import typing from enum import Enum from importlib.metadata import PackageNotFoundError, version from pathlib import Path from platform import python_version from typing import Optional import torch from fastapi import Body from fastapi.routing import APIRouter from pydantic import BaseModel, Field from invokeai.app.api.dependencies import ApiDependencies from invokeai.app.invocations.upscale import ESRGAN_MODELS from invokeai.app.services.invocation_cache.invocation_cache_common import InvocationCacheStatus from invokeai.backend.image_util.infill_methods.patchmatch import PatchMatch from invokeai.backend.util.logging import logging from invokeai.version import __version__ class LogLevel(int, Enum): NotSet = logging.NOTSET Debug = logging.DEBUG Info = logging.INFO Warning = logging.WARNING Error = logging.ERROR Critical = logging.CRITICAL class Upscaler(BaseModel): upscaling_method: str = Field(description="Name of upscaling method") upscaling_models: list[str] = Field(description="List of upscaling models for this method") app_router = APIRouter(prefix="/v1/app", tags=["app"]) class AppVersion(BaseModel): """App Version Response""" version: str = Field(description="App version") class AppDependencyVersions(BaseModel): """App depencency Versions Response""" accelerate: str = Field(description="accelerate version") compel: str = Field(description="compel version") cuda: Optional[str] = Field(description="CUDA version") diffusers: str = Field(description="diffusers version") numpy: str = Field(description="Numpy version") opencv: str = Field(description="OpenCV version") onnx: str = Field(description="ONNX version") pillow: str = Field(description="Pillow (PIL) version") python: str = Field(description="Python version") torch: str = Field(description="PyTorch version") torchvision: str = Field(description="PyTorch Vision version") transformers: str = Field(description="transformers version") xformers: Optional[str] = Field(description="xformers version") class AppConfig(BaseModel): """App Config Response""" infill_methods: list[str] = Field(description="List of available infill methods") upscaling_methods: list[Upscaler] = Field(description="List of upscaling methods") nsfw_methods: list[str] = Field(description="List of NSFW checking methods") watermarking_methods: list[str] = Field(description="List of invisible watermark methods") @app_router.get("/version", operation_id="app_version", status_code=200, response_model=AppVersion) async def get_version() -> AppVersion: return AppVersion(version=__version__) @app_router.get("/app_deps", operation_id="get_app_deps", status_code=200, response_model=AppDependencyVersions) async def get_app_deps() -> AppDependencyVersions: try: xformers = version("xformers") except PackageNotFoundError: xformers = None return AppDependencyVersions( accelerate=version("accelerate"), compel=version("compel"), cuda=torch.version.cuda, diffusers=version("diffusers"), numpy=version("numpy"), opencv=version("opencv-python"), onnx=version("onnx"), pillow=version("pillow"), python=python_version(), torch=torch.version.__version__, torchvision=version("torchvision"), transformers=version("transformers"), xformers=xformers, ) @app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig) async def get_config() -> AppConfig: infill_methods = ["tile", "lama", "cv2", "color"] # TODO: add mosaic back if PatchMatch.patchmatch_available(): infill_methods.append("patchmatch") upscaling_models = [] for model in typing.get_args(ESRGAN_MODELS): upscaling_models.append(str(Path(model).stem)) upscaler = Upscaler(upscaling_method="esrgan", upscaling_models=upscaling_models) nsfw_methods = ["nsfw_checker"] watermarking_methods = ["invisible_watermark"] return AppConfig( infill_methods=infill_methods, upscaling_methods=[upscaler], nsfw_methods=nsfw_methods, watermarking_methods=watermarking_methods, ) @app_router.get( "/logging", operation_id="get_log_level", responses={200: {"description": "The operation was successful"}}, response_model=LogLevel, ) async def get_log_level() -> LogLevel: """Returns the log level""" return LogLevel(ApiDependencies.invoker.services.logger.level) @app_router.post( "/logging", operation_id="set_log_level", responses={200: {"description": "The operation was successful"}}, response_model=LogLevel, ) async def set_log_level( level: LogLevel = Body(description="New log verbosity level"), ) -> LogLevel: """Sets the log verbosity level""" ApiDependencies.invoker.services.logger.setLevel(level) return LogLevel(ApiDependencies.invoker.services.logger.level) @app_router.delete( "/invocation_cache", operation_id="clear_invocation_cache", responses={200: {"description": "The operation was successful"}}, ) async def clear_invocation_cache() -> None: """Clears the invocation cache""" ApiDependencies.invoker.services.invocation_cache.clear() @app_router.put( "/invocation_cache/enable", operation_id="enable_invocation_cache", responses={200: {"description": "The operation was successful"}}, ) async def enable_invocation_cache() -> None: """Clears the invocation cache""" ApiDependencies.invoker.services.invocation_cache.enable() @app_router.put( "/invocation_cache/disable", operation_id="disable_invocation_cache", responses={200: {"description": "The operation was successful"}}, ) async def disable_invocation_cache() -> None: """Clears the invocation cache""" ApiDependencies.invoker.services.invocation_cache.disable() @app_router.get( "/invocation_cache/status", operation_id="get_invocation_cache_status", responses={200: {"model": InvocationCacheStatus}}, ) async def get_invocation_cache_status() -> InvocationCacheStatus: """Clears the invocation cache""" return ApiDependencies.invoker.services.invocation_cache.get_status()