mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
389 lines
15 KiB
Python
389 lines
15 KiB
Python
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654), 2023 Kent Keirsey (https://github.com/hipsterusername), 2023 Lincoln D. Stein
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import pathlib
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from typing import List, Literal, Optional, Union
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from fastapi import Body, Path, Query, Response
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from fastapi.routing import APIRouter
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from pydantic import BaseModel, parse_obj_as
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from starlette.exceptions import HTTPException
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from invokeai.backend import BaseModelType, ModelType
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from invokeai.backend.model_management import MergeInterpolationMethod
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from invokeai.backend.model_management.models import (
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OPENAPI_MODEL_CONFIGS,
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InvalidModelException,
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ModelNotFoundException,
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SchedulerPredictionType,
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)
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from ..dependencies import ApiDependencies
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models_router = APIRouter(prefix="/v1/models", tags=["models"])
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UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
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ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
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ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
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MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
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ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
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class ModelsList(BaseModel):
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models: list[Union[tuple(OPENAPI_MODEL_CONFIGS)]]
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@models_router.get(
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"/",
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operation_id="list_models",
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responses={200: {"model": ModelsList}},
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)
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async def list_models(
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base_models: Optional[List[BaseModelType]] = Query(default=None, description="Base models to include"),
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model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
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) -> ModelsList:
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"""Gets a list of models"""
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if base_models and len(base_models) > 0:
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models_raw = list()
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for base_model in base_models:
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models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
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else:
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models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type)
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models = parse_obj_as(ModelsList, {"models": models_raw})
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return models
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@models_router.patch(
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"/{base_model}/{model_type}/{model_name}",
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operation_id="update_model",
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responses={
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200: {"description": "The model was updated successfully"},
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400: {"description": "Bad request"},
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404: {"description": "The model could not be found"},
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409: {"description": "There is already a model corresponding to the new name"},
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},
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status_code=200,
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response_model=UpdateModelResponse,
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)
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async def update_model(
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base_model: BaseModelType = Path(description="Base model"),
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model_type: ModelType = Path(description="The type of model"),
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model_name: str = Path(description="model name"),
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info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
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) -> UpdateModelResponse:
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"""Update model contents with a new config. If the model name or base fields are changed, then the model is renamed."""
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logger = ApiDependencies.invoker.services.logger
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try:
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previous_info = ApiDependencies.invoker.services.model_manager.list_model(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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)
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# rename operation requested
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if info.model_name != model_name or info.base_model != base_model:
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ApiDependencies.invoker.services.model_manager.rename_model(
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base_model=base_model,
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model_type=model_type,
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model_name=model_name,
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new_name=info.model_name,
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new_base=info.base_model,
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)
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logger.info(f"Successfully renamed {base_model.value}/{model_name}=>{info.base_model}/{info.model_name}")
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# update information to support an update of attributes
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model_name = info.model_name
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base_model = info.base_model
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new_info = ApiDependencies.invoker.services.model_manager.list_model(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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)
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if new_info.get("path") != previous_info.get(
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"path"
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): # model manager moved model path during rename - don't overwrite it
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info.path = new_info.get("path")
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# replace empty string values with None/null to avoid phenomenon of vae: ''
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info_dict = info.dict()
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info_dict = {x: info_dict[x] if info_dict[x] else None for x in info_dict.keys()}
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ApiDependencies.invoker.services.model_manager.update_model(
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model_name=model_name, base_model=base_model, model_type=model_type, model_attributes=info_dict
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)
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model_raw = ApiDependencies.invoker.services.model_manager.list_model(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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)
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model_response = parse_obj_as(UpdateModelResponse, model_raw)
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except ModelNotFoundException as e:
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raise HTTPException(status_code=404, detail=str(e))
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except ValueError as e:
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logger.error(str(e))
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raise HTTPException(status_code=409, detail=str(e))
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except Exception as e:
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logger.error(str(e))
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raise HTTPException(status_code=400, detail=str(e))
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return model_response
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@models_router.post(
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"/import",
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operation_id="import_model",
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responses={
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201: {"description": "The model imported successfully"},
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404: {"description": "The model could not be found"},
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415: {"description": "Unrecognized file/folder format"},
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424: {"description": "The model appeared to import successfully, but could not be found in the model manager"},
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409: {"description": "There is already a model corresponding to this path or repo_id"},
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},
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status_code=201,
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response_model=ImportModelResponse,
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)
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async def import_model(
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location: str = Body(description="A model path, repo_id or URL to import"),
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prediction_type: Optional[Literal["v_prediction", "epsilon", "sample"]] = Body(
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description="Prediction type for SDv2 checkpoints and rare SDv1 checkpoints",
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default=None,
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),
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) -> ImportModelResponse:
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"""Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically"""
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items_to_import = {location}
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prediction_types = {x.value: x for x in SchedulerPredictionType}
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logger = ApiDependencies.invoker.services.logger
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try:
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installed_models = ApiDependencies.invoker.services.model_manager.heuristic_import(
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items_to_import=items_to_import, prediction_type_helper=lambda x: prediction_types.get(prediction_type)
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)
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info = installed_models.get(location)
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if not info:
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logger.error("Import failed")
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raise HTTPException(status_code=415)
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logger.info(f"Successfully imported {location}, got {info}")
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model_raw = ApiDependencies.invoker.services.model_manager.list_model(
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model_name=info.name, base_model=info.base_model, model_type=info.model_type
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)
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return parse_obj_as(ImportModelResponse, model_raw)
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except ModelNotFoundException as e:
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logger.error(str(e))
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raise HTTPException(status_code=404, detail=str(e))
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except InvalidModelException as e:
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logger.error(str(e))
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raise HTTPException(status_code=415)
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except ValueError as e:
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logger.error(str(e))
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raise HTTPException(status_code=409, detail=str(e))
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@models_router.post(
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"/add",
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operation_id="add_model",
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responses={
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201: {"description": "The model added successfully"},
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404: {"description": "The model could not be found"},
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424: {"description": "The model appeared to add successfully, but could not be found in the model manager"},
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409: {"description": "There is already a model corresponding to this path or repo_id"},
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},
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status_code=201,
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response_model=ImportModelResponse,
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)
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async def add_model(
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info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
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) -> ImportModelResponse:
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"""Add a model using the configuration information appropriate for its type. Only local models can be added by path"""
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logger = ApiDependencies.invoker.services.logger
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try:
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ApiDependencies.invoker.services.model_manager.add_model(
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info.model_name, info.base_model, info.model_type, model_attributes=info.dict()
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)
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logger.info(f"Successfully added {info.model_name}")
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model_raw = ApiDependencies.invoker.services.model_manager.list_model(
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model_name=info.model_name, base_model=info.base_model, model_type=info.model_type
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)
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return parse_obj_as(ImportModelResponse, model_raw)
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except ModelNotFoundException as e:
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logger.error(str(e))
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raise HTTPException(status_code=404, detail=str(e))
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except ValueError as e:
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logger.error(str(e))
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raise HTTPException(status_code=409, detail=str(e))
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@models_router.delete(
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"/{base_model}/{model_type}/{model_name}",
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operation_id="del_model",
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responses={204: {"description": "Model deleted successfully"}, 404: {"description": "Model not found"}},
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status_code=204,
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response_model=None,
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)
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async def delete_model(
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base_model: BaseModelType = Path(description="Base model"),
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model_type: ModelType = Path(description="The type of model"),
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model_name: str = Path(description="model name"),
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) -> Response:
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"""Delete Model"""
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logger = ApiDependencies.invoker.services.logger
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try:
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ApiDependencies.invoker.services.model_manager.del_model(
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model_name, base_model=base_model, model_type=model_type
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)
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logger.info(f"Deleted model: {model_name}")
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return Response(status_code=204)
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except ModelNotFoundException as e:
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logger.error(str(e))
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raise HTTPException(status_code=404, detail=str(e))
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@models_router.put(
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"/convert/{base_model}/{model_type}/{model_name}",
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operation_id="convert_model",
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responses={
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200: {"description": "Model converted successfully"},
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400: {"description": "Bad request"},
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404: {"description": "Model not found"},
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},
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status_code=200,
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response_model=ConvertModelResponse,
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)
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async def convert_model(
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base_model: BaseModelType = Path(description="Base model"),
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model_type: ModelType = Path(description="The type of model"),
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model_name: str = Path(description="model name"),
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convert_dest_directory: Optional[str] = Query(
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default=None, description="Save the converted model to the designated directory"
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),
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) -> ConvertModelResponse:
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"""Convert a checkpoint model into a diffusers model, optionally saving to the indicated destination directory, or `models` if none."""
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logger = ApiDependencies.invoker.services.logger
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try:
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logger.info(f"Converting model: {model_name}")
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dest = pathlib.Path(convert_dest_directory) if convert_dest_directory else None
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ApiDependencies.invoker.services.model_manager.convert_model(
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model_name,
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base_model=base_model,
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model_type=model_type,
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convert_dest_directory=dest,
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)
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model_raw = ApiDependencies.invoker.services.model_manager.list_model(
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model_name, base_model=base_model, model_type=model_type
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)
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response = parse_obj_as(ConvertModelResponse, model_raw)
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except ModelNotFoundException as e:
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raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}")
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except ValueError as e:
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raise HTTPException(status_code=400, detail=str(e))
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return response
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@models_router.get(
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"/search",
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operation_id="search_for_models",
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responses={
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200: {"description": "Directory searched successfully"},
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404: {"description": "Invalid directory path"},
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},
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status_code=200,
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response_model=List[pathlib.Path],
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)
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async def search_for_models(
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search_path: pathlib.Path = Query(description="Directory path to search for models"),
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) -> List[pathlib.Path]:
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if not search_path.is_dir():
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raise HTTPException(
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status_code=404, detail=f"The search path '{search_path}' does not exist or is not directory"
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)
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return ApiDependencies.invoker.services.model_manager.search_for_models(search_path)
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@models_router.get(
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"/ckpt_confs",
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operation_id="list_ckpt_configs",
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responses={
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200: {"description": "paths retrieved successfully"},
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},
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status_code=200,
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response_model=List[pathlib.Path],
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)
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async def list_ckpt_configs() -> List[pathlib.Path]:
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"""Return a list of the legacy checkpoint configuration files stored in `ROOT/configs/stable-diffusion`, relative to ROOT."""
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return ApiDependencies.invoker.services.model_manager.list_checkpoint_configs()
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@models_router.post(
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"/sync",
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operation_id="sync_to_config",
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responses={
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201: {"description": "synchronization successful"},
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},
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status_code=201,
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response_model=bool,
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)
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async def sync_to_config() -> bool:
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"""Call after making changes to models.yaml, autoimport directories or models directory to synchronize
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in-memory data structures with disk data structures."""
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ApiDependencies.invoker.services.model_manager.sync_to_config()
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return True
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@models_router.put(
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"/merge/{base_model}",
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operation_id="merge_models",
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responses={
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200: {"description": "Model converted successfully"},
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400: {"description": "Incompatible models"},
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404: {"description": "One or more models not found"},
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},
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status_code=200,
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response_model=MergeModelResponse,
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)
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async def merge_models(
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base_model: BaseModelType = Path(description="Base model"),
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model_names: List[str] = Body(description="model name", min_items=2, max_items=3),
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merged_model_name: Optional[str] = Body(description="Name of destination model"),
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alpha: Optional[float] = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
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interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method"),
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force: Optional[bool] = Body(
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description="Force merging of models created with different versions of diffusers", default=False
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),
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merge_dest_directory: Optional[str] = Body(
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description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
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default=None,
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),
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) -> MergeModelResponse:
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"""Convert a checkpoint model into a diffusers model"""
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logger = ApiDependencies.invoker.services.logger
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try:
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logger.info(f"Merging models: {model_names} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")
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dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None
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result = ApiDependencies.invoker.services.model_manager.merge_models(
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model_names,
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base_model,
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merged_model_name=merged_model_name or "+".join(model_names),
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alpha=alpha,
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interp=interp,
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force=force,
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merge_dest_directory=dest,
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)
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model_raw = ApiDependencies.invoker.services.model_manager.list_model(
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result.name,
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base_model=base_model,
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model_type=ModelType.Main,
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)
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response = parse_obj_as(ConvertModelResponse, model_raw)
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except ModelNotFoundException:
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raise HTTPException(status_code=404, detail=f"One or more of the models '{model_names}' not found")
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except ValueError as e:
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raise HTTPException(status_code=400, detail=str(e))
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return response
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