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make model manager v2 ready for PR review

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- Replace legacy model manager service with the v2 manager.

- Update invocations to use new load interface.

- Fixed many but not all type checking errors in the invocations. Most
  were unrelated to model manager

- Updated routes. All the new routes live under the route tag
  `model_manager_v2`. To avoid confusion with the old routes,
  they have the URL prefix `/api/v2/models`. The old routes
  have been de-registered.

- Added a pytest for the loader.

- Updated documentation in contributing/MODEL_MANAGER.md
This commit is contained in:
Lincoln Stein
2024-02-10 18:09:45 -05:00
committed by Brandon Rising
parent 721ff58e44
commit d56337f2d8
36 changed files with 680 additions and 435 deletions
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4
invokeai/backend/embeddings/model_patcher.py
View File

@ -64,7 +64,7 @@ class ModelPatcher:
def apply_lora_unet(
cls,
unet: UNet2DConditionModel,
loras: List[Tuple[LoRAModelRaw, float]],
loras: Iterator[Tuple[LoRAModelRaw, float]],
) -> None:
with cls.apply_lora(unet, loras, "lora_unet_"):
yield
@ -307,7 +307,7 @@ class ONNXModelPatcher:
def apply_lora_unet(
cls,
unet: OnnxRuntimeModel,
loras: List[Tuple[LoRAModelRaw, float]],
loras: Iterator[Tuple[LoRAModelRaw, float]],
) -> None:
with cls.apply_lora(unet, loras, "lora_unet_"):
yield

2
invokeai/backend/image_util/safety_checker.py
View File

@ -8,8 +8,8 @@ from PIL import Image
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.backend import SilenceWarnings
from invokeai.backend.util.devices import choose_torch_device
from invokeai.backend.util.silence_warnings import SilenceWarnings
config = InvokeAIAppConfig.get_config()

4
invokeai/backend/ip_adapter/ip_adapter.py
View File

@ -8,7 +8,6 @@ from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from invokeai.backend.ip_adapter.ip_attention_weights import IPAttentionWeights
from invokeai.backend.model_management.models.base import calc_model_size_by_data
from .resampler import Resampler
@ -124,6 +123,9 @@ class IPAdapter:
self.attn_weights.to(device=self.device, dtype=self.dtype)
def calc_size(self):
# workaround for circular import
from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
return calc_model_size_by_data(self._image_proj_model) + calc_model_size_by_data(self.attn_weights)
def _init_image_proj_model(self, state_dict):

13
invokeai/backend/model_manager/config.py
View File

@ -21,7 +21,7 @@ Validation errors will raise an InvalidModelConfigException error.
"""
import time
from enum import Enum
from typing import Literal, Optional, Type, Union, Class
from typing import Literal, Optional, Type, Union
import torch
from diffusers import ModelMixin
@ -335,7 +335,7 @@ class ModelConfigFactory(object):
cls,
model_data: Union[Dict[str, Any], AnyModelConfig],
key: Optional[str] = None,
dest_class: Optional[Type[Class]] = None,
dest_class: Optional[Type[ModelConfigBase]] = None,
timestamp: Optional[float] = None,
) -> AnyModelConfig:
"""
@ -347,14 +347,17 @@ class ModelConfigFactory(object):
:param dest_class: The config class to be returned. If not provided, will
be selected automatically.
"""
model: Optional[ModelConfigBase] = None
if isinstance(model_data, ModelConfigBase):
model = model_data
elif dest_class:
model = dest_class.validate_python(model_data)
model = dest_class.model_validate(model_data)
else:
model = AnyModelConfigValidator.validate_python(model_data)
# mypy doesn't typecheck TypeAdapters well?
model = AnyModelConfigValidator.validate_python(model_data) # type: ignore
assert model is not None
if key:
model.key = key
if timestamp:
model.last_modified = timestamp
return model
return model # type: ignore

18
invokeai/backend/model_manager/load/load_base.py
View File

@ -18,8 +18,16 @@ from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple, Type
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.backend.model_manager import AnyModelConfig, BaseModelType, ModelFormat, ModelType, SubModelType
from invokeai.backend.model_manager.config import AnyModel, VaeCheckpointConfig, VaeDiffusersConfig
from invokeai.backend.model_manager.config import (
AnyModel,
AnyModelConfig,
BaseModelType,
ModelFormat,
ModelType,
SubModelType,
VaeCheckpointConfig,
VaeDiffusersConfig,
)
from invokeai.backend.model_manager.load.convert_cache.convert_cache_base import ModelConvertCacheBase
from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
from invokeai.backend.util.logging import InvokeAILogger
@ -32,7 +40,7 @@ class LoadedModel:
config: AnyModelConfig
locker: ModelLockerBase
def __enter__(self) -> AnyModel: # I think load_file() always returns a dict
def __enter__(self) -> AnyModel:
"""Context entry."""
self.locker.lock()
return self.model
@ -171,6 +179,10 @@ class AnyModelLoader:
def decorator(subclass: Type[ModelLoaderBase]) -> Type[ModelLoaderBase]:
cls._logger.debug(f"Registering class {subclass.__name__} to load models of type {base}/{type}/{format}")
key = cls._to_registry_key(base, type, format)
if key in cls._registry:
raise Exception(
f"{subclass.__name__} is trying to register as a loader for {base}/{type}/{format}, but this type of model has already been registered by {cls._registry[key].__name__}"
)
cls._registry[key] = subclass
return subclass

2
invokeai/backend/model_manager/load/load_default.py
View File

@ -169,7 +169,7 @@ class ModelLoader(ModelLoaderBase):
raise InvalidModelConfigException("An expected config.json file is missing from this model.") from e
# This needs to be implemented in subclasses that handle checkpoints
def _convert_model(self, config: AnyModelConfig, weights_path: Path, output_path: Path) -> Path:
def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
raise NotImplementedError
# This needs to be implemented in the subclass

2
invokeai/backend/model_manager/load/model_cache/model_cache_default.py
View File

@ -246,7 +246,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
def move_model_to_device(self, cache_entry: CacheRecord[AnyModel], target_device: torch.device) -> None:
"""Move model into the indicated device."""
# These attributes are not in the base ModelMixin class but in derived classes.
# These attributes are not in the base ModelMixin class but in various derived classes.
# Some models don't have these attributes, in which case they run in RAM/CPU.
self.logger.debug(f"Called to move {cache_entry.key} to {target_device}")
if not (hasattr(cache_entry.model, "device") and hasattr(cache_entry.model, "to")):

12
invokeai/backend/model_manager/load/model_loaders/controlnet.py
View File

@ -35,28 +35,28 @@ class ControlnetLoader(GenericDiffusersLoader):
else:
return True
def _convert_model(self, config: AnyModelConfig, weights_path: Path, output_path: Path) -> Path:
def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
if config.base not in {BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2}:
raise Exception(f"Vae conversion not supported for model type: {config.base}")
else:
assert hasattr(config, "config")
config_file = config.config
if weights_path.suffix == ".safetensors":
checkpoint = safetensors.torch.load_file(weights_path, device="cpu")
if model_path.suffix == ".safetensors":
checkpoint = safetensors.torch.load_file(model_path, device="cpu")
else:
checkpoint = torch.load(weights_path, map_location="cpu")
checkpoint = torch.load(model_path, map_location="cpu")
# sometimes weights are hidden under "state_dict", and sometimes not
if "state_dict" in checkpoint:
checkpoint = checkpoint["state_dict"]
convert_controlnet_to_diffusers(
weights_path,
model_path,
output_path,
original_config_file=self._app_config.root_path / config_file,
image_size=512,
scan_needed=True,
from_safetensors=weights_path.suffix == ".safetensors",
from_safetensors=model_path.suffix == ".safetensors",
)
return output_path

5
invokeai/backend/model_manager/load/model_loaders/generic_diffusers.py
View File

@ -12,8 +12,9 @@ from invokeai.backend.model_manager import (
ModelType,
SubModelType,
)
from invokeai.backend.model_manager.load.load_base import AnyModelLoader
from invokeai.backend.model_manager.load.load_default import ModelLoader
from ..load_base import AnyModelLoader
from ..load_default import ModelLoader
@AnyModelLoader.register(base=BaseModelType.Any, type=ModelType.CLIPVision, format=ModelFormat.Diffusers)

8
invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py
View File

@ -65,7 +65,7 @@ class StableDiffusionDiffusersModel(ModelLoader):
else:
return True
def _convert_model(self, config: AnyModelConfig, weights_path: Path, output_path: Path) -> Path:
def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
assert isinstance(config, MainCheckpointConfig)
variant = config.variant
base = config.base
@ -75,9 +75,9 @@ class StableDiffusionDiffusersModel(ModelLoader):
config_file = config.config
self._logger.info(f"Converting {weights_path} to diffusers format")
self._logger.info(f"Converting {model_path} to diffusers format")
convert_ckpt_to_diffusers(
weights_path,
model_path,
output_path,
model_type=self.model_base_to_model_type[base],
model_version=base,
@ -86,7 +86,7 @@ class StableDiffusionDiffusersModel(ModelLoader):
extract_ema=True,
scan_needed=True,
pipeline_class=pipeline_class,
from_safetensors=weights_path.suffix == ".safetensors",
from_safetensors=model_path.suffix == ".safetensors",
precision=self._torch_dtype,
load_safety_checker=False,
)

8
invokeai/backend/model_manager/load/model_loaders/vae.py
View File

@ -37,7 +37,7 @@ class VaeLoader(GenericDiffusersLoader):
else:
return True
def _convert_model(self, config: AnyModelConfig, weights_path: Path, output_path: Path) -> Path:
def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
# TO DO: check whether sdxl VAE models convert.
if config.base not in {BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2}:
raise Exception(f"Vae conversion not supported for model type: {config.base}")
@ -46,10 +46,10 @@ class VaeLoader(GenericDiffusersLoader):
"v1-inference.yaml" if config.base == BaseModelType.StableDiffusion1 else "v2-inference-v.yaml"
)
if weights_path.suffix == ".safetensors":
checkpoint = safetensors.torch.load_file(weights_path, device="cpu")
if model_path.suffix == ".safetensors":
checkpoint = safetensors.torch.load_file(model_path, device="cpu")
else:
checkpoint = torch.load(weights_path, map_location="cpu")
checkpoint = torch.load(model_path, map_location="cpu")
# sometimes weights are hidden under "state_dict", and sometimes not
if "state_dict" in checkpoint:

2
invokeai/backend/model_manager/load/model_util.py
View File

@ -65,7 +65,7 @@ def calc_model_size_by_fs(model_path: Path, subfolder: Optional[str] = None, var
bit8_files = {f for f in all_files if ".8bit." in f.name or ".8bit-" in f.name}
other_files = set(all_files) - fp16_files - bit8_files
if variant is None:
if not variant: # ModelRepoVariant.DEFAULT evaluates to empty string for compatability with HF
files = other_files
elif variant == "fp16":
files = fp16_files

3
invokeai/backend/model_manager/search.py
View File

@ -22,11 +22,12 @@ Example usage:
import os
from abc import ABC, abstractmethod
from logging import Logger
from pathlib import Path
from typing import Callable, Optional, Set, Union
from pydantic import BaseModel, Field
from logging import Logger
from invokeai.backend.util.logging import InvokeAILogger
default_logger: Logger = InvokeAILogger.get_logger()

2
invokeai/backend/stable_diffusion/schedulers/__init__.py
View File

@ -1 +1,3 @@
from .schedulers import SCHEDULER_MAP # noqa: F401
__all__ = ["SCHEDULER_MAP"]