[DiffusionPipeline.from_pretrained] add warning when passing unused k… (huggingface#870)

[DiffusionPipeline.from_pretrained] add warning when passing unused kwargs

Author: patrickvonplaten

src/diffusers/pipeline_utils.py

@@ -350,6 +350,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         """
         cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
         resume_download = kwargs.pop("resume_download", False)
+        force_download = kwargs.pop("force_download", False)
         proxies = kwargs.pop("proxies", None)
         local_files_only = kwargs.pop("local_files_only", False)
         use_auth_token = kwargs.pop("use_auth_token", None)
@@ -367,6 +368,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                 pretrained_model_name_or_path,
                 cache_dir=cache_dir,
                 resume_download=resume_download,
+                force_download=force_download,
                 proxies=proxies,
                 local_files_only=local_files_only,
                 use_auth_token=use_auth_token,
@@ -439,7 +441,10 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         expected_modules = set(inspect.signature(pipeline_class.__init__).parameters.keys()) - set(["self"])
         passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
 
-        init_dict, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
+        init_dict, unused_kwargs = pipeline_class.extract_init_dict(config_dict, **kwargs)
+
+        if len(unused_kwargs) > 0:
+            logger.warning(f"Keyword arguments {unused_kwargs} not recognized.")
 
         init_kwargs = {}
 

src/diffusers/utils/testing_utils.py

@@ -1,9 +1,11 @@
 import inspect
+import logging
 import os
 import random
 import re
 import unittest
 from distutils.util import strtobool
+from io import StringIO
 from pathlib import Path
 from typing import Union
 
@@ -284,3 +286,42 @@ def summary_failures_short(tr):
     tr._tw = orig_writer
     tr.reportchars = orig_reportchars
     config.option.tbstyle = orig_tbstyle
+
+
+class CaptureLogger:
+    """
+    Args:
+    Context manager to capture `logging` streams
+        logger: 'logging` logger object
+    Returns:
+        The captured output is available via `self.out`
+    Example:
+    ```python
+    >>> from diffusers import logging
+    >>> from diffusers.testing_utils import CaptureLogger
+
+    >>> msg = "Testing 1, 2, 3"
+    >>> logging.set_verbosity_info()
+    >>> logger = logging.get_logger("diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.py")
+    >>> with CaptureLogger(logger) as cl:
+    ...     logger.info(msg)
+    >>> assert cl.out, msg + "\n"
+    ```
+    """
+
+    def __init__(self, logger):
+        self.logger = logger
+        self.io = StringIO()
+        self.sh = logging.StreamHandler(self.io)
+        self.out = ""
+
+    def __enter__(self):
+        self.logger.addHandler(self.sh)
+        return self
+
+    def __exit__(self, *exc):
+        self.logger.removeHandler(self.sh)
+        self.out = self.io.getvalue()
+
+    def __repr__(self):
+        return f"captured: {self.out}\n"

tests/test_pipelines.py

@@ -51,11 +51,12 @@
     UNet2DConditionModel,
     UNet2DModel,
     VQModel,
+    logging,
 )
 from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
 from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, load_image, slow, torch_device
-from diffusers.utils.testing_utils import get_tests_dir
+from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir
 from packaging import version
 from PIL import Image
 from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
@@ -1473,6 +1474,15 @@ def test_smart_download(self):
             # is not downloaded, but all the expected ones
             assert not os.path.isfile(os.path.join(snapshot_dir, "big_array.npy"))
 
+    def test_warning_unused_kwargs(self):
+        model_id = "hf-internal-testing/unet-pipeline-dummy"
+        logger = logging.get_logger("diffusers.pipeline_utils")
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            with CaptureLogger(logger) as cap_logger:
+                DiffusionPipeline.from_pretrained(model_id, not_used=True, cache_dir=tmpdirname, force_download=True)
+
+        assert cap_logger.out == "Keyword arguments {'not_used': True} not recognized.\n"
+
     @property
     def dummy_safety_checker(self):
         def check(images, *args, **kwargs):