InvokeAI/invokeai/app/api/routers/models.py

# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654), 2023 Kent Keirsey (https://github.com/hipsterusername), 2023 Lincoln D. Stein


import pathlib
from enum import Enum
from typing import Any, List, Literal, Optional, Union

from fastapi import Body, Path, Query, Response
from fastapi.routing import APIRouter
from pydantic import BaseModel, parse_obj_as
from starlette.exceptions import HTTPException

from invokeai.app.api.dependencies import ApiDependencies
from invokeai.backend import BaseModelType, ModelType
from invokeai.backend.model_manager import (
    OPENAPI_MODEL_CONFIGS,
    DuplicateModelException,
    InvalidModelException,
    ModelConfigBase,
    SchedulerPredictionType,
    UnknownModelException,
)
from invokeai.backend.model_manager.download import DownloadJobStatus, UnknownJobIDException
from invokeai.backend.model_manager.merge import MergeInterpolationMethod

models_router = APIRouter(prefix="/v1/models", tags=["models"])

# NOTE: The generic configuration classes defined in invokeai.backend.model_manager.config
# such as "MainCheckpointConfig" are repackaged by code originally written by Stalker
# into base-specific classes such as `abc.StableDiffusion1ModelCheckpointConfig`
# This is the reason for the calls to dict() followed by pydantic.parse_obj_as()

# There are still numerous mypy errors here because it does not seem to like this
# way of dynamically generating the typing hints below.
InvokeAIModelConfig: Any = Union[tuple(OPENAPI_MODEL_CONFIGS)]


class ModelsList(BaseModel):
    models: List[InvokeAIModelConfig]


class ModelImportStatus(BaseModel):
    """Return information about a background installation job."""

    job_id: int
    source: str
    priority: int
    bytes: int
    total_bytes: int
    status: DownloadJobStatus


class JobControlOperation(str, Enum):
    START = "Start"
    PAUSE = "Pause"
    CANCEL = "Cancel"
    CHANGE_PRIORITY = "Change Priority"


@models_router.get(
    "/",
    operation_id="list_models",
    responses={200: {"model": ModelsList}},
)
async def list_models(
    base_models: Optional[List[BaseModelType]] = Query(default=None, description="Base models to include"),
    model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
) -> ModelsList:
    """Get a list of models."""
    manager = ApiDependencies.invoker.services.model_manager
    if base_models and len(base_models) > 0:
        models_raw = list()
        for base_model in base_models:
            models_raw.extend([x.dict() for x in manager.list_models(base_model=base_model, model_type=model_type)])
    else:
        models_raw = [x.dict() for x in manager.list_models(model_type=model_type)]
    models = parse_obj_as(ModelsList, {"models": models_raw})
    return models


@models_router.patch(
    "/{key}",
    operation_id="update_model",
    responses={
        200: {"description": "The model was updated successfully"},
        400: {"description": "Bad request"},
        404: {"description": "The model could not be found"},
        409: {"description": "There is already a model corresponding to the new name"},
    },
    status_code=200,
    response_model=InvokeAIModelConfig,
)
async def update_model(
    key: str = Path(description="Unique key of model"),
    info: InvokeAIModelConfig = Body(description="Model configuration"),
) -> InvokeAIModelConfig:
    """Update model contents with a new config. If the model name or base fields are changed, then the model is renamed."""
    logger = ApiDependencies.invoker.services.logger

    try:
        info_dict = info.dict()
        info_dict = {x: info_dict[x] if info_dict[x] else None for x in info_dict.keys()}
        new_config = ApiDependencies.invoker.services.model_manager.update_model(key, new_config=info_dict)
        model_response = parse_obj_as(InvokeAIModelConfig, new_config.dict())
    except UnknownModelException as e:
        raise HTTPException(status_code=404, detail=str(e))
    except ValueError as e:
        logger.error(str(e))
        raise HTTPException(status_code=409, detail=str(e))
    except Exception as e:
        logger.error(str(e))
        raise HTTPException(status_code=400, detail=str(e))

    return model_response


@models_router.post(
    "/import",
    operation_id="import_model",
    responses={
        201: {"description": "The model imported successfully"},
        404: {"description": "The model could not be found"},
        415: {"description": "Unrecognized file/folder format"},
        424: {"description": "The model appeared to import successfully, but could not be found in the model manager"},
        409: {"description": "There is already a model corresponding to this path or repo_id"},
    },
    status_code=201,
    response_model=ModelImportStatus,
)
async def import_model(
    location: str = Body(description="A model path, repo_id or URL to import"),
    prediction_type: Optional[Literal["v_prediction", "epsilon", "sample"]] = Body(
        description="Prediction type for SDv2 checkpoints and rare SDv1 checkpoints",
        default=None,
    ),
    priority: Optional[int] = Body(
        description="Which import jobs run first. Lower values run before higher ones.",
        default=10,
    ),
) -> ModelImportStatus:
    """
    Add a model using its local path, repo_id, or remote URL.

    Models will be downloaded, probed, configured and installed in a
    series of background threads. The return object has a `job_id` property
    that can be used to control the download job.

    The priority controls which import jobs run first. Lower values run before
    higher ones.

    The prediction_type applies to SDv2 models only and can be one of
    "v_prediction", "epsilon", or "sample". Default if not provided is
    "v_prediction".

    Listen on the event bus for a series of `model_event` events with an `id`
    matching the returned job id to get the progress, completion status, errors,
    and information on the model that was installed.
    """
    logger = ApiDependencies.invoker.services.logger

    try:
        result = ApiDependencies.invoker.services.model_manager.install_model(
            location,
            model_attributes={"prediction_type": SchedulerPredictionType(prediction_type)},
            priority=priority,
        )
        return ModelImportStatus(
            job_id=result.id,
            source=result.source,
            priority=result.priority,
            bytes=result.bytes,
            total_bytes=result.total_bytes,
            status=result.status,
        )
    except UnknownModelException as e:
        logger.error(str(e))
        raise HTTPException(status_code=404, detail=str(e))
    except InvalidModelException as e:
        logger.error(str(e))
        raise HTTPException(status_code=415)
    except ValueError as e:
        logger.error(str(e))
        raise HTTPException(status_code=409, detail=str(e))


@models_router.post(
    "/add",
    operation_id="add_model",
    responses={
        201: {"description": "The model added successfully"},
        404: {"description": "The model could not be found"},
        424: {"description": "The model appeared to add successfully, but could not be found in the model manager"},
        409: {"description": "There is already a model corresponding to this path or repo_id"},
    },
    status_code=201,
    response_model=InvokeAIModelConfig,
)
async def add_model(
    info: InvokeAIModelConfig = Body(description="Model configuration"),
) -> InvokeAIModelConfig:
    """
    Add a model using the configuration information appropriate for its type. Only local models can be added by path.
    This call will block until the model is installed.
    """

    logger = ApiDependencies.invoker.services.logger
    try:
        path = info.path
        job = ApiDependencies.invoker.services.model_manager.add_model(path)
        ApiDependencies.invoker.services.model_manager.wait_for_installs()
        key = job.model_key
        logger.info(f"Created model {key} for {path}")

        # update with the provided info
        info_dict = info.dict()
        info_dict = {x: info_dict[x] if info_dict[x] else None for x in info_dict.keys()}
        new_config = ApiDependencies.invoker.services.model_manager.update_model(key, new_config=info_dict)
        return parse_obj_as(InvokeAIModelConfig, new_config.dict())
    except UnknownModelException as e:
        logger.error(str(e))
        raise HTTPException(status_code=404, detail=str(e))
    except ValueError as e:
        logger.error(str(e))
        raise HTTPException(status_code=409, detail=str(e))


@models_router.delete(
    "/{key}",
    operation_id="del_model",
    responses={204: {"description": "Model deleted successfully"}, 404: {"description": "Model not found"}},
    status_code=204,
    response_model=None,
)
async def delete_model(
    key: str = Path(description="Unique key of model to remove from model registry."),
    delete_files: Optional[bool] = Query(description="Delete underlying files and directories as well.", default=False),
) -> Response:
    """Delete Model"""
    logger = ApiDependencies.invoker.services.logger

    try:
        ApiDependencies.invoker.services.model_manager.del_model(key, delete_files=delete_files)
        logger.info(f"Deleted model: {key}")
        return Response(status_code=204)
    except UnknownModelException as e:
        logger.error(str(e))
        raise HTTPException(status_code=404, detail=str(e))


@models_router.put(
    "/convert/{key}",
    operation_id="convert_model",
    responses={
        200: {"description": "Model converted successfully"},
        400: {"description": "Bad request"},
        404: {"description": "Model not found"},
    },
    status_code=200,
    response_model=InvokeAIModelConfig,
)
async def convert_model(
    key: str = Path(description="Unique key of model to remove from model registry."),
    convert_dest_directory: Optional[str] = Query(
        default=None, description="Save the converted model to the designated directory"
    ),
) -> InvokeAIModelConfig:
    """Convert a checkpoint model into a diffusers model, optionally saving to the indicated destination directory, or `models` if none."""
    try:
        dest = pathlib.Path(convert_dest_directory) if convert_dest_directory else None
        ApiDependencies.invoker.services.model_manager.convert_model(key, convert_dest_directory=dest)
        model_raw = ApiDependencies.invoker.services.model_manager.model_info(key).dict()
        response = parse_obj_as(InvokeAIModelConfig, model_raw)
    except UnknownModelException as e:
        raise HTTPException(status_code=404, detail=f"Model '{key}' not found: {str(e)}")
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e))
    return response


@models_router.get(
    "/search",
    operation_id="search_for_models",
    responses={
        200: {"description": "Directory searched successfully"},
        404: {"description": "Invalid directory path"},
    },
    status_code=200,
    response_model=List[pathlib.Path],
)
async def search_for_models(
    search_path: pathlib.Path = Query(description="Directory path to search for models"),
) -> List[pathlib.Path]:
    """Search for all models in a server-local path."""
    if not search_path.is_dir():
        raise HTTPException(
            status_code=404, detail=f"The search path '{search_path}' does not exist or is not directory"
        )
    return ApiDependencies.invoker.services.model_manager.search_for_models(search_path)


@models_router.get(
    "/ckpt_confs",
    operation_id="list_ckpt_configs",
    responses={
        200: {"description": "paths retrieved successfully"},
    },
    status_code=200,
    response_model=List[pathlib.Path],
)
async def list_ckpt_configs() -> List[pathlib.Path]:
    """Return a list of the legacy checkpoint configuration files stored in `ROOT/configs/stable-diffusion`, relative to ROOT."""
    return ApiDependencies.invoker.services.model_manager.list_checkpoint_configs()


@models_router.post(
    "/sync",
    operation_id="sync_to_config",
    responses={
        201: {"description": "synchronization successful"},
    },
    status_code=201,
    response_model=bool,
)
async def sync_to_config() -> bool:
    """
    Synchronize model in-memory data structures with disk.

    Call after making changes to models.yaml, autoimport directories
    or models directory.
    """
    ApiDependencies.invoker.services.model_manager.sync_to_config()
    return True


@models_router.put(
    "/merge",
    operation_id="merge_models",
    responses={
        200: {"description": "Model converted successfully"},
        400: {"description": "Incompatible models"},
        404: {"description": "One or more models not found"},
        409: {"description": "An identical merged model is already installed"},
    },
    status_code=200,
    response_model=InvokeAIModelConfig,
)
async def merge_models(
    keys: List[str] = Body(description="model name", min_items=2, max_items=3),
    merged_model_name: Optional[str] = Body(description="Name of destination model", default=None),
    alpha: Optional[float] = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
    interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method"),
    force: Optional[bool] = Body(
        description="Force merging of models created with different versions of diffusers", default=False
    ),
    merge_dest_directory: Optional[str] = Body(
        description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
        default=None,
    ),
) -> InvokeAIModelConfig:
    """Merge the indicated diffusers model."""
    logger = ApiDependencies.invoker.services.logger
    try:
        logger.info(f"Merging models: {keys} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")
        dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None
        result: ModelConfigBase = ApiDependencies.invoker.services.model_manager.merge_models(
            model_keys=keys,
            merged_model_name=merged_model_name,
            alpha=alpha,
            interp=interp,
            force=force,
            merge_dest_directory=dest,
        )
        response = parse_obj_as(InvokeAIModelConfig, result.dict())
    except DuplicateModelException as e:
        raise HTTPException(status_code=409, detail=str(e))
    except UnknownModelException:
        raise HTTPException(status_code=404, detail=f"One or more of the models '{keys}' not found")
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e))
    return response


@models_router.get(
    "/jobs",
    operation_id="list_install_jobs",
    responses={
        200: {"description": "The control job was updated successfully"},
        400: {"description": "Bad request"},
    },
    status_code=200,
    response_model=List[ModelImportStatus],
)
async def list_install_jobs() -> List[ModelImportStatus]:
    """List active and pending model installation jobs."""
    logger = ApiDependencies.invoker.services.logger
    mgr = ApiDependencies.invoker.services.model_manager
    try:
        jobs = mgr.list_install_jobs()
        return [
            ModelImportStatus(
                job_id=x.id,
                source=x.source,
                priority=x.priority,
                bytes=x.bytes,
                total_bytes=x.total_bytes,
                status=x.status,
            )
            for x in jobs
        ]
    except Exception as e:
        logger.error(str(e))
        raise HTTPException(status_code=400, detail=str(e))


@models_router.patch(
    "/jobs/control/{operation}/{job_id}",
    operation_id="control_install_jobs",
    responses={
        200: {"description": "The control job was updated successfully"},
        400: {"description": "Bad request"},
        404: {"description": "The job could not be found"},
    },
    status_code=200,
    response_model=ModelImportStatus,
)
async def control_install_jobs(
    job_id: int = Path(description="Install job_id for start, pause and cancel operations"),
    operation: JobControlOperation = Path(description="The operation to perform on the job."),
    priority_delta: Optional[int] = Body(
        description="Change in job priority for priority operations only. Negative numbers increase priority.",
        default=None,
    ),
) -> ModelImportStatus:
    """Start, pause, cancel, or change the run priority of a running model install job."""
    logger = ApiDependencies.invoker.services.logger
    mgr = ApiDependencies.invoker.services.model_manager
    try:
        job = mgr.id_to_job(job_id)

        if operation == JobControlOperation.START:
            mgr.start_job(job_id)

        elif operation == JobControlOperation.PAUSE:
            mgr.pause_job(job_id)

        elif operation == JobControlOperation.CANCEL:
            mgr.cancel_job(job_id)

        elif operation == JobControlOperation.CHANGE_PRIORITY:
            mgr.change_job_priority(job_id, priority_delta)

        else:
            raise ValueError(f"Unknown operation {JobControlOperation.value}")

        return ModelImportStatus(
            job_id=job_id,
            source=job.source,
            priority=job.priority,
            status=job.status,
            bytes=job.bytes,
            total_bytes=job.total_bytes,
        )
    except UnknownJobIDException as e:
        raise HTTPException(status_code=404, detail=str(e))
    except ValueError as e:
        logger.error(str(e))
        raise HTTPException(status_code=409, detail=str(e))
    except Exception as e:
        logger.error(str(e))
        raise HTTPException(status_code=400, detail=str(e))


@models_router.patch(
    "/jobs/cancel_all",
    operation_id="cancel_all_jobs",
    responses={
        204: {"description": "All jobs cancelled successfully"},
        400: {"description": "Bad request"},
    },
    status_code=200,
    response_model=ModelImportStatus,
)
async def cancel_install_jobs():
    """Cancel all pending install jobs."""
    logger = ApiDependencies.invoker.services.logger
    try:
        mgr = ApiDependencies.invoker.services.model_manager
        logger.info("Cancelling all running model installation jobs.")
        mgr.cancel_all_jobs()
        return Response(status_code=204)
    except Exception as e:
        logger.error(str(e))
        raise HTTPException(status_code=400, detail=str(e))


@models_router.patch(
    "/jobs/prune",
    operation_id="prune_jobs",
    responses={
        204: {"description": "All jobs cancelled successfully"},
        400: {"description": "Bad request"},
    },
    status_code=200,
    response_model=ModelImportStatus,
)
async def prune_jobs():
    """Prune all completed and errored jobs."""
    logger = ApiDependencies.invoker.services.logger
    try:
        mgr = ApiDependencies.invoker.services.model_manager
        mgr.prune_jobs()
        return Response(status_code=204)
    except Exception as e:
        logger.error(str(e))
        raise HTTPException(status_code=400, detail=str(e))