diff --git a/invokeai/app/api/dependencies.py b/invokeai/app/api/dependencies.py
index 153176cd95..454c2bbbf6 100644
--- a/invokeai/app/api/dependencies.py
+++ b/invokeai/app/api/dependencies.py
@@ -2,7 +2,7 @@
 
 import os
 
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 
 from ..services.default_graphs import create_system_graphs
 from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
@@ -49,7 +49,7 @@ class ApiDependencies:
         Globals.disable_xformers = not config.xformers
         Globals.ckpt_convert = config.ckpt_convert
 
-        log.info(f"Internet connectivity is {Globals.internet_available}")
+        logger.info(f"Internet connectivity is {Globals.internet_available}")
 
         events = FastAPIEventService(event_handler_id)
 
diff --git a/invokeai/app/api/routers/models.py b/invokeai/app/api/routers/models.py
index 94dca8321e..9e567bab45 100644
--- a/invokeai/app/api/routers/models.py
+++ b/invokeai/app/api/routers/models.py
@@ -4,7 +4,7 @@ import shutil
 import asyncio
 from typing import Annotated, Any, List, Literal, Optional, Union
 
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from fastapi.routing import APIRouter, HTTPException
 from pydantic import BaseModel, Field, parse_obj_as
 from pathlib import Path
@@ -116,16 +116,16 @@ async def delete_model(model_name: str) -> None:
     model_exists = model_name in model_names
 
     # check if model exists
-    log.info(f"Checking for model {model_name}...")
+    logger.info(f"Checking for model {model_name}...")
            
     if model_exists:
-        log.info(f"Deleting Model: {model_name}")
+        logger.info(f"Deleting Model: {model_name}")
         ApiDependencies.invoker.services.model_manager.del_model(model_name, delete_files=True)
-        log.info(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:
-        log.error(f"Model not found")
+        logger.error(f"Model not found")
         raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
     
 
diff --git a/invokeai/app/cli/commands.py b/invokeai/app/cli/commands.py
index 156887da9d..01cd99bc35 100644
--- a/invokeai/app/cli/commands.py
+++ b/invokeai/app/cli/commands.py
@@ -7,7 +7,7 @@ from pydantic import BaseModel, Field
 import networkx as nx
 import matplotlib.pyplot as plt
 
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from ..invocations.baseinvocation import BaseInvocation
 from ..invocations.image import ImageField
 from ..services.graph import GraphExecutionState, LibraryGraph, Edge
@@ -230,7 +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)
-            log.info(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 b3e5b983a2..c84c430bd7 100644
--- a/invokeai/app/cli/completer.py
+++ b/invokeai/app/cli/completer.py
@@ -10,7 +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 log
+import invokeai.backend.util.logging as logger
 from ...backend import ModelManager, Globals
 from ..invocations.baseinvocation import BaseInvocation
 from .commands import BaseCommand
@@ -161,7 +161,7 @@ def set_autocompleter(model_manager: ModelManager) -> Completer:
         pass
     except OSError:  # file likely corrupted
         newname = f"{histfile}.old"
-        log.error(
+        logger.error(
             f"Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}"
         )
         histfile.replace(Path(newname))
diff --git a/invokeai/app/cli_app.py b/invokeai/app/cli_app.py
index 0e75bef67f..0d879e98d7 100644
--- a/invokeai/app/cli_app.py
+++ b/invokeai/app/cli_app.py
@@ -14,7 +14,7 @@ from pydantic import BaseModel
 from pydantic.fields import Field
 
 
-import invokeai.backend.util.logging as log
+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
@@ -181,7 +181,7 @@ def invoke_all(context: CliContext):
     # Print any errors
     if context.session.has_error():
         for n in context.session.errors:
-            log.error(
+            logger.error(
                 f"Error in node {n} (source node {context.session.prepared_source_mapping[n]}): {context.session.errors[n]}"
             )
         
@@ -364,12 +364,12 @@ def invoke_cli():
             invoke_all(context)
 
         except InvalidArgs:
-            log.warning('Invalid command, use "help" to list commands')
+            logger.warning('Invalid command, use "help" to list commands')
             continue
 
         except SessionError:
             # Start a new session
-            log.warning("Session error: creating a new session")
+            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 b6fc31644e..99a4d14835 100644
--- a/invokeai/app/invocations/util/choose_model.py
+++ b/invokeai/app/invocations/util/choose_model.py
@@ -1,4 +1,4 @@
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from invokeai.backend.model_management.model_manager import ModelManager
 
 
@@ -8,6 +8,6 @@ def choose_model(model_manager: ModelManager, model_name: str):
         model = model_manager.get_model(model_name)
     else:
         model = model_manager.get_model()
-        log.warning(f"{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/model_manager_initializer.py b/invokeai/app/services/model_manager_initializer.py
index 7f1ed28e49..7f78f303f2 100644
--- a/invokeai/app/services/model_manager_initializer.py
+++ b/invokeai/app/services/model_manager_initializer.py
@@ -7,7 +7,7 @@ from omegaconf import OmegaConf
 from pathlib import Path
 
 import invokeai.version
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from ...backend import ModelManager
 from ...backend.util import choose_precision, choose_torch_device
 from ...backend import Globals
@@ -21,8 +21,8 @@ def get_model_manager(config: Args) -> ModelManager:
                 config, FileNotFoundError(f"The file {config_file} could not be found.")
             )
 
-    log.info(f"{invokeai.version.__app_name__}, version {invokeai.version.__version__}")
-    log.info(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
@@ -67,7 +67,7 @@ def get_model_manager(config: Args) -> ModelManager:
     except (FileNotFoundError, TypeError, AssertionError) as e:
         report_model_error(config, e)
     except (IOError, KeyError) as e:
-        log.error(f"{e}. Aborting.")
+        logger.error(f"{e}. Aborting.")
         sys.exit(-1)
 
     # try to autoconvert new models
@@ -81,13 +81,13 @@ def get_model_manager(config: Args) -> ModelManager:
     return model_manager
 
 def report_model_error(opt: Namespace, e: Exception):
-    log.error(f'An error occurred while attempting to initialize the model: "{str(e)}"')
-    log.error(
+    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:
-        log.warning
+        logger.warning
             "Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
         )
     else:
@@ -97,7 +97,7 @@ def report_model_error(opt: Namespace, e: Exception):
         if response.startswith(("n", "N")):
             return
 
-    log.info("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
diff --git a/invokeai/app/services/restoration_services.py b/invokeai/app/services/restoration_services.py
index 5e908c0abe..7501b11e6b 100644
--- a/invokeai/app/services/restoration_services.py
+++ b/invokeai/app/services/restoration_services.py
@@ -1,7 +1,7 @@
 import sys
 import traceback
 import torch
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from ...backend.restoration import Restoration
 from ...backend.util import choose_torch_device, CPU_DEVICE, MPS_DEVICE
 
@@ -21,16 +21,16 @@ class RestorationServices:
                         args.gfpgan_model_path
                     )
                 else:
-                    log.info("Face restoration disabled")
+                    logger.info("Face restoration disabled")
                     if args.esrgan:
                         esrgan = restoration.load_esrgan(args.esrgan_bg_tile)
                     else:
-                        log.info("Upscaling disabled")
+                        logger.info("Upscaling disabled")
             else:
-                log.info("Face restoration and upscaling disabled")
+                logger.info("Face restoration and upscaling disabled")
         except (ModuleNotFoundError, ImportError):
             print(traceback.format_exc(), file=sys.stderr)
-            log.info("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
@@ -59,14 +59,14 @@ class RestorationServices:
                     if self.gfpgan is not None or self.codeformer is not None:
                         if facetool == "gfpgan":
                             if self.gfpgan is None:
-                                log.info(
+                                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:
-                                log.info(
+                                logger.info(
                                     "CodeFormer not found. Face restoration is disabled."
                                 )
                             else:
@@ -81,7 +81,7 @@ class RestorationServices:
                                     fidelity=codeformer_fidelity,
                                 )
                     else:
-                        log.info("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:
@@ -94,9 +94,9 @@ class RestorationServices:
                             denoise_str=upscale_denoise_str,
                         )
                     else:
-                        log.info("ESRGAN is disabled. Image not upscaled.")
+                        logger.info("ESRGAN is disabled. Image not upscaled.")
             except Exception as e:
-                log.info(
+                logger.info(
                     f"Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
                 )
 
diff --git a/invokeai/backend/args.py b/invokeai/backend/args.py
index 4a1aea094c..eb8b396ee0 100644
--- a/invokeai/backend/args.py
+++ b/invokeai/backend/args.py
@@ -96,7 +96,7 @@ from pathlib import Path
 from typing import List
 
 import invokeai.version
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from invokeai.backend.image_util import retrieve_metadata
 
 from .globals import Globals
@@ -190,7 +190,7 @@ class Args(object):
                 print(f"{APP_NAME} {APP_VERSION}")
                 sys.exit(0)
 
-            log.info("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
 
@@ -198,12 +198,12 @@ class Args(object):
             initfile = os.path.expanduser(os.path.join(Globals.root, Globals.initfile))
             legacyinit = os.path.expanduser("~/.invokeai")
             if os.path.exists(initfile):
-                log.info(
+                logger.info(
                     f"Initialization file {initfile} found. Loading...",
                 )
                 sysargs.insert(0, f"@{initfile}")
             elif os.path.exists(legacyinit):
-                log.warning(
+                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}")
@@ -214,7 +214,7 @@ class Args(object):
             self._arg_switches = self._arg_parser.parse_args(sysargs)
             return self._arg_switches
         except Exception as e:
-            log.error(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):
-    log.warning("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
 
-        log.error("Could not read metadata")
+        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 dfec9df59f..4f3df60f1c 100644
--- a/invokeai/backend/generate.py
+++ b/invokeai/backend/generate.py
@@ -27,7 +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 log
+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
@@ -196,12 +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())
-        log.info(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")
-            log.warning("Please remove deprecated --full_precision / -F")
-            log.warning("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)
@@ -209,13 +209,13 @@ class Generate:
 
         if is_xformers_available():
             if torch.cuda.is_available() and not Globals.disable_xformers:
-                log.info("xformers memory-efficient attention is available and enabled")
+                logger.info("xformers memory-efficient attention is available and enabled")
             else:
-                log.info(
+                logger.info(
                     "xformers memory-efficient attention is available but disabled"
                 )
         else:
-            log.info("xformers not installed")
+            logger.info("xformers not installed")
 
         # model caching system for fast switching
         self.model_manager = ModelManager(
@@ -230,7 +230,7 @@ class Generate:
         fallback = self.model_manager.default_model() or FALLBACK_MODEL_NAME
         model = model or fallback
         if not self.model_manager.valid_model(model):
-            log.warning(
+            logger.warning(
                 f'"{model}" is not a known model name; falling back to {fallback}.'
             )
             model = None
@@ -247,10 +247,10 @@ class Generate:
 
         # load safety checker if requested
         if safety_checker:
-            log.info("Initializing NSFW checker")
+            logger.info("Initializing NSFW checker")
             self.safety_checker = SafetyChecker(self.device)
         else:
-            log.info("NSFW checker is disabled")
+            logger.info("NSFW checker is disabled")
 
     def prompt2png(self, prompt, outdir, **kwargs):
         """
@@ -568,7 +568,7 @@ class Generate:
             self.clear_cuda_cache()
 
             if catch_interrupts:
-                log.warning("Interrupted** Partial results will be returned.")
+                logger.warning("Interrupted** Partial results will be returned.")
             else:
                 raise KeyboardInterrupt
         except RuntimeError:
@@ -576,11 +576,11 @@ class Generate:
             self.clear_cuda_cache()
 
             print(traceback.format_exc(), file=sys.stderr)
-            log.info("Could not generate image.")
+            logger.info("Could not generate image.")
 
         toc = time.time()
-        log.info("Usage stats:")
-        log.info(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
 
@@ -610,14 +610,14 @@ class Generate:
     def print_cuda_stats(self):
         if self._has_cuda():
             self.gather_cuda_stats()
-            log.info(
+            logger.info(
                 "Max VRAM used for this generation: "+
                 "%4.2fG. " % (self.max_memory_allocated / 1e9)+
                 "Current VRAM utilization: "+
                 "%4.2fG" % (self.memory_allocated / 1e9)
             )
 
-            log.info(
+            logger.info(
                 "Max VRAM used since script start: " +
                 "%4.2fG" % (self.session_peakmem / 1e9)
             )
@@ -648,7 +648,7 @@ class Generate:
             seed = random.randrange(0, np.iinfo(np.uint32).max)
 
         prompt = opt.prompt or args.prompt or ""
-        log.info(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
@@ -697,7 +697,7 @@ class Generate:
                 try:
                     extend_instructions[direction] = int(pixels)
                 except ValueError:
-                    log.warning(
+                    logger.warning(
                         'invalid extension instruction. Use <directions> <pixels>..., as in "top 64 left 128 right 64 bottom 64"'
                     )
 
@@ -721,7 +721,7 @@ class Generate:
             # fetch the metadata from the image
             generator = self.select_generator(embiggen=True)
             opt.strength = opt.embiggen_strength or 0.40
-            log.info(
+            logger.info(
                 f"Setting img2img strength to {opt.strength} for happy embiggening"
             )
             generator.generate(
@@ -749,12 +749,12 @@ class Generate:
             return restorer.process(opt, args, image_callback=callback, prefix=prefix)
 
         elif tool is None:
-            log.warning(
+            logger.warning(
                 "please provide at least one postprocessing option, such as -G or -U"
             )
             return None
         else:
-            log.warning(f"postprocessing tool {tool} is not yet supported")
+            logger.warning(f"postprocessing tool {tool} is not yet supported")
             return None
 
     def select_generator(
@@ -798,7 +798,7 @@ class Generate:
         image = self._load_img(img)
 
         if image.width < self.width and image.height < self.height:
-            log.warning(
+            logger.warning(
                 f"img2img and inpainting may produce unexpected results with initial images smaller than {self.width}x{self.height} in both dimensions"
             )
 
@@ -810,7 +810,7 @@ class Generate:
         if (image.width * image.height) > (
             self.width * self.height
         ) and self.size_matters:
-            log.info(
+            logger.info(
                 "This input is larger than your defaults. If you run out of memory, please use a smaller image."
             )
             self.size_matters = False
@@ -892,11 +892,11 @@ class Generate:
         try:
             model_data = cache.get_model(model_name)
         except Exception as e:
-            log.warning(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
-            log.warning("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
@@ -963,14 +963,14 @@ class Generate:
                     if self.gfpgan is not None or self.codeformer is not None:
                         if facetool == "gfpgan":
                             if self.gfpgan is None:
-                                log.info(
+                                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:
-                                log.info(
+                                logger.info(
                                     "CodeFormer not found. Face restoration is disabled."
                                 )
                             else:
@@ -985,7 +985,7 @@ class Generate:
                                     fidelity=codeformer_fidelity,
                                 )
                     else:
-                        log.info("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:
@@ -998,9 +998,9 @@ class Generate:
                             denoise_str=upscale_denoise_str,
                         )
                     else:
-                        log.info("ESRGAN is disabled. Image not upscaled.")
+                        logger.info("ESRGAN is disabled. Image not upscaled.")
             except Exception as e:
-                log.info(
+                logger.info(
                     f"Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
                 )
 
@@ -1077,7 +1077,7 @@ class Generate:
             )
             self.sampler = default
 
-        log.info(msg)
+        logger.info(msg)
 
         if not hasattr(self.sampler, "uses_inpainting_model"):
             # FIXME: terrible kludge!
@@ -1086,17 +1086,17 @@ class Generate:
     def _load_img(self, img) -> Image:
         if isinstance(img, Image.Image):
             image = img
-            log.info(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"
 
             image = Image.open(img)
-            log.info(
+            logger.info(
                 f"loaded input image of size {image.width}x{image.height} from {img}"
             )
         else:
             image = Image.open(img)
-            log.info(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
 
@@ -1184,11 +1184,11 @@ class Generate:
 
     def _transparency_check_and_warning(self, image, mask, force_outpaint=False):
         if not mask:
-           log.info(
+           logger.info(
                 "Initial image has transparent areas. Will inpaint in these regions."
             )
            if (not force_outpaint) and self._check_for_erasure(image):
-                log.info(
+                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)."
@@ -1202,10 +1202,10 @@ class Generate:
 
     def _fit_image(self, image, max_dimensions):
         w, h = max_dimensions
-        log.info(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)
-        log.info(
+        logger.info(
             f"after adjusting image dimensions to be multiples of 64, init image is {image.width}x{image.height}"
         )
         return image
@@ -1217,7 +1217,7 @@ class Generate:
         )  # resize to integer multiple of 64
         if h != height or w != width:
             if log:
-                log.info(
+                logger.info(
                     f"Provided width and height must be multiples of 64. Auto-resizing to {w}x{h}"
                 )
             height = h
diff --git a/invokeai/backend/generator/base.py b/invokeai/backend/generator/base.py
index 00ee61af2d..8ad9dec026 100644
--- a/invokeai/backend/generator/base.py
+++ b/invokeai/backend/generator/base.py
@@ -25,7 +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 log
+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
@@ -373,7 +373,7 @@ class Generator:
                     try:
                         x_T = self.get_noise(width, height)
                     except:
-                        log.error("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.
@@ -608,7 +608,7 @@ class Generator:
         image = self.sample_to_image(sample)
         dirname = os.path.dirname(filepath) or "."
         if not os.path.exists(dirname):
-            log.info(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 4ac72016b9..6eae5732b0 100644
--- a/invokeai/backend/generator/embiggen.py
+++ b/invokeai/backend/generator/embiggen.py
@@ -8,7 +8,7 @@ import torch
 from PIL import Image
 from tqdm import trange
 
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 
 from .base import Generator
 from .img2img import Img2Img
@@ -73,21 +73,21 @@ class Embiggen(Generator):
             embiggen = [1.0]  # If not specified, assume no scaling
         elif embiggen[0] < 0:
             embiggen[0] = 1.0
-            log.warning(
+            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
-            log.warning(
+            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
-            log.warning(
+            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 !"
             )
 
@@ -98,7 +98,7 @@ class Embiggen(Generator):
             embiggen_tiles.sort()
 
         if strength >= 0.5:
-            log.warning(
+            logger.warning(
                 f"Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45."
             )
 
@@ -122,7 +122,7 @@ class Embiggen(Generator):
                 from ..restoration.realesrgan import ESRGAN
 
                 esrgan = ESRGAN()
-                log.info(
+                logger.info(
                     f"ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}"
                 )
                 if embiggen[0] > 2:
@@ -313,9 +313,9 @@ class Embiggen(Generator):
         def make_image():
             # Make main tiles -------------------------------------------------
             if embiggen_tiles:
-                log.info(f"Making {len(embiggen_tiles)} Embiggen tiles...")
+                logger.info(f"Making {len(embiggen_tiles)} Embiggen tiles...")
             else:
-                log.info(
+                logger.info(
                     f"Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})..."
                 )
 
@@ -362,11 +362,11 @@ class Embiggen(Generator):
                 # newinitimage.save(newinitimagepath)
 
                 if embiggen_tiles:
-                    log.debug(
+                    logger.debug(
                         f"Making tile #{tile + 1} ({embiggen_tiles.index(tile) + 1} of {len(embiggen_tiles)} requested)"
                     )
                 else:
-                    log.debug(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
@@ -548,7 +548,7 @@ class Embiggen(Generator):
                     # Layer tile onto final image
                     outputsuperimage.alpha_composite(intileimage, (left, top))
             else:
-                log.error(
+                logger.error(
                     "Could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation."
                 )
 
diff --git a/invokeai/backend/generator/txt2img2img.py b/invokeai/backend/generator/txt2img2img.py
index 2dfd2c79e9..1257a44fb1 100644
--- a/invokeai/backend/generator/txt2img2img.py
+++ b/invokeai/backend/generator/txt2img2img.py
@@ -14,7 +14,7 @@ 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 log
+import invokeai.backend.util.logging as logger
 
 class Txt2Img2Img(Generator):
     def __init__(self, model, precision):
@@ -79,7 +79,7 @@ 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
-            log.info(
+            logger.info(
                 f"Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
             )
 
diff --git a/invokeai/backend/image_util/patchmatch.py b/invokeai/backend/image_util/patchmatch.py
index 3989786c58..5b5dd75f68 100644
--- a/invokeai/backend/image_util/patchmatch.py
+++ b/invokeai/backend/image_util/patchmatch.py
@@ -5,7 +5,7 @@ wraps the actual patchmatch object. It respects the global
 be suppressed or deferred
 """
 import numpy as np
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
 class PatchMatch:
@@ -27,12 +27,12 @@ class PatchMatch:
             from patchmatch import patch_match as pm
 
             if pm.patchmatch_available:
-                log.info("Patchmatch initialized")
+                logger.info("Patchmatch initialized")
             else:
-                log.info("Patchmatch not loaded (nonfatal)")
+                logger.info("Patchmatch not loaded (nonfatal)")
             self.patch_match = pm
         else:
-            log.info("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 a82e56ffd7..248f19d81d 100644
--- a/invokeai/backend/image_util/txt2mask.py
+++ b/invokeai/backend/image_util/txt2mask.py
@@ -32,7 +32,7 @@ import torch
 from PIL import Image, ImageOps
 from transformers import AutoProcessor, CLIPSegForImageSegmentation
 
-import invokeai.backend.util.logging as log
+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):
-        log.info("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
diff --git a/invokeai/backend/model_management/convert_ckpt_to_diffusers.py b/invokeai/backend/model_management/convert_ckpt_to_diffusers.py
index d08f146d8e..8aec5a01d9 100644
--- a/invokeai/backend/model_management/convert_ckpt_to_diffusers.py
+++ b/invokeai/backend/model_management/convert_ckpt_to_diffusers.py
@@ -25,7 +25,7 @@ from typing import Union
 import torch
 from safetensors.torch import load_file
 
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import global_cache_dir, global_config_dir
 
 from .model_manager import ModelManager, SDLegacyType
@@ -373,9 +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:
-        log.debug(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:
-            log.debug("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:])
@@ -393,7 +393,7 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
                             key
                         )
         else:
-            log.debug(
+            logger.debug(
                 "Extracting only the non-EMA weights (usually better for fine-tuning)"
             )
 
@@ -1116,7 +1116,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         if "global_step" in checkpoint:
             global_step = checkpoint["global_step"]
         else:
-            log.debug("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
@@ -1230,15 +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:
-            log.debug(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:
-            log.debug("Using checkpoint model's original VAE")
+            logger.debug("Using checkpoint model's original VAE")
 
         if vae:
-            log.debug("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 f10cd8c4f5..3f921828d5 100644
--- a/invokeai/backend/model_management/model_manager.py
+++ b/invokeai/backend/model_management/model_manager.py
@@ -24,7 +24,7 @@ import safetensors
 import safetensors.torch
 import torch
 import transformers
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from diffusers import (
     AutoencoderKL,
     UNet2DConditionModel,
@@ -133,7 +133,7 @@ class ModelManager(object):
             )
 
         if not self.valid_model(model_name):
-            log.error(
+            logger.error(
                 f'"{model_name}" is not a known model name. Please check your models.yaml file'
             )
             return self.current_model
@@ -145,7 +145,7 @@ class ModelManager(object):
 
         if model_name in self.models:
             requested_model = self.models[model_name]["model"]
-            log.info(f"Retrieving model {model_name} from system RAM cache")
+            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"]
@@ -380,7 +380,7 @@ class ModelManager(object):
         """
         omega = self.config
         if model_name not in omega:
-            log.error(f"Unknown model {model_name}")
+            logger.error(f"Unknown model {model_name}")
             return
         # save these for use in deletion later
         conf = omega[model_name]
@@ -393,13 +393,13 @@ class ModelManager(object):
             self.stack.remove(model_name)
         if delete_files:
             if weights:
-                log.info(f"Deleting file {weights}")
+                logger.info(f"Deleting file {weights}")
                 Path(weights).unlink(missing_ok=True)
             elif path:
-                log.info(f"Deleting directory {path}")
+                logger.info(f"Deleting directory {path}")
                 rmtree(path, ignore_errors=True)
             elif repo_id:
-                log.info(f"Deleting the cached model directory for {repo_id}")
+                logger.info(f"Deleting the cached model directory for {repo_id}")
                 self._delete_model_from_cache(repo_id)
 
     def add_model(
@@ -440,7 +440,7 @@ 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:
-            log.error(
+            logger.error(
                 f'"{model_name}" is not a known model name. Please check your models.yaml file'
             )
             return
@@ -458,7 +458,7 @@ class ModelManager(object):
         model_format = mconfig.get("format", "ckpt")
         if model_format == "ckpt":
             weights = mconfig.weights
-            log.info(f"Loading {model_name} from {weights}")
+            logger.info(f"Loading {model_name} from {weights}")
             model, width, height, model_hash = self._load_ckpt_model(
                 model_name, mconfig
             )
@@ -474,13 +474,13 @@ class ModelManager(object):
 
         # usage statistics
         toc = time.time()
-        log.info("Model loaded in " + "%4.2fs" % (toc - tic))
+        logger.info("Model loaded in " + "%4.2fs" % (toc - tic))
         if self._has_cuda():
-            log.info(
+            logger.info(
                 "Max VRAM used to load the model: "+
                 "%4.2fG" % (torch.cuda.max_memory_allocated() / 1e9)
             )
-            log.info(
+            logger.info(
                 "Current VRAM usage: "+
                 "%4.2fG" % (torch.cuda.memory_allocated() / 1e9)
             )
@@ -490,11 +490,11 @@ class ModelManager(object):
         name_or_path = self.model_name_or_path(mconfig)
         using_fp16 = self.precision == "float16"
 
-        log.info(f"Loading diffusers model from {name_or_path}")
+        logger.info(f"Loading diffusers model from {name_or_path}")
         if using_fp16:
-            log.debug("Using faster float16 precision")
+            logger.debug("Using faster float16 precision")
         else:
-            log.debug("Using more accurate float32 precision")
+            logger.debug("Using more accurate float32 precision")
 
         # TODO: scan weights maybe?
         pipeline_args: dict[str, Any] = dict(
@@ -526,7 +526,7 @@ class ModelManager(object):
                 if str(e).startswith("fp16 is not a valid"):
                     pass
                 else:
-                    log.error(
+                    logger.error(
                         f"An unexpected error occurred while downloading the model: {e})"
                     )
             if pipeline:
@@ -545,7 +545,7 @@ class ModelManager(object):
         # square images???
         width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor
         height = width
-        log.debug(f"Default image dimensions = {width} x {height}")
+        logger.debug(f"Default image dimensions = {width} x {height}")
 
         return pipeline, width, height, model_hash
 
@@ -562,7 +562,7 @@ class ModelManager(object):
             weights = os.path.normpath(os.path.join(Globals.root, weights))
 
         # Convert to diffusers and return a diffusers pipeline
-        log.info(f"Converting legacy checkpoint {model_name} into a diffusers model...")
+        logger.info(f"Converting legacy checkpoint {model_name} into a diffusers model...")
 
         from . import load_pipeline_from_original_stable_diffusion_ckpt
 
@@ -627,7 +627,7 @@ class ModelManager(object):
         if model_name not in self.models:
             return
 
-        log.info(f"Offloading {model_name} to CPU")
+        logger.info(f"Offloading {model_name} to CPU")
         model = self.models[model_name]["model"]
         model.offload_all()
         self.current_model = None
@@ -643,26 +643,26 @@ class ModelManager(object):
         and option to exit if an infected file is identified.
         """
         # scan model
-        log.debug(f"Scanning Model: {model_name}")
+        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:
-                log.critical(f"Issues Found In Model: {scan_result.issues_count}")
-                log.critical("The model you are trying to load seems to be infected.")
-                log.critical("For your safety, InvokeAI will not load this model.")
-                log.critical("Please use checkpoints from trusted sources.")
-                log.critical("Exiting InvokeAI")
+                logger.critical(f"Issues Found In Model: {scan_result.issues_count}")
+                logger.critical("The model you are trying to load seems to be infected.")
+                logger.critical("For your safety, InvokeAI will not load this model.")
+                logger.critical("Please use checkpoints from trusted sources.")
+                logger.critical("Exiting InvokeAI")
                 sys.exit()
             else:
-                log.warning("InvokeAI was unable to scan the model you are using.")
+                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":
-                    log.critical("Exiting InvokeAI")
+                    logger.critical("Exiting InvokeAI")
                     sys.exit()
         else:
-            log.debug("Model scanned ok")
+            logger.debug("Model scanned ok")
 
     def import_diffuser_model(
         self,
@@ -779,24 +779,24 @@ class ModelManager(object):
         model_path: Path = None
         thing = path_url_or_repo  # to save typing
 
-        log.info(f"Probing {thing} for import")
+        logger.info(f"Probing {thing} for import")
 
         if thing.startswith(("http:", "https:", "ftp:")):
-            log.info(f"{thing} appears to be a URL")
+            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"]:
-                log.debug(f"{Path(thing).name} appears to be part of a diffusers model. Skipping import")
+                logger.debug(f"{Path(thing).name} appears to be part of a diffusers model. Skipping import")
                 return
             else:
-                log.debug(f"{thing} appears to be a checkpoint file on disk")
+                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():
-            log.debug(f"{thing} appears to be a diffusers file on disk")
+            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"),
@@ -807,30 +807,30 @@ class ModelManager(object):
 
         elif Path(thing).is_dir():
             if (Path(thing) / "model_index.json").exists():
-                log.debug(f"{thing} appears to be a diffusers model.")
+                logger.debug(f"{thing} appears to be a diffusers model.")
                 model_name = self.import_diffuser_model(
                     thing, commit_to_conf=commit_to_conf
                 )
             else:
-                log.debug(f"{thing} appears to be a directory. Will scan for models to import")
+                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
                     ):
-                        log.info(f"{model_name} successfully imported")
+                        logger.info(f"{model_name} successfully imported")
                 return model_name
 
         elif re.match(r"^[\w.+-]+/[\w.+-]+$", thing):
-            log.debug(f"{thing} appears to be a HuggingFace diffusers repo_id")
+            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:
-            log.warning(f"{thing}: Unknown thing. Please provide a URL, file path, directory or HuggingFace repo_id")
+            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
@@ -838,7 +838,7 @@ class ModelManager(object):
             return
 
         if model_path.stem in self.config:  # already imported
-            log.debug("Already imported. Skipping")
+            logger.debug("Already imported. Skipping")
             return model_path.stem
 
         # another round of heuristics to guess the correct config file.
@@ -854,38 +854,38 @@ 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")
-                log.debug(f"Using config file {model_config_file.name}")
+                logger.debug(f"Using config file {model_config_file.name}")
 
             else:
                 model_type = self.probe_model_type(checkpoint)
                 if model_type == SDLegacyType.V1:
-                    log.debug("SD-v1 model detected")
+                    logger.debug("SD-v1 model detected")
                     model_config_file = Path(
                         Globals.root, "configs/stable-diffusion/v1-inference.yaml"
                     )
                 elif model_type == SDLegacyType.V1_INPAINT:
-                    log.debug("SD-v1 inpainting model detected")
+                    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:
-                    log.debug("SD-v2-v model detected")
+                    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:
-                    log.debug("SD-v2-e model detected")
+                    logger.debug("SD-v2-e model detected")
                     model_config_file = Path(
                         Globals.root, "configs/stable-diffusion/v2-inference.yaml"
                     )
                 elif model_type == SDLegacyType.V2:
-                    log.warning(
+                    logger.warning(
                         f"{thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide configuration file path."
                     )
                     return
                 else:
-                    log.warning(
+                    logger.warning(
                         f"{thing} is a legacy checkpoint file but not a known Stable Diffusion model. Please provide configuration file path."
                     )
                     return
@@ -902,7 +902,7 @@ 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}")
-                log.debug(f"Using VAE file {vae_path.name}")
+                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(
@@ -948,14 +948,14 @@ class ModelManager(object):
         from . import convert_ckpt_to_diffusers
 
         if diffusers_path.exists():
-            log.error(
+            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}"
-        log.debug(f"Converting {model_name} to diffusers (30-60s)")
+        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
@@ -972,10 +972,10 @@ class ModelManager(object):
                 vae_path=vae_path,
                 scan_needed=scan_needed,
             )
-            log.debug(
+            logger.debug(
                 f"Success. Converted model is now located at {str(diffusers_path)}"
             )
-            log.debug(f"Writing new config file entry for {model_name}")
+            logger.debug(f"Writing new config file entry for {model_name}")
             new_config = dict(
                 path=str(diffusers_path),
                 description=model_description,
@@ -986,17 +986,17 @@ class ModelManager(object):
             self.add_model(model_name, new_config, True)
             if commit_to_conf:
                 self.commit(commit_to_conf)
-            log.debug("Conversion succeeded")
+            logger.debug("Conversion succeeded")
         except Exception as e:
-            log.warning(f"Conversion failed: {str(e)}")
-            log.warning(
+            logger.warning(f"Conversion failed: {str(e)}")
+            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):
-        log.info(f"Finding Models In: {search_folder}")
+        logger.info(f"Finding Models In: {search_folder}")
         models_folder_ckpt = Path(search_folder).glob("**/*.ckpt")
         models_folder_safetensors = Path(search_folder).glob("**/*.safetensors")
 
@@ -1020,7 +1020,7 @@ class ModelManager(object):
         num_loaded_models = len(self.models)
         if num_loaded_models >= self.max_loaded_models:
             least_recent_model = self._pop_oldest_model()
-            log.info(
+            logger.info(
                 f"Cache limit (max={self.max_loaded_models}) reached. Purging {least_recent_model}"
             )
             if least_recent_model is not None:
@@ -1029,7 +1029,7 @@ class ModelManager(object):
 
     def print_vram_usage(self) -> None:
         if self._has_cuda:
-            log.info(
+            logger.info(
                 "Current VRAM usage:"+
                 "%4.2fG" % (torch.cuda.memory_allocated() / 1e9),
             )
@@ -1119,10 +1119,10 @@ class ModelManager(object):
             dest = hub / model.stem
             if dest.exists() and not source.exists():
                 continue
-            log.info(f"{source} => {dest}")
+            logger.info(f"{source} => {dest}")
             if source.exists():
                 if dest.is_symlink():
-                    log.warning(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)
@@ -1139,7 +1139,7 @@ class ModelManager(object):
         ]
         for d in empty:
             os.rmdir(d)
-        log.info("Migration is done. Continuing...")
+        logger.info("Migration is done. Continuing...")
 
     def _resolve_path(
         self, source: Union[str, Path], dest_directory: str
@@ -1182,14 +1182,14 @@ class ModelManager(object):
 
     def _add_embeddings_to_model(self, model: StableDiffusionGeneratorPipeline):
         if self.embedding_path is not None:
-            log.info(f"Loading embeddings from {self.embedding_path}")
+            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
                     )
-            log.info(
+            logger.info(
                 f'Textual inversion triggers: {", ".join(sorted(model.textual_inversion_manager.get_all_trigger_strings()))}'
             )
 
@@ -1212,7 +1212,7 @@ class ModelManager(object):
             with open(hashpath) as f:
                 hash = f.read()
             return hash
-        log.debug("Calculating sha256 hash of model files")
+        logger.debug("Calculating sha256 hash of model files")
         tic = time.time()
         sha = hashlib.sha256()
         count = 0
@@ -1224,7 +1224,7 @@ class ModelManager(object):
                         sha.update(chunk)
         hash = sha.hexdigest()
         toc = time.time()
-        log.debug(f"sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
+        logger.debug(f"sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
         with open(hashpath, "w") as f:
             f.write(hash)
         return hash
@@ -1242,13 +1242,13 @@ class ModelManager(object):
                 hash = f.read()
             return hash
 
-        log.debug("Calculating sha256 hash of weights file")
+        logger.debug("Calculating sha256 hash of weights file")
         tic = time.time()
         sha = hashlib.sha256()
         sha.update(data)
         hash = sha.hexdigest()
         toc = time.time()
-        log.debug(f"sha256 = {hash} "+"(%4.2fs)" % (toc - tic))
+        logger.debug(f"sha256 = {hash} "+"(%4.2fs)" % (toc - tic))
 
         with open(hashpath, "w") as f:
             f.write(hash)
@@ -1269,12 +1269,12 @@ class ModelManager(object):
             local_files_only=not Globals.internet_available,
         )
 
-        log.debug(f"Loading diffusers VAE from {name_or_path}")
+        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:
-            log.debug("Using more accurate float32 precision")
+            logger.debug("Using more accurate float32 precision")
             fp_args_list = [{}]
 
         vae = None
@@ -1298,7 +1298,7 @@ class ModelManager(object):
                 break
 
         if not vae and deferred_error:
-            log.warning(f"Could not load VAE {name_or_path}: {str(deferred_error)}")
+            logger.warning(f"Could not load VAE {name_or_path}: {str(deferred_error)}")
 
         return vae
 
@@ -1314,7 +1314,7 @@ class ModelManager(object):
                 for revision in repo.revisions:
                     hashes_to_delete.add(revision.commit_hash)
         strategy = cache_info.delete_revisions(*hashes_to_delete)
-        log.warning(
+        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 355cba2a6f..d9130ace04 100644
--- a/invokeai/backend/prompting/conditioning.py
+++ b/invokeai/backend/prompting/conditioning.py
@@ -18,7 +18,7 @@ from compel.prompt_parser import (
     PromptParser,
 )
 
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
 from ..stable_diffusion import InvokeAIDiffuserComponent
@@ -163,8 +163,8 @@ def log_tokenization(
     negative_prompt: Union[Blend, FlattenedPrompt],
     tokenizer,
 ):
-    log.info(f"[TOKENLOG] Parsed Prompt: {positive_prompt}")
-    log.info(f"[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(
@@ -238,12 +238,12 @@ def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_t
             usedTokens += 1
 
     if usedTokens > 0:
-        log.info(f'[TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
-        log.debug(f"{tokenized}\x1b[0m")
+        logger.info(f'[TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
+        logger.debug(f"{tokenized}\x1b[0m")
 
     if discarded != "":
-        log.info(f"[TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
-        log.debug(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]:
@@ -296,7 +296,7 @@ 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:
-        log.warning(
+        logger.warning(
             "Subprompt weights add up to zero. Discarding and using even weights instead."
         )
         equal_weight = 1 / max(len(parsed_prompts), 1)
diff --git a/invokeai/backend/restoration/base.py b/invokeai/backend/restoration/base.py
index 794d7502da..f6f01da17d 100644
--- a/invokeai/backend/restoration/base.py
+++ b/invokeai/backend/restoration/base.py
@@ -1,4 +1,4 @@
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 
 class Restoration:
     def __init__(self) -> None:
@@ -10,17 +10,17 @@ class Restoration:
         # Load GFPGAN
         gfpgan = self.load_gfpgan(gfpgan_model_path)
         if gfpgan.gfpgan_model_exists:
-            log.info("GFPGAN Initialized")
+            logger.info("GFPGAN Initialized")
         else:
-            log.info("GFPGAN Disabled")
+            logger.info("GFPGAN Disabled")
             gfpgan = None
 
         # Load CodeFormer
         codeformer = self.load_codeformer()
         if codeformer.codeformer_model_exists:
-            log.info("CodeFormer Initialized")
+            logger.info("CodeFormer Initialized")
         else:
-            log.info("CodeFormer Disabled")
+            logger.info("CodeFormer Disabled")
             codeformer = None
 
         return gfpgan, codeformer
@@ -41,5 +41,5 @@ class Restoration:
         from .realesrgan import ESRGAN
 
         esrgan = ESRGAN(esrgan_bg_tile)
-        log.info("ESRGAN Initialized")
+        logger.info("ESRGAN Initialized")
         return esrgan
diff --git a/invokeai/backend/restoration/codeformer.py b/invokeai/backend/restoration/codeformer.py
index db2a4fb1dd..5b578af082 100644
--- a/invokeai/backend/restoration/codeformer.py
+++ b/invokeai/backend/restoration/codeformer.py
@@ -5,7 +5,7 @@ import warnings
 import numpy as np
 import torch
 
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from ..globals import Globals
 
 pretrained_model_url = (
@@ -24,12 +24,12 @@ class CodeFormerRestoration:
         self.codeformer_model_exists = os.path.isfile(self.model_path)
 
         if not self.codeformer_model_exists:
-            log.error("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:
-            log.info(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)
@@ -98,7 +98,7 @@ class CodeFormerRestoration:
                     del output
                     torch.cuda.empty_cache()
                 except RuntimeError as error:
-                    log.error(f"Failed 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 adcab2424c..b5c0278362 100644
--- a/invokeai/backend/restoration/gfpgan.py
+++ b/invokeai/backend/restoration/gfpgan.py
@@ -6,7 +6,7 @@ import numpy as np
 import torch
 from PIL import Image
 
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
 class GFPGAN:
@@ -19,7 +19,7 @@ class GFPGAN:
         self.gfpgan_model_exists = os.path.isfile(self.model_path)
 
         if not self.gfpgan_model_exists:
-            log.error("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:
-            log.info(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)
@@ -47,13 +47,13 @@ class GFPGAN:
             except Exception:
                 import traceback
 
-                log.error("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:
-            log.warning("WARNING: GFPGAN not initialized.")
-            log.warning(
+            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}"
             )
 
diff --git a/invokeai/backend/restoration/outcrop.py b/invokeai/backend/restoration/outcrop.py
index bc508b01ea..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 log
+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
-            log.info(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 15afb1f92f..9f26cc63ac 100644
--- a/invokeai/backend/restoration/realesrgan.py
+++ b/invokeai/backend/restoration/realesrgan.py
@@ -6,7 +6,7 @@ import torch
 from PIL import Image
 from PIL.Image import Image as ImageType
 
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
 class ESRGAN:
@@ -69,15 +69,15 @@ class ESRGAN:
                 import sys
                 import traceback
 
-                log.error("Error loading Real-ESRGAN:")
+                logger.error("Error loading Real-ESRGAN:")
                 print(traceback.format_exc(), file=sys.stderr)
 
         if upsampler_scale == 0:
-            log.warning("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:
-            log.info(
+            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
diff --git a/invokeai/backend/safety_checker.py b/invokeai/backend/safety_checker.py
index 97e3387fb4..3003981888 100644
--- a/invokeai/backend/safety_checker.py
+++ b/invokeai/backend/safety_checker.py
@@ -14,7 +14,7 @@ from PIL import Image, ImageFilter
 from transformers import AutoFeatureExtractor
 
 import invokeai.assets.web as web_assets
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from .globals import global_cache_dir
 from .util import CPU_DEVICE
 
@@ -41,7 +41,7 @@ class SafetyChecker(object):
                 cache_dir=safety_model_path,
             )
         except Exception:
-            log.error(
+            logger.error(
                 "An error was encountered while installing the safety checker:"
             )
             print(traceback.format_exc())
@@ -66,7 +66,7 @@ class SafetyChecker(object):
         )
         self.safety_checker.to(CPU_DEVICE) # offload
         if has_nsfw_concept[0]:
-            log.warning(
+            logger.warning(
                 "An image with potential non-safe content has been detected. A blurred image will be returned."
             )
             return self.blur(image)
diff --git a/invokeai/backend/stable_diffusion/concepts_lib.py b/invokeai/backend/stable_diffusion/concepts_lib.py
index 85b3d81287..ebbcc9c3e9 100644
--- a/invokeai/backend/stable_diffusion/concepts_lib.py
+++ b/invokeai/backend/stable_diffusion/concepts_lib.py
@@ -17,7 +17,7 @@ from huggingface_hub import (
     hf_hub_url,
 )
 
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
 
@@ -67,10 +67,10 @@ 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:
-                log.warning(
+                logger.warning(
                     f"Hugging Face textual inversion concepts libraries could not be loaded. The error was {str(e)}."
                 )
-                log.warning(
+                logger.warning(
                     "You may load .bin and .pt file(s) manually using the --embedding_directory argument."
                 )
             return self.concept_list
@@ -84,7 +84,7 @@ class HuggingFaceConceptsLibrary(object):
         be downloaded.
         """
         if not concept_name in self.list_concepts():
-            log.warning(
+            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
@@ -222,7 +222,7 @@ class HuggingFaceConceptsLibrary(object):
             if chunk == 0:
                 bytes += total
 
-        log.info(f"Downloading {repo_id}...", end="")
+        logger.info(f"Downloading {repo_id}...", end="")
         try:
             for file in (
                 "README.md",
@@ -236,22 +236,22 @@ class HuggingFaceConceptsLibrary(object):
                 )
         except ul_error.HTTPError as e:
             if e.code == 404:
-                log.warning(
+                logger.warning(
                     f"Concept {concept_name} is not known to the Hugging Face library. Generation will continue without the concept."
                 )
             else:
-                log.warning(
+                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:
-            log.error(
+            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
-        log.info("...{:.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 1959269711..dfd19ea964 100644
--- a/invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py
+++ b/invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py
@@ -13,7 +13,7 @@ from compel.cross_attention_control import Arguments
 from diffusers.models.attention_processor import AttentionProcessor
 from torch import nn
 
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from ...util import torch_dtype
 
 class CrossAttentionType(enum.Enum):
@@ -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.
-        log.error(
+        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 827e114be8..b0c85e9fd3 100644
--- a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
+++ b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
@@ -8,7 +8,7 @@ import torch
 from diffusers.models.attention_processor import AttentionProcessor
 from typing_extensions import TypeAlias
 
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
 from .cross_attention_control import (
@@ -467,13 +467,13 @@ class InvokeAIDiffuserComponent:
             outside = torch.count_nonzero(
                 (latents < -current_threshold) | (latents > current_threshold)
             )
-            log.info(
+            logger.info(
                 f"Threshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})"
                 )
-            log.debug(
+            logger.debug(
                 f"min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}"
             )
-            log.debug(   
+            logger.debug(   
                 f"{outside / latents.numel() * 100:.2f}% values outside threshold"
             )
 
@@ -501,10 +501,10 @@ class InvokeAIDiffuserComponent:
             )
 
         if self.debug_thresholding:
-            log.debug(
+            logger.debug(
                 f"min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})"
             )
-            log.debug(
+            logger.debug(
                 f"{num_altered / latents.numel() * 100:.2f}% values altered"
             )
 
diff --git a/invokeai/backend/stable_diffusion/image_degradation/utils_image.py b/invokeai/backend/stable_diffusion/image_degradation/utils_image.py
index 31f5300949..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 log
+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()
-        log.error("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 1d27c7b813..9476c12dc5 100644
--- a/invokeai/backend/stable_diffusion/textual_inversion_manager.py
+++ b/invokeai/backend/stable_diffusion/textual_inversion_manager.py
@@ -10,7 +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 log
+import invokeai.backend.util.logging as logger
 from .concepts_lib import HuggingFaceConceptsLibrary
 
 @dataclass
@@ -60,12 +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
-                log.info(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
-            log.info(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
 
@@ -86,7 +86,7 @@ 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):
-                log.warning(
+                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
@@ -106,7 +106,7 @@ class TextualInversionManager(BaseTextualInversionManager):
                     if ckpt_path.name == "learned_embeds.bin"
                     else f"<{ckpt_path.stem}>"
                 )
-                log.info(
+                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
@@ -121,8 +121,8 @@ class TextualInversionManager(BaseTextualInversionManager):
                 self.trigger_to_sourcefile[trigger_str] = sourcefile
 
             except ValueError as e:
-                log.debug(f'Ignoring incompatible embedding {embedding_info["name"]}')
-                log.debug(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
@@ -134,7 +134,7 @@ 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]:
-            log.warning(
+            logger.warning(
                 f"TextualInversionManager refusing to overwrite already-loaded token '{trigger_str}'"
             )
             return
@@ -156,10 +156,10 @@ class TextualInversionManager(BaseTextualInversionManager):
 
         except ValueError as e:
             if str(e).startswith("Warning"):
-                log.warning(f"{str(e)}")
+                logger.warning(f"{str(e)}")
             else:
                 traceback.print_exc()
-                log.error(
+                logger.error(
                     f"TextualInversionManager was unable to add a textual inversion with trigger string {trigger_str}."
                 )
                 raise
@@ -220,16 +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:
-                    log.info(
+                    logger.info(
                         f"Preparing tokens for textual inversion {ti.trigger_string}..."
                     )
                 try:
                     self._inject_tokens_and_assign_embeddings(ti)
                 except ValueError as e:
-                    log.debug(
+                    logger.debug(
                         f"Ignoring incompatible embedding trigger {ti.trigger_string}"
                     )
-                    log.debug(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)
@@ -307,16 +307,16 @@ class TextualInversionManager(BaseTextualInversionManager):
             if suffix in [".pt",".ckpt",".bin"]:
                 scan_result = scan_file_path(embedding_file)
                 if scan_result.infected_files > 0:
-                    log.critical(
+                    logger.critical(
                         f"Security Issues Found in Model: {scan_result.issues_count}"
                     )
-                    log.critical("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:
-            log.warning(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
@@ -335,7 +335,7 @@ class TextualInversionManager(BaseTextualInversionManager):
 
     def _parse_embedding_v1(self, embedding_ckpt: dict, file_path: str)->List[EmbeddingInfo]:
         basename = Path(file_path).stem
-        log.debug(f'Loading v1 embedding file: {basename}')
+        logger.debug(f'Loading v1 embedding file: {basename}')
 
         embeddings = list()
         token_counter = -1
@@ -366,7 +366,7 @@ class TextualInversionManager(BaseTextualInversionManager):
         This handles embedding .pt file variant #2.
         """
         basename = Path(file_path).stem
-        log.debug(f'Loading v2 embedding file: {basename}')
+        logger.debug(f'Loading v2 embedding file: {basename}')
         embeddings = list()
         
         if isinstance(
@@ -385,7 +385,7 @@ class TextualInversionManager(BaseTextualInversionManager):
                 )
                 embeddings.append(embedding_info)
         else:
-            log.warning(f"{basename}: Unrecognized embedding format")
+            logger.warning(f"{basename}: Unrecognized embedding format")
 
         return embeddings
 
@@ -394,7 +394,7 @@ class TextualInversionManager(BaseTextualInversionManager):
         Parse 'version 3' of the .pt textual inversion embedding files.
         """
         basename = Path(file_path).stem
-        log.debug(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}>',
@@ -412,11 +412,11 @@ class TextualInversionManager(BaseTextualInversionManager):
         basename = Path(filepath).stem
         short_path = Path(filepath).parents[0].name+'/'+Path(filepath).name
         
-        log.debug(f'Loading v4 embedding file: {short_path}')
+        logger.debug(f'Loading v4 embedding file: {short_path}')
         
         embeddings = list()
         if list(embedding_ckpt.keys()) == 0:
-            log.warning(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
index 1f820977ed..73f980aeff 100644
--- a/invokeai/backend/util/logging.py
+++ b/invokeai/backend/util/logging.py
@@ -26,7 +26,7 @@ Console messages:
 
 Another way:
 import invokeai.backend.util.logging as ialog
-ialog.debug('this is a debugging message')
+ialogger.debug('this is a debugging message')
 """
 import logging
 
diff --git a/invokeai/backend/util/util.py b/invokeai/backend/util/util.py
index 95403a57e6..edc6a7b04b 100644
--- a/invokeai/backend/util/util.py
+++ b/invokeai/backend/util/util.py
@@ -18,7 +18,7 @@ import torch
 from PIL import Image, ImageDraw, ImageFont
 from tqdm import tqdm
 
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from .devices import torch_dtype
 
 
@@ -39,7 +39,7 @@ def log_txt_as_img(wh, xc, size=10):
         try:
             draw.text((0, 0), lines, fill="black", font=font)
         except UnicodeEncodeError:
-            log.warning("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)
@@ -81,7 +81,7 @@ def mean_flat(tensor):
 def count_params(model, verbose=False):
     total_params = sum(p.numel() for p in model.parameters())
     if verbose:
-        log.debug(
+        logger.debug(
             f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params."
         )
     return total_params
@@ -133,7 +133,7 @@ def parallel_data_prefetch(
         raise ValueError("list expected but function got ndarray.")
     elif isinstance(data, abc.Iterable):
         if isinstance(data, dict):
-            log.warning(
+            logger.warning(
                 '"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
             )
             data = list(data.values())
@@ -176,7 +176,7 @@ def parallel_data_prefetch(
         processes += [p]
 
     # start processes
-    log.info("Start prefetching...")
+    logger.info("Start prefetching...")
     import time
 
     start = time.time()
@@ -195,7 +195,7 @@ def parallel_data_prefetch(
                 gather_res[res[0]] = res[1]
 
     except Exception as e:
-        log.error("Exception: ", e)
+        logger.error("Exception: ", e)
         for p in processes:
             p.terminate()
 
@@ -203,7 +203,7 @@ def parallel_data_prefetch(
     finally:
         for p in processes:
             p.join()
-        log.info(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):
@@ -319,23 +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:
-        log.warning("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:
-        log.warning(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:
-        log.warning(f"{dest}: partial file found. Resuming...")
+        logger.warning(f"{dest}: partial file found. Resuming...")
     elif resp.status_code != 200:
-        log.error(f"An error occurred during downloading {dest}: {resp.reason}")
+        logger.error(f"An error occurred during downloading {dest}: {resp.reason}")
     else:
-        log.error(f"{dest}: Downloading...")
+        logger.error(f"{dest}: Downloading...")
 
     try:
         if content_length < 2000:
-            log.error(f"ERROR DOWNLOADING {url}: {resp.text}")
+            logger.error(f"ERROR DOWNLOADING {url}: {resp.text}")
             return None
 
         with open(dest, open_mode) as file, tqdm(
@@ -350,7 +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:
-        log.error(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 e7c02d212a..84478d5cb6 100644
--- a/invokeai/backend/web/invoke_ai_web_server.py
+++ b/invokeai/backend/web/invoke_ai_web_server.py
@@ -19,7 +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 log
+import invokeai.backend.util.logging as logger
 import invokeai.frontend.web.dist as frontend
 
 from .. import Generate
@@ -214,7 +214,7 @@ class InvokeAIWebServer:
         self.load_socketio_listeners(self.socketio)
 
         if args.gui:
-            log.info("Launching Invoke AI GUI")
+            logger.info("Launching Invoke AI GUI")
             try:
                 from flaskwebgui import FlaskUI
 
@@ -232,16 +232,16 @@ class InvokeAIWebServer:
                 sys.exit(0)
         else:
             useSSL = args.certfile or args.keyfile
-            log.info("Started Invoke AI Web Server")
+            logger.info("Started Invoke AI Web Server")
             if self.host == "0.0.0.0":
-                log.info(
+                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:
-                log.info(
+                logger.info(
                     "Default host address now 127.0.0.1 (localhost). Use --host 0.0.0.0 to bind any address."
                 )
-                log.info(
+                logger.info(
                     f"Point your browser at http{'s' if useSSL else ''}://{self.host}:{self.port}"
                 )
             if not useSSL:
@@ -274,7 +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)
@@ -291,7 +291,7 @@ class InvokeAIWebServer:
     def load_socketio_listeners(self, socketio):
         @socketio.on("requestSystemConfig")
         def handle_request_capabilities():
-            log.info("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()
@@ -331,7 +331,7 @@ class InvokeAIWebServer:
                 if model_name in current_model_list:
                     update = True
 
-                log.info(f"Adding New Model: {model_name}")
+                logger.info(f"Adding New Model: {model_name}")
 
                 self.generate.model_manager.add_model(
                     model_name=model_name,
@@ -349,14 +349,14 @@ class InvokeAIWebServer:
                         "update": update,
                     },
                 )
-                log.info(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:
-                log.info(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()
@@ -367,14 +367,14 @@ class InvokeAIWebServer:
                         "model_list": updated_model_list,
                     },
                 )
-                log.info(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:
-                log.info(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:
@@ -455,7 +455,7 @@ class InvokeAIWebServer:
                         "update": True,
                     },
                 )
-                log.info(f"Model Converted: {model_name}")
+                logger.info(f"Model Converted: {model_name}")
             except Exception as e:
                 self.handle_exceptions(e)
 
@@ -491,7 +491,7 @@ class InvokeAIWebServer:
                 if vae := self.generate.model_manager.config[models_to_merge[0]].get(
                     "vae", None
                 ):
-                    log.info(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(
@@ -508,8 +508,8 @@ class InvokeAIWebServer:
                         "update": True,
                     },
                 )
-                log.info(f"Models Merged: {models_to_merge}")
-                log.info(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)
 
@@ -699,7 +699,7 @@ class InvokeAIWebServer:
                             }
                         )
                     except Exception as e:
-                        log.info(f"Unable to load {path}")
+                        logger.info(f"Unable to load {path}")
                         socketio.emit(
                             "error", {"message": f"Unable to load {path}: {str(e)}"}
                         )
@@ -736,9 +736,9 @@ class InvokeAIWebServer:
                         printable_parameters["init_mask"][:64] + "..."
                     )
 
-                log.info(f"Image Generation Parameters:\n\n{printable_parameters}\n")
-                log.info(f"ESRGAN Parameters: {esrgan_parameters}")
-                log.info(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,
@@ -751,7 +751,7 @@ class InvokeAIWebServer:
         @socketio.on("runPostprocessing")
         def handle_run_postprocessing(original_image, postprocessing_parameters):
             try:
-                log.info(
+                logger.info(
                     f'Postprocessing requested for "{original_image["url"]}": {postprocessing_parameters}'
                 )
 
@@ -862,14 +862,14 @@ class InvokeAIWebServer:
 
         @socketio.on("cancel")
         def handle_cancel():
-            log.info("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:
-                log.info(f'Delete requested "{url}"')
+                logger.info(f'Delete requested "{url}"')
                 from send2trash import send2trash
 
                 path = self.get_image_path_from_url(url)
@@ -1264,7 +1264,7 @@ class InvokeAIWebServer:
                     image, os.path.basename(path), self.thumbnail_image_path
                 )
 
-                log.info(f'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:
@@ -1330,7 +1330,7 @@ class InvokeAIWebServer:
         except Exception as e:
             # Clear the CUDA cache on an exception
             self.empty_cuda_cache()
-            log.error(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 85a4d1a37d..aa0c4bea5f 100644
--- a/invokeai/frontend/CLI/CLI.py
+++ b/invokeai/frontend/CLI/CLI.py
@@ -16,7 +16,7 @@ if sys.platform == "darwin":
 import pyparsing  # type: ignore
 
 import invokeai.version as invokeai
-import invokeai.backend.util.logging as log
+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
@@ -70,7 +70,7 @@ def main():
     # run any post-install patches needed
     run_patches()
 
-    log.info(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")
@@ -79,8 +79,8 @@ def main():
                 opt, FileNotFoundError(f"The file {config_file} could not be found.")
             )
 
-    log.info(f"{invokeai.__app_name__}, version {invokeai.__version__}")
-    log.info(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
@@ -122,7 +122,7 @@ def main():
             else:
                 raise FileNotFoundError(f"{opt.infile} not found.")
         except (FileNotFoundError, IOError) as e:
-            log.critical('Aborted',exc_info=True)
+            logger.critical('Aborted',exc_info=True)
             sys.exit(-1)
 
     # creating a Generate object:
@@ -144,11 +144,11 @@ def main():
     except (FileNotFoundError, TypeError, AssertionError) as e:
         report_model_error(opt, e)
     except (IOError, KeyError):
-        log.critical("Aborted",exc_info=True)
+        logger.critical("Aborted",exc_info=True)
         sys.exit(-1)
 
     if opt.seamless:
-        log.info("Changed to seamless tiling mode")
+        logger.info("Changed to seamless tiling mode")
 
     # preload the model
     try:
@@ -181,7 +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:
-        log.error("An error occurred",exc_info=True)
+        logger.error("An error occurred",exc_info=True)
 
 # TODO: main_loop() has gotten busy. Needs to be refactored.
 def main_loop(gen, opt):
@@ -247,7 +247,7 @@ def main_loop(gen, opt):
                 if not opt.prompt:
                     oldargs = metadata_from_png(opt.init_img)
                     opt.prompt = oldargs.prompt
-                    log.info(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
 
@@ -264,9 +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]
-                log.info(f"Reusing previous image {opt.init_img}")
+                logger.info(f"Reusing previous image {opt.init_img}")
             except IndexError:
-                log.info(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
 
@@ -287,9 +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]
-                log.info(f"Reusing previous seed {opt.seed}")
+                logger.info(f"Reusing previous seed {opt.seed}")
             except IndexError:
-                log.info(f"No previous seed at position {opt.seed} found")
+                logger.info(f"No previous seed at position {opt.seed} found")
                 opt.seed = None
                 continue
 
@@ -308,7 +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)
 
-            log.info('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):
@@ -438,13 +438,13 @@ def main_loop(gen, opt):
                         **vars(opt),
                     )
                 except (PromptParser.ParsingException, pyparsing.ParseException):
-                    log.error("An error occurred while processing your prompt",exc_info=True)
+                    logger.error("An error occurred while processing your prompt",exc_info=True)
             elif operation == "postprocess":
-                log.info(f"fixing {opt.prompt}")
+                logger.info(f"fixing {opt.prompt}")
                 opt.last_operation = do_postprocess(gen, opt, image_writer)
 
             elif operation == "mask":
-                log.info(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:
@@ -468,11 +468,11 @@ def main_loop(gen, opt):
                 results = [[path, formatted_dream_prompt]]
 
         except AssertionError:
-            log.error(e)
+            logger.error(e)
             continue
 
         except OSError as e:
-            log.error(e)
+            logger.error(e)
             continue
 
         print("Outputs:")
@@ -511,7 +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:
-            log.error(e)
+            logger.error(e)
         except Exception as e:
             report_model_error(opt, e)
         completer.add_history(command)
@@ -525,7 +525,7 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple:
     elif command.startswith("!import"):
         path = shlex.split(command)
         if len(path) < 2:
-            log.warning(
+            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:
@@ -539,7 +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:
-            log.warning("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)
@@ -551,7 +551,7 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple:
     elif command.startswith("!edit"):
         path = shlex.split(command)
         if len(path) < 2:
-            log.warning("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)
@@ -560,7 +560,7 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple:
     elif command.startswith("!del"):
         path = shlex.split(command)
         if len(path) < 2:
-            log.warning("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)
@@ -641,7 +641,7 @@ def import_model(model_path: str, gen, opt, completer):
                 default_name = url_attachment_name(model_path)
                 default_name = Path(default_name).stem
             except Exception:
-                log.warning(f"A problem occurred while assigning the name of the downloaded model",exc_info=True)
+                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,
@@ -662,11 +662,11 @@ def import_model(model_path: str, gen, opt, completer):
                 model_config_file=config_file,
             )
     if not imported_name:
-        log.error("Aborting import.")
+        logger.error("Aborting import.")
         return
 
     if not _verify_load(imported_name, gen):
-        log.error("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):
@@ -674,7 +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())
-    log.info(f"{imported_name} successfully installed")
+    logger.info(f"{imported_name} successfully installed")
 
 def _pick_configuration_file(completer)->Path:
     print(
@@ -718,21 +718,21 @@ Please select the type of this model:
     return choice
 
 def _verify_load(model_name: str, gen) -> bool:
-    log.info("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:
-        log.warning(f"model failed to load: {str(e)}")
-        log.warning(
+        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:
-        log.info("Restoring previous model")
+        logger.info("Restoring previous model")
         gen.set_model(current_model)
     return True
 
@@ -755,7 +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:
-        log.warning("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:
@@ -765,7 +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:
-            log.warning(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,
@@ -786,16 +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)
-        log.warning(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:
-        log.warning("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:
-        log.warning(f"Unknown model {model_name}")
+        logger.warning(f"Unknown model {model_name}")
         return
 
     if not click.confirm(
@@ -808,17 +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)
-    log.warning(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)):
-        log.warning(f"** Unknown model {model_name}")
+        logger.warning(f"** Unknown model {model_name}")
         return
     print()
-    log.info(f"Editing model {model_name} from configuration file {opt.conf}")
+    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():
@@ -856,7 +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())
-    log.info("Model successfully updated")
+    logger.info("Model successfully updated")
 
 
 def _get_model_name(existing_names, completer, default_name: str = "") -> str:
@@ -867,11 +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):
-            log.warning(
+            logger.warning(
                 'model name must contain only words, digits and the characters "._+:/-" **'
             )
         elif model_name != default_name and model_name in existing_names:
-            log.warning(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
@@ -938,10 +938,10 @@ def do_postprocess(gen, opt, callback):
             opt=opt,
         )
     except OSError:
-        log.error(f"{file_path}: file could not be read",exc_info=True)
+        logger.error(f"{file_path}: file could not be read",exc_info=True)
         return
     except (KeyError, AttributeError):
-        log.error(f"an error occurred while applying the {tool} postprocessor",exc_info=True)
+        logger.error(f"an error occurred while applying the {tool} postprocessor",exc_info=True)
         return
     return opt.last_operation
 
@@ -996,12 +996,12 @@ def prepare_image_metadata(
         try:
             filename = opt.fnformat.format(**wildcards)
         except KeyError as e:
-            log.error(
+            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:
-            log.error(
+            logger.error(
                 "The filename format is broken or complete. Will use '{prefix}.{seed}.png' instead"
             )
             filename = f"{prefix}.{seed}.png"
@@ -1091,14 +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:
-            log.warning(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:
-            log.warning(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])
@@ -1122,23 +1122,23 @@ def load_face_restoration(opt):
                     opt.gfpgan_model_path
                 )
             else:
-                log.info("Face restoration disabled")
+                logger.info("Face restoration disabled")
             if opt.esrgan:
                 esrgan = restoration.load_esrgan(opt.esrgan_bg_tile)
             else:
-                log.info("Upscaling disabled")
+                logger.info("Upscaling disabled")
         else:
-            log.info("Face restoration and upscaling disabled")
+            logger.info("Face restoration and upscaling disabled")
     except (ModuleNotFoundError, ImportError):
         print(traceback.format_exc(), file=sys.stderr)
-        log.info("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)
-    log.info(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
@@ -1180,11 +1180,11 @@ def retrieve_dream_command(opt, command, completer):
     try:
         cmd = dream_cmd_from_png(path)
     except OSError:
-        log.error(f"{tokens[0]}: file could not be read")
+        logger.error(f"{tokens[0]}: file could not be read")
     except (KeyError, AttributeError, IndexError):
-        log.error(f"{tokens[0]}: file has no metadata")
+        logger.error(f"{tokens[0]}: file has no metadata")
     except:
-        log.error(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)
 
@@ -1193,7 +1193,7 @@ def write_commands(opt, file_path: str, outfilepath: str):
     try:
         paths = sorted(list(Path(dir).glob(basename)))
     except ValueError:
-        log.error(f'"{basename}": unacceptable pattern')
+        logger.error(f'"{basename}": unacceptable pattern')
         return
 
     commands = []
@@ -1202,9 +1202,9 @@ def write_commands(opt, file_path: str, outfilepath: str):
         try:
             cmd = dream_cmd_from_png(path)
         except (KeyError, AttributeError, IndexError):
-            log.error(f"{path}: file has no metadata")
+            logger.error(f"{path}: file has no metadata")
         except:
-            log.error(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)
@@ -1214,17 +1214,17 @@ 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))
-        log.info(f"File {outfilepath} with commands created")
+        logger.info(f"File {outfilepath} with commands created")
 
 
 def report_model_error(opt: Namespace, e: Exception):
-    log.warning(f'An error occurred while attempting to initialize the model: "{str(e)}"')
-    log.warning(
+    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:
-        log.warning(
+        logger.warning(
             "Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
         )
     else:
@@ -1234,7 +1234,7 @@ def report_model_error(opt: Namespace, e: Exception):
         ):
             return
 
-    log.info("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
@@ -1251,7 +1251,7 @@ def report_model_error(opt: Namespace, e: Exception):
     from ..install import invokeai_configure
 
     invokeai_configure()
-    log.warning("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 12213b8b67..c12104033f 100644
--- a/invokeai/frontend/install/model_install.py
+++ b/invokeai/frontend/install/model_install.py
@@ -22,7 +22,7 @@ import torch
 from npyscreen import widget
 from omegaconf import OmegaConf
 
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals, global_config_dir
 
 from ...backend.config.model_install_backend import (
@@ -456,7 +456,7 @@ def main():
     Globals.root = os.path.expanduser(get_root(opt.root) or "")
 
     if not global_config_dir().exists():
-        log.info(
+        logger.info(
             "Your InvokeAI root directory is not set up. Calling invokeai-configure."
         )
         from invokeai.frontend.install import invokeai_configure
@@ -467,17 +467,17 @@ def main():
     try:
         select_and_download_models(opt)
     except AssertionError as e:
-        log.error(e)
+        logger.error(e)
         sys.exit(-1)
     except KeyboardInterrupt:
-        log.info("Goodbye! Come back soon.")
+        logger.info("Goodbye! Come back soon.")
     except widget.NotEnoughSpaceForWidget as e:
         if str(e).startswith("Height of 1 allocated"):
-            log.error(
+            logger.error(
                 "Insufficient vertical space for the interface. Please make your window taller and try again"
             )
         elif str(e).startswith("addwstr"):
-            log.error(
+            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 7c2b621025..524118ba7c 100644
--- a/invokeai/frontend/merge/merge_diffusers.py
+++ b/invokeai/frontend/merge/merge_diffusers.py
@@ -28,7 +28,7 @@ from ...backend.globals import (
     global_set_root,
 )
 
-import invokeai.backend.util.logging as log
+import invokeai.backend.util.logging as logger
 from ...backend.model_management import ModelManager
 from ...frontend.install.widgets import FloatTitleSlider
 
@@ -115,7 +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):
-        log.info(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)
@@ -414,7 +414,7 @@ def run_gui(args: Namespace):
 
     args = mergeapp.merge_arguments
     merge_diffusion_models_and_commit(**args)
-    log.info(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):
@@ -425,7 +425,7 @@ def run_cli(args: Namespace):
 
     if not args.merged_model_name:
         args.merged_model_name = "+".join(args.models)
-        log.info(
+        logger.info(
             f'No --merged_model_name provided. Defaulting to "{args.merged_model_name}"'
         )
 
@@ -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))
-        log.info(f'Models merged into new model: "{args.merged_model_name}".')
+        logger.info(f'Models merged into new model: "{args.merged_model_name}".')
 
 
 def main():
@@ -455,16 +455,16 @@ def main():
             run_cli(args)
     except widget.NotEnoughSpaceForWidget as e:
         if str(e).startswith("Height of 1 allocated"):
-            log.error(
+            logger.error(
                 "You need to have at least two diffusers models defined in models.yaml in order to merge"
             )
         else:
-            log.error(
+            logger.error(
                 "Not enough room for the user interface. Try making this window larger."
             )
         sys.exit(-1)
     except Exception as e:
-        log.error(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 82fe450d93..23134d2736 100755
--- a/invokeai/frontend/training/textual_inversion.py
+++ b/invokeai/frontend/training/textual_inversion.py
@@ -20,7 +20,7 @@ import npyscreen
 from npyscreen import widget
 from omegaconf import OmegaConf
 
-import invokeai.backend.util.logging as log
+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
@@ -369,14 +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)
-    log.info(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:
-        log.info(f'Keeping {args["output_dir"]}')
+        logger.info(f'Keeping {args["output_dir"]}')
 
 
 def save_args(args: dict):
@@ -423,10 +423,10 @@ def do_front_end(args: Namespace):
             do_textual_inversion_training(**args)
             copy_to_embeddings_folder(args)
         except Exception as e:
-            log.error("An exception occurred during training. The exception was:")
-            log.error(str(e))
-            log.error("DETAILS:")
-            log.error(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():
@@ -438,21 +438,21 @@ def main():
         else:
             do_textual_inversion_training(**vars(args))
     except AssertionError as e:
-        log.error(e)
+        logger.error(e)
         sys.exit(-1)
     except KeyboardInterrupt:
         pass
     except (widget.NotEnoughSpaceForWidget, Exception) as e:
         if str(e).startswith("Height of 1 allocated"):
-            log.error(
+            logger.error(
                 "You need to have at least one diffusers models defined in models.yaml in order to train"
             )
         elif str(e).startswith("addwstr"):
-            log.error(
+            logger.error(
                 "Not enough window space for the interface. Please make your window larger and try again."
             )
         else:
-            log.error(e)
+            logger.error(e)
         sys.exit(-1)