lora, textual_inversion and model_patcher modules
- Introduce a RawModel base class to simplfy the Union returned by the
  model loaders.
- Tidy up the model manager 2-related tests. Add useful fixtures, and
  a finalizer to the queue and installer fixtures that will stop the
  services and release threads.
This commit is contained in:
Lincoln Stein
2024-02-17 11:45:32 -05:00
committed by psychedelicious
parent ba1f8878dd
commit 2ad0752582
89 changed files with 355 additions and 1609 deletions
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1
tests/backend/model_manager/data/invokeai_root/README Normal file
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This is an empty invokeai root that is used as a template for model manager tests.

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tests/backend/model_manager/data/invokeai_root/configs/stable-diffusion/v1-inference.yaml Normal file
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model:
base_learning_rate: 1.0e-04
target: invokeai.backend.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 64
channels: 4
cond_stage_trainable: false # Note: different from the one we trained before
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
scheduler_config: # 10000 warmup steps
target: invokeai.backend.stable_diffusion.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 10000 ]
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
f_start: [ 1.e-6 ]
f_max: [ 1. ]
f_min: [ 1. ]
personalization_config:
target: invokeai.backend.stable_diffusion.embedding_manager.EmbeddingManager
params:
placeholder_strings: ["*"]
initializer_words: ['sculpture']
per_image_tokens: false
num_vectors_per_token: 1
progressive_words: False
unet_config:
target: invokeai.backend.stable_diffusion.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: invokeai.backend.stable_diffusion.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: invokeai.backend.stable_diffusion.encoders.modules.WeightedFrozenCLIPEmbedder

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tests/backend/model_manager/data/invokeai_root/databases/README Normal file
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This is a template empty invokeai root directory used to test model management.

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tests/backend/model_manager/data/invokeai_root/models/README Normal file
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This is a template empty invokeai root directory used to test model management.

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tests/backend/model_manager/data/test_files/test-diffusers-main/model_index.json Normal file
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{
"_class_name": "StableDiffusionXLPipeline",
"_diffusers_version": "0.23.0",
"_name_or_path": "stabilityai/sdxl-turbo",
"force_zeros_for_empty_prompt": true,
"scheduler": [
"diffusers",
"EulerAncestralDiscreteScheduler"
],
"text_encoder": [
"transformers",
"CLIPTextModel"
],
"text_encoder_2": [
"transformers",
"CLIPTextModelWithProjection"
],
"tokenizer": [
"transformers",
"CLIPTokenizer"
],
"tokenizer_2": [
"transformers",
"CLIPTokenizer"
],
"unet": [
"diffusers",
"UNet2DConditionModel"
],
"vae": [
"diffusers",
"AutoencoderKL"
]
}

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tests/backend/model_manager/data/test_files/test-diffusers-main/scheduler/scheduler_config.json Normal file
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{
"_class_name": "EulerAncestralDiscreteScheduler",
"_diffusers_version": "0.23.0",
"beta_end": 0.012,
"beta_schedule": "scaled_linear",
"beta_start": 0.00085,
"clip_sample": false,
"interpolation_type": "linear",
"num_train_timesteps": 1000,
"prediction_type": "epsilon",
"sample_max_value": 1.0,
"set_alpha_to_one": false,
"skip_prk_steps": true,
"steps_offset": 1,
"timestep_spacing": "trailing",
"trained_betas": null
}

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tests/backend/model_manager/data/test_files/test-diffusers-main/text_encoder/config.json Normal file
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{
"_name_or_path": "/home/lstein/.cache/huggingface/hub/models--stabilityai--sdxl-turbo/snapshots/fbda35297a8280789ffe2e25206800702fa5c4c1/text_encoder",
"architectures": [
"CLIPTextModel"
],
"attention_dropout": 0.0,
"bos_token_id": 0,
"dropout": 0.0,
"eos_token_id": 2,
"hidden_act": "quick_gelu",
"hidden_size": 768,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 77,
"model_type": "clip_text_model",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"projection_dim": 768,
"torch_dtype": "float16",
"transformers_version": "4.35.0",
"vocab_size": 49408
}

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tests/backend/model_manager/data/test_files/test-diffusers-main/text_encoder/model.fp16.safetensors Normal file
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tests/backend/model_manager/data/test_files/test-diffusers-main/text_encoder_2/config.json Normal file
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{
"_name_or_path": "/home/lstein/.cache/huggingface/hub/models--stabilityai--sdxl-turbo/snapshots/fbda35297a8280789ffe2e25206800702fa5c4c1/text_encoder_2",
"architectures": [
"CLIPTextModelWithProjection"
],
"attention_dropout": 0.0,
"bos_token_id": 0,
"dropout": 0.0,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_size": 1280,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"intermediate_size": 5120,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 77,
"model_type": "clip_text_model",
"num_attention_heads": 20,
"num_hidden_layers": 32,
"pad_token_id": 1,
"projection_dim": 1280,
"torch_dtype": "float16",
"transformers_version": "4.35.0",
"vocab_size": 49408
}

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tests/backend/model_manager/data/test_files/test-diffusers-main/tokenizer/merges.txt Normal file
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tests/backend/model_manager/data/test_files/test-diffusers-main/tokenizer/special_tokens_map.json Normal file
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{
"bos_token": {
"content": "<|startoftext|>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
}
}

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tests/backend/model_manager/data/test_files/test-diffusers-main/tokenizer/tokenizer_config.json Normal file
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{
"add_prefix_space": false,
"added_tokens_decoder": {
"49406": {
"content": "<|startoftext|>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false,
"special": true
},
"49407": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false,
"special": true
}
},
"bos_token": "<|startoftext|>",
"clean_up_tokenization_spaces": true,
"do_lower_case": true,
"eos_token": "<|endoftext|>",
"errors": "replace",
"model_max_length": 77,
"pad_token": "<|endoftext|>",
"tokenizer_class": "CLIPTokenizer",
"unk_token": "<|endoftext|>"
}

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tests/backend/model_manager/data/test_files/test-diffusers-main/tokenizer/vocab.json Normal file
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tests/backend/model_manager/data/test_files/test-diffusers-main/tokenizer_2/merges.txt Normal file
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tests/backend/model_manager/data/test_files/test-diffusers-main/tokenizer_2/special_tokens_map.json Normal file
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{
"bos_token": {
"content": "<|startoftext|>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "!",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
}
}

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tests/backend/model_manager/data/test_files/test-diffusers-main/tokenizer_2/tokenizer_config.json Normal file
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{
"add_prefix_space": false,
"added_tokens_decoder": {
"0": {
"content": "!",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"49406": {
"content": "<|startoftext|>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false,
"special": true
},
"49407": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false,
"special": true
}
},
"bos_token": "<|startoftext|>",
"clean_up_tokenization_spaces": true,
"do_lower_case": true,
"eos_token": "<|endoftext|>",
"errors": "replace",
"model_max_length": 77,
"pad_token": "!",
"tokenizer_class": "CLIPTokenizer",
"unk_token": "<|endoftext|>"
}

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tests/backend/model_manager/data/test_files/test-diffusers-main/tokenizer_2/vocab.json Normal file
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tests/backend/model_manager/data/test_files/test-diffusers-main/unet/config.json Normal file
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{
"_class_name": "UNet2DConditionModel",
"_diffusers_version": "0.23.0",
"_name_or_path": "/home/lstein/.cache/huggingface/hub/models--stabilityai--sdxl-turbo/snapshots/fbda35297a8280789ffe2e25206800702fa5c4c1/unet",
"act_fn": "silu",
"addition_embed_type": "text_time",
"addition_embed_type_num_heads": 64,
"addition_time_embed_dim": 256,
"attention_head_dim": [
5,
10,
20
],
"attention_type": "default",
"block_out_channels": [
320,
640,
1280
],
"center_input_sample": false,
"class_embed_type": null,
"class_embeddings_concat": false,
"conv_in_kernel": 3,
"conv_out_kernel": 3,
"cross_attention_dim": 2048,
"cross_attention_norm": null,
"down_block_types": [
"DownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D"
],
"downsample_padding": 1,
"dropout": 0.0,
"dual_cross_attention": false,
"encoder_hid_dim": null,
"encoder_hid_dim_type": null,
"flip_sin_to_cos": true,
"freq_shift": 0,
"in_channels": 4,
"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": 2816,
"resnet_out_scale_factor": 1.0,
"resnet_skip_time_act": false,
"resnet_time_scale_shift": "default",
"reverse_transformer_layers_per_block": null,
"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,
2,
10
],
"up_block_types": [
"CrossAttnUpBlock2D",
"CrossAttnUpBlock2D",
"UpBlock2D"
],
"upcast_attention": null,
"use_linear_projection": true
}

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tests/backend/model_manager/data/test_files/test-diffusers-main/vae/config.json Normal file
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{
"_class_name": "AutoencoderKL",
"_diffusers_version": "0.23.0",
"_name_or_path": "/home/lstein/.cache/huggingface/hub/models--stabilityai--sdxl-turbo/snapshots/fbda35297a8280789ffe2e25206800702fa5c4c1/vae",
"act_fn": "silu",
"block_out_channels": [
128,
256,
512,
512
],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D"
],
"force_upcast": true,
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 2,
"norm_num_groups": 32,
"out_channels": 3,
"sample_size": 1024,
"scaling_factor": 0.13025,
"up_block_types": [
"UpDecoderBlock2D",
"UpDecoderBlock2D",
"UpDecoderBlock2D",
"UpDecoderBlock2D"
]
}

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tests/backend/model_manager/model_loading/test_model_load.py Normal file
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"""
Test model loading
"""
from pathlib import Path
from invokeai.app.services.model_install import ModelInstallServiceBase
from invokeai.app.services.model_load import ModelLoadServiceBase
from invokeai.backend.textual_inversion import TextualInversionModelRaw
from tests.backend.model_manager.model_manager_fixtures import * # noqa F403
def test_loading(mm2_installer: ModelInstallServiceBase, mm2_loader: ModelLoadServiceBase, embedding_file: Path):
store = mm2_installer.record_store
matches = store.search_by_attr(model_name="test_embedding")
assert len(matches) == 0
key = mm2_installer.register_path(embedding_file)
loaded_model = mm2_loader.load_model_by_config(store.get_model(key))
assert loaded_model is not None
assert loaded_model.config.key == key
with loaded_model as model:
assert isinstance(model, TextualInversionModelRaw)

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tests/backend/model_manager/model_manager_fixtures.py Normal file
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# Fixtures to support testing of the model_manager v2 installer, metadata and record store
import os
import shutil
from pathlib import Path
from typing import Any, Dict, List
import pytest
from pytest import FixtureRequest
from pydantic import BaseModel
from requests.sessions import Session
from requests_testadapter import TestAdapter, TestSession
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.app.services.download import DownloadQueueServiceBase, DownloadQueueService
from invokeai.app.services.events.events_base import EventServiceBase
from invokeai.app.services.model_manager import ModelManagerServiceBase, ModelManagerService
from invokeai.app.services.model_load import ModelLoadServiceBase, ModelLoadService
from invokeai.app.services.model_install import ModelInstallService, ModelInstallServiceBase
from invokeai.app.services.model_metadata import ModelMetadataStoreBase, ModelMetadataStoreSQL
from invokeai.app.services.model_records import ModelRecordServiceBase, ModelRecordServiceSQL
from invokeai.backend.model_manager.config import (
BaseModelType,
ModelFormat,
ModelType,
)
from invokeai.backend.model_manager.load import ModelCache, ModelConvertCache
from invokeai.backend.util.logging import InvokeAILogger
from tests.backend.model_manager.model_metadata.metadata_examples import (
RepoCivitaiModelMetadata1,
RepoCivitaiVersionMetadata1,
RepoHFMetadata1,
RepoHFMetadata1_nofp16,
RepoHFModelJson1,
)
from tests.fixtures.sqlite_database import create_mock_sqlite_database
class DummyEvent(BaseModel):
"""Dummy Event to use with Dummy Event service."""
event_name: str
payload: Dict[str, Any]
class DummyEventService(EventServiceBase):
"""Dummy event service for testing."""
events: List[DummyEvent]
def __init__(self) -> None:
super().__init__()
self.events = []
def dispatch(self, event_name: str, payload: Any) -> None:
"""Dispatch an event by appending it to self.events."""
self.events.append(DummyEvent(event_name=payload["event"], payload=payload["data"]))
# Create a temporary directory using the contents of `./data/invokeai_root` as the template
@pytest.fixture
def mm2_root_dir(tmp_path_factory) -> Path:
root_template = Path(__file__).resolve().parent / "data" / "invokeai_root"
temp_dir: Path = tmp_path_factory.mktemp("data") / "invokeai_root"
shutil.copytree(root_template, temp_dir)
return temp_dir
@pytest.fixture
def mm2_model_files(tmp_path_factory) -> Path:
root_template = Path(__file__).resolve().parent / "data" / "test_files"
temp_dir: Path = tmp_path_factory.mktemp("data") / "test_files"
shutil.copytree(root_template, temp_dir)
return temp_dir
@pytest.fixture
def embedding_file(mm2_model_files: Path) -> Path:
return mm2_model_files / "test_embedding.safetensors"
@pytest.fixture
def diffusers_dir(mm2_model_files: Path) -> Path:
return mm2_model_files / "test-diffusers-main"
@pytest.fixture
def mm2_app_config(mm2_root_dir: Path) -> InvokeAIAppConfig:
app_config = InvokeAIAppConfig(
root=mm2_root_dir,
models_dir=mm2_root_dir / "models",
log_level="info",
)
return app_config
@pytest.fixture
def mm2_download_queue(mm2_session: Session,
request: FixtureRequest
) -> DownloadQueueServiceBase:
download_queue = DownloadQueueService(requests_session=mm2_session)
download_queue.start()
def stop_queue() -> None:
download_queue.stop()
request.addfinalizer(stop_queue)
return download_queue
@pytest.fixture
def mm2_metadata_store(mm2_record_store: ModelRecordServiceSQL) -> ModelMetadataStoreBase:
return mm2_record_store.metadata_store
@pytest.fixture
def mm2_loader(mm2_app_config: InvokeAIAppConfig, mm2_record_store: ModelRecordServiceBase) -> ModelLoadServiceBase:
ram_cache = ModelCache(
logger=InvokeAILogger.get_logger(),
max_cache_size=mm2_app_config.ram_cache_size,
max_vram_cache_size=mm2_app_config.vram_cache_size
)
convert_cache = ModelConvertCache(mm2_app_config.models_convert_cache_path)
return ModelLoadService(app_config=mm2_app_config,
record_store=mm2_record_store,
ram_cache=ram_cache,
convert_cache=convert_cache,
)
@pytest.fixture
def mm2_installer(mm2_app_config: InvokeAIAppConfig,
mm2_download_queue: DownloadQueueServiceBase,
mm2_session: Session,
request: FixtureRequest,
) -> ModelInstallServiceBase:
logger = InvokeAILogger.get_logger()
db = create_mock_sqlite_database(mm2_app_config, logger)
events = DummyEventService()
store = ModelRecordServiceSQL(db, ModelMetadataStoreSQL(db))
installer = ModelInstallService(
app_config=mm2_app_config,
record_store=store,
download_queue=mm2_download_queue,
event_bus=events,
session=mm2_session,
)
installer.start()
def stop_installer() -> None:
installer.stop()
request.addfinalizer(stop_installer)
return installer
@pytest.fixture
def mm2_record_store(mm2_app_config: InvokeAIAppConfig) -> ModelRecordServiceBase:
logger = InvokeAILogger.get_logger(config=mm2_app_config)
db = create_mock_sqlite_database(mm2_app_config, logger)
store = ModelRecordServiceSQL(db, ModelMetadataStoreSQL(db))
# add five simple config records to the database
raw1 = {
"path": "/tmp/foo1",
"format": ModelFormat("diffusers"),
"name": "test2",
"base": BaseModelType("sd-2"),
"type": ModelType("vae"),
"original_hash": "111222333444",
"source": "stabilityai/sdxl-vae",
}
raw2 = {
"path": "/tmp/foo2.ckpt",
"name": "model1",
"format": ModelFormat("checkpoint"),
"base": BaseModelType("sd-1"),
"type": "main",
"config": "/tmp/foo.yaml",
"variant": "normal",
"original_hash": "111222333444",
"source": "https://civitai.com/models/206883/split",
}
raw3 = {
"path": "/tmp/foo3",
"format": ModelFormat("diffusers"),
"name": "test3",
"base": BaseModelType("sdxl"),
"type": ModelType("main"),
"original_hash": "111222333444",
"source": "author3/model3",
"description": "This is test 3",
}
raw4 = {
"path": "/tmp/foo4",
"format": ModelFormat("diffusers"),
"name": "test4",
"base": BaseModelType("sdxl"),
"type": ModelType("lora"),
"original_hash": "111222333444",
"source": "author4/model4",
}
raw5 = {
"path": "/tmp/foo5",
"format": ModelFormat("diffusers"),
"name": "test5",
"base": BaseModelType("sd-1"),
"type": ModelType("lora"),
"original_hash": "111222333444",
"source": "author4/model5",
}
store.add_model("test_config_1", raw1)
store.add_model("test_config_2", raw2)
store.add_model("test_config_3", raw3)
store.add_model("test_config_4", raw4)
store.add_model("test_config_5", raw5)
return store
@pytest.fixture
def mm2_model_manager(mm2_record_store: ModelRecordServiceBase,
mm2_installer: ModelInstallServiceBase,
mm2_loader: ModelLoadServiceBase) -> ModelManagerServiceBase:
return ModelManagerService(
store=mm2_record_store,
install=mm2_installer,
load=mm2_loader
)
@pytest.fixture
def mm2_session(embedding_file: Path, diffusers_dir: Path) -> Session:
"""This fixtures defines a series of mock URLs for testing download and installation."""
sess: Session = TestSession()
sess.mount(
"https://test.com/missing_model.safetensors",
TestAdapter(
b"missing",
status=404,
),
)
sess.mount(
"https://huggingface.co/api/models/stabilityai/sdxl-turbo",
TestAdapter(
RepoHFMetadata1,
headers={"Content-Type": "application/json; charset=utf-8", "Content-Length": len(RepoHFMetadata1)},
),
)
sess.mount(
"https://huggingface.co/api/models/stabilityai/sdxl-turbo-nofp16",
TestAdapter(
RepoHFMetadata1_nofp16,
headers={"Content-Type": "application/json; charset=utf-8", "Content-Length": len(RepoHFMetadata1_nofp16)},
),
)
sess.mount(
"https://civitai.com/api/v1/model-versions/242807",
TestAdapter(
RepoCivitaiVersionMetadata1,
headers={
"Content-Length": len(RepoCivitaiVersionMetadata1),
},
),
)
sess.mount(
"https://civitai.com/api/v1/models/215485",
TestAdapter(
RepoCivitaiModelMetadata1,
headers={
"Content-Length": len(RepoCivitaiModelMetadata1),
},
),
)
sess.mount(
"https://huggingface.co/stabilityai/sdxl-turbo/resolve/main/model_index.json",
TestAdapter(
RepoHFModelJson1,
headers={
"Content-Length": len(RepoHFModelJson1),
},
),
)
with open(embedding_file, "rb") as f:
data = f.read() # file is small - just 15K
sess.mount(
"https://www.test.foo/download/test_embedding.safetensors",
TestAdapter(data, headers={"Content-Type": "application/octet-stream", "Content-Length": len(data)}),
)
sess.mount(
"https://huggingface.co/api/models/stabilityai/sdxl-turbo",
TestAdapter(
RepoHFMetadata1,
headers={"Content-Type": "application/json; charset=utf-8", "Content-Length": len(RepoHFMetadata1)},
),
)
for root, _, files in os.walk(diffusers_dir):
for name in files:
path = Path(root, name)
url_base = path.relative_to(diffusers_dir).as_posix()
url = f"https://huggingface.co/stabilityai/sdxl-turbo/resolve/main/{url_base}"
with open(path, "rb") as f:
data = f.read()
sess.mount(
url,
TestAdapter(
data,
headers={
"Content-Type": "application/json; charset=utf-8",
"Content-Length": len(data),
},
),
)
return sess

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"""
Test model metadata fetching and storage.
"""
import datetime
from pathlib import Path
import pytest
from pydantic.networks import HttpUrl
from requests.sessions import Session
from invokeai.app.services.model_metadata import ModelMetadataStoreBase
from invokeai.backend.model_manager.config import ModelRepoVariant
from invokeai.backend.model_manager.metadata import (
CivitaiMetadata,
CivitaiMetadataFetch,
CommercialUsage,
HuggingFaceMetadata,
HuggingFaceMetadataFetch,
UnknownMetadataException,
)
from invokeai.backend.model_manager.util import select_hf_files
from tests.backend.model_manager.model_manager_fixtures import * # noqa F403
def test_metadata_store_put_get(mm2_metadata_store: ModelMetadataStoreBase) -> None:
tags = {"text-to-image", "diffusers"}
input_metadata = HuggingFaceMetadata(
name="sdxl-vae",
author="stabilityai",
tags=tags,
id="stabilityai/sdxl-vae",
tag_dict={"license": "other"},
last_modified=datetime.datetime.now(),
)
mm2_metadata_store.add_metadata("test_config_1", input_metadata)
output_metadata = mm2_metadata_store.get_metadata("test_config_1")
assert input_metadata == output_metadata
with pytest.raises(UnknownMetadataException):
mm2_metadata_store.add_metadata("unknown_key", input_metadata)
assert mm2_metadata_store.list_tags() == tags
def test_metadata_store_update(mm2_metadata_store: ModelMetadataStoreBase) -> None:
input_metadata = HuggingFaceMetadata(
name="sdxl-vae",
author="stabilityai",
tags={"text-to-image", "diffusers"},
id="stabilityai/sdxl-vae",
tag_dict={"license": "other"},
last_modified=datetime.datetime.now(),
)
mm2_metadata_store.add_metadata("test_config_1", input_metadata)
input_metadata.name = "new-name"
mm2_metadata_store.update_metadata("test_config_1", input_metadata)
output_metadata = mm2_metadata_store.get_metadata("test_config_1")
assert output_metadata.name == "new-name"
assert input_metadata == output_metadata
def test_metadata_search(mm2_metadata_store: ModelMetadataStoreBase) -> None:
metadata1 = HuggingFaceMetadata(
name="sdxl-vae",
author="stabilityai",
tags={"text-to-image", "diffusers"},
id="stabilityai/sdxl-vae",
tag_dict={"license": "other"},
last_modified=datetime.datetime.now(),
)
metadata2 = HuggingFaceMetadata(
name="model2",
author="stabilityai",
tags={"text-to-image", "diffusers", "community-contributed"},
id="author2/model2",
tag_dict={"license": "other"},
last_modified=datetime.datetime.now(),
)
metadata3 = HuggingFaceMetadata(
name="model3",
author="author3",
tags={"text-to-image", "checkpoint", "community-contributed"},
id="author3/model3",
tag_dict={"license": "other"},
last_modified=datetime.datetime.now(),
)
mm2_metadata_store.add_metadata("test_config_1", metadata1)
mm2_metadata_store.add_metadata("test_config_2", metadata2)
mm2_metadata_store.add_metadata("test_config_3", metadata3)
matches = mm2_metadata_store.search_by_author("stabilityai")
assert len(matches) == 2
assert "test_config_1" in matches
assert "test_config_2" in matches
matches = mm2_metadata_store.search_by_author("Sherlock Holmes")
assert not matches
matches = mm2_metadata_store.search_by_name("model3")
assert len(matches) == 1
assert "test_config_3" in matches
matches = mm2_metadata_store.search_by_tag({"text-to-image"})
assert len(matches) == 3
matches = mm2_metadata_store.search_by_tag({"text-to-image", "diffusers"})
assert len(matches) == 2
assert "test_config_1" in matches
assert "test_config_2" in matches
matches = mm2_metadata_store.search_by_tag({"checkpoint", "community-contributed"})
assert len(matches) == 1
assert "test_config_3" in matches
# does the tag table update correctly?
matches = mm2_metadata_store.search_by_tag({"checkpoint", "licensed-for-commercial-use"})
assert not matches
assert mm2_metadata_store.list_tags() == {"text-to-image", "diffusers", "community-contributed", "checkpoint"}
metadata3.tags.add("licensed-for-commercial-use")
mm2_metadata_store.update_metadata("test_config_3", metadata3)
assert mm2_metadata_store.list_tags() == {
"text-to-image",
"diffusers",
"community-contributed",
"checkpoint",
"licensed-for-commercial-use",
}
matches = mm2_metadata_store.search_by_tag({"checkpoint", "licensed-for-commercial-use"})
assert len(matches) == 1
def test_metadata_civitai_fetch(mm2_session: Session) -> None:
fetcher = CivitaiMetadataFetch(mm2_session)
metadata = fetcher.from_url(HttpUrl("https://civitai.com/models/215485/SDXL-turbo"))
assert isinstance(metadata, CivitaiMetadata)
assert metadata.id == 215485
assert metadata.author == "test_author" # note that this is not the same as the original from Civitai
assert metadata.allow_commercial_use # changed to make sure we are reading locally not remotely
assert metadata.restrictions.AllowCommercialUse == CommercialUsage("RentCivit")
assert metadata.version_id == 242807
assert metadata.tags == {"tool", "turbo", "sdxl turbo"}
def test_metadata_hf_fetch(mm2_session: Session) -> None:
fetcher = HuggingFaceMetadataFetch(mm2_session)
metadata = fetcher.from_url(HttpUrl("https://huggingface.co/stabilityai/sdxl-turbo"))
assert isinstance(metadata, HuggingFaceMetadata)
assert metadata.author == "test_author" # this is not the same as the original
assert metadata.files
assert metadata.tags == {
"diffusers",
"onnx",
"safetensors",
"text-to-image",
"license:other",
"has_space",
"diffusers:StableDiffusionXLPipeline",
"region:us",
}
def test_metadata_hf_filter(mm2_session: Session) -> None:
metadata = HuggingFaceMetadataFetch(mm2_session).from_url(HttpUrl("https://huggingface.co/stabilityai/sdxl-turbo"))
assert isinstance(metadata, HuggingFaceMetadata)
files = [x.path for x in metadata.files]
fp16_files = select_hf_files.filter_files(files, variant=ModelRepoVariant("fp16"))
assert Path("sdxl-turbo/text_encoder/model.fp16.safetensors") in fp16_files
assert Path("sdxl-turbo/text_encoder/model.safetensors") not in fp16_files
fp32_files = select_hf_files.filter_files(files, variant=ModelRepoVariant("fp32"))
assert Path("sdxl-turbo/text_encoder/model.safetensors") in fp32_files
assert Path("sdxl-turbo/text_encoder/model.16.safetensors") not in fp32_files
onnx_files = select_hf_files.filter_files(files, variant=ModelRepoVariant("onnx"))
assert Path("sdxl-turbo/text_encoder/model.onnx") in onnx_files
assert Path("sdxl-turbo/text_encoder/model.safetensors") not in onnx_files
default_files = select_hf_files.filter_files(files)
assert Path("sdxl-turbo/text_encoder/model.safetensors") in default_files
assert Path("sdxl-turbo/text_encoder/model.16.safetensors") not in default_files
openvino_files = select_hf_files.filter_files(files, variant=ModelRepoVariant("openvino"))
print(openvino_files)
assert len(openvino_files) == 0
flax_files = select_hf_files.filter_files(files, variant=ModelRepoVariant("flax"))
print(flax_files)
assert not flax_files
metadata = HuggingFaceMetadataFetch(mm2_session).from_url(
HttpUrl("https://huggingface.co/stabilityai/sdxl-turbo-nofp16")
)
assert isinstance(metadata, HuggingFaceMetadata)
files = [x.path for x in metadata.files]
filtered_files = select_hf_files.filter_files(files, variant=ModelRepoVariant("fp16"))
assert (
Path("sdxl-turbo-nofp16/text_encoder/model.safetensors") in filtered_files
) # confirm that default is returned
assert Path("sdxl-turbo-nofp16/text_encoder/model.16.safetensors") not in filtered_files
def test_metadata_hf_urls(mm2_session: Session) -> None:
metadata = HuggingFaceMetadataFetch(mm2_session).from_url(HttpUrl("https://huggingface.co/stabilityai/sdxl-turbo"))
assert isinstance(metadata, HuggingFaceMetadata)

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import pytest
from invokeai.backend.model_manager.util.libc_util import LibcUtil, Struct_mallinfo2
def test_libc_util_mallinfo2():
"""Smoke test of LibcUtil().mallinfo2()."""
try:
libc = LibcUtil()
except OSError:
# TODO: Set the expected result preemptively based on the system properties.
pytest.xfail("libc shared library is not available on this system.")
try:
info = libc.mallinfo2()
except AttributeError:
pytest.xfail("`mallinfo2` is not available on this system, likely due to glibc < 2.33.")
assert info.arena > 0
def test_struct_mallinfo2_to_str():
"""Smoke test of Struct_mallinfo2.__str__()."""
info = Struct_mallinfo2()
info_str = str(info)
assert len(info_str) > 0

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# test that if the model's device changes while the lora is applied, the weights can still be restored
# test that LoRA patching works on both CPU and CUDA
import pytest
import torch
from invokeai.backend.model_patcher import ModelPatcher
from invokeai.backend.lora import LoRALayer, LoRAModelRaw
@pytest.mark.parametrize(
"device",
[
"cpu",
pytest.param("cuda", marks=pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA device")),
],
)
@torch.no_grad()
def test_apply_lora(device):
"""Test the basic behavior of ModelPatcher.apply_lora(...). Check that patching and unpatching produce the correct
result, and that model/LoRA tensors are moved between devices as expected.
"""
linear_in_features = 4
linear_out_features = 8
lora_dim = 2
model = torch.nn.ModuleDict(
{"linear_layer_1": torch.nn.Linear(linear_in_features, linear_out_features, device=device, dtype=torch.float16)}
)
lora_layers = {
"linear_layer_1": LoRALayer(
layer_key="linear_layer_1",
values={
"lora_down.weight": torch.ones((lora_dim, linear_in_features), device="cpu", dtype=torch.float16),
"lora_up.weight": torch.ones((linear_out_features, lora_dim), device="cpu", dtype=torch.float16),
},
)
}
lora = LoRAModelRaw("lora_name", lora_layers)
lora_weight = 0.5
orig_linear_weight = model["linear_layer_1"].weight.data.detach().clone()
expected_patched_linear_weight = orig_linear_weight + (lora_dim * lora_weight)
with ModelPatcher.apply_lora(model, [(lora, lora_weight)], prefix=""):
# After patching, all LoRA layer weights should have been moved back to the cpu.
assert lora_layers["linear_layer_1"].up.device.type == "cpu"
assert lora_layers["linear_layer_1"].down.device.type == "cpu"
# After patching, the patched model should still be on its original device.
assert model["linear_layer_1"].weight.data.device.type == device
torch.testing.assert_close(model["linear_layer_1"].weight.data, expected_patched_linear_weight)
# After unpatching, the original model weights should have been restored on the original device.
assert model["linear_layer_1"].weight.data.device.type == device
torch.testing.assert_close(model["linear_layer_1"].weight.data, orig_linear_weight)
@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA device")
@torch.no_grad()
def test_apply_lora_change_device():
"""Test that if LoRA patching is applied on the CPU, and then the patched model is moved to the GPU, unpatching
still behaves correctly.
"""
linear_in_features = 4
linear_out_features = 8
lora_dim = 2
# Initialize the model on the CPU.
model = torch.nn.ModuleDict(
{"linear_layer_1": torch.nn.Linear(linear_in_features, linear_out_features, device="cpu", dtype=torch.float16)}
)
lora_layers = {
"linear_layer_1": LoRALayer(
layer_key="linear_layer_1",
values={
"lora_down.weight": torch.ones((lora_dim, linear_in_features), device="cpu", dtype=torch.float16),
"lora_up.weight": torch.ones((linear_out_features, lora_dim), device="cpu", dtype=torch.float16),
},
)
}
lora = LoRAModelRaw("lora_name", lora_layers)
orig_linear_weight = model["linear_layer_1"].weight.data.detach().clone()
with ModelPatcher.apply_lora(model, [(lora, 0.5)], prefix=""):
# After patching, all LoRA layer weights should have been moved back to the cpu.
assert lora_layers["linear_layer_1"].up.device.type == "cpu"
assert lora_layers["linear_layer_1"].down.device.type == "cpu"
# After patching, the patched model should still be on the CPU.
assert model["linear_layer_1"].weight.data.device.type == "cpu"
# Move the model to the GPU.
assert model.to("cuda")
# After unpatching, the original model weights should have been restored on the GPU.
assert model["linear_layer_1"].weight.data.device.type == "cuda"
torch.testing.assert_close(model["linear_layer_1"].weight.data, orig_linear_weight, check_device=False)

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import pytest
from invokeai.backend.model_manager.util.libc_util import Struct_mallinfo2
from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot, get_pretty_snapshot_diff
def test_memory_snapshot_capture():
"""Smoke test of MemorySnapshot.capture()."""
snapshot = MemorySnapshot.capture()
# We just check process_ram, because it is the only field that should be supported on all platforms.
assert snapshot.process_ram > 0
snapshots = [
MemorySnapshot(process_ram=1, vram=2, malloc_info=Struct_mallinfo2()),
MemorySnapshot(process_ram=1, vram=2, malloc_info=None),
MemorySnapshot(process_ram=1, vram=None, malloc_info=Struct_mallinfo2()),
MemorySnapshot(process_ram=1, vram=None, malloc_info=None),
None,
]
@pytest.mark.parametrize("snapshot_1", snapshots)
@pytest.mark.parametrize("snapshot_2", snapshots)
def test_get_pretty_snapshot_diff(snapshot_1, snapshot_2):
"""Test that get_pretty_snapshot_diff() works with various combinations of missing MemorySnapshot fields."""
msg = get_pretty_snapshot_diff(snapshot_1, snapshot_2)
print(msg)
expected_lines = 0
if snapshot_1 is not None and snapshot_2 is not None:
expected_lines += 1
if snapshot_1.vram is not None and snapshot_2.vram is not None:
expected_lines += 1
if snapshot_1.malloc_info is not None and snapshot_2.malloc_info is not None:
expected_lines += 5
assert len(msg.splitlines()) == expected_lines

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import pytest
import torch
from invokeai.backend.model_manager.load.optimizations import _no_op, skip_torch_weight_init
@pytest.mark.parametrize(
["torch_module", "layer_args"],
[
(torch.nn.Linear, {"in_features": 10, "out_features": 20}),
(torch.nn.Conv1d, {"in_channels": 10, "out_channels": 20, "kernel_size": 3}),
(torch.nn.Conv2d, {"in_channels": 10, "out_channels": 20, "kernel_size": 3}),
(torch.nn.Conv3d, {"in_channels": 10, "out_channels": 20, "kernel_size": 3}),
(torch.nn.Embedding, {"num_embeddings": 10, "embedding_dim": 10}),
],
)
def test_skip_torch_weight_init_linear(torch_module, layer_args):
"""Test the interactions between `skip_torch_weight_init()` and various torch modules."""
seed = 123
# Initialize a torch layer *before* applying `skip_torch_weight_init()`.
reset_params_fn_before = torch_module.reset_parameters
torch.manual_seed(seed)
layer_before = torch_module(**layer_args)
# Initialize a torch layer while `skip_torch_weight_init()` is applied.
with skip_torch_weight_init():
reset_params_fn_during = torch_module.reset_parameters
torch.manual_seed(123)
layer_during = torch_module(**layer_args)
# Initialize a torch layer *after* applying `skip_torch_weight_init()`.
reset_params_fn_after = torch_module.reset_parameters
torch.manual_seed(123)
layer_after = torch_module(**layer_args)
# Check that reset_parameters is skipped while `skip_torch_weight_init()` is active.
assert reset_params_fn_during == _no_op
assert not torch.allclose(layer_before.weight, layer_during.weight)
if hasattr(layer_before, "bias"):
assert not torch.allclose(layer_before.bias, layer_during.bias)
# Check that the original behavior is restored after `skip_torch_weight_init()` ends.
assert reset_params_fn_before is reset_params_fn_after
assert torch.allclose(layer_before.weight, layer_after.weight)
if hasattr(layer_before, "bias"):
assert torch.allclose(layer_before.bias, layer_after.bias)
def test_skip_torch_weight_init_restores_base_class_behavior():
"""Test that `skip_torch_weight_init()` correctly restores the original behavior of torch.nn.Conv*d modules. This
test was created to catch a previous bug where `reset_parameters` was being copied from the base `_ConvNd` class to
its child classes (like `Conv1d`).
"""
with skip_torch_weight_init():
# There is no need to do anything while the context manager is applied, we're just testing that the original
# behavior is restored correctly.
pass
# Mock the behavior of another library that monkey patches `torch.nn.modules.conv._ConvNd.reset_parameters` and
# expects it to affect all of the sub-classes (e.g. `torch.nn.Conv1D`, `torch.nn.Conv2D`, etc.).
called_monkey_patched_fn = False
def monkey_patched_fn(*args, **kwargs):
nonlocal called_monkey_patched_fn
called_monkey_patched_fn = True
saved_fn = torch.nn.modules.conv._ConvNd.reset_parameters
torch.nn.modules.conv._ConvNd.reset_parameters = monkey_patched_fn
_ = torch.nn.Conv1d(10, 20, 3)
torch.nn.modules.conv._ConvNd.reset_parameters = saved_fn
assert called_monkey_patched_fn

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from pathlib import Path
from typing import List
import pytest
from invokeai.backend.model_manager.config import ModelRepoVariant
from invokeai.backend.model_manager.util.select_hf_files import filter_files
# This is the full list of model paths returned by the HF API for sdxl-base
@pytest.fixture
def sdxl_base_files() -> List[Path]:
return [
Path(x)
for x in [
".gitattributes",
"01.png",
"LICENSE.md",
"README.md",
"comparison.png",
"model_index.json",
"pipeline.png",
"scheduler/scheduler_config.json",
"sd_xl_base_1.0.safetensors",
"sd_xl_base_1.0_0.9vae.safetensors",
"sd_xl_offset_example-lora_1.0.safetensors",
"text_encoder/config.json",
"text_encoder/flax_model.msgpack",
"text_encoder/model.fp16.safetensors",
"text_encoder/model.onnx",
"text_encoder/model.safetensors",
"text_encoder/openvino_model.bin",
"text_encoder/openvino_model.xml",
"text_encoder_2/config.json",
"text_encoder_2/flax_model.msgpack",
"text_encoder_2/model.fp16.safetensors",
"text_encoder_2/model.onnx",
"text_encoder_2/model.onnx_data",
"text_encoder_2/model.safetensors",
"text_encoder_2/openvino_model.bin",
"text_encoder_2/openvino_model.xml",
"tokenizer/merges.txt",
"tokenizer/special_tokens_map.json",
"tokenizer/tokenizer_config.json",
"tokenizer/vocab.json",
"tokenizer_2/merges.txt",
"tokenizer_2/special_tokens_map.json",
"tokenizer_2/tokenizer_config.json",
"tokenizer_2/vocab.json",
"unet/config.json",
"unet/diffusion_flax_model.msgpack",
"unet/diffusion_pytorch_model.fp16.safetensors",
"unet/diffusion_pytorch_model.safetensors",
"unet/model.onnx",
"unet/model.onnx_data",
"unet/openvino_model.bin",
"unet/openvino_model.xml",
"vae/config.json",
"vae/diffusion_flax_model.msgpack",
"vae/diffusion_pytorch_model.fp16.safetensors",
"vae/diffusion_pytorch_model.safetensors",
"vae_1_0/config.json",
"vae_1_0/diffusion_pytorch_model.fp16.safetensors",
"vae_1_0/diffusion_pytorch_model.safetensors",
"vae_decoder/config.json",
"vae_decoder/model.onnx",
"vae_decoder/openvino_model.bin",
"vae_decoder/openvino_model.xml",
"vae_encoder/config.json",
"vae_encoder/model.onnx",
"vae_encoder/openvino_model.bin",
"vae_encoder/openvino_model.xml",
]
]
# This are what we expect to get when various diffusers variants are requested
@pytest.mark.parametrize(
"variant,expected_list",
[
(
None,
[
"model_index.json",
"scheduler/scheduler_config.json",
"text_encoder/config.json",
"text_encoder/model.safetensors",
"text_encoder_2/config.json",
"text_encoder_2/model.safetensors",
"tokenizer/merges.txt",
"tokenizer/special_tokens_map.json",
"tokenizer/tokenizer_config.json",
"tokenizer/vocab.json",
"tokenizer_2/merges.txt",
"tokenizer_2/special_tokens_map.json",
"tokenizer_2/tokenizer_config.json",
"tokenizer_2/vocab.json",
"unet/config.json",
"unet/diffusion_pytorch_model.safetensors",
"vae/config.json",
"vae/diffusion_pytorch_model.safetensors",
"vae_1_0/config.json",
"vae_1_0/diffusion_pytorch_model.safetensors",
],
),
(
ModelRepoVariant.DEFAULT,
[
"model_index.json",
"scheduler/scheduler_config.json",
"text_encoder/config.json",
"text_encoder/model.safetensors",
"text_encoder_2/config.json",
"text_encoder_2/model.safetensors",
"tokenizer/merges.txt",
"tokenizer/special_tokens_map.json",
"tokenizer/tokenizer_config.json",
"tokenizer/vocab.json",
"tokenizer_2/merges.txt",
"tokenizer_2/special_tokens_map.json",
"tokenizer_2/tokenizer_config.json",
"tokenizer_2/vocab.json",
"unet/config.json",
"unet/diffusion_pytorch_model.safetensors",
"vae/config.json",
"vae/diffusion_pytorch_model.safetensors",
"vae_1_0/config.json",
"vae_1_0/diffusion_pytorch_model.safetensors",
],
),
(
ModelRepoVariant.OPENVINO,
[
"model_index.json",
"scheduler/scheduler_config.json",
"text_encoder/config.json",
"text_encoder/openvino_model.bin",
"text_encoder/openvino_model.xml",
"text_encoder_2/config.json",
"text_encoder_2/openvino_model.bin",
"text_encoder_2/openvino_model.xml",
"tokenizer/merges.txt",
"tokenizer/special_tokens_map.json",
"tokenizer/tokenizer_config.json",
"tokenizer/vocab.json",
"tokenizer_2/merges.txt",
"tokenizer_2/special_tokens_map.json",
"tokenizer_2/tokenizer_config.json",
"tokenizer_2/vocab.json",
"unet/config.json",
"unet/openvino_model.bin",
"unet/openvino_model.xml",
"vae_decoder/config.json",
"vae_decoder/openvino_model.bin",
"vae_decoder/openvino_model.xml",
"vae_encoder/config.json",
"vae_encoder/openvino_model.bin",
"vae_encoder/openvino_model.xml",
],
),
(
ModelRepoVariant.FP16,
[
"model_index.json",
"scheduler/scheduler_config.json",
"text_encoder/config.json",
"text_encoder/model.fp16.safetensors",
"text_encoder_2/config.json",
"text_encoder_2/model.fp16.safetensors",
"tokenizer/merges.txt",
"tokenizer/special_tokens_map.json",
"tokenizer/tokenizer_config.json",
"tokenizer/vocab.json",
"tokenizer_2/merges.txt",
"tokenizer_2/special_tokens_map.json",
"tokenizer_2/tokenizer_config.json",
"tokenizer_2/vocab.json",
"unet/config.json",
"unet/diffusion_pytorch_model.fp16.safetensors",
"vae/config.json",
"vae/diffusion_pytorch_model.fp16.safetensors",
"vae_1_0/config.json",
"vae_1_0/diffusion_pytorch_model.fp16.safetensors",
],
),
(
ModelRepoVariant.ONNX,
[
"model_index.json",
"scheduler/scheduler_config.json",
"text_encoder/config.json",
"text_encoder/model.onnx",
"text_encoder_2/config.json",
"text_encoder_2/model.onnx",
"text_encoder_2/model.onnx_data",
"tokenizer/merges.txt",
"tokenizer/special_tokens_map.json",
"tokenizer/tokenizer_config.json",
"tokenizer/vocab.json",
"tokenizer_2/merges.txt",
"tokenizer_2/special_tokens_map.json",
"tokenizer_2/tokenizer_config.json",
"tokenizer_2/vocab.json",
"unet/config.json",
"unet/model.onnx",
"unet/model.onnx_data",
"vae_decoder/config.json",
"vae_decoder/model.onnx",
"vae_encoder/config.json",
"vae_encoder/model.onnx",
],
),
(
ModelRepoVariant.FLAX,
[
"model_index.json",
"scheduler/scheduler_config.json",
"text_encoder/config.json",
"text_encoder/flax_model.msgpack",
"text_encoder_2/config.json",
"text_encoder_2/flax_model.msgpack",
"tokenizer/merges.txt",
"tokenizer/special_tokens_map.json",
"tokenizer/tokenizer_config.json",
"tokenizer/vocab.json",
"tokenizer_2/merges.txt",
"tokenizer_2/special_tokens_map.json",
"tokenizer_2/tokenizer_config.json",
"tokenizer_2/vocab.json",
"unet/config.json",
"unet/diffusion_flax_model.msgpack",
"vae/config.json",
"vae/diffusion_flax_model.msgpack",
],
),
],
)
def test_select(sdxl_base_files: List[Path], variant: ModelRepoVariant, expected_list: List[Path]) -> None:
print(f"testing variant {variant}")
filtered_files = filter_files(sdxl_base_files, variant)
assert set(filtered_files) == {Path(x) for x in expected_list}