diff --git a/invokeai/app/api/dependencies.py b/invokeai/app/api/dependencies.py index f33bfff26e..99127c4332 100644 --- a/invokeai/app/api/dependencies.py +++ b/invokeai/app/api/dependencies.py @@ -1,14 +1,12 @@ # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) import os -from argparse import Namespace -from invokeai.app.services.metadata import PngMetadataService, MetadataServiceBase +import invokeai.backend.util.logging as logger +from typing import types from ..services.default_graphs import create_system_graphs - from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage - from ...backend import Globals from ..services.model_manager_initializer import get_model_manager from ..services.restoration_services import RestorationServices @@ -19,6 +17,7 @@ from ..services.invocation_services import InvocationServices from ..services.invoker import Invoker from ..services.processor import DefaultInvocationProcessor from ..services.sqlite import SqliteItemStorage +from ..services.metadata import PngMetadataService from .events import FastAPIEventService @@ -44,15 +43,16 @@ class ApiDependencies: invoker: Invoker = None @staticmethod - def initialize(config, event_handler_id: int): + def initialize(config, event_handler_id: int, logger: types.ModuleType=logger): Globals.try_patchmatch = config.patchmatch Globals.always_use_cpu = config.always_use_cpu Globals.internet_available = config.internet_available and check_internet() Globals.disable_xformers = not config.xformers Globals.ckpt_convert = config.ckpt_convert - # TODO: Use a logger - print(f">> Internet connectivity is {Globals.internet_available}") + # TO DO: Use the config to select the logger rather than use the default + # invokeai logging module + logger.info(f"Internet connectivity is {Globals.internet_available}") events = FastAPIEventService(event_handler_id) @@ -70,8 +70,9 @@ class ApiDependencies: db_location = os.path.join(output_folder, "invokeai.db") services = InvocationServices( - model_manager=get_model_manager(config), + model_manager=get_model_manager(config,logger), events=events, + logger=logger, latents=latents, images=images, metadata=metadata, @@ -83,7 +84,7 @@ class ApiDependencies: filename=db_location, table_name="graph_executions" ), processor=DefaultInvocationProcessor(), - restoration=RestorationServices(config), + restoration=RestorationServices(config,logger), ) create_system_graphs(services.graph_library) diff --git a/invokeai/app/api/routers/models.py b/invokeai/app/api/routers/models.py index 2de079cd6d..ca83b44bf3 100644 --- a/invokeai/app/api/routers/models.py +++ b/invokeai/app/api/routers/models.py @@ -8,10 +8,6 @@ from fastapi.routing import APIRouter, HTTPException from pydantic import BaseModel, Field, parse_obj_as from pathlib import Path from ..dependencies import ApiDependencies -from invokeai.backend.globals import Globals, global_converted_ckpts_dir -from invokeai.backend.args import Args - - models_router = APIRouter(prefix="/v1/models", tags=["models"]) @@ -112,19 +108,20 @@ async def update_model( async def delete_model(model_name: str) -> None: """Delete Model""" model_names = ApiDependencies.invoker.services.model_manager.model_names() + logger = ApiDependencies.invoker.services.logger model_exists = model_name in model_names # check if model exists - print(f">> Checking for model {model_name}...") + logger.info(f"Checking for model {model_name}...") if model_exists: - print(f">> Deleting Model: {model_name}") + logger.info(f"Deleting Model: {model_name}") ApiDependencies.invoker.services.model_manager.del_model(model_name, delete_files=True) - print(f">> Model Deleted: {model_name}") + logger.info(f"Model Deleted: {model_name}") raise HTTPException(status_code=204, detail=f"Model '{model_name}' deleted successfully") else: - print(f">> Model not found") + logger.error(f"Model not found") raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found") @@ -248,4 +245,4 @@ async def delete_model(model_name: str) -> None: # ) # print(f">> Models Merged: {models_to_merge}") # print(f">> New Model Added: {model_merge_info['merged_model_name']}") - # except Exception as e: \ No newline at end of file + # except Exception as e: diff --git a/invokeai/app/api_app.py b/invokeai/app/api_app.py index ab05cb3344..f935de542e 100644 --- a/invokeai/app/api_app.py +++ b/invokeai/app/api_app.py @@ -3,6 +3,7 @@ import asyncio from inspect import signature import uvicorn +import invokeai.backend.util.logging as logger from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html @@ -16,7 +17,6 @@ from ..backend import Args from .api.dependencies import ApiDependencies from .api.routers import images, sessions, models from .api.sockets import SocketIO -from .invocations import * from .invocations.baseinvocation import BaseInvocation # Create the app @@ -56,7 +56,7 @@ async def startup_event(): config.parse_args() ApiDependencies.initialize( - config=config, event_handler_id=event_handler_id + config=config, event_handler_id=event_handler_id, logger=logger ) diff --git a/invokeai/app/cli/commands.py b/invokeai/app/cli/commands.py index 5ad4827eb0..01cd99bc35 100644 --- a/invokeai/app/cli/commands.py +++ b/invokeai/app/cli/commands.py @@ -2,14 +2,15 @@ from abc import ABC, abstractmethod import argparse -from typing import Any, Callable, Iterable, Literal, get_args, get_origin, get_type_hints +from typing import Any, Callable, Iterable, Literal, Union, get_args, get_origin, get_type_hints from pydantic import BaseModel, Field import networkx as nx import matplotlib.pyplot as plt +import invokeai.backend.util.logging as logger from ..invocations.baseinvocation import BaseInvocation from ..invocations.image import ImageField -from ..services.graph import GraphExecutionState, LibraryGraph, GraphInvocation, Edge +from ..services.graph import GraphExecutionState, LibraryGraph, Edge from ..services.invoker import Invoker @@ -229,7 +230,7 @@ class HistoryCommand(BaseCommand): for i in range(min(self.count, len(history))): entry_id = history[-1 - i] entry = context.get_session().graph.get_node(entry_id) - print(f"{entry_id}: {get_invocation_command(entry)}") + logger.info(f"{entry_id}: {get_invocation_command(entry)}") class SetDefaultCommand(BaseCommand): diff --git a/invokeai/app/cli/completer.py b/invokeai/app/cli/completer.py index 86d3e100c3..c84c430bd7 100644 --- a/invokeai/app/cli/completer.py +++ b/invokeai/app/cli/completer.py @@ -10,6 +10,7 @@ import shlex from pathlib import Path from typing import List, Dict, Literal, get_args, get_type_hints, get_origin +import invokeai.backend.util.logging as logger from ...backend import ModelManager, Globals from ..invocations.baseinvocation import BaseInvocation from .commands import BaseCommand @@ -160,8 +161,8 @@ def set_autocompleter(model_manager: ModelManager) -> Completer: pass except OSError: # file likely corrupted newname = f"{histfile}.old" - print( - f"## Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}" + logger.error( + f"Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}" ) histfile.replace(Path(newname)) atexit.register(readline.write_history_file, histfile) diff --git a/invokeai/app/cli_app.py b/invokeai/app/cli_app.py index 9ac156916b..abe672820b 100644 --- a/invokeai/app/cli_app.py +++ b/invokeai/app/cli_app.py @@ -13,21 +13,20 @@ from typing import ( from pydantic import BaseModel from pydantic.fields import Field + +import invokeai.backend.util.logging as logger from invokeai.app.services.metadata import PngMetadataService - from .services.default_graphs import create_system_graphs - from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage from ..backend import Args -from .cli.commands import BaseCommand, CliContext, ExitCli, add_graph_parsers, add_parsers, get_graph_execution_history +from .cli.commands import BaseCommand, CliContext, ExitCli, add_graph_parsers, add_parsers from .cli.completer import set_autocompleter -from .invocations import * from .invocations.baseinvocation import BaseInvocation from .services.events import EventServiceBase from .services.model_manager_initializer import get_model_manager from .services.restoration_services import RestorationServices -from .services.graph import Edge, EdgeConnection, ExposedNodeInput, GraphExecutionState, GraphInvocation, LibraryGraph, are_connection_types_compatible +from .services.graph import Edge, EdgeConnection, GraphExecutionState, GraphInvocation, LibraryGraph, are_connection_types_compatible from .services.default_graphs import default_text_to_image_graph_id from .services.image_storage import DiskImageStorage from .services.invocation_queue import MemoryInvocationQueue @@ -182,7 +181,7 @@ def invoke_all(context: CliContext): # Print any errors if context.session.has_error(): for n in context.session.errors: - print( + context.invoker.services.logger.error( f"Error in node {n} (source node {context.session.prepared_source_mapping[n]}): {context.session.errors[n]}" ) @@ -192,13 +191,13 @@ def invoke_all(context: CliContext): def invoke_cli(): config = Args() config.parse_args() - model_manager = get_model_manager(config) + model_manager = get_model_manager(config,logger=logger) # This initializes the autocompleter and returns it. # Currently nothing is done with the returned Completer # object, but the object can be used to change autocompletion # behavior on the fly, if desired. - completer = set_autocompleter(model_manager) + set_autocompleter(model_manager) events = EventServiceBase() @@ -225,7 +224,8 @@ def invoke_cli(): filename=db_location, table_name="graph_executions" ), processor=DefaultInvocationProcessor(), - restoration=RestorationServices(config), + restoration=RestorationServices(config,logger=logger), + logger=logger, ) system_graphs = create_system_graphs(services.graph_library) @@ -365,12 +365,12 @@ def invoke_cli(): invoke_all(context) except InvalidArgs: - print('Invalid command, use "help" to list commands') + invoker.services.logger.warning('Invalid command, use "help" to list commands') continue except SessionError: # Start a new session - print("Session error: creating a new session") + invoker.services.logger.warning("Session error: creating a new session") context.reset() except ExitCli: diff --git a/invokeai/app/invocations/util/choose_model.py b/invokeai/app/invocations/util/choose_model.py index f0f2dc7120..cd03ce87a8 100644 --- a/invokeai/app/invocations/util/choose_model.py +++ b/invokeai/app/invocations/util/choose_model.py @@ -3,12 +3,11 @@ from invokeai.backend.model_management.model_manager import ModelManager def choose_model(model_manager: ModelManager, model_name: str): """Returns the default model if the `model_name` not a valid model, else returns the selected model.""" + logger = model_manager.logger if model_manager.valid_model(model_name): model = model_manager.get_model(model_name) else: model = model_manager.get_model() - print( - f"* Warning: '{model_name}' is not a valid model name. Using default model \'{model['model_name']}\' instead." - ) + logger.warning(f"{model_name}' is not a valid model name. Using default model \'{model['model_name']}\' instead.") return model diff --git a/invokeai/app/services/invocation_services.py b/invokeai/app/services/invocation_services.py index 1ff42f063d..47b3b6cf07 100644 --- a/invokeai/app/services/invocation_services.py +++ b/invokeai/app/services/invocation_services.py @@ -1,4 +1,6 @@ -# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) +# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team + +from typing import types from invokeai.app.services.metadata import MetadataServiceBase from invokeai.backend import ModelManager @@ -29,6 +31,7 @@ class InvocationServices: self, model_manager: ModelManager, events: EventServiceBase, + logger: types.ModuleType, latents: LatentsStorageBase, images: ImageStorageBase, metadata: MetadataServiceBase, @@ -40,6 +43,7 @@ class InvocationServices: ): self.model_manager = model_manager self.events = events + self.logger = logger self.latents = latents self.images = images self.metadata = metadata diff --git a/invokeai/app/services/model_manager_initializer.py b/invokeai/app/services/model_manager_initializer.py index 3ef79f0b7e..2b1aac1f36 100644 --- a/invokeai/app/services/model_manager_initializer.py +++ b/invokeai/app/services/model_manager_initializer.py @@ -5,6 +5,7 @@ from argparse import Namespace from invokeai.backend import Args from omegaconf import OmegaConf from pathlib import Path +from typing import types import invokeai.version from ...backend import ModelManager @@ -12,16 +13,16 @@ from ...backend.util import choose_precision, choose_torch_device from ...backend import Globals # TODO: Replace with an abstract class base ModelManagerBase -def get_model_manager(config: Args) -> ModelManager: +def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManager: if not config.conf: config_file = os.path.join(Globals.root, "configs", "models.yaml") if not os.path.exists(config_file): report_model_error( - config, FileNotFoundError(f"The file {config_file} could not be found.") + config, FileNotFoundError(f"The file {config_file} could not be found."), logger ) - print(f">> {invokeai.version.__app_name__}, version {invokeai.version.__version__}") - print(f'>> InvokeAI runtime directory is "{Globals.root}"') + logger.info(f"{invokeai.version.__app_name__}, version {invokeai.version.__version__}") + logger.info(f'InvokeAI runtime directory is "{Globals.root}"') # these two lines prevent a horrible warning message from appearing # when the frozen CLIP tokenizer is imported @@ -62,11 +63,12 @@ def get_model_manager(config: Args) -> ModelManager: device_type=device, max_loaded_models=config.max_loaded_models, embedding_path = Path(embedding_path), + logger = logger, ) except (FileNotFoundError, TypeError, AssertionError) as e: - report_model_error(config, e) + report_model_error(config, e, logger) except (IOError, KeyError) as e: - print(f"{e}. Aborting.") + logger.error(f"{e}. Aborting.") sys.exit(-1) # try to autoconvert new models @@ -76,18 +78,18 @@ def get_model_manager(config: Args) -> ModelManager: conf_path=config.conf, weights_directory=path, ) - + logger.info('Model manager initialized') return model_manager -def report_model_error(opt: Namespace, e: Exception): - print(f'** An error occurred while attempting to initialize the model: "{str(e)}"') - print( - "** This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models." +def report_model_error(opt: Namespace, e: Exception, logger: types.ModuleType): + logger.error(f'An error occurred while attempting to initialize the model: "{str(e)}"') + logger.error( + "This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models." ) yes_to_all = os.environ.get("INVOKE_MODEL_RECONFIGURE") if yes_to_all: - print( - "** Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE" + logger.warning( + "Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE" ) else: response = input( @@ -96,13 +98,12 @@ def report_model_error(opt: Namespace, e: Exception): if response.startswith(("n", "N")): return - print("invokeai-configure is launching....\n") + logger.info("invokeai-configure is launching....\n") # Match arguments that were set on the CLI # only the arguments accepted by the configuration script are parsed root_dir = ["--root", opt.root_dir] if opt.root_dir is not None else [] config = ["--config", opt.conf] if opt.conf is not None else [] - previous_config = sys.argv sys.argv = ["invokeai-configure"] sys.argv.extend(root_dir) sys.argv.extend(config.to_dict()) diff --git a/invokeai/app/services/restoration_services.py b/invokeai/app/services/restoration_services.py index f5fc687c11..7bd264444e 100644 --- a/invokeai/app/services/restoration_services.py +++ b/invokeai/app/services/restoration_services.py @@ -1,6 +1,7 @@ import sys import traceback import torch +from typing import types from ...backend.restoration import Restoration from ...backend.util import choose_torch_device, CPU_DEVICE, MPS_DEVICE @@ -10,7 +11,7 @@ from ...backend.util import choose_torch_device, CPU_DEVICE, MPS_DEVICE class RestorationServices: '''Face restoration and upscaling''' - def __init__(self,args): + def __init__(self,args,logger:types.ModuleType): try: gfpgan, codeformer, esrgan = None, None, None if args.restore or args.esrgan: @@ -20,20 +21,22 @@ class RestorationServices: args.gfpgan_model_path ) else: - print(">> Face restoration disabled") + logger.info("Face restoration disabled") if args.esrgan: esrgan = restoration.load_esrgan(args.esrgan_bg_tile) else: - print(">> Upscaling disabled") + logger.info("Upscaling disabled") else: - print(">> Face restoration and upscaling disabled") + logger.info("Face restoration and upscaling disabled") except (ModuleNotFoundError, ImportError): print(traceback.format_exc(), file=sys.stderr) - print(">> You may need to install the ESRGAN and/or GFPGAN modules") + logger.info("You may need to install the ESRGAN and/or GFPGAN modules") self.device = torch.device(choose_torch_device()) self.gfpgan = gfpgan self.codeformer = codeformer self.esrgan = esrgan + self.logger = logger + self.logger.info('Face restoration initialized') # note that this one method does gfpgan and codepath reconstruction, as well as # esrgan upscaling @@ -58,15 +61,15 @@ class RestorationServices: if self.gfpgan is not None or self.codeformer is not None: if facetool == "gfpgan": if self.gfpgan is None: - print( - ">> GFPGAN not found. Face restoration is disabled." + self.logger.info( + "GFPGAN not found. Face restoration is disabled." ) else: image = self.gfpgan.process(image, strength, seed) if facetool == "codeformer": if self.codeformer is None: - print( - ">> CodeFormer not found. Face restoration is disabled." + self.logger.info( + "CodeFormer not found. Face restoration is disabled." ) else: cf_device = ( @@ -80,7 +83,7 @@ class RestorationServices: fidelity=codeformer_fidelity, ) else: - print(">> Face Restoration is disabled.") + self.logger.info("Face Restoration is disabled.") if upscale is not None: if self.esrgan is not None: if len(upscale) < 2: @@ -93,10 +96,10 @@ class RestorationServices: denoise_str=upscale_denoise_str, ) else: - print(">> ESRGAN is disabled. Image not upscaled.") + self.logger.info("ESRGAN is disabled. Image not upscaled.") except Exception as e: - print( - f">> Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}" + self.logger.info( + f"Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}" ) if image_callback is not None: diff --git a/invokeai/backend/args.py b/invokeai/backend/args.py index b6c2608b20..eb8b396ee0 100644 --- a/invokeai/backend/args.py +++ b/invokeai/backend/args.py @@ -96,6 +96,7 @@ from pathlib import Path from typing import List import invokeai.version +import invokeai.backend.util.logging as logger from invokeai.backend.image_util import retrieve_metadata from .globals import Globals @@ -189,7 +190,7 @@ class Args(object): print(f"{APP_NAME} {APP_VERSION}") sys.exit(0) - print("* Initializing, be patient...") + logger.info("Initializing, be patient...") Globals.root = Path(os.path.abspath(switches.root_dir or Globals.root)) Globals.try_patchmatch = switches.patchmatch @@ -197,14 +198,13 @@ class Args(object): initfile = os.path.expanduser(os.path.join(Globals.root, Globals.initfile)) legacyinit = os.path.expanduser("~/.invokeai") if os.path.exists(initfile): - print( - f">> Initialization file {initfile} found. Loading...", - file=sys.stderr, + logger.info( + f"Initialization file {initfile} found. Loading...", ) sysargs.insert(0, f"@{initfile}") elif os.path.exists(legacyinit): - print( - f">> WARNING: Old initialization file found at {legacyinit}. This location is deprecated. Please move it to {Globals.root}/invokeai.init." + logger.warning( + f"Old initialization file found at {legacyinit}. This location is deprecated. Please move it to {Globals.root}/invokeai.init." ) sysargs.insert(0, f"@{legacyinit}") Globals.log_tokenization = self._arg_parser.parse_args( @@ -214,7 +214,7 @@ class Args(object): self._arg_switches = self._arg_parser.parse_args(sysargs) return self._arg_switches except Exception as e: - print(f"An exception has occurred: {e}") + logger.error(f"An exception has occurred: {e}") return None def parse_cmd(self, cmd_string): @@ -1154,7 +1154,7 @@ class Args(object): def format_metadata(**kwargs): - print("format_metadata() is deprecated. Please use metadata_dumps()") + logger.warning("format_metadata() is deprecated. Please use metadata_dumps()") return metadata_dumps(kwargs) @@ -1326,7 +1326,7 @@ def metadata_loads(metadata) -> list: import sys import traceback - print(">> could not read metadata", file=sys.stderr) + logger.error("Could not read metadata") print(traceback.format_exc(), file=sys.stderr) return results diff --git a/invokeai/backend/generate.py b/invokeai/backend/generate.py index 1b19a1aa7e..4f3df60f1c 100644 --- a/invokeai/backend/generate.py +++ b/invokeai/backend/generate.py @@ -27,6 +27,7 @@ from diffusers.utils.import_utils import is_xformers_available from omegaconf import OmegaConf from pathlib import Path +import invokeai.backend.util.logging as logger from .args import metadata_from_png from .generator import infill_methods from .globals import Globals, global_cache_dir @@ -195,12 +196,12 @@ class Generate: # device to Generate(). However the device was then ignored, so # it wasn't actually doing anything. This logic could be reinstated. self.device = torch.device(choose_torch_device()) - print(f">> Using device_type {self.device.type}") + logger.info(f"Using device_type {self.device.type}") if full_precision: if self.precision != "auto": raise ValueError("Remove --full_precision / -F if using --precision") - print("Please remove deprecated --full_precision / -F") - print("If auto config does not work you can use --precision=float32") + logger.warning("Please remove deprecated --full_precision / -F") + logger.warning("If auto config does not work you can use --precision=float32") self.precision = "float32" if self.precision == "auto": self.precision = choose_precision(self.device) @@ -208,13 +209,13 @@ class Generate: if is_xformers_available(): if torch.cuda.is_available() and not Globals.disable_xformers: - print(">> xformers memory-efficient attention is available and enabled") + logger.info("xformers memory-efficient attention is available and enabled") else: - print( - ">> xformers memory-efficient attention is available but disabled" + logger.info( + "xformers memory-efficient attention is available but disabled" ) else: - print(">> xformers not installed") + logger.info("xformers not installed") # model caching system for fast switching self.model_manager = ModelManager( @@ -229,8 +230,8 @@ class Generate: fallback = self.model_manager.default_model() or FALLBACK_MODEL_NAME model = model or fallback if not self.model_manager.valid_model(model): - print( - f'** "{model}" is not a known model name; falling back to {fallback}.' + logger.warning( + f'"{model}" is not a known model name; falling back to {fallback}.' ) model = None self.model_name = model or fallback @@ -246,10 +247,10 @@ class Generate: # load safety checker if requested if safety_checker: - print(">> Initializing NSFW checker") + logger.info("Initializing NSFW checker") self.safety_checker = SafetyChecker(self.device) else: - print(">> NSFW checker is disabled") + logger.info("NSFW checker is disabled") def prompt2png(self, prompt, outdir, **kwargs): """ @@ -567,7 +568,7 @@ class Generate: self.clear_cuda_cache() if catch_interrupts: - print("**Interrupted** Partial results will be returned.") + logger.warning("Interrupted** Partial results will be returned.") else: raise KeyboardInterrupt except RuntimeError: @@ -575,11 +576,11 @@ class Generate: self.clear_cuda_cache() print(traceback.format_exc(), file=sys.stderr) - print(">> Could not generate image.") + logger.info("Could not generate image.") toc = time.time() - print("\n>> Usage stats:") - print(f">> {len(results)} image(s) generated in", "%4.2fs" % (toc - tic)) + logger.info("Usage stats:") + logger.info(f"{len(results)} image(s) generated in "+"%4.2fs" % (toc - tic)) self.print_cuda_stats() return results @@ -609,16 +610,16 @@ class Generate: def print_cuda_stats(self): if self._has_cuda(): self.gather_cuda_stats() - print( - ">> Max VRAM used for this generation:", - "%4.2fG." % (self.max_memory_allocated / 1e9), - "Current VRAM utilization:", - "%4.2fG" % (self.memory_allocated / 1e9), + logger.info( + "Max VRAM used for this generation: "+ + "%4.2fG. " % (self.max_memory_allocated / 1e9)+ + "Current VRAM utilization: "+ + "%4.2fG" % (self.memory_allocated / 1e9) ) - print( - ">> Max VRAM used since script start: ", - "%4.2fG" % (self.session_peakmem / 1e9), + logger.info( + "Max VRAM used since script start: " + + "%4.2fG" % (self.session_peakmem / 1e9) ) # this needs to be generalized to all sorts of postprocessors, which should be wrapped @@ -647,7 +648,7 @@ class Generate: seed = random.randrange(0, np.iinfo(np.uint32).max) prompt = opt.prompt or args.prompt or "" - print(f'>> using seed {seed} and prompt "{prompt}" for {image_path}') + logger.info(f'using seed {seed} and prompt "{prompt}" for {image_path}') # try to reuse the same filename prefix as the original file. # we take everything up to the first period @@ -696,8 +697,8 @@ class Generate: try: extend_instructions[direction] = int(pixels) except ValueError: - print( - '** invalid extension instruction. Use ..., as in "top 64 left 128 right 64 bottom 64"' + logger.warning( + 'invalid extension instruction. Use ..., as in "top 64 left 128 right 64 bottom 64"' ) opt.seed = seed @@ -720,8 +721,8 @@ class Generate: # fetch the metadata from the image generator = self.select_generator(embiggen=True) opt.strength = opt.embiggen_strength or 0.40 - print( - f">> Setting img2img strength to {opt.strength} for happy embiggening" + logger.info( + f"Setting img2img strength to {opt.strength} for happy embiggening" ) generator.generate( prompt, @@ -748,12 +749,12 @@ class Generate: return restorer.process(opt, args, image_callback=callback, prefix=prefix) elif tool is None: - print( - "* please provide at least one postprocessing option, such as -G or -U" + logger.warning( + "please provide at least one postprocessing option, such as -G or -U" ) return None else: - print(f"* postprocessing tool {tool} is not yet supported") + logger.warning(f"postprocessing tool {tool} is not yet supported") return None def select_generator( @@ -797,8 +798,8 @@ class Generate: image = self._load_img(img) if image.width < self.width and image.height < self.height: - print( - f">> WARNING: img2img and inpainting may produce unexpected results with initial images smaller than {self.width}x{self.height} in both dimensions" + logger.warning( + f"img2img and inpainting may produce unexpected results with initial images smaller than {self.width}x{self.height} in both dimensions" ) # if image has a transparent area and no mask was provided, then try to generate mask @@ -809,8 +810,8 @@ class Generate: if (image.width * image.height) > ( self.width * self.height ) and self.size_matters: - print( - ">> This input is larger than your defaults. If you run out of memory, please use a smaller image." + logger.info( + "This input is larger than your defaults. If you run out of memory, please use a smaller image." ) self.size_matters = False @@ -891,11 +892,11 @@ class Generate: try: model_data = cache.get_model(model_name) except Exception as e: - print(f"** model {model_name} could not be loaded: {str(e)}") + logger.warning(f"model {model_name} could not be loaded: {str(e)}") print(traceback.format_exc(), file=sys.stderr) if previous_model_name is None: raise e - print("** trying to reload previous model") + logger.warning("trying to reload previous model") model_data = cache.get_model(previous_model_name) # load previous if model_data is None: raise e @@ -962,15 +963,15 @@ class Generate: if self.gfpgan is not None or self.codeformer is not None: if facetool == "gfpgan": if self.gfpgan is None: - print( - ">> GFPGAN not found. Face restoration is disabled." + logger.info( + "GFPGAN not found. Face restoration is disabled." ) else: image = self.gfpgan.process(image, strength, seed) if facetool == "codeformer": if self.codeformer is None: - print( - ">> CodeFormer not found. Face restoration is disabled." + logger.info( + "CodeFormer not found. Face restoration is disabled." ) else: cf_device = ( @@ -984,7 +985,7 @@ class Generate: fidelity=codeformer_fidelity, ) else: - print(">> Face Restoration is disabled.") + logger.info("Face Restoration is disabled.") if upscale is not None: if self.esrgan is not None: if len(upscale) < 2: @@ -997,10 +998,10 @@ class Generate: denoise_str=upscale_denoise_str, ) else: - print(">> ESRGAN is disabled. Image not upscaled.") + logger.info("ESRGAN is disabled. Image not upscaled.") except Exception as e: - print( - f">> Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}" + logger.info( + f"Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}" ) if image_callback is not None: @@ -1066,17 +1067,17 @@ class Generate: if self.sampler_name in scheduler_map: sampler_class = scheduler_map[self.sampler_name] msg = ( - f">> Setting Sampler to {self.sampler_name} ({sampler_class.__name__})" + f"Setting Sampler to {self.sampler_name} ({sampler_class.__name__})" ) self.sampler = sampler_class.from_config(self.model.scheduler.config) else: msg = ( - f">> Unsupported Sampler: {self.sampler_name} " + f" Unsupported Sampler: {self.sampler_name} "+ f"Defaulting to {default}" ) self.sampler = default - print(msg) + logger.info(msg) if not hasattr(self.sampler, "uses_inpainting_model"): # FIXME: terrible kludge! @@ -1085,17 +1086,17 @@ class Generate: def _load_img(self, img) -> Image: if isinstance(img, Image.Image): image = img - print(f">> using provided input image of size {image.width}x{image.height}") + logger.info(f"using provided input image of size {image.width}x{image.height}") elif isinstance(img, str): - assert os.path.exists(img), f">> {img}: File not found" + assert os.path.exists(img), f"{img}: File not found" image = Image.open(img) - print( - f">> loaded input image of size {image.width}x{image.height} from {img}" + logger.info( + f"loaded input image of size {image.width}x{image.height} from {img}" ) else: image = Image.open(img) - print(f">> loaded input image of size {image.width}x{image.height}") + logger.info(f"loaded input image of size {image.width}x{image.height}") image = ImageOps.exif_transpose(image) return image @@ -1183,14 +1184,14 @@ class Generate: def _transparency_check_and_warning(self, image, mask, force_outpaint=False): if not mask: - print( - ">> Initial image has transparent areas. Will inpaint in these regions." + logger.info( + "Initial image has transparent areas. Will inpaint in these regions." ) - if (not force_outpaint) and self._check_for_erasure(image): - print( - ">> WARNING: Colors underneath the transparent region seem to have been erased.\n", - ">> Inpainting will be suboptimal. Please preserve the colors when making\n", - ">> a transparency mask, or provide mask explicitly using --init_mask (-M).", + if (not force_outpaint) and self._check_for_erasure(image): + logger.info( + "Colors underneath the transparent region seem to have been erased.\n" + + "Inpainting will be suboptimal. Please preserve the colors when making\n" + + "a transparency mask, or provide mask explicitly using --init_mask (-M)." ) def _squeeze_image(self, image): @@ -1201,11 +1202,11 @@ class Generate: def _fit_image(self, image, max_dimensions): w, h = max_dimensions - print(f">> image will be resized to fit inside a box {w}x{h} in size.") + logger.info(f"image will be resized to fit inside a box {w}x{h} in size.") # note that InitImageResizer does the multiple of 64 truncation internally image = InitImageResizer(image).resize(width=w, height=h) - print( - f">> after adjusting image dimensions to be multiples of 64, init image is {image.width}x{image.height}" + logger.info( + f"after adjusting image dimensions to be multiples of 64, init image is {image.width}x{image.height}" ) return image @@ -1216,8 +1217,8 @@ class Generate: ) # resize to integer multiple of 64 if h != height or w != width: if log: - print( - f">> Provided width and height must be multiples of 64. Auto-resizing to {w}x{h}" + logger.info( + f"Provided width and height must be multiples of 64. Auto-resizing to {w}x{h}" ) height = h width = w diff --git a/invokeai/backend/generator/base.py b/invokeai/backend/generator/base.py index ee56077fa8..8ad9dec026 100644 --- a/invokeai/backend/generator/base.py +++ b/invokeai/backend/generator/base.py @@ -25,6 +25,7 @@ from typing import Callable, List, Iterator, Optional, Type from dataclasses import dataclass, field from diffusers.schedulers import SchedulerMixin as Scheduler +import invokeai.backend.util.logging as logger from ..image_util import configure_model_padding from ..util.util import rand_perlin_2d from ..safety_checker import SafetyChecker @@ -372,7 +373,7 @@ class Generator: try: x_T = self.get_noise(width, height) except: - print("** An error occurred while getting initial noise **") + logger.error("An error occurred while getting initial noise") print(traceback.format_exc()) # Pass on the seed in case a layer beneath us needs to generate noise on its own. @@ -607,7 +608,7 @@ class Generator: image = self.sample_to_image(sample) dirname = os.path.dirname(filepath) or "." if not os.path.exists(dirname): - print(f"** creating directory {dirname}") + logger.info(f"creating directory {dirname}") os.makedirs(dirname, exist_ok=True) image.save(filepath, "PNG") diff --git a/invokeai/backend/generator/embiggen.py b/invokeai/backend/generator/embiggen.py index ce9ef4d1b6..6eae5732b0 100644 --- a/invokeai/backend/generator/embiggen.py +++ b/invokeai/backend/generator/embiggen.py @@ -8,10 +8,11 @@ import torch from PIL import Image from tqdm import trange +import invokeai.backend.util.logging as logger + from .base import Generator from .img2img import Img2Img - class Embiggen(Generator): def __init__(self, model, precision): super().__init__(model, precision) @@ -72,22 +73,22 @@ class Embiggen(Generator): embiggen = [1.0] # If not specified, assume no scaling elif embiggen[0] < 0: embiggen[0] = 1.0 - print( - ">> Embiggen scaling factor cannot be negative, fell back to the default of 1.0 !" + logger.warning( + "Embiggen scaling factor cannot be negative, fell back to the default of 1.0 !" ) if len(embiggen) < 2: embiggen.append(0.75) elif embiggen[1] > 1.0 or embiggen[1] < 0: embiggen[1] = 0.75 - print( - ">> Embiggen upscaling strength for ESRGAN must be between 0 and 1, fell back to the default of 0.75 !" + logger.warning( + "Embiggen upscaling strength for ESRGAN must be between 0 and 1, fell back to the default of 0.75 !" ) if len(embiggen) < 3: embiggen.append(0.25) elif embiggen[2] < 0: embiggen[2] = 0.25 - print( - ">> Overlap size for Embiggen must be a positive ratio between 0 and 1 OR a number of pixels, fell back to the default of 0.25 !" + logger.warning( + "Overlap size for Embiggen must be a positive ratio between 0 and 1 OR a number of pixels, fell back to the default of 0.25 !" ) # Convert tiles from their user-freindly count-from-one to count-from-zero, because we need to do modulo math @@ -97,8 +98,8 @@ class Embiggen(Generator): embiggen_tiles.sort() if strength >= 0.5: - print( - f"* WARNING: Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45." + logger.warning( + f"Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45." ) # Prep img2img generator, since we wrap over it @@ -121,8 +122,8 @@ class Embiggen(Generator): from ..restoration.realesrgan import ESRGAN esrgan = ESRGAN() - print( - f">> ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}" + logger.info( + f"ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}" ) if embiggen[0] > 2: initsuperimage = esrgan.process( @@ -312,10 +313,10 @@ class Embiggen(Generator): def make_image(): # Make main tiles ------------------------------------------------- if embiggen_tiles: - print(f">> Making {len(embiggen_tiles)} Embiggen tiles...") + logger.info(f"Making {len(embiggen_tiles)} Embiggen tiles...") else: - print( - f">> Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})..." + logger.info( + f"Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})..." ) emb_tile_store = [] @@ -361,11 +362,11 @@ class Embiggen(Generator): # newinitimage.save(newinitimagepath) if embiggen_tiles: - print( + logger.debug( f"Making tile #{tile + 1} ({embiggen_tiles.index(tile) + 1} of {len(embiggen_tiles)} requested)" ) else: - print(f"Starting {tile + 1} of {(emb_tiles_x * emb_tiles_y)} tiles") + logger.debug(f"Starting {tile + 1} of {(emb_tiles_x * emb_tiles_y)} tiles") # create a torch tensor from an Image newinitimage = np.array(newinitimage).astype(np.float32) / 255.0 @@ -547,8 +548,8 @@ class Embiggen(Generator): # Layer tile onto final image outputsuperimage.alpha_composite(intileimage, (left, top)) else: - print( - "Error: could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation." + logger.error( + "Could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation." ) # after internal loops and patching up return Embiggen image diff --git a/invokeai/backend/generator/txt2img2img.py b/invokeai/backend/generator/txt2img2img.py index 1e24a8b729..1257a44fb1 100644 --- a/invokeai/backend/generator/txt2img2img.py +++ b/invokeai/backend/generator/txt2img2img.py @@ -14,6 +14,8 @@ from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeli from ..stable_diffusion.diffusers_pipeline import ConditioningData from ..stable_diffusion.diffusers_pipeline import trim_to_multiple_of +import invokeai.backend.util.logging as logger + class Txt2Img2Img(Generator): def __init__(self, model, precision): super().__init__(model, precision) @@ -77,8 +79,8 @@ class Txt2Img2Img(Generator): # the message below is accurate. init_width = first_pass_latent_output.size()[3] * self.downsampling_factor init_height = first_pass_latent_output.size()[2] * self.downsampling_factor - print( - f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling" + logger.info( + f"Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling" ) # resizing diff --git a/invokeai/backend/image_util/patchmatch.py b/invokeai/backend/image_util/patchmatch.py index 8753298f51..5b5dd75f68 100644 --- a/invokeai/backend/image_util/patchmatch.py +++ b/invokeai/backend/image_util/patchmatch.py @@ -5,10 +5,9 @@ wraps the actual patchmatch object. It respects the global be suppressed or deferred """ import numpy as np - +import invokeai.backend.util.logging as logger from invokeai.backend.globals import Globals - class PatchMatch: """ Thin class wrapper around the patchmatch function. @@ -28,12 +27,12 @@ class PatchMatch: from patchmatch import patch_match as pm if pm.patchmatch_available: - print(">> Patchmatch initialized") + logger.info("Patchmatch initialized") else: - print(">> Patchmatch not loaded (nonfatal)") + logger.info("Patchmatch not loaded (nonfatal)") self.patch_match = pm else: - print(">> Patchmatch loading disabled") + logger.info("Patchmatch loading disabled") self.tried_load = True @classmethod diff --git a/invokeai/backend/image_util/txt2mask.py b/invokeai/backend/image_util/txt2mask.py index bc7e56d397..248f19d81d 100644 --- a/invokeai/backend/image_util/txt2mask.py +++ b/invokeai/backend/image_util/txt2mask.py @@ -30,9 +30,9 @@ work fine. import numpy as np import torch from PIL import Image, ImageOps -from torchvision import transforms from transformers import AutoProcessor, CLIPSegForImageSegmentation +import invokeai.backend.util.logging as logger from invokeai.backend.globals import global_cache_dir CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined" @@ -83,7 +83,7 @@ class Txt2Mask(object): """ def __init__(self, device="cpu", refined=False): - print(">> Initializing clipseg model for text to mask inference") + logger.info("Initializing clipseg model for text to mask inference") # BUG: we are not doing anything with the device option at this time self.device = device @@ -101,18 +101,6 @@ class Txt2Mask(object): provided image and returns a SegmentedGrayscale object in which the brighter pixels indicate where the object is inferred to be. """ - transform = transforms.Compose( - [ - transforms.ToTensor(), - transforms.Normalize( - mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] - ), - transforms.Resize( - (CLIPSEG_SIZE, CLIPSEG_SIZE) - ), # must be multiple of 64... - ] - ) - if type(image) is str: image = Image.open(image).convert("RGB") diff --git a/invokeai/backend/model_management/convert_ckpt_to_diffusers.py b/invokeai/backend/model_management/convert_ckpt_to_diffusers.py index b46586611d..8aec5a01d9 100644 --- a/invokeai/backend/model_management/convert_ckpt_to_diffusers.py +++ b/invokeai/backend/model_management/convert_ckpt_to_diffusers.py @@ -25,6 +25,7 @@ from typing import Union import torch from safetensors.torch import load_file +import invokeai.backend.util.logging as logger from invokeai.backend.globals import global_cache_dir, global_config_dir from .model_manager import ModelManager, SDLegacyType @@ -372,9 +373,9 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False unet_key = "model.diffusion_model." # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA if sum(k.startswith("model_ema") for k in keys) > 100: - print(f" | Checkpoint {path} has both EMA and non-EMA weights.") + logger.debug(f"Checkpoint {path} has both EMA and non-EMA weights.") if extract_ema: - print(" | Extracting EMA weights (usually better for inference)") + logger.debug("Extracting EMA weights (usually better for inference)") for key in keys: if key.startswith("model.diffusion_model"): flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) @@ -392,8 +393,8 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False key ) else: - print( - " | Extracting only the non-EMA weights (usually better for fine-tuning)" + logger.debug( + "Extracting only the non-EMA weights (usually better for fine-tuning)" ) for key in keys: @@ -1115,7 +1116,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt( if "global_step" in checkpoint: global_step = checkpoint["global_step"] else: - print(" | global_step key not found in model") + logger.debug("global_step key not found in model") global_step = None # sometimes there is a state_dict key and sometimes not @@ -1229,15 +1230,15 @@ def load_pipeline_from_original_stable_diffusion_ckpt( # If a replacement VAE path was specified, we'll incorporate that into # the checkpoint model and then convert it if vae_path: - print(f" | Converting VAE {vae_path}") + logger.debug(f"Converting VAE {vae_path}") replace_checkpoint_vae(checkpoint,vae_path) # otherwise we use the original VAE, provided that # an externally loaded diffusers VAE was not passed elif not vae: - print(" | Using checkpoint model's original VAE") + logger.debug("Using checkpoint model's original VAE") if vae: - print(" | Using replacement diffusers VAE") + logger.debug("Using replacement diffusers VAE") else: # convert the original or replacement VAE vae_config = create_vae_diffusers_config( original_config, image_size=image_size diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 68ecd3bcca..9ba1e8779c 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -18,12 +18,13 @@ import warnings from enum import Enum, auto from pathlib import Path from shutil import move, rmtree -from typing import Any, Optional, Union, Callable +from typing import Any, Optional, Union, Callable, types import safetensors import safetensors.torch import torch import transformers +import invokeai.backend.util.logging as logger from diffusers import ( AutoencoderKL, UNet2DConditionModel, @@ -75,6 +76,8 @@ class ModelManager(object): Model manager handles loading, caching, importing, deleting, converting, and editing models. """ + logger: types.ModuleType = logger + def __init__( self, config: OmegaConf | Path, @@ -83,6 +86,7 @@ class ModelManager(object): max_loaded_models=DEFAULT_MAX_MODELS, sequential_offload=False, embedding_path: Path = None, + logger: types.ModuleType = logger, ): """ Initialize with the path to the models.yaml config file or @@ -104,6 +108,7 @@ class ModelManager(object): self.current_model = None self.sequential_offload = sequential_offload self.embedding_path = embedding_path + self.logger = logger def valid_model(self, model_name: str) -> bool: """ @@ -132,8 +137,8 @@ class ModelManager(object): ) if not self.valid_model(model_name): - print( - f'** "{model_name}" is not a known model name. Please check your models.yaml file' + self.logger.error( + f'"{model_name}" is not a known model name. Please check your models.yaml file' ) return self.current_model @@ -144,7 +149,7 @@ class ModelManager(object): if model_name in self.models: requested_model = self.models[model_name]["model"] - print(f">> Retrieving model {model_name} from system RAM cache") + self.logger.info(f"Retrieving model {model_name} from system RAM cache") requested_model.ready() width = self.models[model_name]["width"] height = self.models[model_name]["height"] @@ -379,7 +384,7 @@ class ModelManager(object): """ omega = self.config if model_name not in omega: - print(f"** Unknown model {model_name}") + self.logger.error(f"Unknown model {model_name}") return # save these for use in deletion later conf = omega[model_name] @@ -392,13 +397,13 @@ class ModelManager(object): self.stack.remove(model_name) if delete_files: if weights: - print(f"** Deleting file {weights}") + self.logger.info(f"Deleting file {weights}") Path(weights).unlink(missing_ok=True) elif path: - print(f"** Deleting directory {path}") + self.logger.info(f"Deleting directory {path}") rmtree(path, ignore_errors=True) elif repo_id: - print(f"** Deleting the cached model directory for {repo_id}") + self.logger.info(f"Deleting the cached model directory for {repo_id}") self._delete_model_from_cache(repo_id) def add_model( @@ -439,7 +444,7 @@ class ModelManager(object): def _load_model(self, model_name: str): """Load and initialize the model from configuration variables passed at object creation time""" if model_name not in self.config: - print( + self.logger.error( f'"{model_name}" is not a known model name. Please check your models.yaml file' ) return @@ -457,7 +462,7 @@ class ModelManager(object): model_format = mconfig.get("format", "ckpt") if model_format == "ckpt": weights = mconfig.weights - print(f">> Loading {model_name} from {weights}") + self.logger.info(f"Loading {model_name} from {weights}") model, width, height, model_hash = self._load_ckpt_model( model_name, mconfig ) @@ -473,13 +478,15 @@ class ModelManager(object): # usage statistics toc = time.time() - print(">> Model loaded in", "%4.2fs" % (toc - tic)) + self.logger.info("Model loaded in " + "%4.2fs" % (toc - tic)) if self._has_cuda(): - print( - ">> Max VRAM used to load the model:", - "%4.2fG" % (torch.cuda.max_memory_allocated() / 1e9), - "\n>> Current VRAM usage:" - "%4.2fG" % (torch.cuda.memory_allocated() / 1e9), + self.logger.info( + "Max VRAM used to load the model: "+ + "%4.2fG" % (torch.cuda.max_memory_allocated() / 1e9) + ) + self.logger.info( + "Current VRAM usage: "+ + "%4.2fG" % (torch.cuda.memory_allocated() / 1e9) ) return model, width, height, model_hash @@ -487,11 +494,11 @@ class ModelManager(object): name_or_path = self.model_name_or_path(mconfig) using_fp16 = self.precision == "float16" - print(f">> Loading diffusers model from {name_or_path}") + self.logger.info(f"Loading diffusers model from {name_or_path}") if using_fp16: - print(" | Using faster float16 precision") + self.logger.debug("Using faster float16 precision") else: - print(" | Using more accurate float32 precision") + self.logger.debug("Using more accurate float32 precision") # TODO: scan weights maybe? pipeline_args: dict[str, Any] = dict( @@ -523,8 +530,8 @@ class ModelManager(object): if str(e).startswith("fp16 is not a valid"): pass else: - print( - f"** An unexpected error occurred while downloading the model: {e})" + self.logger.error( + f"An unexpected error occurred while downloading the model: {e})" ) if pipeline: break @@ -542,7 +549,7 @@ class ModelManager(object): # square images??? width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor height = width - print(f" | Default image dimensions = {width} x {height}") + self.logger.debug(f"Default image dimensions = {width} x {height}") return pipeline, width, height, model_hash @@ -559,14 +566,14 @@ class ModelManager(object): weights = os.path.normpath(os.path.join(Globals.root, weights)) # Convert to diffusers and return a diffusers pipeline - print(f">> Converting legacy checkpoint {model_name} into a diffusers model...") + self.logger.info(f"Converting legacy checkpoint {model_name} into a diffusers model...") from . import load_pipeline_from_original_stable_diffusion_ckpt try: if self.list_models()[self.current_model]["status"] == "active": self.offload_model(self.current_model) - except Exception as e: + except Exception: pass vae_path = None @@ -624,7 +631,7 @@ class ModelManager(object): if model_name not in self.models: return - print(f">> Offloading {model_name} to CPU") + self.logger.info(f"Offloading {model_name} to CPU") model = self.models[model_name]["model"] model.offload_all() self.current_model = None @@ -640,30 +647,26 @@ class ModelManager(object): and option to exit if an infected file is identified. """ # scan model - print(f" | Scanning Model: {model_name}") + self.logger.debug(f"Scanning Model: {model_name}") scan_result = scan_file_path(checkpoint) if scan_result.infected_files != 0: if scan_result.infected_files == 1: - print(f"\n### Issues Found In Model: {scan_result.issues_count}") - print( - "### WARNING: The model you are trying to load seems to be infected." - ) - print("### For your safety, InvokeAI will not load this model.") - print("### Please use checkpoints from trusted sources.") - print("### Exiting InvokeAI") + self.logger.critical(f"Issues Found In Model: {scan_result.issues_count}") + self.logger.critical("The model you are trying to load seems to be infected.") + self.logger.critical("For your safety, InvokeAI will not load this model.") + self.logger.critical("Please use checkpoints from trusted sources.") + self.logger.critical("Exiting InvokeAI") sys.exit() else: - print( - "\n### WARNING: InvokeAI was unable to scan the model you are using." - ) + self.logger.warning("InvokeAI was unable to scan the model you are using.") model_safe_check_fail = ask_user( "Do you want to to continue loading the model?", ["y", "n"] ) if model_safe_check_fail.lower() != "y": - print("### Exiting InvokeAI") + self.logger.critical("Exiting InvokeAI") sys.exit() else: - print(" | Model scanned ok") + self.logger.debug("Model scanned ok") def import_diffuser_model( self, @@ -780,26 +783,24 @@ class ModelManager(object): model_path: Path = None thing = path_url_or_repo # to save typing - print(f">> Probing {thing} for import") + self.logger.info(f"Probing {thing} for import") if thing.startswith(("http:", "https:", "ftp:")): - print(f" | {thing} appears to be a URL") + self.logger.info(f"{thing} appears to be a URL") model_path = self._resolve_path( thing, "models/ldm/stable-diffusion-v1" ) # _resolve_path does a download if needed elif Path(thing).is_file() and thing.endswith((".ckpt", ".safetensors")): if Path(thing).stem in ["model", "diffusion_pytorch_model"]: - print( - f" | {Path(thing).name} appears to be part of a diffusers model. Skipping import" - ) + self.logger.debug(f"{Path(thing).name} appears to be part of a diffusers model. Skipping import") return else: - print(f" | {thing} appears to be a checkpoint file on disk") + self.logger.debug(f"{thing} appears to be a checkpoint file on disk") model_path = self._resolve_path(thing, "models/ldm/stable-diffusion-v1") elif Path(thing).is_dir() and Path(thing, "model_index.json").exists(): - print(f" | {thing} appears to be a diffusers file on disk") + self.logger.debug(f"{thing} appears to be a diffusers file on disk") model_name = self.import_diffuser_model( thing, vae=dict(repo_id="stabilityai/sd-vae-ft-mse"), @@ -810,34 +811,30 @@ class ModelManager(object): elif Path(thing).is_dir(): if (Path(thing) / "model_index.json").exists(): - print(f" | {thing} appears to be a diffusers model.") + self.logger.debug(f"{thing} appears to be a diffusers model.") model_name = self.import_diffuser_model( thing, commit_to_conf=commit_to_conf ) else: - print( - f" |{thing} appears to be a directory. Will scan for models to import" - ) + self.logger.debug(f"{thing} appears to be a directory. Will scan for models to import") for m in list(Path(thing).rglob("*.ckpt")) + list( Path(thing).rglob("*.safetensors") ): if model_name := self.heuristic_import( str(m), commit_to_conf=commit_to_conf ): - print(f" >> {model_name} successfully imported") + self.logger.info(f"{model_name} successfully imported") return model_name elif re.match(r"^[\w.+-]+/[\w.+-]+$", thing): - print(f" | {thing} appears to be a HuggingFace diffusers repo_id") + self.logger.debug(f"{thing} appears to be a HuggingFace diffusers repo_id") model_name = self.import_diffuser_model( thing, commit_to_conf=commit_to_conf ) pipeline, _, _, _ = self._load_diffusers_model(self.config[model_name]) return model_name else: - print( - f"** {thing}: Unknown thing. Please provide a URL, file path, directory or HuggingFace repo_id" - ) + self.logger.warning(f"{thing}: Unknown thing. Please provide a URL, file path, directory or HuggingFace repo_id") # Model_path is set in the event of a legacy checkpoint file. # If not set, we're all done @@ -845,7 +842,7 @@ class ModelManager(object): return if model_path.stem in self.config: # already imported - print(" | Already imported. Skipping") + self.logger.debug("Already imported. Skipping") return model_path.stem # another round of heuristics to guess the correct config file. @@ -861,39 +858,39 @@ class ModelManager(object): # look for a like-named .yaml file in same directory if model_path.with_suffix(".yaml").exists(): model_config_file = model_path.with_suffix(".yaml") - print(f" | Using config file {model_config_file.name}") + self.logger.debug(f"Using config file {model_config_file.name}") else: model_type = self.probe_model_type(checkpoint) if model_type == SDLegacyType.V1: - print(" | SD-v1 model detected") + self.logger.debug("SD-v1 model detected") model_config_file = Path( Globals.root, "configs/stable-diffusion/v1-inference.yaml" ) elif model_type == SDLegacyType.V1_INPAINT: - print(" | SD-v1 inpainting model detected") + self.logger.debug("SD-v1 inpainting model detected") model_config_file = Path( Globals.root, "configs/stable-diffusion/v1-inpainting-inference.yaml", ) elif model_type == SDLegacyType.V2_v: - print(" | SD-v2-v model detected") + self.logger.debug("SD-v2-v model detected") model_config_file = Path( Globals.root, "configs/stable-diffusion/v2-inference-v.yaml" ) elif model_type == SDLegacyType.V2_e: - print(" | SD-v2-e model detected") + self.logger.debug("SD-v2-e model detected") model_config_file = Path( Globals.root, "configs/stable-diffusion/v2-inference.yaml" ) elif model_type == SDLegacyType.V2: - print( - f"** {thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide configuration file path." + self.logger.warning( + f"{thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide configuration file path." ) return else: - print( - f"** {thing} is a legacy checkpoint file but not a known Stable Diffusion model. Please provide configuration file path." + self.logger.warning( + f"{thing} is a legacy checkpoint file but not a known Stable Diffusion model. Please provide configuration file path." ) return @@ -909,7 +906,7 @@ class ModelManager(object): for suffix in ["pt", "ckpt", "safetensors"]: if (model_path.with_suffix(f".vae.{suffix}")).exists(): vae_path = model_path.with_suffix(f".vae.{suffix}") - print(f" | Using VAE file {vae_path.name}") + self.logger.debug(f"Using VAE file {vae_path.name}") vae = None if vae_path else dict(repo_id="stabilityai/sd-vae-ft-mse") diffuser_path = Path( @@ -955,14 +952,14 @@ class ModelManager(object): from . import convert_ckpt_to_diffusers if diffusers_path.exists(): - print( - f"ERROR: The path {str(diffusers_path)} already exists. Please move or remove it and try again." + self.logger.error( + f"The path {str(diffusers_path)} already exists. Please move or remove it and try again." ) return model_name = model_name or diffusers_path.name model_description = model_description or f"Converted version of {model_name}" - print(f" | Converting {model_name} to diffusers (30-60s)") + self.logger.debug(f"Converting {model_name} to diffusers (30-60s)") try: # By passing the specified VAE to the conversion function, the autoencoder # will be built into the model rather than tacked on afterward via the config file @@ -979,10 +976,10 @@ class ModelManager(object): vae_path=vae_path, scan_needed=scan_needed, ) - print( - f" | Success. Converted model is now located at {str(diffusers_path)}" + self.logger.debug( + f"Success. Converted model is now located at {str(diffusers_path)}" ) - print(f" | Writing new config file entry for {model_name}") + self.logger.debug(f"Writing new config file entry for {model_name}") new_config = dict( path=str(diffusers_path), description=model_description, @@ -993,17 +990,17 @@ class ModelManager(object): self.add_model(model_name, new_config, True) if commit_to_conf: self.commit(commit_to_conf) - print(" | Conversion succeeded") + self.logger.debug("Conversion succeeded") except Exception as e: - print(f"** Conversion failed: {str(e)}") - print( - "** If you are trying to convert an inpainting or 2.X model, please indicate the correct config file (e.g. v1-inpainting-inference.yaml)" + self.logger.warning(f"Conversion failed: {str(e)}") + self.logger.warning( + "If you are trying to convert an inpainting or 2.X model, please indicate the correct config file (e.g. v1-inpainting-inference.yaml)" ) return model_name def search_models(self, search_folder): - print(f">> Finding Models In: {search_folder}") + self.logger.info(f"Finding Models In: {search_folder}") models_folder_ckpt = Path(search_folder).glob("**/*.ckpt") models_folder_safetensors = Path(search_folder).glob("**/*.safetensors") @@ -1027,8 +1024,8 @@ class ModelManager(object): num_loaded_models = len(self.models) if num_loaded_models >= self.max_loaded_models: least_recent_model = self._pop_oldest_model() - print( - f">> Cache limit (max={self.max_loaded_models}) reached. Purging {least_recent_model}" + self.logger.info( + f"Cache limit (max={self.max_loaded_models}) reached. Purging {least_recent_model}" ) if least_recent_model is not None: del self.models[least_recent_model] @@ -1036,8 +1033,8 @@ class ModelManager(object): def print_vram_usage(self) -> None: if self._has_cuda: - print( - ">> Current VRAM usage: ", + self.logger.info( + "Current VRAM usage:"+ "%4.2fG" % (torch.cuda.memory_allocated() / 1e9), ) @@ -1123,10 +1120,10 @@ class ModelManager(object): dest = hub / model.stem if dest.exists() and not source.exists(): continue - print(f"** {source} => {dest}") + cls.logger.info(f"{source} => {dest}") if source.exists(): if dest.is_symlink(): - print(f"** Found symlink at {dest.name}. Not migrating.") + logger.warning(f"Found symlink at {dest.name}. Not migrating.") elif dest.exists(): if source.is_dir(): rmtree(source) @@ -1143,7 +1140,7 @@ class ModelManager(object): ] for d in empty: os.rmdir(d) - print("** Migration is done. Continuing...") + cls.logger.info("Migration is done. Continuing...") def _resolve_path( self, source: Union[str, Path], dest_directory: str @@ -1186,15 +1183,15 @@ class ModelManager(object): def _add_embeddings_to_model(self, model: StableDiffusionGeneratorPipeline): if self.embedding_path is not None: - print(f">> Loading embeddings from {self.embedding_path}") + self.logger.info(f"Loading embeddings from {self.embedding_path}") for root, _, files in os.walk(self.embedding_path): for name in files: ti_path = os.path.join(root, name) model.textual_inversion_manager.load_textual_inversion( ti_path, defer_injecting_tokens=True ) - print( - f'>> Textual inversion triggers: {", ".join(sorted(model.textual_inversion_manager.get_all_trigger_strings()))}' + self.logger.info( + f'Textual inversion triggers: {", ".join(sorted(model.textual_inversion_manager.get_all_trigger_strings()))}' ) def _has_cuda(self) -> bool: @@ -1216,7 +1213,7 @@ class ModelManager(object): with open(hashpath) as f: hash = f.read() return hash - print(" | Calculating sha256 hash of model files") + self.logger.debug("Calculating sha256 hash of model files") tic = time.time() sha = hashlib.sha256() count = 0 @@ -1228,7 +1225,7 @@ class ModelManager(object): sha.update(chunk) hash = sha.hexdigest() toc = time.time() - print(f" | sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic)) + self.logger.debug(f"sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic)) with open(hashpath, "w") as f: f.write(hash) return hash @@ -1246,13 +1243,13 @@ class ModelManager(object): hash = f.read() return hash - print(" | Calculating sha256 hash of weights file") + self.logger.debug("Calculating sha256 hash of weights file") tic = time.time() sha = hashlib.sha256() sha.update(data) hash = sha.hexdigest() toc = time.time() - print(f">> sha256 = {hash}", "(%4.2fs)" % (toc - tic)) + self.logger.debug(f"sha256 = {hash} "+"(%4.2fs)" % (toc - tic)) with open(hashpath, "w") as f: f.write(hash) @@ -1273,12 +1270,12 @@ class ModelManager(object): local_files_only=not Globals.internet_available, ) - print(f" | Loading diffusers VAE from {name_or_path}") + self.logger.debug(f"Loading diffusers VAE from {name_or_path}") if using_fp16: vae_args.update(torch_dtype=torch.float16) fp_args_list = [{"revision": "fp16"}, {}] else: - print(" | Using more accurate float32 precision") + self.logger.debug("Using more accurate float32 precision") fp_args_list = [{}] vae = None @@ -1302,12 +1299,12 @@ class ModelManager(object): break if not vae and deferred_error: - print(f"** Could not load VAE {name_or_path}: {str(deferred_error)}") + self.logger.warning(f"Could not load VAE {name_or_path}: {str(deferred_error)}") return vae - @staticmethod - def _delete_model_from_cache(repo_id): + @classmethod + def _delete_model_from_cache(cls,repo_id): cache_info = scan_cache_dir(global_cache_dir("hub")) # I'm sure there is a way to do this with comprehensions @@ -1318,8 +1315,8 @@ class ModelManager(object): for revision in repo.revisions: hashes_to_delete.add(revision.commit_hash) strategy = cache_info.delete_revisions(*hashes_to_delete) - print( - f"** Deletion of this model is expected to free {strategy.expected_freed_size_str}" + cls.logger.warning( + f"Deletion of this model is expected to free {strategy.expected_freed_size_str}" ) strategy.execute() diff --git a/invokeai/backend/prompting/conditioning.py b/invokeai/backend/prompting/conditioning.py index 1ddae1e93d..d9130ace04 100644 --- a/invokeai/backend/prompting/conditioning.py +++ b/invokeai/backend/prompting/conditioning.py @@ -18,6 +18,7 @@ from compel.prompt_parser import ( PromptParser, ) +import invokeai.backend.util.logging as logger from invokeai.backend.globals import Globals from ..stable_diffusion import InvokeAIDiffuserComponent @@ -162,8 +163,8 @@ def log_tokenization( negative_prompt: Union[Blend, FlattenedPrompt], tokenizer, ): - print(f"\n>> [TOKENLOG] Parsed Prompt: {positive_prompt}") - print(f"\n>> [TOKENLOG] Parsed Negative Prompt: {negative_prompt}") + logger.info(f"[TOKENLOG] Parsed Prompt: {positive_prompt}") + logger.info(f"[TOKENLOG] Parsed Negative Prompt: {negative_prompt}") log_tokenization_for_prompt_object(positive_prompt, tokenizer) log_tokenization_for_prompt_object( @@ -237,12 +238,12 @@ def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_t usedTokens += 1 if usedTokens > 0: - print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):') - print(f"{tokenized}\x1b[0m") + logger.info(f'[TOKENLOG] Tokens {display_label or ""} ({usedTokens}):') + logger.debug(f"{tokenized}\x1b[0m") if discarded != "": - print(f"\n>> [TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):") - print(f"{discarded}\x1b[0m") + logger.info(f"[TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):") + logger.debug(f"{discarded}\x1b[0m") def try_parse_legacy_blend(text: str, skip_normalize: bool = False) -> Optional[Blend]: @@ -295,8 +296,8 @@ def split_weighted_subprompts(text, skip_normalize=False) -> list: return parsed_prompts weight_sum = sum(map(lambda x: x[1], parsed_prompts)) if weight_sum == 0: - print( - "* Warning: Subprompt weights add up to zero. Discarding and using even weights instead." + logger.warning( + "Subprompt weights add up to zero. Discarding and using even weights instead." ) equal_weight = 1 / max(len(parsed_prompts), 1) return [(x[0], equal_weight) for x in parsed_prompts] diff --git a/invokeai/backend/restoration/base.py b/invokeai/backend/restoration/base.py index 0957811fc3..f6f01da17d 100644 --- a/invokeai/backend/restoration/base.py +++ b/invokeai/backend/restoration/base.py @@ -1,3 +1,5 @@ +import invokeai.backend.util.logging as logger + class Restoration: def __init__(self) -> None: pass @@ -8,17 +10,17 @@ class Restoration: # Load GFPGAN gfpgan = self.load_gfpgan(gfpgan_model_path) if gfpgan.gfpgan_model_exists: - print(">> GFPGAN Initialized") + logger.info("GFPGAN Initialized") else: - print(">> GFPGAN Disabled") + logger.info("GFPGAN Disabled") gfpgan = None # Load CodeFormer codeformer = self.load_codeformer() if codeformer.codeformer_model_exists: - print(">> CodeFormer Initialized") + logger.info("CodeFormer Initialized") else: - print(">> CodeFormer Disabled") + logger.info("CodeFormer Disabled") codeformer = None return gfpgan, codeformer @@ -39,5 +41,5 @@ class Restoration: from .realesrgan import ESRGAN esrgan = ESRGAN(esrgan_bg_tile) - print(">> ESRGAN Initialized") + logger.info("ESRGAN Initialized") return esrgan diff --git a/invokeai/backend/restoration/codeformer.py b/invokeai/backend/restoration/codeformer.py index 94add72b00..5b578af082 100644 --- a/invokeai/backend/restoration/codeformer.py +++ b/invokeai/backend/restoration/codeformer.py @@ -5,6 +5,7 @@ import warnings import numpy as np import torch +import invokeai.backend.util.logging as logger from ..globals import Globals pretrained_model_url = ( @@ -23,12 +24,12 @@ class CodeFormerRestoration: self.codeformer_model_exists = os.path.isfile(self.model_path) if not self.codeformer_model_exists: - print("## NOT FOUND: CodeFormer model not found at " + self.model_path) + logger.error("NOT FOUND: CodeFormer model not found at " + self.model_path) sys.path.append(os.path.abspath(codeformer_dir)) def process(self, image, strength, device, seed=None, fidelity=0.75): if seed is not None: - print(f">> CodeFormer - Restoring Faces for image seed:{seed}") + logger.info(f"CodeFormer - Restoring Faces for image seed:{seed}") with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=DeprecationWarning) warnings.filterwarnings("ignore", category=UserWarning) @@ -97,7 +98,7 @@ class CodeFormerRestoration: del output torch.cuda.empty_cache() except RuntimeError as error: - print(f"\tFailed inference for CodeFormer: {error}.") + logger.error(f"Failed inference for CodeFormer: {error}.") restored_face = cropped_face restored_face = restored_face.astype("uint8") diff --git a/invokeai/backend/restoration/gfpgan.py b/invokeai/backend/restoration/gfpgan.py index d13745d0c6..b5c0278362 100644 --- a/invokeai/backend/restoration/gfpgan.py +++ b/invokeai/backend/restoration/gfpgan.py @@ -6,9 +6,9 @@ import numpy as np import torch from PIL import Image +import invokeai.backend.util.logging as logger from invokeai.backend.globals import Globals - class GFPGAN: def __init__(self, gfpgan_model_path="models/gfpgan/GFPGANv1.4.pth") -> None: if not os.path.isabs(gfpgan_model_path): @@ -19,7 +19,7 @@ class GFPGAN: self.gfpgan_model_exists = os.path.isfile(self.model_path) if not self.gfpgan_model_exists: - print("## NOT FOUND: GFPGAN model not found at " + self.model_path) + logger.error("NOT FOUND: GFPGAN model not found at " + self.model_path) return None def model_exists(self): @@ -27,7 +27,7 @@ class GFPGAN: def process(self, image, strength: float, seed: str = None): if seed is not None: - print(f">> GFPGAN - Restoring Faces for image seed:{seed}") + logger.info(f"GFPGAN - Restoring Faces for image seed:{seed}") with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=DeprecationWarning) @@ -47,14 +47,14 @@ class GFPGAN: except Exception: import traceback - print(">> Error loading GFPGAN:", file=sys.stderr) + logger.error("Error loading GFPGAN:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) os.chdir(cwd) if self.gfpgan is None: - print(f">> WARNING: GFPGAN not initialized.") - print( - f">> Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to {self.model_path}" + logger.warning("WARNING: GFPGAN not initialized.") + logger.warning( + f"Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to {self.model_path}" ) image = image.convert("RGB") diff --git a/invokeai/backend/restoration/outcrop.py b/invokeai/backend/restoration/outcrop.py index e0f110f71e..07f76d6bf9 100644 --- a/invokeai/backend/restoration/outcrop.py +++ b/invokeai/backend/restoration/outcrop.py @@ -1,7 +1,7 @@ import math from PIL import Image - +import invokeai.backend.util.logging as logger class Outcrop(object): def __init__( @@ -82,7 +82,7 @@ class Outcrop(object): pixels = extents[direction] # round pixels up to the nearest 64 pixels = math.ceil(pixels / 64) * 64 - print(f">> extending image {direction}ward by {pixels} pixels") + logger.info(f"extending image {direction}ward by {pixels} pixels") image = self._rotate(image, direction) image = self._extend(image, pixels) image = self._rotate(image, direction, reverse=True) diff --git a/invokeai/backend/restoration/realesrgan.py b/invokeai/backend/restoration/realesrgan.py index ad6ad556f1..9f26cc63ac 100644 --- a/invokeai/backend/restoration/realesrgan.py +++ b/invokeai/backend/restoration/realesrgan.py @@ -6,18 +6,13 @@ import torch from PIL import Image from PIL.Image import Image as ImageType +import invokeai.backend.util.logging as logger from invokeai.backend.globals import Globals - class ESRGAN: def __init__(self, bg_tile_size=400) -> None: self.bg_tile_size = bg_tile_size - if not torch.cuda.is_available(): # CPU or MPS on M1 - use_half_precision = False - else: - use_half_precision = True - def load_esrgan_bg_upsampler(self, denoise_str): if not torch.cuda.is_available(): # CPU or MPS on M1 use_half_precision = False @@ -74,16 +69,16 @@ class ESRGAN: import sys import traceback - print(">> Error loading Real-ESRGAN:", file=sys.stderr) + logger.error("Error loading Real-ESRGAN:") print(traceback.format_exc(), file=sys.stderr) if upsampler_scale == 0: - print(">> Real-ESRGAN: Invalid scaling option. Image not upscaled.") + logger.warning("Real-ESRGAN: Invalid scaling option. Image not upscaled.") return image if seed is not None: - print( - f">> Real-ESRGAN Upscaling seed:{seed}, scale:{upsampler_scale}x, tile:{self.bg_tile_size}, denoise:{denoise_str}" + logger.info( + f"Real-ESRGAN Upscaling seed:{seed}, scale:{upsampler_scale}x, tile:{self.bg_tile_size}, denoise:{denoise_str}" ) # ESRGAN outputs images with partial transparency if given RGBA images; convert to RGB image = image.convert("RGB") diff --git a/invokeai/backend/safety_checker.py b/invokeai/backend/safety_checker.py index 2e6c4fd479..3003981888 100644 --- a/invokeai/backend/safety_checker.py +++ b/invokeai/backend/safety_checker.py @@ -14,6 +14,7 @@ from PIL import Image, ImageFilter from transformers import AutoFeatureExtractor import invokeai.assets.web as web_assets +import invokeai.backend.util.logging as logger from .globals import global_cache_dir from .util import CPU_DEVICE @@ -40,8 +41,8 @@ class SafetyChecker(object): cache_dir=safety_model_path, ) except Exception: - print( - "** An error was encountered while installing the safety checker:" + logger.error( + "An error was encountered while installing the safety checker:" ) print(traceback.format_exc()) @@ -65,8 +66,8 @@ class SafetyChecker(object): ) self.safety_checker.to(CPU_DEVICE) # offload if has_nsfw_concept[0]: - print( - "** An image with potential non-safe content has been detected. A blurred image will be returned. **" + logger.warning( + "An image with potential non-safe content has been detected. A blurred image will be returned." ) return self.blur(image) else: diff --git a/invokeai/backend/stable_diffusion/concepts_lib.py b/invokeai/backend/stable_diffusion/concepts_lib.py index 129dd430f4..ebbcc9c3e9 100644 --- a/invokeai/backend/stable_diffusion/concepts_lib.py +++ b/invokeai/backend/stable_diffusion/concepts_lib.py @@ -17,6 +17,7 @@ from huggingface_hub import ( hf_hub_url, ) +import invokeai.backend.util.logging as logger from invokeai.backend.globals import Globals @@ -66,11 +67,11 @@ class HuggingFaceConceptsLibrary(object): # when init, add all in dir. when not init, add only concepts added between init and now self.concept_list.extend(list(local_concepts_to_add)) except Exception as e: - print( - f" ** WARNING: Hugging Face textual inversion concepts libraries could not be loaded. The error was {str(e)}." + logger.warning( + f"Hugging Face textual inversion concepts libraries could not be loaded. The error was {str(e)}." ) - print( - " ** You may load .bin and .pt file(s) manually using the --embedding_directory argument." + logger.warning( + "You may load .bin and .pt file(s) manually using the --embedding_directory argument." ) return self.concept_list else: @@ -83,7 +84,7 @@ class HuggingFaceConceptsLibrary(object): be downloaded. """ if not concept_name in self.list_concepts(): - print( + logger.warning( f"{concept_name} is not a local embedding trigger, nor is it a HuggingFace concept. Generation will continue without the concept." ) return None @@ -221,7 +222,7 @@ class HuggingFaceConceptsLibrary(object): if chunk == 0: bytes += total - print(f">> Downloading {repo_id}...", end="") + logger.info(f"Downloading {repo_id}...", end="") try: for file in ( "README.md", @@ -235,22 +236,22 @@ class HuggingFaceConceptsLibrary(object): ) except ul_error.HTTPError as e: if e.code == 404: - print( + logger.warning( f"Concept {concept_name} is not known to the Hugging Face library. Generation will continue without the concept." ) else: - print( + logger.warning( f"Failed to download {concept_name}/{file} ({str(e)}. Generation will continue without the concept.)" ) os.rmdir(dest) return False except ul_error.URLError as e: - print( - f"ERROR while downloading {concept_name}: {str(e)}. This may reflect a network issue. Generation will continue without the concept." + logger.error( + f"an error occurred while downloading {concept_name}: {str(e)}. This may reflect a network issue. Generation will continue without the concept." ) os.rmdir(dest) return False - print("...{:.2f}Kb".format(bytes / 1024)) + logger.info("...{:.2f}Kb".format(bytes / 1024)) return succeeded def _concept_id(self, concept_name: str) -> str: diff --git a/invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py b/invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py index d6c90503fe..dfd19ea964 100644 --- a/invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py +++ b/invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py @@ -13,9 +13,9 @@ from compel.cross_attention_control import Arguments from diffusers.models.attention_processor import AttentionProcessor from torch import nn +import invokeai.backend.util.logging as logger from ...util import torch_dtype - class CrossAttentionType(enum.Enum): SELF = 1 TOKENS = 2 @@ -421,7 +421,7 @@ def get_cross_attention_modules( expected_count = 16 if cross_attention_modules_in_model_count != expected_count: # non-fatal error but .swap() won't work. - print( + logger.error( f"Error! CrossAttentionControl found an unexpected number of {cross_attention_class} modules in the model " + f"(expected {expected_count}, found {cross_attention_modules_in_model_count}). Either monkey-patching failed " + "or some assumption has changed about the structure of the model itself. Please fix the monkey-patching, " diff --git a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py index 1137aa52e4..b0c85e9fd3 100644 --- a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py +++ b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py @@ -8,6 +8,7 @@ import torch from diffusers.models.attention_processor import AttentionProcessor from typing_extensions import TypeAlias +import invokeai.backend.util.logging as logger from invokeai.backend.globals import Globals from .cross_attention_control import ( @@ -466,10 +467,14 @@ class InvokeAIDiffuserComponent: outside = torch.count_nonzero( (latents < -current_threshold) | (latents > current_threshold) ) - print( - f"\nThreshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})\n" - f" | min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}\n" - f" | {outside / latents.numel() * 100:.2f}% values outside threshold" + logger.info( + f"Threshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})" + ) + logger.debug( + f"min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}" + ) + logger.debug( + f"{outside / latents.numel() * 100:.2f}% values outside threshold" ) if maxval < current_threshold and minval > -current_threshold: @@ -496,9 +501,11 @@ class InvokeAIDiffuserComponent: ) if self.debug_thresholding: - print( - f" | min, , max = {minval:.3f}, , {maxval:.3f}\t(scaled by {scale})\n" - f" | {num_altered / latents.numel() * 100:.2f}% values altered" + logger.debug( + f"min, , max = {minval:.3f}, , {maxval:.3f}\t(scaled by {scale})" + ) + logger.debug( + f"{num_altered / latents.numel() * 100:.2f}% values altered" ) return latents diff --git a/invokeai/backend/stable_diffusion/image_degradation/utils_image.py b/invokeai/backend/stable_diffusion/image_degradation/utils_image.py index 08505edde0..c4d37a24bf 100644 --- a/invokeai/backend/stable_diffusion/image_degradation/utils_image.py +++ b/invokeai/backend/stable_diffusion/image_degradation/utils_image.py @@ -10,7 +10,7 @@ from torchvision.utils import make_grid # import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py - +import invokeai.backend.util.logging as logger os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" @@ -191,7 +191,7 @@ def mkdirs(paths): def mkdir_and_rename(path): if os.path.exists(path): new_name = path + "_archived_" + get_timestamp() - print("Path already exists. Rename it to [{:s}]".format(new_name)) + logger.error("Path already exists. Rename it to [{:s}]".format(new_name)) os.replace(path, new_name) os.makedirs(path) diff --git a/invokeai/backend/stable_diffusion/textual_inversion_manager.py b/invokeai/backend/stable_diffusion/textual_inversion_manager.py index 2dba2b88d3..9476c12dc5 100644 --- a/invokeai/backend/stable_diffusion/textual_inversion_manager.py +++ b/invokeai/backend/stable_diffusion/textual_inversion_manager.py @@ -10,6 +10,7 @@ from compel.embeddings_provider import BaseTextualInversionManager from picklescan.scanner import scan_file_path from transformers import CLIPTextModel, CLIPTokenizer +import invokeai.backend.util.logging as logger from .concepts_lib import HuggingFaceConceptsLibrary @dataclass @@ -59,12 +60,12 @@ class TextualInversionManager(BaseTextualInversionManager): or self.has_textual_inversion_for_trigger_string(concept_name) or self.has_textual_inversion_for_trigger_string(f"<{concept_name}>") ): # in case a token with literal angle brackets encountered - print(f">> Loaded local embedding for trigger {concept_name}") + logger.info(f"Loaded local embedding for trigger {concept_name}") continue bin_file = self.hf_concepts_library.get_concept_model_path(concept_name) if not bin_file: continue - print(f">> Loaded remote embedding for trigger {concept_name}") + logger.info(f"Loaded remote embedding for trigger {concept_name}") self.load_textual_inversion(bin_file) self.hf_concepts_library.concepts_loaded[concept_name] = True @@ -85,8 +86,8 @@ class TextualInversionManager(BaseTextualInversionManager): embedding_list = self._parse_embedding(str(ckpt_path)) for embedding_info in embedding_list: if (self.text_encoder.get_input_embeddings().weight.data[0].shape[0] != embedding_info.token_dim): - print( - f" ** Notice: {ckpt_path.parents[0].name}/{ckpt_path.name} was trained on a model with an incompatible token dimension: {self.text_encoder.get_input_embeddings().weight.data[0].shape[0]} vs {embedding_info.token_dim}." + logger.warning( + f"Notice: {ckpt_path.parents[0].name}/{ckpt_path.name} was trained on a model with an incompatible token dimension: {self.text_encoder.get_input_embeddings().weight.data[0].shape[0]} vs {embedding_info.token_dim}." ) continue @@ -105,8 +106,8 @@ class TextualInversionManager(BaseTextualInversionManager): if ckpt_path.name == "learned_embeds.bin" else f"<{ckpt_path.stem}>" ) - print( - f">> {sourcefile}: Trigger token '{trigger_str}' is already claimed by '{self.trigger_to_sourcefile[trigger_str]}'. Trigger this concept with {replacement_trigger_str}" + logger.info( + f"{sourcefile}: Trigger token '{trigger_str}' is already claimed by '{self.trigger_to_sourcefile[trigger_str]}'. Trigger this concept with {replacement_trigger_str}" ) trigger_str = replacement_trigger_str @@ -120,8 +121,8 @@ class TextualInversionManager(BaseTextualInversionManager): self.trigger_to_sourcefile[trigger_str] = sourcefile except ValueError as e: - print(f' | Ignoring incompatible embedding {embedding_info["name"]}') - print(f" | The error was {str(e)}") + logger.debug(f'Ignoring incompatible embedding {embedding_info["name"]}') + logger.debug(f"The error was {str(e)}") def _add_textual_inversion( self, trigger_str, embedding, defer_injecting_tokens=False @@ -133,8 +134,8 @@ class TextualInversionManager(BaseTextualInversionManager): :return: The token id for the added embedding, either existing or newly-added. """ if trigger_str in [ti.trigger_string for ti in self.textual_inversions]: - print( - f"** TextualInversionManager refusing to overwrite already-loaded token '{trigger_str}'" + logger.warning( + f"TextualInversionManager refusing to overwrite already-loaded token '{trigger_str}'" ) return if not self.full_precision: @@ -155,11 +156,11 @@ class TextualInversionManager(BaseTextualInversionManager): except ValueError as e: if str(e).startswith("Warning"): - print(f">> {str(e)}") + logger.warning(f"{str(e)}") else: traceback.print_exc() - print( - f"** TextualInversionManager was unable to add a textual inversion with trigger string {trigger_str}." + logger.error( + f"TextualInversionManager was unable to add a textual inversion with trigger string {trigger_str}." ) raise @@ -219,16 +220,16 @@ class TextualInversionManager(BaseTextualInversionManager): for ti in self.textual_inversions: if ti.trigger_token_id is None and ti.trigger_string in prompt_string: if ti.embedding_vector_length > 1: - print( - f">> Preparing tokens for textual inversion {ti.trigger_string}..." + logger.info( + f"Preparing tokens for textual inversion {ti.trigger_string}..." ) try: self._inject_tokens_and_assign_embeddings(ti) except ValueError as e: - print( - f" | Ignoring incompatible embedding trigger {ti.trigger_string}" + logger.debug( + f"Ignoring incompatible embedding trigger {ti.trigger_string}" ) - print(f" | The error was {str(e)}") + logger.debug(f"The error was {str(e)}") continue injected_token_ids.append(ti.trigger_token_id) injected_token_ids.extend(ti.pad_token_ids) @@ -306,16 +307,16 @@ class TextualInversionManager(BaseTextualInversionManager): if suffix in [".pt",".ckpt",".bin"]: scan_result = scan_file_path(embedding_file) if scan_result.infected_files > 0: - print( - f" ** Security Issues Found in Model: {scan_result.issues_count}" + logger.critical( + f"Security Issues Found in Model: {scan_result.issues_count}" ) - print(" ** For your safety, InvokeAI will not load this embed.") + logger.critical("For your safety, InvokeAI will not load this embed.") return list() ckpt = torch.load(embedding_file,map_location="cpu") else: ckpt = safetensors.torch.load_file(embedding_file) except Exception as e: - print(f" ** Notice: unrecognized embedding file format: {embedding_file}: {e}") + logger.warning(f"Notice: unrecognized embedding file format: {embedding_file}: {e}") return list() # try to figure out what kind of embedding file it is and parse accordingly @@ -334,7 +335,7 @@ class TextualInversionManager(BaseTextualInversionManager): def _parse_embedding_v1(self, embedding_ckpt: dict, file_path: str)->List[EmbeddingInfo]: basename = Path(file_path).stem - print(f' | Loading v1 embedding file: {basename}') + logger.debug(f'Loading v1 embedding file: {basename}') embeddings = list() token_counter = -1 @@ -342,7 +343,7 @@ class TextualInversionManager(BaseTextualInversionManager): if token_counter < 0: trigger = embedding_ckpt["name"] elif token_counter == 0: - trigger = f'' + trigger = '' else: trigger = f'<{basename}-{int(token_counter:=token_counter)}>' token_counter += 1 @@ -365,7 +366,7 @@ class TextualInversionManager(BaseTextualInversionManager): This handles embedding .pt file variant #2. """ basename = Path(file_path).stem - print(f' | Loading v2 embedding file: {basename}') + logger.debug(f'Loading v2 embedding file: {basename}') embeddings = list() if isinstance( @@ -384,7 +385,7 @@ class TextualInversionManager(BaseTextualInversionManager): ) embeddings.append(embedding_info) else: - print(f" ** {basename}: Unrecognized embedding format") + logger.warning(f"{basename}: Unrecognized embedding format") return embeddings @@ -393,7 +394,7 @@ class TextualInversionManager(BaseTextualInversionManager): Parse 'version 3' of the .pt textual inversion embedding files. """ basename = Path(file_path).stem - print(f' | Loading v3 embedding file: {basename}') + logger.debug(f'Loading v3 embedding file: {basename}') embedding = embedding_ckpt['emb_params'] embedding_info = EmbeddingInfo( name = f'<{basename}>', @@ -411,11 +412,11 @@ class TextualInversionManager(BaseTextualInversionManager): basename = Path(filepath).stem short_path = Path(filepath).parents[0].name+'/'+Path(filepath).name - print(f' | Loading v4 embedding file: {short_path}') + logger.debug(f'Loading v4 embedding file: {short_path}') embeddings = list() if list(embedding_ckpt.keys()) == 0: - print(f" ** Invalid embeddings file: {short_path}") + logger.warning(f"Invalid embeddings file: {short_path}") else: for token,embedding in embedding_ckpt.items(): embedding_info = EmbeddingInfo( diff --git a/invokeai/backend/util/logging.py b/invokeai/backend/util/logging.py new file mode 100644 index 0000000000..73f980aeff --- /dev/null +++ b/invokeai/backend/util/logging.py @@ -0,0 +1,109 @@ +# Copyright (c) 2023 Lincoln D. Stein and The InvokeAI Development Team + +"""invokeai.util.logging + +Logging class for InvokeAI that produces console messages that follow +the conventions established in InvokeAI 1.X through 2.X. + + +One way to use it: + +from invokeai.backend.util.logging import InvokeAILogger + +logger = InvokeAILogger.getLogger(__name__) +logger.critical('this is critical') +logger.error('this is an error') +logger.warning('this is a warning') +logger.info('this is info') +logger.debug('this is debugging') + +Console messages: + ### this is critical + *** this is an error *** + ** this is a warning + >> this is info + | this is debugging + +Another way: +import invokeai.backend.util.logging as ialog +ialogger.debug('this is a debugging message') +""" +import logging + +# module level functions +def debug(msg, *args, **kwargs): + InvokeAILogger.getLogger().debug(msg, *args, **kwargs) + +def info(msg, *args, **kwargs): + InvokeAILogger.getLogger().info(msg, *args, **kwargs) + +def warning(msg, *args, **kwargs): + InvokeAILogger.getLogger().warning(msg, *args, **kwargs) + +def error(msg, *args, **kwargs): + InvokeAILogger.getLogger().error(msg, *args, **kwargs) + +def critical(msg, *args, **kwargs): + InvokeAILogger.getLogger().critical(msg, *args, **kwargs) + +def log(level, msg, *args, **kwargs): + InvokeAILogger.getLogger().log(level, msg, *args, **kwargs) + +def disable(level=logging.CRITICAL): + InvokeAILogger.getLogger().disable(level) + +def basicConfig(**kwargs): + InvokeAILogger.getLogger().basicConfig(**kwargs) + +def getLogger(name: str=None)->logging.Logger: + return InvokeAILogger.getLogger(name) + +class InvokeAILogFormatter(logging.Formatter): + ''' + Repurposed from: + https://stackoverflow.com/questions/14844970/modifying-logging-message-format-based-on-message-logging-level-in-python3 + ''' + crit_fmt = "### %(msg)s" + err_fmt = "*** %(msg)s" + warn_fmt = "** %(msg)s" + info_fmt = ">> %(msg)s" + dbg_fmt = " | %(msg)s" + + def __init__(self): + super().__init__(fmt="%(levelno)d: %(msg)s", datefmt=None, style='%') + + def format(self, record): + # Remember the format used when the logging module + # was installed (in the event that this formatter is + # used with the vanilla logging module. + format_orig = self._style._fmt + if record.levelno == logging.DEBUG: + self._style._fmt = InvokeAILogFormatter.dbg_fmt + if record.levelno == logging.INFO: + self._style._fmt = InvokeAILogFormatter.info_fmt + if record.levelno == logging.WARNING: + self._style._fmt = InvokeAILogFormatter.warn_fmt + if record.levelno == logging.ERROR: + self._style._fmt = InvokeAILogFormatter.err_fmt + if record.levelno == logging.CRITICAL: + self._style._fmt = InvokeAILogFormatter.crit_fmt + + # parent class does the work + result = super().format(record) + self._style._fmt = format_orig + return result + +class InvokeAILogger(object): + loggers = dict() + + @classmethod + def getLogger(self, name:str='invokeai')->logging.Logger: + if name not in self.loggers: + logger = logging.getLogger(name) + logger.setLevel(logging.DEBUG) + ch = logging.StreamHandler() + fmt = InvokeAILogFormatter() + ch.setFormatter(fmt) + logger.addHandler(ch) + self.loggers[name] = logger + return self.loggers[name] diff --git a/invokeai/backend/util/util.py b/invokeai/backend/util/util.py index d5239af834..edc6a7b04b 100644 --- a/invokeai/backend/util/util.py +++ b/invokeai/backend/util/util.py @@ -18,6 +18,7 @@ import torch from PIL import Image, ImageDraw, ImageFont from tqdm import tqdm +import invokeai.backend.util.logging as logger from .devices import torch_dtype @@ -38,7 +39,7 @@ def log_txt_as_img(wh, xc, size=10): try: draw.text((0, 0), lines, fill="black", font=font) except UnicodeEncodeError: - print("Cant encode string for logging. Skipping.") + logger.warning("Cant encode string for logging. Skipping.") txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 txts.append(txt) @@ -80,8 +81,8 @@ def mean_flat(tensor): def count_params(model, verbose=False): total_params = sum(p.numel() for p in model.parameters()) if verbose: - print( - f" | {model.__class__.__name__} has {total_params * 1.e-6:.2f} M params." + logger.debug( + f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params." ) return total_params @@ -132,8 +133,8 @@ def parallel_data_prefetch( raise ValueError("list expected but function got ndarray.") elif isinstance(data, abc.Iterable): if isinstance(data, dict): - print( - 'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.' + logger.warning( + '"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.' ) data = list(data.values()) if target_data_type == "ndarray": @@ -175,7 +176,7 @@ def parallel_data_prefetch( processes += [p] # start processes - print("Start prefetching...") + logger.info("Start prefetching...") import time start = time.time() @@ -194,7 +195,7 @@ def parallel_data_prefetch( gather_res[res[0]] = res[1] except Exception as e: - print("Exception: ", e) + logger.error("Exception: ", e) for p in processes: p.terminate() @@ -202,7 +203,7 @@ def parallel_data_prefetch( finally: for p in processes: p.join() - print(f"Prefetching complete. [{time.time() - start} sec.]") + logger.info(f"Prefetching complete. [{time.time() - start} sec.]") if target_data_type == "ndarray": if not isinstance(gather_res[0], np.ndarray): @@ -318,23 +319,23 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path resp = requests.get(url, headers=header, stream=True) # new request with range if exist_size > content_length: - print("* corrupt existing file found. re-downloading") + logger.warning("corrupt existing file found. re-downloading") os.remove(dest) exist_size = 0 if resp.status_code == 416 or exist_size == content_length: - print(f"* {dest}: complete file found. Skipping.") + logger.warning(f"{dest}: complete file found. Skipping.") return dest elif resp.status_code == 206 or exist_size > 0: - print(f"* {dest}: partial file found. Resuming...") + logger.warning(f"{dest}: partial file found. Resuming...") elif resp.status_code != 200: - print(f"** An error occurred during downloading {dest}: {resp.reason}") + logger.error(f"An error occurred during downloading {dest}: {resp.reason}") else: - print(f"* {dest}: Downloading...") + logger.error(f"{dest}: Downloading...") try: if content_length < 2000: - print(f"*** ERROR DOWNLOADING {url}: {resp.text}") + logger.error(f"ERROR DOWNLOADING {url}: {resp.text}") return None with open(dest, open_mode) as file, tqdm( @@ -349,7 +350,7 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path size = file.write(data) bar.update(size) except Exception as e: - print(f"An error occurred while downloading {dest}: {str(e)}") + logger.error(f"An error occurred while downloading {dest}: {str(e)}") return None return dest diff --git a/invokeai/backend/web/invoke_ai_web_server.py b/invokeai/backend/web/invoke_ai_web_server.py index 7209e31449..84478d5cb6 100644 --- a/invokeai/backend/web/invoke_ai_web_server.py +++ b/invokeai/backend/web/invoke_ai_web_server.py @@ -19,6 +19,7 @@ from PIL import Image from PIL.Image import Image as ImageType from werkzeug.utils import secure_filename +import invokeai.backend.util.logging as logger import invokeai.frontend.web.dist as frontend from .. import Generate @@ -213,7 +214,7 @@ class InvokeAIWebServer: self.load_socketio_listeners(self.socketio) if args.gui: - print(">> Launching Invoke AI GUI") + logger.info("Launching Invoke AI GUI") try: from flaskwebgui import FlaskUI @@ -231,17 +232,17 @@ class InvokeAIWebServer: sys.exit(0) else: useSSL = args.certfile or args.keyfile - print(">> Started Invoke AI Web Server") + logger.info("Started Invoke AI Web Server") if self.host == "0.0.0.0": - print( + logger.info( f"Point your browser at http{'s' if useSSL else ''}://localhost:{self.port} or use the host's DNS name or IP address." ) else: - print( - ">> Default host address now 127.0.0.1 (localhost). Use --host 0.0.0.0 to bind any address." + logger.info( + "Default host address now 127.0.0.1 (localhost). Use --host 0.0.0.0 to bind any address." ) - print( - f">> Point your browser at http{'s' if useSSL else ''}://{self.host}:{self.port}" + logger.info( + f"Point your browser at http{'s' if useSSL else ''}://{self.host}:{self.port}" ) if not useSSL: self.socketio.run(app=self.app, host=self.host, port=self.port) @@ -273,7 +274,7 @@ class InvokeAIWebServer: # path for thumbnail images self.thumbnail_image_path = os.path.join(self.result_path, "thumbnails/") # txt log - self.log_path = os.path.join(self.result_path, "invoke_log.txt") + self.log_path = os.path.join(self.result_path, "invoke_logger.txt") # make all output paths [ os.makedirs(path, exist_ok=True) @@ -290,7 +291,7 @@ class InvokeAIWebServer: def load_socketio_listeners(self, socketio): @socketio.on("requestSystemConfig") def handle_request_capabilities(): - print(">> System config requested") + logger.info("System config requested") config = self.get_system_config() config["model_list"] = self.generate.model_manager.list_models() config["infill_methods"] = infill_methods() @@ -330,7 +331,7 @@ class InvokeAIWebServer: if model_name in current_model_list: update = True - print(f">> Adding New Model: {model_name}") + logger.info(f"Adding New Model: {model_name}") self.generate.model_manager.add_model( model_name=model_name, @@ -348,14 +349,14 @@ class InvokeAIWebServer: "update": update, }, ) - print(f">> New Model Added: {model_name}") + logger.info(f"New Model Added: {model_name}") except Exception as e: self.handle_exceptions(e) @socketio.on("deleteModel") def handle_delete_model(model_name: str): try: - print(f">> Deleting Model: {model_name}") + logger.info(f"Deleting Model: {model_name}") self.generate.model_manager.del_model(model_name) self.generate.model_manager.commit(opt.conf) updated_model_list = self.generate.model_manager.list_models() @@ -366,14 +367,14 @@ class InvokeAIWebServer: "model_list": updated_model_list, }, ) - print(f">> Model Deleted: {model_name}") + logger.info(f"Model Deleted: {model_name}") except Exception as e: self.handle_exceptions(e) @socketio.on("requestModelChange") def handle_set_model(model_name: str): try: - print(f">> Model change requested: {model_name}") + logger.info(f"Model change requested: {model_name}") model = self.generate.set_model(model_name) model_list = self.generate.model_manager.list_models() if model is None: @@ -454,7 +455,7 @@ class InvokeAIWebServer: "update": True, }, ) - print(f">> Model Converted: {model_name}") + logger.info(f"Model Converted: {model_name}") except Exception as e: self.handle_exceptions(e) @@ -490,7 +491,7 @@ class InvokeAIWebServer: if vae := self.generate.model_manager.config[models_to_merge[0]].get( "vae", None ): - print(f">> Using configured VAE assigned to {models_to_merge[0]}") + logger.info(f"Using configured VAE assigned to {models_to_merge[0]}") merged_model_config.update(vae=vae) self.generate.model_manager.import_diffuser_model( @@ -507,8 +508,8 @@ class InvokeAIWebServer: "update": True, }, ) - print(f">> Models Merged: {models_to_merge}") - print(f">> New Model Added: {model_merge_info['merged_model_name']}") + logger.info(f"Models Merged: {models_to_merge}") + logger.info(f"New Model Added: {model_merge_info['merged_model_name']}") except Exception as e: self.handle_exceptions(e) @@ -698,7 +699,7 @@ class InvokeAIWebServer: } ) except Exception as e: - print(f">> Unable to load {path}") + logger.info(f"Unable to load {path}") socketio.emit( "error", {"message": f"Unable to load {path}: {str(e)}"} ) @@ -735,9 +736,9 @@ class InvokeAIWebServer: printable_parameters["init_mask"][:64] + "..." ) - print(f"\n>> Image Generation Parameters:\n\n{printable_parameters}\n") - print(f">> ESRGAN Parameters: {esrgan_parameters}") - print(f">> Facetool Parameters: {facetool_parameters}") + logger.info(f"Image Generation Parameters:\n\n{printable_parameters}\n") + logger.info(f"ESRGAN Parameters: {esrgan_parameters}") + logger.info(f"Facetool Parameters: {facetool_parameters}") self.generate_images( generation_parameters, @@ -750,8 +751,8 @@ class InvokeAIWebServer: @socketio.on("runPostprocessing") def handle_run_postprocessing(original_image, postprocessing_parameters): try: - print( - f'>> Postprocessing requested for "{original_image["url"]}": {postprocessing_parameters}' + logger.info( + f'Postprocessing requested for "{original_image["url"]}": {postprocessing_parameters}' ) progress = Progress() @@ -861,14 +862,14 @@ class InvokeAIWebServer: @socketio.on("cancel") def handle_cancel(): - print(">> Cancel processing requested") + logger.info("Cancel processing requested") self.canceled.set() # TODO: I think this needs a safety mechanism. @socketio.on("deleteImage") def handle_delete_image(url, thumbnail, uuid, category): try: - print(f'>> Delete requested "{url}"') + logger.info(f'Delete requested "{url}"') from send2trash import send2trash path = self.get_image_path_from_url(url) @@ -1263,7 +1264,7 @@ class InvokeAIWebServer: image, os.path.basename(path), self.thumbnail_image_path ) - print(f'\n\n>> Image generated: "{path}"\n') + logger.info(f'Image generated: "{path}"\n') self.write_log_message(f'[Generated] "{path}": {command}') if progress.total_iterations > progress.current_iteration: @@ -1329,7 +1330,7 @@ class InvokeAIWebServer: except Exception as e: # Clear the CUDA cache on an exception self.empty_cuda_cache() - print(e) + logger.error(e) self.handle_exceptions(e) def empty_cuda_cache(self): diff --git a/invokeai/frontend/CLI/CLI.py b/invokeai/frontend/CLI/CLI.py index 352ee83812..aa0c4bea5f 100644 --- a/invokeai/frontend/CLI/CLI.py +++ b/invokeai/frontend/CLI/CLI.py @@ -16,6 +16,7 @@ if sys.platform == "darwin": import pyparsing # type: ignore import invokeai.version as invokeai +import invokeai.backend.util.logging as logger from ...backend import Generate, ModelManager from ...backend.args import Args, dream_cmd_from_png, metadata_dumps, metadata_from_png @@ -69,7 +70,7 @@ def main(): # run any post-install patches needed run_patches() - print(f">> Internet connectivity is {Globals.internet_available}") + logger.info(f"Internet connectivity is {Globals.internet_available}") if not args.conf: config_file = os.path.join(Globals.root, "configs", "models.yaml") @@ -78,8 +79,8 @@ def main(): opt, FileNotFoundError(f"The file {config_file} could not be found.") ) - print(f">> {invokeai.__app_name__}, version {invokeai.__version__}") - print(f'>> InvokeAI runtime directory is "{Globals.root}"') + logger.info(f"{invokeai.__app_name__}, version {invokeai.__version__}") + logger.info(f'InvokeAI runtime directory is "{Globals.root}"') # loading here to avoid long delays on startup # these two lines prevent a horrible warning message from appearing @@ -121,7 +122,7 @@ def main(): else: raise FileNotFoundError(f"{opt.infile} not found.") except (FileNotFoundError, IOError) as e: - print(f"{e}. Aborting.") + logger.critical('Aborted',exc_info=True) sys.exit(-1) # creating a Generate object: @@ -142,12 +143,12 @@ def main(): ) except (FileNotFoundError, TypeError, AssertionError) as e: report_model_error(opt, e) - except (IOError, KeyError) as e: - print(f"{e}. Aborting.") + except (IOError, KeyError): + logger.critical("Aborted",exc_info=True) sys.exit(-1) if opt.seamless: - print(">> changed to seamless tiling mode") + logger.info("Changed to seamless tiling mode") # preload the model try: @@ -180,9 +181,7 @@ def main(): f'\nGoodbye!\nYou can start InvokeAI again by running the "invoke.bat" (or "invoke.sh") script from {Globals.root}' ) except Exception: - print(">> An error occurred:") - traceback.print_exc() - + logger.error("An error occurred",exc_info=True) # TODO: main_loop() has gotten busy. Needs to be refactored. def main_loop(gen, opt): @@ -248,7 +247,7 @@ def main_loop(gen, opt): if not opt.prompt: oldargs = metadata_from_png(opt.init_img) opt.prompt = oldargs.prompt - print(f'>> Retrieved old prompt "{opt.prompt}" from {opt.init_img}') + logger.info(f'Retrieved old prompt "{opt.prompt}" from {opt.init_img}') except (OSError, AttributeError, KeyError): pass @@ -265,9 +264,9 @@ def main_loop(gen, opt): if opt.init_img is not None and re.match("^-\\d+$", opt.init_img): try: opt.init_img = last_results[int(opt.init_img)][0] - print(f">> Reusing previous image {opt.init_img}") + logger.info(f"Reusing previous image {opt.init_img}") except IndexError: - print(f">> No previous initial image at position {opt.init_img} found") + logger.info(f"No previous initial image at position {opt.init_img} found") opt.init_img = None continue @@ -288,9 +287,9 @@ def main_loop(gen, opt): if opt.seed is not None and opt.seed < 0 and operation != "postprocess": try: opt.seed = last_results[opt.seed][1] - print(f">> Reusing previous seed {opt.seed}") + logger.info(f"Reusing previous seed {opt.seed}") except IndexError: - print(f">> No previous seed at position {opt.seed} found") + logger.info(f"No previous seed at position {opt.seed} found") opt.seed = None continue @@ -309,7 +308,7 @@ def main_loop(gen, opt): subdir = subdir[: (path_max - 39 - len(os.path.abspath(opt.outdir)))] current_outdir = os.path.join(opt.outdir, subdir) - print('Writing files to directory: "' + current_outdir + '"') + logger.info('Writing files to directory: "' + current_outdir + '"') # make sure the output directory exists if not os.path.exists(current_outdir): @@ -438,15 +437,14 @@ def main_loop(gen, opt): catch_interrupts=catch_ctrl_c, **vars(opt), ) - except (PromptParser.ParsingException, pyparsing.ParseException) as e: - print("** An error occurred while processing your prompt **") - print(f"** {str(e)} **") + except (PromptParser.ParsingException, pyparsing.ParseException): + logger.error("An error occurred while processing your prompt",exc_info=True) elif operation == "postprocess": - print(f">> fixing {opt.prompt}") + logger.info(f"fixing {opt.prompt}") opt.last_operation = do_postprocess(gen, opt, image_writer) elif operation == "mask": - print(f">> generating masks from {opt.prompt}") + logger.info(f"generating masks from {opt.prompt}") do_textmask(gen, opt, image_writer) if opt.grid and len(grid_images) > 0: @@ -469,12 +467,12 @@ def main_loop(gen, opt): ) results = [[path, formatted_dream_prompt]] - except AssertionError as e: - print(e) + except AssertionError: + logger.error(e) continue except OSError as e: - print(e) + logger.error(e) continue print("Outputs:") @@ -513,7 +511,7 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple: gen.set_model(model_name) add_embedding_terms(gen, completer) except KeyError as e: - print(str(e)) + logger.error(e) except Exception as e: report_model_error(opt, e) completer.add_history(command) @@ -527,8 +525,8 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple: elif command.startswith("!import"): path = shlex.split(command) if len(path) < 2: - print( - "** please provide (1) a URL to a .ckpt file to import; (2) a local path to a .ckpt file; or (3) a diffusers repository id in the form stabilityai/stable-diffusion-2-1" + logger.warning( + "please provide (1) a URL to a .ckpt file to import; (2) a local path to a .ckpt file; or (3) a diffusers repository id in the form stabilityai/stable-diffusion-2-1" ) else: try: @@ -541,7 +539,7 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple: elif command.startswith(("!convert", "!optimize")): path = shlex.split(command) if len(path) < 2: - print("** please provide the path to a .ckpt or .safetensors model") + logger.warning("please provide the path to a .ckpt or .safetensors model") else: try: convert_model(path[1], gen, opt, completer) @@ -553,7 +551,7 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple: elif command.startswith("!edit"): path = shlex.split(command) if len(path) < 2: - print("** please provide the name of a model") + logger.warning("please provide the name of a model") else: edit_model(path[1], gen, opt, completer) completer.add_history(command) @@ -562,7 +560,7 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple: elif command.startswith("!del"): path = shlex.split(command) if len(path) < 2: - print("** please provide the name of a model") + logger.warning("please provide the name of a model") else: del_config(path[1], gen, opt, completer) completer.add_history(command) @@ -642,8 +640,8 @@ def import_model(model_path: str, gen, opt, completer): try: default_name = url_attachment_name(model_path) default_name = Path(default_name).stem - except Exception as e: - print(f"** URL: {str(e)}") + except Exception: + logger.warning(f"A problem occurred while assigning the name of the downloaded model",exc_info=True) model_name, model_desc = _get_model_name_and_desc( gen.model_manager, completer, @@ -664,11 +662,11 @@ def import_model(model_path: str, gen, opt, completer): model_config_file=config_file, ) if not imported_name: - print("** Aborting import.") + logger.error("Aborting import.") return if not _verify_load(imported_name, gen): - print("** model failed to load. Discarding configuration entry") + logger.error("model failed to load. Discarding configuration entry") gen.model_manager.del_model(imported_name) return if click.confirm("Make this the default model?", default=False): @@ -676,7 +674,7 @@ def import_model(model_path: str, gen, opt, completer): gen.model_manager.commit(opt.conf) completer.update_models(gen.model_manager.list_models()) - print(f">> {imported_name} successfully installed") + logger.info(f"{imported_name} successfully installed") def _pick_configuration_file(completer)->Path: print( @@ -720,21 +718,21 @@ Please select the type of this model: return choice def _verify_load(model_name: str, gen) -> bool: - print(">> Verifying that new model loads...") + logger.info("Verifying that new model loads...") current_model = gen.model_name try: if not gen.set_model(model_name): return except Exception as e: - print(f"** model failed to load: {str(e)}") - print( + logger.warning(f"model failed to load: {str(e)}") + logger.warning( "** note that importing 2.X checkpoints is not supported. Please use !convert_model instead." ) return False if click.confirm("Keep model loaded?", default=True): gen.set_model(model_name) else: - print(">> Restoring previous model") + logger.info("Restoring previous model") gen.set_model(current_model) return True @@ -757,7 +755,7 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer): ckpt_path = None original_config_file = None if model_name_or_path == gen.model_name: - print("** Can't convert the active model. !switch to another model first. **") + logger.warning("Can't convert the active model. !switch to another model first. **") return elif model_info := manager.model_info(model_name_or_path): if "weights" in model_info: @@ -767,7 +765,7 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer): model_description = model_info["description"] vae_path = model_info.get("vae") else: - print(f"** {model_name_or_path} is not a legacy .ckpt weights file") + logger.warning(f"{model_name_or_path} is not a legacy .ckpt weights file") return model_name = manager.convert_and_import( ckpt_path, @@ -788,16 +786,16 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer): manager.commit(opt.conf) if click.confirm(f"Delete the original .ckpt file at {ckpt_path}?", default=False): ckpt_path.unlink(missing_ok=True) - print(f"{ckpt_path} deleted") + logger.warning(f"{ckpt_path} deleted") def del_config(model_name: str, gen, opt, completer): current_model = gen.model_name if model_name == current_model: - print("** Can't delete active model. !switch to another model first. **") + logger.warning("Can't delete active model. !switch to another model first. **") return if model_name not in gen.model_manager.config: - print(f"** Unknown model {model_name}") + logger.warning(f"Unknown model {model_name}") return if not click.confirm( @@ -810,17 +808,17 @@ def del_config(model_name: str, gen, opt, completer): ) gen.model_manager.del_model(model_name, delete_files=delete_completely) gen.model_manager.commit(opt.conf) - print(f"** {model_name} deleted") + logger.warning(f"{model_name} deleted") completer.update_models(gen.model_manager.list_models()) def edit_model(model_name: str, gen, opt, completer): manager = gen.model_manager if not (info := manager.model_info(model_name)): - print(f"** Unknown model {model_name}") + logger.warning(f"** Unknown model {model_name}") return - - print(f"\n>> Editing model {model_name} from configuration file {opt.conf}") + print() + logger.info(f"Editing model {model_name} from configuration file {opt.conf}") new_name = _get_model_name(manager.list_models(), completer, model_name) for attribute in info.keys(): @@ -858,7 +856,7 @@ def edit_model(model_name: str, gen, opt, completer): manager.set_default_model(new_name) manager.commit(opt.conf) completer.update_models(manager.list_models()) - print(">> Model successfully updated") + logger.info("Model successfully updated") def _get_model_name(existing_names, completer, default_name: str = "") -> str: @@ -869,11 +867,11 @@ def _get_model_name(existing_names, completer, default_name: str = "") -> str: if len(model_name) == 0: model_name = default_name if not re.match("^[\w._+:/-]+$", model_name): - print( - '** model name must contain only words, digits and the characters "._+:/-" **' + logger.warning( + 'model name must contain only words, digits and the characters "._+:/-" **' ) elif model_name != default_name and model_name in existing_names: - print(f"** the name {model_name} is already in use. Pick another.") + logger.warning(f"the name {model_name} is already in use. Pick another.") else: done = True return model_name @@ -940,11 +938,10 @@ def do_postprocess(gen, opt, callback): opt=opt, ) except OSError: - print(traceback.format_exc(), file=sys.stderr) - print(f"** {file_path}: file could not be read") + logger.error(f"{file_path}: file could not be read",exc_info=True) return except (KeyError, AttributeError): - print(traceback.format_exc(), file=sys.stderr) + logger.error(f"an error occurred while applying the {tool} postprocessor",exc_info=True) return return opt.last_operation @@ -999,13 +996,13 @@ def prepare_image_metadata( try: filename = opt.fnformat.format(**wildcards) except KeyError as e: - print( - f"** The filename format contains an unknown key '{e.args[0]}'. Will use {{prefix}}.{{seed}}.png' instead" + logger.error( + f"The filename format contains an unknown key '{e.args[0]}'. Will use {{prefix}}.{{seed}}.png' instead" ) filename = f"{prefix}.{seed}.png" except IndexError: - print( - "** The filename format is broken or complete. Will use '{prefix}.{seed}.png' instead" + logger.error( + "The filename format is broken or complete. Will use '{prefix}.{seed}.png' instead" ) filename = f"{prefix}.{seed}.png" @@ -1094,14 +1091,14 @@ def split_variations(variations_string) -> list: for part in variations_string.split(","): seed_and_weight = part.split(":") if len(seed_and_weight) != 2: - print(f'** Could not parse with_variation part "{part}"') + logger.warning(f'Could not parse with_variation part "{part}"') broken = True break try: seed = int(seed_and_weight[0]) weight = float(seed_and_weight[1]) except ValueError: - print(f'** Could not parse with_variation part "{part}"') + logger.warning(f'Could not parse with_variation part "{part}"') broken = True break parts.append([seed, weight]) @@ -1125,23 +1122,23 @@ def load_face_restoration(opt): opt.gfpgan_model_path ) else: - print(">> Face restoration disabled") + logger.info("Face restoration disabled") if opt.esrgan: esrgan = restoration.load_esrgan(opt.esrgan_bg_tile) else: - print(">> Upscaling disabled") + logger.info("Upscaling disabled") else: - print(">> Face restoration and upscaling disabled") + logger.info("Face restoration and upscaling disabled") except (ModuleNotFoundError, ImportError): print(traceback.format_exc(), file=sys.stderr) - print(">> You may need to install the ESRGAN and/or GFPGAN modules") + logger.info("You may need to install the ESRGAN and/or GFPGAN modules") return gfpgan, codeformer, esrgan def make_step_callback(gen, opt, prefix): destination = os.path.join(opt.outdir, "intermediates", prefix) os.makedirs(destination, exist_ok=True) - print(f">> Intermediate images will be written into {destination}") + logger.info(f"Intermediate images will be written into {destination}") def callback(state: PipelineIntermediateState): latents = state.latents @@ -1183,21 +1180,20 @@ def retrieve_dream_command(opt, command, completer): try: cmd = dream_cmd_from_png(path) except OSError: - print(f"## {tokens[0]}: file could not be read") + logger.error(f"{tokens[0]}: file could not be read") except (KeyError, AttributeError, IndexError): - print(f"## {tokens[0]}: file has no metadata") + logger.error(f"{tokens[0]}: file has no metadata") except: - print(f"## {tokens[0]}: file could not be processed") + logger.error(f"{tokens[0]}: file could not be processed") if len(cmd) > 0: completer.set_line(cmd) - def write_commands(opt, file_path: str, outfilepath: str): dir, basename = os.path.split(file_path) try: paths = sorted(list(Path(dir).glob(basename))) except ValueError: - print(f'## "{basename}": unacceptable pattern') + logger.error(f'"{basename}": unacceptable pattern') return commands = [] @@ -1206,9 +1202,9 @@ def write_commands(opt, file_path: str, outfilepath: str): try: cmd = dream_cmd_from_png(path) except (KeyError, AttributeError, IndexError): - print(f"## {path}: file has no metadata") + logger.error(f"{path}: file has no metadata") except: - print(f"## {path}: file could not be processed") + logger.error(f"{path}: file could not be processed") if cmd: commands.append(f"# {path}") commands.append(cmd) @@ -1218,18 +1214,18 @@ def write_commands(opt, file_path: str, outfilepath: str): outfilepath = os.path.join(opt.outdir, basename) with open(outfilepath, "w", encoding="utf-8") as f: f.write("\n".join(commands)) - print(f">> File {outfilepath} with commands created") + logger.info(f"File {outfilepath} with commands created") def report_model_error(opt: Namespace, e: Exception): - print(f'** An error occurred while attempting to initialize the model: "{str(e)}"') - print( - "** This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models." + logger.warning(f'An error occurred while attempting to initialize the model: "{str(e)}"') + logger.warning( + "This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models." ) yes_to_all = os.environ.get("INVOKE_MODEL_RECONFIGURE") if yes_to_all: - print( - "** Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE" + logger.warning( + "Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE" ) else: if not click.confirm( @@ -1238,7 +1234,7 @@ def report_model_error(opt: Namespace, e: Exception): ): return - print("invokeai-configure is launching....\n") + logger.info("invokeai-configure is launching....\n") # Match arguments that were set on the CLI # only the arguments accepted by the configuration script are parsed @@ -1255,7 +1251,7 @@ def report_model_error(opt: Namespace, e: Exception): from ..install import invokeai_configure invokeai_configure() - print("** InvokeAI will now restart") + logger.warning("InvokeAI will now restart") sys.argv = previous_args main() # would rather do a os.exec(), but doesn't exist? sys.exit(0) diff --git a/invokeai/frontend/install/model_install.py b/invokeai/frontend/install/model_install.py index 18ec6d55df..c12104033f 100644 --- a/invokeai/frontend/install/model_install.py +++ b/invokeai/frontend/install/model_install.py @@ -22,6 +22,7 @@ import torch from npyscreen import widget from omegaconf import OmegaConf +import invokeai.backend.util.logging as logger from invokeai.backend.globals import Globals, global_config_dir from ...backend.config.model_install_backend import ( @@ -455,8 +456,8 @@ def main(): Globals.root = os.path.expanduser(get_root(opt.root) or "") if not global_config_dir().exists(): - print( - ">> Your InvokeAI root directory is not set up. Calling invokeai-configure." + logger.info( + "Your InvokeAI root directory is not set up. Calling invokeai-configure." ) from invokeai.frontend.install import invokeai_configure @@ -466,18 +467,18 @@ def main(): try: select_and_download_models(opt) except AssertionError as e: - print(str(e)) + logger.error(e) sys.exit(-1) except KeyboardInterrupt: - print("\nGoodbye! Come back soon.") + logger.info("Goodbye! Come back soon.") except widget.NotEnoughSpaceForWidget as e: if str(e).startswith("Height of 1 allocated"): - print( - "** Insufficient vertical space for the interface. Please make your window taller and try again" + logger.error( + "Insufficient vertical space for the interface. Please make your window taller and try again" ) elif str(e).startswith("addwstr"): - print( - "** Insufficient horizontal space for the interface. Please make your window wider and try again." + logger.error( + "Insufficient horizontal space for the interface. Please make your window wider and try again." ) diff --git a/invokeai/frontend/merge/merge_diffusers.py b/invokeai/frontend/merge/merge_diffusers.py index 1538826d54..524118ba7c 100644 --- a/invokeai/frontend/merge/merge_diffusers.py +++ b/invokeai/frontend/merge/merge_diffusers.py @@ -27,6 +27,8 @@ from ...backend.globals import ( global_models_dir, global_set_root, ) + +import invokeai.backend.util.logging as logger from ...backend.model_management import ModelManager from ...frontend.install.widgets import FloatTitleSlider @@ -113,7 +115,7 @@ def merge_diffusion_models_and_commit( model_name=merged_model_name, description=f'Merge of models {", ".join(models)}' ) if vae := model_manager.config[models[0]].get("vae", None): - print(f">> Using configured VAE assigned to {models[0]}") + logger.info(f"Using configured VAE assigned to {models[0]}") import_args.update(vae=vae) model_manager.import_diffuser_model(dump_path, **import_args) model_manager.commit(config_file) @@ -391,10 +393,8 @@ class mergeModelsForm(npyscreen.FormMultiPageAction): for name in self.model_manager.model_names() if self.model_manager.model_info(name).get("format") == "diffusers" ] - print(model_names) return sorted(model_names) - class Mergeapp(npyscreen.NPSAppManaged): def __init__(self): super().__init__() @@ -414,7 +414,7 @@ def run_gui(args: Namespace): args = mergeapp.merge_arguments merge_diffusion_models_and_commit(**args) - print(f'>> Models merged into new model: "{args["merged_model_name"]}".') + logger.info(f'Models merged into new model: "{args["merged_model_name"]}".') def run_cli(args: Namespace): @@ -425,8 +425,8 @@ def run_cli(args: Namespace): if not args.merged_model_name: args.merged_model_name = "+".join(args.models) - print( - f'>> No --merged_model_name provided. Defaulting to "{args.merged_model_name}"' + logger.info( + f'No --merged_model_name provided. Defaulting to "{args.merged_model_name}"' ) model_manager = ModelManager(OmegaConf.load(global_config_file())) @@ -435,7 +435,7 @@ def run_cli(args: Namespace): ), f'A model named "{args.merged_model_name}" already exists. Use --clobber to overwrite.' merge_diffusion_models_and_commit(**vars(args)) - print(f'>> Models merged into new model: "{args.merged_model_name}".') + logger.info(f'Models merged into new model: "{args.merged_model_name}".') def main(): @@ -455,17 +455,16 @@ def main(): run_cli(args) except widget.NotEnoughSpaceForWidget as e: if str(e).startswith("Height of 1 allocated"): - print( - "** You need to have at least two diffusers models defined in models.yaml in order to merge" + logger.error( + "You need to have at least two diffusers models defined in models.yaml in order to merge" ) else: - print( - "** Not enough room for the user interface. Try making this window larger." + logger.error( + "Not enough room for the user interface. Try making this window larger." ) sys.exit(-1) - except Exception: - print(">> An error occurred:") - traceback.print_exc() + except Exception as e: + logger.error(e) sys.exit(-1) except KeyboardInterrupt: sys.exit(-1) diff --git a/invokeai/frontend/training/textual_inversion.py b/invokeai/frontend/training/textual_inversion.py index e97284da3d..23134d2736 100755 --- a/invokeai/frontend/training/textual_inversion.py +++ b/invokeai/frontend/training/textual_inversion.py @@ -20,6 +20,7 @@ import npyscreen from npyscreen import widget from omegaconf import OmegaConf +import invokeai.backend.util.logging as logger from invokeai.backend.globals import Globals, global_set_root from ...backend.training import do_textual_inversion_training, parse_args @@ -368,14 +369,14 @@ def copy_to_embeddings_folder(args: dict): dest_dir_name = args["placeholder_token"].strip("<>") destination = Path(Globals.root, "embeddings", dest_dir_name) os.makedirs(destination, exist_ok=True) - print(f">> Training completed. Copying learned_embeds.bin into {str(destination)}") + logger.info(f"Training completed. Copying learned_embeds.bin into {str(destination)}") shutil.copy(source, destination) if ( input("Delete training logs and intermediate checkpoints? [y] ") or "y" ).startswith(("y", "Y")): shutil.rmtree(Path(args["output_dir"])) else: - print(f'>> Keeping {args["output_dir"]}') + logger.info(f'Keeping {args["output_dir"]}') def save_args(args: dict): @@ -422,10 +423,10 @@ def do_front_end(args: Namespace): do_textual_inversion_training(**args) copy_to_embeddings_folder(args) except Exception as e: - print("** An exception occurred during training. The exception was:") - print(str(e)) - print("** DETAILS:") - print(traceback.format_exc()) + logger.error("An exception occurred during training. The exception was:") + logger.error(str(e)) + logger.error("DETAILS:") + logger.error(traceback.format_exc()) def main(): @@ -437,21 +438,21 @@ def main(): else: do_textual_inversion_training(**vars(args)) except AssertionError as e: - print(str(e)) + logger.error(e) sys.exit(-1) except KeyboardInterrupt: pass except (widget.NotEnoughSpaceForWidget, Exception) as e: if str(e).startswith("Height of 1 allocated"): - print( - "** You need to have at least one diffusers models defined in models.yaml in order to train" + logger.error( + "You need to have at least one diffusers models defined in models.yaml in order to train" ) elif str(e).startswith("addwstr"): - print( - "** Not enough window space for the interface. Please make your window larger and try again." + logger.error( + "Not enough window space for the interface. Please make your window larger and try again." ) else: - print(f"** An error has occurred: {str(e)}") + logger.error(e) sys.exit(-1) diff --git a/tests/nodes/test_graph_execution_state.py b/tests/nodes/test_graph_execution_state.py index 2476786e41..3c262cf88e 100644 --- a/tests/nodes/test_graph_execution_state.py +++ b/tests/nodes/test_graph_execution_state.py @@ -25,6 +25,7 @@ def mock_services(): return InvocationServices( model_manager = None, # type: ignore events = None, # type: ignore + logger = None, # type: ignore images = None, # type: ignore latents = None, # type: ignore metadata = None, # type: ignore diff --git a/tests/nodes/test_invoker.py b/tests/nodes/test_invoker.py index d187c1b171..66c6b94d6f 100644 --- a/tests/nodes/test_invoker.py +++ b/tests/nodes/test_invoker.py @@ -23,6 +23,7 @@ def mock_services() -> InvocationServices: return InvocationServices( model_manager = None, # type: ignore events = TestEventService(), + logger = None, # type: ignore images = None, # type: ignore latents = None, # type: ignore metadata = None, # type: ignore