mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Revert "[MM2] Use typed ModelRecordChanges for model_install() rather than un…"
This reverts commit 633bbb4e85.
This commit is contained in:
psychedelicious
GitHub
parent
633bbb4e85
commit
8d22f5741d
@ -6,7 +6,7 @@ import pathlib
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import traceback
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from copy import deepcopy
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from tempfile import TemporaryDirectory
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from typing import List, Optional, Type
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from typing import Any, Dict, List, Optional, Type
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from fastapi import Body, Path, Query, Response, UploadFile
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from fastapi.responses import FileResponse, HTMLResponse
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@ -430,11 +430,13 @@ async def delete_model_image(
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async def install_model(
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source: str = Query(description="Model source to install, can be a local path, repo_id, or remote URL"),
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inplace: Optional[bool] = Query(description="Whether or not to install a local model in place", default=False),
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access_token: Optional[str] = Query(description="access token for the remote resource", default=None),
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config: ModelRecordChanges = Body(
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description="Object containing fields that override auto-probed values in the model config record, such as name, description and prediction_type ",
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# TODO(MM2): Can we type this?
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config: Optional[Dict[str, Any]] = Body(
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description="Dict of fields that override auto-probed values in the model config record, such as name, description and prediction_type ",
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default=None,
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example={"name": "string", "description": "string"},
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),
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access_token: Optional[str] = None,
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) -> ModelInstallJob:
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"""Install a model using a string identifier.
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@ -449,9 +451,8 @@ async def install_model(
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- model/name:fp16:path/to/model.safetensors
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- model/name::path/to/model.safetensors
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`config` is a ModelRecordChanges object. Fields in this object will override
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the ones that are probed automatically. Pass an empty object to accept
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all the defaults.
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`config` is an optional dict containing model configuration values that will override
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the ones that are probed automatically.
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`access_token` is an optional access token for use with Urls that require
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authentication.
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@ -736,7 +737,7 @@ async def convert_model(
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# write the converted file to the convert path
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raw_model = converted_model.model
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assert hasattr(raw_model, "save_pretrained")
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raw_model.save_pretrained(convert_path) # type: ignore
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raw_model.save_pretrained(convert_path)
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assert convert_path.exists()
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# temporarily rename the original safetensors file so that there is no naming conflict
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@ -749,12 +750,12 @@ async def convert_model(
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try:
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new_key = installer.install_path(
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convert_path,
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config=ModelRecordChanges(
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name=original_name,
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description=model_config.description,
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hash=model_config.hash,
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source=model_config.source,
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),
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config={
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"name": original_name,
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"description": model_config.description,
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"hash": model_config.hash,
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"source": model_config.source,
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},
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)
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except Exception as e:
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logger.error(str(e))
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@ -3,7 +3,7 @@
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from abc import ABC, abstractmethod
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from pathlib import Path
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from typing import List, Optional, Union
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from typing import Any, Dict, List, Optional, Union
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from pydantic.networks import AnyHttpUrl
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@ -12,7 +12,7 @@ from invokeai.app.services.download import DownloadQueueServiceBase
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from invokeai.app.services.events.events_base import EventServiceBase
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from invokeai.app.services.invoker import Invoker
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from invokeai.app.services.model_install.model_install_common import ModelInstallJob, ModelSource
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from invokeai.app.services.model_records import ModelRecordChanges, ModelRecordServiceBase
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from invokeai.app.services.model_records import ModelRecordServiceBase
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from invokeai.backend.model_manager import AnyModelConfig
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@ -64,7 +64,7 @@ class ModelInstallServiceBase(ABC):
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def register_path(
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self,
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model_path: Union[Path, str],
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config: Optional[ModelRecordChanges] = None,
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config: Optional[Dict[str, Any]] = None,
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) -> str:
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"""
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Probe and register the model at model_path.
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@ -72,7 +72,7 @@ class ModelInstallServiceBase(ABC):
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This keeps the model in its current location.
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:param model_path: Filesystem Path to the model.
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:param config: ModelRecordChanges object that will override autoassigned model record values.
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:param config: Dict of attributes that will override autoassigned values.
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:returns id: The string ID of the registered model.
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"""
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@ -92,7 +92,7 @@ class ModelInstallServiceBase(ABC):
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def install_path(
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self,
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model_path: Union[Path, str],
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config: Optional[ModelRecordChanges] = None,
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config: Optional[Dict[str, Any]] = None,
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) -> str:
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"""
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Probe, register and install the model in the models directory.
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@ -101,7 +101,7 @@ class ModelInstallServiceBase(ABC):
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the models directory handled by InvokeAI.
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:param model_path: Filesystem Path to the model.
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:param config: ModelRecordChanges object that will override autoassigned model record values.
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:param config: Dict of attributes that will override autoassigned values.
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:returns id: The string ID of the registered model.
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"""
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@ -109,14 +109,14 @@ class ModelInstallServiceBase(ABC):
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def heuristic_import(
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self,
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source: str,
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config: Optional[ModelRecordChanges] = None,
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config: Optional[Dict[str, Any]] = None,
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access_token: Optional[str] = None,
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inplace: Optional[bool] = False,
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) -> ModelInstallJob:
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r"""Install the indicated model using heuristics to interpret user intentions.
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:param source: String source
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:param config: Optional ModelRecordChanges object. Any fields in this object
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:param config: Optional dict. Any fields in this dict
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will override corresponding autoassigned probe fields in the
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model's config record as described in `import_model()`.
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:param access_token: Optional access token for remote sources.
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@ -147,7 +147,7 @@ class ModelInstallServiceBase(ABC):
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def import_model(
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self,
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source: ModelSource,
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config: Optional[ModelRecordChanges] = None,
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config: Optional[Dict[str, Any]] = None,
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) -> ModelInstallJob:
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"""Install the indicated model.
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@ -2,14 +2,13 @@ import re
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import traceback
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from enum import Enum
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from pathlib import Path
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from typing import Literal, Optional, Set, Union
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from typing import Any, Dict, Literal, Optional, Set, Union
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from pydantic import BaseModel, Field, PrivateAttr, field_validator
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from pydantic.networks import AnyHttpUrl
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from typing_extensions import Annotated
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from invokeai.app.services.download import DownloadJob, MultiFileDownloadJob
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from invokeai.app.services.model_records import ModelRecordChanges
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from invokeai.backend.model_manager import AnyModelConfig, ModelRepoVariant
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from invokeai.backend.model_manager.config import ModelSourceType
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from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
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@ -134,9 +133,8 @@ class ModelInstallJob(BaseModel):
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id: int = Field(description="Unique ID for this job")
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status: InstallStatus = Field(default=InstallStatus.WAITING, description="Current status of install process")
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error_reason: Optional[str] = Field(default=None, description="Information about why the job failed")
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config_in: ModelRecordChanges = Field(
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default_factory=ModelRecordChanges,
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description="Configuration information (e.g. 'description') to apply to model.",
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config_in: Dict[str, Any] = Field(
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default_factory=dict, description="Configuration information (e.g. 'description') to apply to model."
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)
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config_out: Optional[AnyModelConfig] = Field(
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default=None, description="After successful installation, this will hold the configuration object."
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@ -163,27 +163,26 @@ class ModelInstallService(ModelInstallServiceBase):
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def register_path(
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self,
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model_path: Union[Path, str],
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config: Optional[ModelRecordChanges] = None,
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config: Optional[Dict[str, Any]] = None,
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) -> str: # noqa D102
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model_path = Path(model_path)
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config = config or ModelRecordChanges()
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if not config.source:
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config.source = model_path.resolve().as_posix()
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config.source_type = ModelSourceType.Path
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config = config or {}
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if not config.get("source"):
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config["source"] = model_path.resolve().as_posix()
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config["source_type"] = ModelSourceType.Path
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return self._register(model_path, config)
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def install_path(
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self,
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model_path: Union[Path, str],
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config: Optional[ModelRecordChanges] = None,
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config: Optional[Dict[str, Any]] = None,
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) -> str: # noqa D102
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model_path = Path(model_path)
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config = config or ModelRecordChanges()
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info: AnyModelConfig = ModelProbe.probe(
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Path(model_path), config.model_dump(), hash_algo=self._app_config.hashing_algorithm
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) # type: ignore
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config = config or {}
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if preferred_name := config.name:
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info: AnyModelConfig = ModelProbe.probe(Path(model_path), config, hash_algo=self._app_config.hashing_algorithm)
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if preferred_name := config.get("name"):
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preferred_name = Path(preferred_name).with_suffix(model_path.suffix)
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dest_path = (
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@ -205,7 +204,7 @@ class ModelInstallService(ModelInstallServiceBase):
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def heuristic_import(
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self,
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source: str,
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config: Optional[ModelRecordChanges] = None,
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config: Optional[Dict[str, Any]] = None,
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access_token: Optional[str] = None,
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inplace: Optional[bool] = False,
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) -> ModelInstallJob:
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@ -217,7 +216,7 @@ class ModelInstallService(ModelInstallServiceBase):
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source_obj.access_token = access_token
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return self.import_model(source_obj, config)
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def import_model(self, source: ModelSource, config: Optional[ModelRecordChanges] = None) -> ModelInstallJob: # noqa D102
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def import_model(self, source: ModelSource, config: Optional[Dict[str, Any]] = None) -> ModelInstallJob: # noqa D102
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similar_jobs = [x for x in self.list_jobs() if x.source == source and not x.in_terminal_state]
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if similar_jobs:
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self._logger.warning(f"There is already an active install job for {source}. Not enqueuing.")
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@ -319,17 +318,16 @@ class ModelInstallService(ModelInstallServiceBase):
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model_path = self._app_config.models_path / model_path
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model_path = model_path.resolve()
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config = ModelRecordChanges(
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name=model_name,
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description=stanza.get("description"),
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)
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config: dict[str, Any] = {}
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config["name"] = model_name
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config["description"] = stanza.get("description")
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legacy_config_path = stanza.get("config")
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if legacy_config_path:
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# In v3, these paths were relative to the root. Migrate them to be relative to the legacy_conf_dir.
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legacy_config_path = self._app_config.root_path / legacy_config_path
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if legacy_config_path.is_relative_to(self._app_config.legacy_conf_path):
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legacy_config_path = legacy_config_path.relative_to(self._app_config.legacy_conf_path)
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config.config_path = str(legacy_config_path)
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config["config_path"] = str(legacy_config_path)
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try:
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id = self.register_path(model_path=model_path, config=config)
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self._logger.info(f"Migrated {model_name} with id {id}")
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@ -502,11 +500,11 @@ class ModelInstallService(ModelInstallServiceBase):
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job.total_bytes = self._stat_size(job.local_path)
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job.bytes = job.total_bytes
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self._signal_job_running(job)
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job.config_in.source = str(job.source)
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job.config_in.source_type = MODEL_SOURCE_TO_TYPE_MAP[job.source.__class__]
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job.config_in["source"] = str(job.source)
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job.config_in["source_type"] = MODEL_SOURCE_TO_TYPE_MAP[job.source.__class__]
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# enter the metadata, if there is any
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if isinstance(job.source_metadata, (HuggingFaceMetadata)):
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job.config_in.source_api_response = job.source_metadata.api_response
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job.config_in["source_api_response"] = job.source_metadata.api_response
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if job.inplace:
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key = self.register_path(job.local_path, job.config_in)
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@ -641,11 +639,11 @@ class ModelInstallService(ModelInstallServiceBase):
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return new_path
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def _register(
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self, model_path: Path, config: Optional[ModelRecordChanges] = None, info: Optional[AnyModelConfig] = None
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self, model_path: Path, config: Optional[Dict[str, Any]] = None, info: Optional[AnyModelConfig] = None
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) -> str:
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config = config or ModelRecordChanges()
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config = config or {}
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info = info or ModelProbe.probe(model_path, config.model_dump(), hash_algo=self._app_config.hashing_algorithm) # type: ignore
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info = info or ModelProbe.probe(model_path, config, hash_algo=self._app_config.hashing_algorithm)
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model_path = model_path.resolve()
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@ -676,13 +674,11 @@ class ModelInstallService(ModelInstallServiceBase):
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precision = TorchDevice.choose_torch_dtype()
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return ModelRepoVariant.FP16 if precision == torch.float16 else None
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def _import_local_model(
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self, source: LocalModelSource, config: Optional[ModelRecordChanges] = None
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) -> ModelInstallJob:
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def _import_local_model(self, source: LocalModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
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return ModelInstallJob(
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id=self._next_id(),
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source=source,
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config_in=config or ModelRecordChanges(),
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config_in=config or {},
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local_path=Path(source.path),
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inplace=source.inplace or False,
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)
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@ -690,7 +686,7 @@ class ModelInstallService(ModelInstallServiceBase):
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def _import_from_hf(
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self,
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source: HFModelSource,
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config: Optional[ModelRecordChanges] = None,
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config: Optional[Dict[str, Any]] = None,
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) -> ModelInstallJob:
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# Add user's cached access token to HuggingFace requests
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if source.access_token is None:
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@ -706,7 +702,7 @@ class ModelInstallService(ModelInstallServiceBase):
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def _import_from_url(
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self,
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source: URLModelSource,
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config: Optional[ModelRecordChanges] = None,
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config: Optional[Dict[str, Any]],
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) -> ModelInstallJob:
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remote_files, metadata = self._remote_files_from_source(source)
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return self._import_remote_model(
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@ -721,7 +717,7 @@ class ModelInstallService(ModelInstallServiceBase):
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source: HFModelSource | URLModelSource,
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remote_files: List[RemoteModelFile],
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metadata: Optional[AnyModelRepoMetadata],
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config: Optional[ModelRecordChanges],
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config: Optional[Dict[str, Any]],
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) -> ModelInstallJob:
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if len(remote_files) == 0:
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raise ValueError(f"{source}: No downloadable files found")
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@ -734,7 +730,7 @@ class ModelInstallService(ModelInstallServiceBase):
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install_job = ModelInstallJob(
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id=self._next_id(),
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source=source,
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config_in=config or ModelRecordChanges(),
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config_in=config or {},
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source_metadata=metadata,
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local_path=destdir, # local path may change once the download has started due to content-disposition handling
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bytes=0,
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@ -18,7 +18,6 @@ from invokeai.backend.model_manager.config import (
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ControlAdapterDefaultSettings,
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MainModelDefaultSettings,
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ModelFormat,
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ModelSourceType,
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ModelType,
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ModelVariantType,
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SchedulerPredictionType,
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@ -67,16 +66,10 @@ class ModelRecordChanges(BaseModelExcludeNull):
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"""A set of changes to apply to a model."""
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# Changes applicable to all models
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source: Optional[str] = Field(description="original source of the model", default=None)
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source_type: Optional[ModelSourceType] = Field(description="type of model source", default=None)
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source_api_response: Optional[str] = Field(description="metadata from remote source", default=None)
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name: Optional[str] = Field(description="Name of the model.", default=None)
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path: Optional[str] = Field(description="Path to the model.", default=None)
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description: Optional[str] = Field(description="Model description", default=None)
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base: Optional[BaseModelType] = Field(description="The base model.", default=None)
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type: Optional[ModelType] = Field(description="Type of model", default=None)
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key: Optional[str] = Field(description="Database ID for this model", default=None)
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hash: Optional[str] = Field(description="hash of model file", default=None)
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trigger_phrases: Optional[set[str]] = Field(description="Set of trigger phrases for this model", default=None)
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default_settings: Optional[MainModelDefaultSettings | ControlAdapterDefaultSettings] = Field(
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description="Default settings for this model", default=None
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@ -354,7 +354,7 @@ class CLIPVisionDiffusersConfig(DiffusersConfigBase):
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"""Model config for CLIPVision."""
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type: Literal[ModelType.CLIPVision] = ModelType.CLIPVision
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format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
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format: Literal[ModelFormat.Diffusers]
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@staticmethod
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def get_tag() -> Tag:
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@ -365,7 +365,7 @@ class T2IAdapterConfig(DiffusersConfigBase, ControlAdapterConfigBase):
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"""Model config for T2I."""
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type: Literal[ModelType.T2IAdapter] = ModelType.T2IAdapter
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format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
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format: Literal[ModelFormat.Diffusers]
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@staticmethod
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def get_tag() -> Tag:
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@ -155,8 +155,5 @@
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"vite-plugin-eslint": "^1.8.1",
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"vite-tsconfig-paths": "^4.3.2",
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"vitest": "^1.6.0"
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},
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"engines": {
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"pnpm": "8"
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}
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}
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13326
invokeai/frontend/web/pnpm-lock.yaml
Normal file
13326
invokeai/frontend/web/pnpm-lock.yaml
Normal file
File diff suppressed because it is too large
Load Diff
@ -1,9 +1,11 @@
|
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import { toast } from 'features/toast/toast';
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import { useCallback } from 'react';
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import { useTranslation } from 'react-i18next';
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import { type InstallModelArg, useInstallModelMutation } from 'services/api/endpoints/models';
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import { useInstallModelMutation } from 'services/api/endpoints/models';
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type InstallModelArgWithCallbacks = InstallModelArg & {
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type InstallModelArg = {
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source: string;
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inplace?: boolean;
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onSuccess?: () => void;
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onError?: (error: unknown) => void;
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};
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@ -13,9 +15,8 @@ export const useInstallModel = () => {
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const [_installModel, request] = useInstallModelMutation();
|
||||
|
||||
const installModel = useCallback(
|
||||
({ source, inplace, config, onSuccess, onError }: InstallModelArgWithCallbacks) => {
|
||||
config ||= {};
|
||||
_installModel({ source, inplace, config })
|
||||
({ source, inplace, onSuccess, onError }: InstallModelArg) => {
|
||||
_installModel({ source, inplace })
|
||||
.unwrap()
|
||||
.then((_) => {
|
||||
if (onSuccess) {
|
||||
|
||||
@ -12,19 +12,17 @@ type Props = {
|
||||
export const StarterModelsResultItem = ({ result }: Props) => {
|
||||
const { t } = useTranslation();
|
||||
const allSources = useMemo(() => {
|
||||
const _allSources = [{ source: result.source, config: { name: result.name, description: result.description } }];
|
||||
const _allSources = [result.source];
|
||||
if (result.dependencies) {
|
||||
for (const d of result.dependencies) {
|
||||
_allSources.push({ source: d.source, config: { name: d.name, description: d.description } });
|
||||
}
|
||||
_allSources.push(...result.dependencies.map((d) => d.source));
|
||||
}
|
||||
return _allSources;
|
||||
}, [result]);
|
||||
const [installModel] = useInstallModel();
|
||||
|
||||
const onClick = useCallback(() => {
|
||||
for (const { config, source } of allSources) {
|
||||
installModel({ config, source });
|
||||
for (const source of allSources) {
|
||||
installModel({ source });
|
||||
}
|
||||
}, [allSources, installModel]);
|
||||
|
||||
|
||||
@ -39,10 +39,9 @@ type DeleteModelImageResponse = void;
|
||||
type ConvertMainModelResponse =
|
||||
paths['/api/v2/models/convert/{key}']['put']['responses']['200']['content']['application/json'];
|
||||
|
||||
export type InstallModelArg = {
|
||||
type InstallModelArg = {
|
||||
source: paths['/api/v2/models/install']['post']['parameters']['query']['source'];
|
||||
inplace?: paths['/api/v2/models/install']['post']['parameters']['query']['inplace'];
|
||||
config?: paths['/api/v2/models/install']['post']['requestBody']['content']['application/json'];
|
||||
};
|
||||
type InstallModelResponse = paths['/api/v2/models/install']['post']['responses']['201']['content']['application/json'];
|
||||
|
||||
@ -125,12 +124,11 @@ export const modelsApi = api.injectEndpoints({
|
||||
invalidatesTags: [{ type: 'ModelConfig', id: LIST_TAG }],
|
||||
}),
|
||||
installModel: build.mutation<InstallModelResponse, InstallModelArg>({
|
||||
query: ({ source, inplace = true, config }) => {
|
||||
query: ({ source, inplace = true }) => {
|
||||
return {
|
||||
url: buildModelsUrl('install'),
|
||||
params: { source, inplace },
|
||||
method: 'POST',
|
||||
body: config,
|
||||
};
|
||||
},
|
||||
invalidatesTags: ['ModelInstalls'],
|
||||
|
||||
@ -103,9 +103,8 @@ export type paths = {
|
||||
* - model/name:fp16:path/to/model.safetensors
|
||||
* - model/name::path/to/model.safetensors
|
||||
*
|
||||
* `config` is a ModelRecordChanges object. Fields in this object will override
|
||||
* the ones that are probed automatically. Pass an empty object to accept
|
||||
* all the defaults.
|
||||
* `config` is an optional dict containing model configuration values that will override
|
||||
* the ones that are probed automatically.
|
||||
*
|
||||
* `access_token` is an optional access token for use with Urls that require
|
||||
* authentication.
|
||||
@ -1590,7 +1589,6 @@ export type components = {
|
||||
cover_image?: string | null;
|
||||
/**
|
||||
* Format
|
||||
* @default diffusers
|
||||
* @constant
|
||||
* @enum {string}
|
||||
*/
|
||||
@ -3173,7 +3171,7 @@ export type components = {
|
||||
/**
|
||||
* Fp32
|
||||
* @description Whether or not to use full float32 precision
|
||||
* @default true
|
||||
* @default false
|
||||
*/
|
||||
fp32?: boolean;
|
||||
/**
|
||||
@ -3256,7 +3254,7 @@ export type components = {
|
||||
/**
|
||||
* Fp32
|
||||
* @description Whether or not to use full float32 precision
|
||||
* @default true
|
||||
* @default false
|
||||
*/
|
||||
fp32?: boolean;
|
||||
/**
|
||||
@ -6577,7 +6575,7 @@ export type components = {
|
||||
/**
|
||||
* Fp32
|
||||
* @description Whether or not to use full float32 precision
|
||||
* @default true
|
||||
* @default false
|
||||
*/
|
||||
fp32?: boolean;
|
||||
/**
|
||||
@ -7306,146 +7304,146 @@ export type components = {
|
||||
project_id: string | null;
|
||||
};
|
||||
InvocationOutputMap: {
|
||||
integer: components["schemas"]["IntegerOutput"];
|
||||
heuristic_resize: components["schemas"]["ImageOutput"];
|
||||
range_of_size: components["schemas"]["IntegerCollectionOutput"];
|
||||
sdxl_compel_prompt: components["schemas"]["ConditioningOutput"];
|
||||
midas_depth_image_processor: components["schemas"]["ImageOutput"];
|
||||
dw_openpose_image_processor: components["schemas"]["ImageOutput"];
|
||||
color: components["schemas"]["ColorOutput"];
|
||||
merge_tiles_to_image: components["schemas"]["ImageOutput"];
|
||||
merge_metadata: components["schemas"]["MetadataOutput"];
|
||||
denoise_latents: components["schemas"]["LatentsOutput"];
|
||||
model_identifier: components["schemas"]["ModelIdentifierOutput"];
|
||||
sdxl_refiner_compel_prompt: components["schemas"]["ConditioningOutput"];
|
||||
img_resize: components["schemas"]["ImageOutput"];
|
||||
float_to_int: components["schemas"]["IntegerOutput"];
|
||||
img_scale: components["schemas"]["ImageOutput"];
|
||||
string_collection: components["schemas"]["StringCollectionOutput"];
|
||||
compel: components["schemas"]["ConditioningOutput"];
|
||||
calculate_image_tiles_min_overlap: components["schemas"]["CalculateImageTilesOutput"];
|
||||
infill_cv2: components["schemas"]["ImageOutput"];
|
||||
string_join: components["schemas"]["StringOutput"];
|
||||
lineart_anime_image_processor: components["schemas"]["ImageOutput"];
|
||||
infill_rgba: components["schemas"]["ImageOutput"];
|
||||
sdxl_lora_collection_loader: components["schemas"]["SDXLLoRALoaderOutput"];
|
||||
string_join_three: components["schemas"]["StringOutput"];
|
||||
lblend: components["schemas"]["LatentsOutput"];
|
||||
lscale: components["schemas"]["LatentsOutput"];
|
||||
normalbae_image_processor: components["schemas"]["ImageOutput"];
|
||||
img_channel_multiply: components["schemas"]["ImageOutput"];
|
||||
float_collection: components["schemas"]["FloatCollectionOutput"];
|
||||
range: components["schemas"]["IntegerCollectionOutput"];
|
||||
infill_patchmatch: components["schemas"]["ImageOutput"];
|
||||
face_off: components["schemas"]["FaceOffOutput"];
|
||||
mlsd_image_processor: components["schemas"]["ImageOutput"];
|
||||
string_replace: components["schemas"]["StringOutput"];
|
||||
image_mask_to_tensor: components["schemas"]["MaskOutput"];
|
||||
depth_anything_image_processor: components["schemas"]["ImageOutput"];
|
||||
infill_lama: components["schemas"]["ImageOutput"];
|
||||
metadata_item: components["schemas"]["MetadataItemOutput"];
|
||||
lora_loader: components["schemas"]["LoRALoaderOutput"];
|
||||
latents_collection: components["schemas"]["LatentsCollectionOutput"];
|
||||
alpha_mask_to_tensor: components["schemas"]["MaskOutput"];
|
||||
rand_float: components["schemas"]["FloatOutput"];
|
||||
noise: components["schemas"]["NoiseOutput"];
|
||||
face_mask_detection: components["schemas"]["FaceMaskOutput"];
|
||||
ideal_size: components["schemas"]["IdealSizeOutput"];
|
||||
lora_collection_loader: components["schemas"]["LoRALoaderOutput"];
|
||||
color_map_image_processor: components["schemas"]["ImageOutput"];
|
||||
img_paste: components["schemas"]["ImageOutput"];
|
||||
img_scale: components["schemas"]["ImageOutput"];
|
||||
zoe_depth_image_processor: components["schemas"]["ImageOutput"];
|
||||
tile_to_properties: components["schemas"]["TileToPropertiesOutput"];
|
||||
clip_skip: components["schemas"]["CLIPSkipInvocationOutput"];
|
||||
save_image: components["schemas"]["ImageOutput"];
|
||||
collect: components["schemas"]["CollectInvocationOutput"];
|
||||
face_identifier: components["schemas"]["ImageOutput"];
|
||||
img_blur: components["schemas"]["ImageOutput"];
|
||||
img_paste: components["schemas"]["ImageOutput"];
|
||||
segment_anything_processor: components["schemas"]["ImageOutput"];
|
||||
add: components["schemas"]["IntegerOutput"];
|
||||
tiled_multi_diffusion_denoise_latents: components["schemas"]["LatentsOutput"];
|
||||
img_crop: components["schemas"]["ImageOutput"];
|
||||
conditioning: components["schemas"]["ConditioningOutput"];
|
||||
esrgan: components["schemas"]["ImageOutput"];
|
||||
lineart_image_processor: components["schemas"]["ImageOutput"];
|
||||
mul: components["schemas"]["IntegerOutput"];
|
||||
img_nsfw: components["schemas"]["ImageOutput"];
|
||||
merge_tiles_to_image: components["schemas"]["ImageOutput"];
|
||||
latents_collection: components["schemas"]["LatentsCollectionOutput"];
|
||||
img_mul: components["schemas"]["ImageOutput"];
|
||||
sdxl_model_loader: components["schemas"]["SDXLModelLoaderOutput"];
|
||||
infill_tile: components["schemas"]["ImageOutput"];
|
||||
float: components["schemas"]["FloatOutput"];
|
||||
sdxl_lora_loader: components["schemas"]["SDXLLoRALoaderOutput"];
|
||||
scheduler: components["schemas"]["SchedulerOutput"];
|
||||
tomask: components["schemas"]["ImageOutput"];
|
||||
image_mask_to_tensor: components["schemas"]["MaskOutput"];
|
||||
conditioning_collection: components["schemas"]["ConditioningCollectionOutput"];
|
||||
img_ilerp: components["schemas"]["ImageOutput"];
|
||||
integer_math: components["schemas"]["IntegerOutput"];
|
||||
lora_selector: components["schemas"]["LoRASelectorOutput"];
|
||||
sub: components["schemas"]["IntegerOutput"];
|
||||
crop_latents: components["schemas"]["LatentsOutput"];
|
||||
string_join_three: components["schemas"]["StringOutput"];
|
||||
cv_inpaint: components["schemas"]["ImageOutput"];
|
||||
main_model_loader: components["schemas"]["ModelLoaderOutput"];
|
||||
hed_image_processor: components["schemas"]["ImageOutput"];
|
||||
create_gradient_mask: components["schemas"]["GradientMaskOutput"];
|
||||
create_denoise_mask: components["schemas"]["DenoiseMaskOutput"];
|
||||
mask_combine: components["schemas"]["ImageOutput"];
|
||||
img_pad_crop: components["schemas"]["ImageOutput"];
|
||||
freeu: components["schemas"]["UNetOutput"];
|
||||
lresize: components["schemas"]["LatentsOutput"];
|
||||
metadata: components["schemas"]["MetadataOutput"];
|
||||
color_map_image_processor: components["schemas"]["ImageOutput"];
|
||||
image_collection: components["schemas"]["ImageCollectionOutput"];
|
||||
l2i: components["schemas"]["ImageOutput"];
|
||||
show_image: components["schemas"]["ImageOutput"];
|
||||
t2i_adapter: components["schemas"]["T2IAdapterOutput"];
|
||||
round_float: components["schemas"]["FloatOutput"];
|
||||
canvas_paste_back: components["schemas"]["ImageOutput"];
|
||||
img_hue_adjust: components["schemas"]["ImageOutput"];
|
||||
sdxl_refiner_model_loader: components["schemas"]["SDXLRefinerModelLoaderOutput"];
|
||||
spandrel_image_to_image: components["schemas"]["ImageOutput"];
|
||||
step_param_easing: components["schemas"]["FloatCollectionOutput"];
|
||||
calculate_image_tiles_even_split: components["schemas"]["CalculateImageTilesOutput"];
|
||||
color_correct: components["schemas"]["ImageOutput"];
|
||||
float_range: components["schemas"]["FloatCollectionOutput"];
|
||||
mediapipe_face_processor: components["schemas"]["ImageOutput"];
|
||||
prompt_from_file: components["schemas"]["StringCollectionOutput"];
|
||||
random_range: components["schemas"]["IntegerCollectionOutput"];
|
||||
invert_tensor_mask: components["schemas"]["MaskOutput"];
|
||||
img_conv: components["schemas"]["ImageOutput"];
|
||||
seamless: components["schemas"]["SeamlessModeOutput"];
|
||||
ip_adapter: components["schemas"]["IPAdapterOutput"];
|
||||
i2l: components["schemas"]["LatentsOutput"];
|
||||
integer_collection: components["schemas"]["IntegerCollectionOutput"];
|
||||
vae_loader: components["schemas"]["VAEOutput"];
|
||||
leres_image_processor: components["schemas"]["ImageOutput"];
|
||||
blank_image: components["schemas"]["ImageOutput"];
|
||||
mask_from_id: components["schemas"]["ImageOutput"];
|
||||
pair_tile_image: components["schemas"]["PairTileImageOutput"];
|
||||
conditioning: components["schemas"]["ConditioningOutput"];
|
||||
dynamic_prompt: components["schemas"]["StringCollectionOutput"];
|
||||
mask_edge: components["schemas"]["ImageOutput"];
|
||||
img_channel_multiply: components["schemas"]["ImageOutput"];
|
||||
controlnet: components["schemas"]["ControlOutput"];
|
||||
latents: components["schemas"]["LatentsOutput"];
|
||||
unsharp_mask: components["schemas"]["ImageOutput"];
|
||||
tomask: components["schemas"]["ImageOutput"];
|
||||
mul: components["schemas"]["IntegerOutput"];
|
||||
seamless: components["schemas"]["SeamlessModeOutput"];
|
||||
canny_image_processor: components["schemas"]["ImageOutput"];
|
||||
core_metadata: components["schemas"]["MetadataOutput"];
|
||||
div: components["schemas"]["IntegerOutput"];
|
||||
metadata_item: components["schemas"]["MetadataItemOutput"];
|
||||
add: components["schemas"]["IntegerOutput"];
|
||||
crop_latents: components["schemas"]["LatentsOutput"];
|
||||
integer_collection: components["schemas"]["IntegerCollectionOutput"];
|
||||
sdxl_model_loader: components["schemas"]["SDXLModelLoaderOutput"];
|
||||
string_split: components["schemas"]["String2Output"];
|
||||
img_chan: components["schemas"]["ImageOutput"];
|
||||
img_channel_offset: components["schemas"]["ImageOutput"];
|
||||
calculate_image_tiles: components["schemas"]["CalculateImageTilesOutput"];
|
||||
rand_int: components["schemas"]["IntegerOutput"];
|
||||
string_split_neg: components["schemas"]["StringPosNegOutput"];
|
||||
face_off: components["schemas"]["FaceOffOutput"];
|
||||
boolean: components["schemas"]["BooleanOutput"];
|
||||
string: components["schemas"]["StringOutput"];
|
||||
float_math: components["schemas"]["FloatOutput"];
|
||||
pidi_image_processor: components["schemas"]["ImageOutput"];
|
||||
img_watermark: components["schemas"]["ImageOutput"];
|
||||
content_shuffle_image_processor: components["schemas"]["ImageOutput"];
|
||||
iterate: components["schemas"]["IterateInvocationOutput"];
|
||||
img_lerp: components["schemas"]["ImageOutput"];
|
||||
image: components["schemas"]["ImageOutput"];
|
||||
rectangle_mask: components["schemas"]["MaskOutput"];
|
||||
tile_image_processor: components["schemas"]["ImageOutput"];
|
||||
infill_cv2: components["schemas"]["ImageOutput"];
|
||||
tiled_multi_diffusion_denoise_latents: components["schemas"]["LatentsOutput"];
|
||||
collect: components["schemas"]["CollectInvocationOutput"];
|
||||
image_collection: components["schemas"]["ImageCollectionOutput"];
|
||||
save_image: components["schemas"]["ImageOutput"];
|
||||
controlnet: components["schemas"]["ControlOutput"];
|
||||
float_math: components["schemas"]["FloatOutput"];
|
||||
sdxl_refiner_compel_prompt: components["schemas"]["ConditioningOutput"];
|
||||
i2l: components["schemas"]["LatentsOutput"];
|
||||
infill_lama: components["schemas"]["ImageOutput"];
|
||||
sub: components["schemas"]["IntegerOutput"];
|
||||
div: components["schemas"]["IntegerOutput"];
|
||||
face_mask_detection: components["schemas"]["FaceMaskOutput"];
|
||||
esrgan: components["schemas"]["ImageOutput"];
|
||||
mask_combine: components["schemas"]["ImageOutput"];
|
||||
ip_adapter: components["schemas"]["IPAdapterOutput"];
|
||||
blank_image: components["schemas"]["ImageOutput"];
|
||||
heuristic_resize: components["schemas"]["ImageOutput"];
|
||||
rand_int: components["schemas"]["IntegerOutput"];
|
||||
lora_selector: components["schemas"]["LoRASelectorOutput"];
|
||||
unsharp_mask: components["schemas"]["ImageOutput"];
|
||||
face_identifier: components["schemas"]["ImageOutput"];
|
||||
sdxl_compel_prompt: components["schemas"]["ConditioningOutput"];
|
||||
infill_patchmatch: components["schemas"]["ImageOutput"];
|
||||
img_nsfw: components["schemas"]["ImageOutput"];
|
||||
lineart_anime_image_processor: components["schemas"]["ImageOutput"];
|
||||
compel: components["schemas"]["ConditioningOutput"];
|
||||
rectangle_mask: components["schemas"]["MaskOutput"];
|
||||
lora_collection_loader: components["schemas"]["LoRALoaderOutput"];
|
||||
freeu: components["schemas"]["UNetOutput"];
|
||||
img_hue_adjust: components["schemas"]["ImageOutput"];
|
||||
pidi_image_processor: components["schemas"]["ImageOutput"];
|
||||
content_shuffle_image_processor: components["schemas"]["ImageOutput"];
|
||||
mediapipe_face_processor: components["schemas"]["ImageOutput"];
|
||||
string_split_neg: components["schemas"]["StringPosNegOutput"];
|
||||
img_conv: components["schemas"]["ImageOutput"];
|
||||
lora_loader: components["schemas"]["LoRALoaderOutput"];
|
||||
color_correct: components["schemas"]["ImageOutput"];
|
||||
img_ilerp: components["schemas"]["ImageOutput"];
|
||||
noise: components["schemas"]["NoiseOutput"];
|
||||
float_range: components["schemas"]["FloatCollectionOutput"];
|
||||
dw_openpose_image_processor: components["schemas"]["ImageOutput"];
|
||||
float_to_int: components["schemas"]["IntegerOutput"];
|
||||
invert_tensor_mask: components["schemas"]["MaskOutput"];
|
||||
random_range: components["schemas"]["IntegerCollectionOutput"];
|
||||
latents: components["schemas"]["LatentsOutput"];
|
||||
leres_image_processor: components["schemas"]["ImageOutput"];
|
||||
t2i_adapter: components["schemas"]["T2IAdapterOutput"];
|
||||
pair_tile_image: components["schemas"]["PairTileImageOutput"];
|
||||
mask_edge: components["schemas"]["ImageOutput"];
|
||||
metadata: components["schemas"]["MetadataOutput"];
|
||||
string_join: components["schemas"]["StringOutput"];
|
||||
core_metadata: components["schemas"]["MetadataOutput"];
|
||||
canvas_paste_back: components["schemas"]["ImageOutput"];
|
||||
sdxl_lora_collection_loader: components["schemas"]["SDXLLoRALoaderOutput"];
|
||||
img_channel_offset: components["schemas"]["ImageOutput"];
|
||||
lineart_image_processor: components["schemas"]["ImageOutput"];
|
||||
midas_depth_image_processor: components["schemas"]["ImageOutput"];
|
||||
lscale: components["schemas"]["LatentsOutput"];
|
||||
string: components["schemas"]["StringOutput"];
|
||||
integer: components["schemas"]["IntegerOutput"];
|
||||
string_replace: components["schemas"]["StringOutput"];
|
||||
depth_anything_image_processor: components["schemas"]["ImageOutput"];
|
||||
main_model_loader: components["schemas"]["ModelLoaderOutput"];
|
||||
image: components["schemas"]["ImageOutput"];
|
||||
prompt_from_file: components["schemas"]["StringCollectionOutput"];
|
||||
sdxl_lora_loader: components["schemas"]["SDXLLoRALoaderOutput"];
|
||||
mask_from_id: components["schemas"]["ImageOutput"];
|
||||
normalbae_image_processor: components["schemas"]["ImageOutput"];
|
||||
infill_rgba: components["schemas"]["ImageOutput"];
|
||||
step_param_easing: components["schemas"]["FloatCollectionOutput"];
|
||||
hed_image_processor: components["schemas"]["ImageOutput"];
|
||||
img_chan: components["schemas"]["ImageOutput"];
|
||||
float: components["schemas"]["FloatOutput"];
|
||||
boolean_collection: components["schemas"]["BooleanCollectionOutput"];
|
||||
segment_anything_processor: components["schemas"]["ImageOutput"];
|
||||
range_of_size: components["schemas"]["IntegerCollectionOutput"];
|
||||
boolean: components["schemas"]["BooleanOutput"];
|
||||
iterate: components["schemas"]["IterateInvocationOutput"];
|
||||
denoise_latents: components["schemas"]["LatentsOutput"];
|
||||
calculate_image_tiles_min_overlap: components["schemas"]["CalculateImageTilesOutput"];
|
||||
color: components["schemas"]["ColorOutput"];
|
||||
calculate_image_tiles_even_split: components["schemas"]["CalculateImageTilesOutput"];
|
||||
scheduler: components["schemas"]["SchedulerOutput"];
|
||||
rand_float: components["schemas"]["FloatOutput"];
|
||||
create_denoise_mask: components["schemas"]["DenoiseMaskOutput"];
|
||||
range: components["schemas"]["IntegerCollectionOutput"];
|
||||
img_watermark: components["schemas"]["ImageOutput"];
|
||||
spandrel_image_to_image: components["schemas"]["ImageOutput"];
|
||||
show_image: components["schemas"]["ImageOutput"];
|
||||
string_collection: components["schemas"]["StringCollectionOutput"];
|
||||
infill_tile: components["schemas"]["ImageOutput"];
|
||||
clip_skip: components["schemas"]["CLIPSkipInvocationOutput"];
|
||||
sdxl_refiner_model_loader: components["schemas"]["SDXLRefinerModelLoaderOutput"];
|
||||
ideal_size: components["schemas"]["IdealSizeOutput"];
|
||||
img_lerp: components["schemas"]["ImageOutput"];
|
||||
l2i: components["schemas"]["ImageOutput"];
|
||||
create_gradient_mask: components["schemas"]["GradientMaskOutput"];
|
||||
vae_loader: components["schemas"]["VAEOutput"];
|
||||
calculate_image_tiles: components["schemas"]["CalculateImageTilesOutput"];
|
||||
alpha_mask_to_tensor: components["schemas"]["MaskOutput"];
|
||||
integer_math: components["schemas"]["IntegerOutput"];
|
||||
model_identifier: components["schemas"]["ModelIdentifierOutput"];
|
||||
img_crop: components["schemas"]["ImageOutput"];
|
||||
img_resize: components["schemas"]["ImageOutput"];
|
||||
round_float: components["schemas"]["FloatOutput"];
|
||||
};
|
||||
/**
|
||||
* InvocationStartedEvent
|
||||
@ -7792,7 +7790,7 @@ export type components = {
|
||||
/**
|
||||
* Fp32
|
||||
* @description Whether or not to use full float32 precision
|
||||
* @default true
|
||||
* @default false
|
||||
*/
|
||||
fp32?: boolean;
|
||||
/**
|
||||
@ -9596,8 +9594,11 @@ export type components = {
|
||||
* @description Information about why the job failed
|
||||
*/
|
||||
error_reason?: string | null;
|
||||
/** @description Configuration information (e.g. 'description') to apply to model. */
|
||||
config_in?: components["schemas"]["ModelRecordChanges"];
|
||||
/**
|
||||
* Config In
|
||||
* @description Configuration information (e.g. 'description') to apply to model.
|
||||
*/
|
||||
config_in?: Record<string, never>;
|
||||
/**
|
||||
* Config Out
|
||||
* @description After successful installation, this will hold the configuration object.
|
||||
@ -9749,18 +9750,6 @@ export type components = {
|
||||
* @description A set of changes to apply to a model.
|
||||
*/
|
||||
ModelRecordChanges: {
|
||||
/**
|
||||
* Source
|
||||
* @description original source of the model
|
||||
*/
|
||||
source?: string | null;
|
||||
/** @description type of model source */
|
||||
source_type?: components["schemas"]["ModelSourceType"] | null;
|
||||
/**
|
||||
* Source Api Response
|
||||
* @description metadata from remote source
|
||||
*/
|
||||
source_api_response?: string | null;
|
||||
/**
|
||||
* Name
|
||||
* @description Name of the model.
|
||||
@ -9778,18 +9767,6 @@ export type components = {
|
||||
description?: string | null;
|
||||
/** @description The base model. */
|
||||
base?: components["schemas"]["BaseModelType"] | null;
|
||||
/** @description Type of model */
|
||||
type?: components["schemas"]["ModelType"] | null;
|
||||
/**
|
||||
* Key
|
||||
* @description Database ID for this model
|
||||
*/
|
||||
key?: string | null;
|
||||
/**
|
||||
* Hash
|
||||
* @description hash of model file
|
||||
*/
|
||||
hash?: string | null;
|
||||
/**
|
||||
* Trigger Phrases
|
||||
* @description Set of trigger phrases for this model
|
||||
@ -12633,7 +12610,6 @@ export type components = {
|
||||
cover_image?: string | null;
|
||||
/**
|
||||
* Format
|
||||
* @default diffusers
|
||||
* @constant
|
||||
* @enum {string}
|
||||
*/
|
||||
@ -14327,9 +14303,8 @@ export type operations = {
|
||||
* - model/name:fp16:path/to/model.safetensors
|
||||
* - model/name::path/to/model.safetensors
|
||||
*
|
||||
* `config` is a ModelRecordChanges object. Fields in this object will override
|
||||
* the ones that are probed automatically. Pass an empty object to accept
|
||||
* all the defaults.
|
||||
* `config` is an optional dict containing model configuration values that will override
|
||||
* the ones that are probed automatically.
|
||||
*
|
||||
* `access_token` is an optional access token for use with Urls that require
|
||||
* authentication.
|
||||
@ -14348,11 +14323,10 @@ export type operations = {
|
||||
source: string;
|
||||
/** @description Whether or not to install a local model in place */
|
||||
inplace?: boolean | null;
|
||||
/** @description access token for the remote resource */
|
||||
access_token?: string | null;
|
||||
};
|
||||
};
|
||||
requestBody: {
|
||||
requestBody?: {
|
||||
content: {
|
||||
/**
|
||||
* @example {
|
||||
@ -14360,7 +14334,7 @@ export type operations = {
|
||||
* "description": "string"
|
||||
* }
|
||||
*/
|
||||
"application/json": components["schemas"]["ModelRecordChanges"];
|
||||
"application/json": Record<string, never> | null;
|
||||
};
|
||||
};
|
||||
responses: {
|
||||
|
||||
@ -72,16 +72,14 @@ 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)):
|
||||
key = mm2_installer.register_path(
|
||||
embedding_file, ModelRecordChanges(name="banana_sushi", type=ModelType("lora"))
|
||||
)
|
||||
key = mm2_installer.register_path(embedding_file, {"name": "banana_sushi", "type": ModelType("lora")})
|
||||
assert key is None
|
||||
|
||||
|
||||
def test_registration_meta_override_succeed(mm2_installer: ModelInstallServiceBase, embedding_file: Path) -> None:
|
||||
store = mm2_installer.record_store
|
||||
key = mm2_installer.register_path(
|
||||
embedding_file, ModelRecordChanges(name="banana_sushi", source="fake/repo_id", key="xyzzy")
|
||||
embedding_file, {"name": "banana_sushi", "source": "fake/repo_id", "key": "xyzzy"}
|
||||
)
|
||||
model_record = store.get_model(key)
|
||||
assert model_record.name == "banana_sushi"
|
||||
@ -133,7 +131,7 @@ def test_background_install(
|
||||
path: Path = request.getfixturevalue(fixture_name)
|
||||
description = "Test of metadata assignment"
|
||||
source = LocalModelSource(path=path, inplace=False)
|
||||
job = mm2_installer.import_model(source, config=ModelRecordChanges(description=description))
|
||||
job = mm2_installer.import_model(source, config={"description": description})
|
||||
assert job is not None
|
||||
assert isinstance(job, ModelInstallJob)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user