[single file] enable telemetry for single file loading when using GGUF. (huggingface#11284)

* enable telemetry for single file loading when using GGUF.

* quality

Author: sayakpaul

src/diffusers/loaders/single_file_model.py

@@ -21,6 +21,7 @@
 from huggingface_hub.utils import validate_hf_hub_args
 from typing_extensions import Self
 
+from .. import __version__
 from ..quantizers import DiffusersAutoQuantizer
 from ..utils import deprecate, is_accelerate_available, logging
 from .single_file_utils import (
@@ -260,6 +261,11 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
         device = kwargs.pop("device", None)
         disable_mmap = kwargs.pop("disable_mmap", False)
 
+        user_agent = {"diffusers": __version__, "file_type": "single_file", "framework": "pytorch"}
+        # In order to ensure popular quantization methods are supported. Can be disable with `disable_telemetry`
+        if quantization_config is not None:
+            user_agent["quant"] = quantization_config.quant_method.value
+
         if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
             torch_dtype = torch.float32
             logger.warning(
@@ -278,6 +284,7 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
                 local_files_only=local_files_only,
                 revision=revision,
                 disable_mmap=disable_mmap,
+                user_agent=user_agent,
             )
         if quantization_config is not None:
             hf_quantizer = DiffusersAutoQuantizer.from_config(quantization_config)

src/diffusers/loaders/single_file_utils.py

@@ -405,13 +405,16 @@ def load_single_file_checkpoint(
     local_files_only=None,
     revision=None,
     disable_mmap=False,
+    user_agent=None,
 ):
+    if user_agent is None:
+        user_agent = {"file_type": "single_file", "framework": "pytorch"}
+
     if os.path.isfile(pretrained_model_link_or_path):
         pretrained_model_link_or_path = pretrained_model_link_or_path
 
     else:
         repo_id, weights_name = _extract_repo_id_and_weights_name(pretrained_model_link_or_path)
-        user_agent = {"file_type": "single_file", "framework": "pytorch"}
         pretrained_model_link_or_path = _get_model_file(
             repo_id,
             weights_name=weights_name,