Tidy SilenceWarnings context manager (#6493)

## Summary

No functional changes, just cleaning some things up as I touch the code.
This PR cleans up the `SilenceWarnings` context manager:
- Fix type errors
- Enable SilenceWarnings to be used as both a context manager and a
decorator
- Remove duplicate implementation
- Check the initial verbosity on `__enter__()` rather than `__init__()`
- Save an indentation level in DenoiseLatents

## QA Instructions

I generated an image to confirm that warnings are still muted.

## Merge Plan

- [x] ⚠️ Merge https://github.com/invoke-ai/InvokeAI/pull/6492 first,
then change the target branch to `main`.

## Checklist

- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_

invokeai/app/invocations/denoise_latents.py

@@ -16,7 +16,9 @@ from pydantic import field_validator
 from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPVisionModelWithProjection
 
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR, SCHEDULER_NAME_VALUES
+from invokeai.app.invocations.controlnet_image_processors import ControlField
 from invokeai.app.invocations.fields import (
     ConditioningField,
     DenoiseMaskField,
@@ -27,6 +29,7 @@ from invokeai.app.invocations.fields import (
     UIType,
 )
 from invokeai.app.invocations.ip_adapter import IPAdapterField
+from invokeai.app.invocations.model import ModelIdentifierField, UNetField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.shared.invocation_context import InvocationContext
@@ -36,6 +39,11 @@ from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import BaseModelType
 from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.stable_diffusion import PipelineIntermediateState, set_seamless
+from invokeai.backend.stable_diffusion.diffusers_pipeline import (
+    ControlNetData,
+    StableDiffusionGeneratorPipeline,
+    T2IAdapterData,
+)
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
     IPAdapterConditioningInfo,
@@ -45,20 +53,11 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     TextConditioningData,
     TextConditioningRegions,
 )
+from invokeai.backend.stable_diffusion.schedulers import SCHEDULER_MAP
+from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.mask import to_standard_float_mask
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
-from ...backend.stable_diffusion.diffusers_pipeline import (
-    ControlNetData,
-    StableDiffusionGeneratorPipeline,
-    T2IAdapterData,
-)
-from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
-from ...backend.util.devices import TorchDevice
-from .baseinvocation import BaseInvocation, invocation
-from .controlnet_image_processors import ControlField
-from .model import ModelIdentifierField, UNetField
-
 
 def get_scheduler(
     context: InvocationContext,
@@ -658,8 +657,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
         return 1 - mask, masked_latents, self.denoise_mask.gradient
 
     @torch.no_grad()
+    @SilenceWarnings()  # This quenches the NSFW nag from diffusers.
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        with SilenceWarnings():  # this quenches NSFW nag from diffusers
         seed = None
         noise = None
         if self.noise is not None:

invokeai/backend/model_manager/probe.py

@@ -10,7 +10,7 @@ from picklescan.scanner import scan_file_path
 import invokeai.backend.util.logging as logger
 from invokeai.app.util.misc import uuid_string
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
-from invokeai.backend.util.util import SilenceWarnings
+from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 from .config import (
     AnyModelConfig,

invokeai/backend/util/silence_warnings.py

@@ -1,29 +1,36 @@
-"""Context class to silence transformers and diffusers warnings."""
-
 import warnings
-from typing import Any
+from contextlib import ContextDecorator
 
-from diffusers import logging as diffusers_logging
+from diffusers.utils import logging as diffusers_logging
 from transformers import logging as transformers_logging
 
 
-class SilenceWarnings(object):
-    """Use in context to temporarily turn off warnings from transformers & diffusers modules.
+# Inherit from ContextDecorator to allow using SilenceWarnings as both a context manager and a decorator.
+class SilenceWarnings(ContextDecorator):
+    """A context manager that disables warnings from transformers & diffusers modules while active.
 
+    As context manager:
+    ```
     with SilenceWarnings():
         # do something
+    ```
+
+    As decorator:
+    ```
+    @SilenceWarnings()
+    def some_function():
+        # do something
+    ```
     """
 
-    def __init__(self) -> None:
-        self.transformers_verbosity = transformers_logging.get_verbosity()
-        self.diffusers_verbosity = diffusers_logging.get_verbosity()
-
     def __enter__(self) -> None:
+        self._transformers_verbosity = transformers_logging.get_verbosity()
+        self._diffusers_verbosity = diffusers_logging.get_verbosity()
         transformers_logging.set_verbosity_error()
         diffusers_logging.set_verbosity_error()
         warnings.simplefilter("ignore")
 
-    def __exit__(self, *args: Any) -> None:
-        transformers_logging.set_verbosity(self.transformers_verbosity)
-        diffusers_logging.set_verbosity(self.diffusers_verbosity)
+    def __exit__(self, *args) -> None:
+        transformers_logging.set_verbosity(self._transformers_verbosity)
+        diffusers_logging.set_verbosity(self._diffusers_verbosity)
         warnings.simplefilter("default")

invokeai/backend/util/util.py

@@ -3,12 +3,9 @@ import io
 import os
 import re
 import unicodedata
-import warnings
 from pathlib import Path
 
-from diffusers import logging as diffusers_logging
 from PIL import Image
-from transformers import logging as transformers_logging
 
 # actual size of a gig
 GIG = 1073741824
@@ -80,21 +77,3 @@ class Chdir(object):
 
     def __exit__(self, *args):
         os.chdir(self.original)
-
-
-class SilenceWarnings(object):
-    """Context manager to temporarily lower verbosity of diffusers & transformers warning messages."""
-
-    def __enter__(self):
-        """Set verbosity to error."""
-        self.transformers_verbosity = transformers_logging.get_verbosity()
-        self.diffusers_verbosity = diffusers_logging.get_verbosity()
-        transformers_logging.set_verbosity_error()
-        diffusers_logging.set_verbosity_error()
-        warnings.simplefilter("ignore")
-
-    def __exit__(self, type, value, traceback):
-        """Restore logger verbosity to state before context was entered."""
-        transformers_logging.set_verbosity(self.transformers_verbosity)
-        diffusers_logging.set_verbosity(self.diffusers_verbosity)
-        warnings.simplefilter("default")