clean up logging

Author: danhipke

src/diffusers/loaders/single_file.py

@@ -99,7 +99,6 @@ def load_single_file_sub_model(
         if original_config:
             cached_model_config_path = None
 
-        logger.warning("dhipke1")
         loaded_sub_model = load_method(
             pretrained_model_link_or_path_or_dict=checkpoint,
             original_config=original_config,
@@ -112,7 +111,6 @@ def load_single_file_sub_model(
         )
 
     elif is_transformers_model and is_clip_model_in_single_file(class_obj, checkpoint):
-        logger.warning("dhipke2")
         loaded_sub_model = create_diffusers_clip_model_from_ldm(
             class_obj,
             checkpoint=checkpoint,
@@ -144,8 +142,6 @@ def load_single_file_sub_model(
         )
 
     else:
-        logger.warning("dhipke3")
-
         if not hasattr(class_obj, "from_pretrained"):
             raise ValueError(
                 (
@@ -363,7 +359,6 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
         revision = kwargs.pop("revision", None)
         torch_dtype = kwargs.pop("torch_dtype", None)
         no_mmap = kwargs.pop("no_mmap", False)
-        logger.warning("no_mmap: " + str(no_mmap))
 
         is_legacy_loading = False
 

src/diffusers/loaders/single_file_model.py

@@ -217,7 +217,6 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
         revision = kwargs.pop("revision", None)
         torch_dtype = kwargs.pop("torch_dtype", None)
         no_mmap = kwargs.pop("no_mmap", False)
-        logger.warning("no_mmap2: " + str(no_mmap))
 
         if isinstance(pretrained_model_link_or_path_or_dict, dict):
             checkpoint = pretrained_model_link_or_path_or_dict
@@ -234,9 +233,7 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
             )
 
         mapping_functions = SINGLE_FILE_LOADABLE_CLASSES[mapping_class_name]
-        logger.warning(mapping_functions)
         checkpoint_mapping_fn = mapping_functions["checkpoint_mapping_fn"]
-        logger.warning(checkpoint_mapping_fn)
         if original_config is not None:
             if "config_mapping_fn" in mapping_functions:
                 config_mapping_fn = mapping_functions["config_mapping_fn"]
@@ -256,7 +253,6 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
                 # If original_config is a URL or filepath fetch the original_config dict
                 original_config = fetch_original_config(original_config, local_files_only=local_files_only)
 
-            logger.warn("hi1")
             config_mapping_kwargs = _get_mapping_function_kwargs(config_mapping_fn, **kwargs)
             diffusers_model_config = config_mapping_fn(
                 original_config=original_config, checkpoint=checkpoint, **config_mapping_kwargs
@@ -314,17 +310,14 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
 
         ctx = init_empty_weights if is_accelerate_available() else nullcontext
         with ctx():
-            logger.warn("hi2")
             model = cls.from_config(diffusers_model_config)
 
-        logger.warn("hi3")
         if is_accelerate_available():
             unexpected_keys = load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)
 
         else:
             _, unexpected_keys = model.load_state_dict(diffusers_format_checkpoint, strict=False, no_mmap=no_mmap)
 
-        logger.warn("hi4")
         if model._keys_to_ignore_on_load_unexpected is not None:
             for pat in model._keys_to_ignore_on_load_unexpected:
                 unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]

src/diffusers/loaders/single_file_utils.py

@@ -382,7 +382,6 @@ def load_single_file_checkpoint(
             revision=revision,
         )
 
-    logger.warn("before load_state_dict. no_mmap: " + str(no_mmap))
     checkpoint = load_state_dict(pretrained_model_link_or_path, no_mmap=no_mmap)
 
     # some checkpoints contain the model state dict under a "state_dict" key