Support both huggingface_hub v0.x and v1.x (huggingface#12389)

* Support huggingface_hub 0.x and 1.x

* httpx

Author: Wauplin

setup.py

@@ -102,7 +102,8 @@
     "filelock",
     "flax>=0.4.1",
     "hf-doc-builder>=0.3.0",
-    "huggingface-hub>=0.34.0",
+    "httpx<1.0.0",
+    "huggingface-hub>=0.34.0,<2.0",
     "requests-mock==1.10.0",
     "importlib_metadata",
     "invisible-watermark>=0.2.0",
@@ -259,6 +260,7 @@ def run(self):
 install_requires = [
     deps["importlib_metadata"],
     deps["filelock"],
+    deps["httpx"],
     deps["huggingface-hub"],
     deps["numpy"],
     deps["regex"],

src/diffusers/configuration_utils.py

@@ -30,11 +30,11 @@
 from huggingface_hub import DDUFEntry, create_repo, hf_hub_download
 from huggingface_hub.utils import (
     EntryNotFoundError,
+    HfHubHTTPError,
     RepositoryNotFoundError,
     RevisionNotFoundError,
     validate_hf_hub_args,
 )
-from requests import HTTPError
 from typing_extensions import Self
 
 from . import __version__
@@ -419,7 +419,7 @@ def load_config(
                 raise EnvironmentError(
                     f"{pretrained_model_name_or_path} does not appear to have a file named {cls.config_name}."
                 )
-            except HTTPError as err:
+            except HfHubHTTPError as err:
                 raise EnvironmentError(
                     "There was a specific connection error when trying to load"
                     f" {pretrained_model_name_or_path}:\n{err}"

src/diffusers/dependency_versions_table.py

@@ -9,7 +9,8 @@
     "filelock": "filelock",
     "flax": "flax>=0.4.1",
     "hf-doc-builder": "hf-doc-builder>=0.3.0",
-    "huggingface-hub": "huggingface-hub>=0.34.0",
+    "httpx": "httpx<1.0.0",
+    "huggingface-hub": "huggingface-hub>=0.34.0,<2.0",
     "requests-mock": "requests-mock==1.10.0",
     "importlib_metadata": "importlib_metadata",
     "invisible-watermark": "invisible-watermark>=0.2.0",

src/diffusers/models/modeling_flax_utils.py

@@ -26,11 +26,11 @@
 from huggingface_hub import create_repo, hf_hub_download
 from huggingface_hub.utils import (
     EntryNotFoundError,
+    HfHubHTTPError,
     RepositoryNotFoundError,
     RevisionNotFoundError,
     validate_hf_hub_args,
 )
-from requests import HTTPError
 
 from .. import __version__, is_torch_available
 from ..utils import (
@@ -385,7 +385,7 @@ def from_pretrained(
                 raise EnvironmentError(
                     f"{pretrained_model_name_or_path} does not appear to have a file named {FLAX_WEIGHTS_NAME}."
                 )
-            except HTTPError as err:
+            except HfHubHTTPError as err:
                 raise EnvironmentError(
                     f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n"
                     f"{err}"

src/diffusers/pipelines/pipeline_loading_utils.py

@@ -19,12 +19,12 @@
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional, Union
 
+import httpx
 import requests
 import torch
 from huggingface_hub import DDUFEntry, ModelCard, model_info, snapshot_download
-from huggingface_hub.utils import OfflineModeIsEnabled, validate_hf_hub_args
+from huggingface_hub.utils import HfHubHTTPError, OfflineModeIsEnabled, validate_hf_hub_args
 from packaging import version
-from requests.exceptions import HTTPError
 
 from .. import __version__
 from ..utils import (
@@ -1110,7 +1110,7 @@ def _download_dduf_file(
     if not local_files_only:
         try:
             info = model_info(pretrained_model_name, token=token, revision=revision)
-        except (HTTPError, OfflineModeIsEnabled, requests.ConnectionError) as e:
+        except (HfHubHTTPError, OfflineModeIsEnabled, requests.ConnectionError, httpx.NetworkError) as e:
             logger.warning(f"Couldn't connect to the Hub: {e}.\nWill try to load from local cache.")
             local_files_only = True
             model_info_call_error = e  # save error to reraise it if model is not cached locally

src/diffusers/pipelines/pipeline_utils.py

@@ -23,6 +23,7 @@
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional, Union, get_args, get_origin
 
+import httpx
 import numpy as np
 import PIL.Image
 import requests
@@ -36,9 +37,8 @@
     read_dduf_file,
     snapshot_download,
 )
-from huggingface_hub.utils import OfflineModeIsEnabled, validate_hf_hub_args
+from huggingface_hub.utils import HfHubHTTPError, OfflineModeIsEnabled, validate_hf_hub_args
 from packaging import version
-from requests.exceptions import HTTPError
 from tqdm.auto import tqdm
 from typing_extensions import Self
 
@@ -1616,7 +1616,7 @@ def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:
         if not local_files_only:
             try:
                 info = model_info(pretrained_model_name, token=token, revision=revision)
-            except (HTTPError, OfflineModeIsEnabled, requests.ConnectionError) as e:
+            except (HfHubHTTPError, OfflineModeIsEnabled, requests.ConnectionError, httpx.NetworkError) as e:
                 logger.warning(f"Couldn't connect to the Hub: {e}.\nWill try to load from local cache.")
                 local_files_only = True
                 model_info_call_error = e  # save error to reraise it if model is not cached locally

src/diffusers/utils/hub_utils.py

@@ -38,13 +38,13 @@
 from huggingface_hub.file_download import REGEX_COMMIT_HASH
 from huggingface_hub.utils import (
     EntryNotFoundError,
+    HfHubHTTPError,
     RepositoryNotFoundError,
     RevisionNotFoundError,
     is_jinja_available,
     validate_hf_hub_args,
 )
 from packaging import version
-from requests import HTTPError
 
 from .. import __version__
 from .constants import (
@@ -316,7 +316,7 @@ def _get_model_file(
             raise EnvironmentError(
                 f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."
             ) from e
-        except HTTPError as e:
+        except HfHubHTTPError as e:
             raise EnvironmentError(
                 f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{e}"
             ) from e
@@ -432,7 +432,7 @@ def _get_checkpoint_shard_files(
 
     # We have already dealt with RepositoryNotFoundError and RevisionNotFoundError when getting the index, so
     # we don't have to catch them here. We have also dealt with EntryNotFoundError.
-    except HTTPError as e:
+    except HfHubHTTPError as e:
         raise EnvironmentError(
             f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load {pretrained_model_name_or_path}. You should try"
             " again after checking your internet connection."

tests/models/test_modeling_common.py

@@ -37,9 +37,8 @@
 import torch.nn as nn
 from accelerate.utils.modeling import _get_proper_dtype, compute_module_sizes, dtype_byte_size
 from huggingface_hub import ModelCard, delete_repo, snapshot_download, try_to_load_from_cache
-from huggingface_hub.utils import is_jinja_available
+from huggingface_hub.utils import HfHubHTTPError, is_jinja_available
 from parameterized import parameterized
-from requests.exceptions import HTTPError
 
 from diffusers.models import FluxTransformer2DModel, SD3Transformer2DModel, UNet2DConditionModel
 from diffusers.models.attention_processor import (
@@ -272,7 +271,7 @@ def test_cached_files_are_used_when_no_internet(self):
         response_mock = mock.Mock()
         response_mock.status_code = 500
         response_mock.headers = {}
-        response_mock.raise_for_status.side_effect = HTTPError
+        response_mock.raise_for_status.side_effect = HfHubHTTPError("Server down", response=mock.Mock())
         response_mock.json.return_value = {}
 
         # Download this model to make sure it's in the cache.
@@ -296,7 +295,7 @@ def test_local_files_only_with_sharded_checkpoint(self):
         error_response = mock.Mock(
             status_code=500,
             headers={},
-            raise_for_status=mock.Mock(side_effect=HTTPError),
+            raise_for_status=mock.Mock(side_effect=HfHubHTTPError("Server down", response=mock.Mock())),
             json=mock.Mock(return_value={}),
         )
 

tests/pipelines/test_pipelines.py

@@ -33,9 +33,9 @@
 import torch
 import torch.nn as nn
 from huggingface_hub import snapshot_download
+from huggingface_hub.utils import HfHubHTTPError
 from parameterized import parameterized
 from PIL import Image
-from requests.exceptions import HTTPError
 from transformers import CLIPImageProcessor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 
 from diffusers import (
@@ -430,7 +430,7 @@ def test_cached_files_are_used_when_no_internet(self):
         response_mock = mock.Mock()
         response_mock.status_code = 500
         response_mock.headers = {}
-        response_mock.raise_for_status.side_effect = HTTPError
+        response_mock.raise_for_status.side_effect = HfHubHTTPError("Server down", response=mock.Mock())
         response_mock.json.return_value = {}
 
         # Download this model to make sure it's in the cache.
@@ -457,7 +457,7 @@ def test_local_files_only_are_used_when_no_internet(self):
         response_mock = mock.Mock()
         response_mock.status_code = 500
         response_mock.headers = {}
-        response_mock.raise_for_status.side_effect = HTTPError
+        response_mock.raise_for_status.side_effect = HfHubHTTPError("Server down", response=mock.Mock())
         response_mock.json.return_value = {}
 
         # first check that with local files only the pipeline can only be used if cached