Add diffusers version and pipeline class to the Hub UA (huggingface#814)

* Add diffusers version and pipeline class to the Hub UA

* Fallback to class name for pipelines

* Update src/diffusers/modeling_utils.py

Co-authored-by: Patrick von Platen <[email protected]>

* Update src/diffusers/modeling_flax_utils.py

Co-authored-by: Patrick von Platen <[email protected]>

* Remove autoclass

Co-authored-by: Patrick von Platen <[email protected]>

Author: 2 people

src/diffusers/modeling_flax_utils.py

@@ -27,7 +27,7 @@
 from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
 from requests import HTTPError
 
-from . import is_torch_available
+from . import __version__, is_torch_available
 from .modeling_flax_pytorch_utils import convert_pytorch_state_dict_to_flax
 from .utils import (
     CONFIG_NAME,
@@ -286,10 +286,13 @@ def from_pretrained(
         local_files_only = kwargs.pop("local_files_only", False)
         use_auth_token = kwargs.pop("use_auth_token", None)
         revision = kwargs.pop("revision", None)
-        from_auto_class = kwargs.pop("_from_auto", False)
         subfolder = kwargs.pop("subfolder", None)
 
-        user_agent = {"file_type": "model", "framework": "flax", "from_auto_class": from_auto_class}
+        user_agent = {
+            "diffusers": __version__,
+            "file_type": "model",
+            "framework": "flax",
+        }
 
         # Load config if we don't provide a configuration
         config_path = config if config is not None else pretrained_model_name_or_path

src/diffusers/modeling_utils.py

@@ -26,6 +26,7 @@
 from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
 from requests import HTTPError
 
+from . import __version__
 from .utils import CONFIG_NAME, DIFFUSERS_CACHE, HUGGINGFACE_CO_RESOLVE_ENDPOINT, WEIGHTS_NAME, logging
 
 
@@ -292,12 +293,15 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         local_files_only = kwargs.pop("local_files_only", False)
         use_auth_token = kwargs.pop("use_auth_token", None)
         revision = kwargs.pop("revision", None)
-        from_auto_class = kwargs.pop("_from_auto", False)
         torch_dtype = kwargs.pop("torch_dtype", None)
         subfolder = kwargs.pop("subfolder", None)
         device_map = kwargs.pop("device_map", None)
 
-        user_agent = {"file_type": "model", "framework": "pytorch", "from_auto_class": from_auto_class}
+        user_agent = {
+            "diffusers": __version__,
+            "file_type": "model",
+            "framework": "pytorch",
+        }
 
         # Load config if we don't provide a configuration
         config_path = pretrained_model_name_or_path

src/diffusers/pipeline_utils.py

@@ -29,6 +29,7 @@
 from PIL import Image
 from tqdm.auto import tqdm
 
+from . import __version__
 from .configuration_utils import ConfigMixin
 from .dynamic_modules_utils import get_class_from_dynamic_module
 from .schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
@@ -376,6 +377,11 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
             if custom_pipeline is not None:
                 allow_patterns += [CUSTOM_PIPELINE_FILE_NAME]
 
+            requested_pipeline_class = config_dict.get("_class_name", cls.__name__)
+            user_agent = {"diffusers": __version__, "pipeline_class": requested_pipeline_class}
+            if custom_pipeline is not None:
+                user_agent["custom_pipeline"] = custom_pipeline
+
             # download all allow_patterns
             cached_folder = snapshot_download(
                 pretrained_model_name_or_path,
@@ -386,6 +392,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                 use_auth_token=use_auth_token,
                 revision=revision,
                 allow_patterns=allow_patterns,
+                user_agent=user_agent,
             )
         else:
             cached_folder = pretrained_model_name_or_path