@@ -113,3 +113,60 @@ jobs:
       with:
         name: pr_${{ matrix.config.report }}_test_reports
         path: reports
+
+  run_staging_tests:
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - name: Hub tests for models, schedulers, and pipelines
+            framework: hub_tests_pytorch
+            runner: docker-cpu
+            image: diffusers/diffusers-pytorch-cpu
+            report: torch_hub
+
+    name: ${{ matrix.config.name }}
+
+    runs-on: ${{ matrix.config.runner }}
+
+    container:
+      image: ${{ matrix.config.image }}
+      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
+
+    defaults:
+      run:
+        shell: bash
+
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: Install dependencies
+      run: |
+        apt-get update && apt-get install libsndfile1-dev libgl1 -y
+        python -m pip install -e .[quality,test]
+
+    - name: Environment
+      run: |
+        python utils/print_env.py
+
+    - name: Run Hub tests for models, schedulers, and pipelines on a staging env
+      if: ${{ matrix.config.framework == 'hub_tests_pytorch' }}
+      run: |
+        HUGGINGFACE_CO_STAGING=true python -m pytest \
+          -m "is_staging_test" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: pr_${{ matrix.config.report }}_test_reports
+        path: reports
@@ -9,4 +9,8 @@ All models are built from the base [`ModelMixin`] class which is a [`torch.nn.mo
 
 ## FlaxModelMixin
 
-[[autodoc]] FlaxModelMixin
+[[autodoc]] FlaxModelMixin
+
+## Pushing to the Hub 
+
+[[autodoc]] utils.PushToHubMixin
@@ -26,7 +26,7 @@
 from typing import Any, Dict, Tuple, Union
 
 import numpy as np
-from huggingface_hub import hf_hub_download
+from huggingface_hub import create_repo, hf_hub_download
 from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
 from requests import HTTPError
 
@@ -144,6 +144,12 @@ def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool
         Args:
             save_directory (`str` or `os.PathLike`):
                 Directory where the configuration JSON file is saved (will be created if it does not exist).
+            push_to_hub (`bool`, *optional*, defaults to `False`):
+                Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the
+                repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
+                namespace).
+            kwargs (`Dict[str, Any]`, *optional*):
+                Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         if os.path.isfile(save_directory):
             raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
@@ -156,6 +162,22 @@ def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool
         self.to_json_file(output_config_file)
         logger.info(f"Configuration saved in {output_config_file}")
 
+        if push_to_hub:
+            commit_message = kwargs.pop("commit_message", None)
+            private = kwargs.pop("private", False)
+            create_pr = kwargs.pop("create_pr", False)
+            token = kwargs.pop("token", None)
+            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
+            repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id
+
+            self._upload_folder(
+                save_directory,
+                repo_id,
+                token=token,
+                commit_message=commit_message,
+                create_pr=create_pr,
+            )
+
     @classmethod
     def from_config(cls, config: Union[FrozenDict, Dict[str, Any]] = None, return_unused_kwargs=False, **kwargs):
         r"""
 
@@ -23,7 +23,7 @@
 from flax.core.frozen_dict import FrozenDict, unfreeze
 from flax.serialization import from_bytes, to_bytes
 from flax.traverse_util import flatten_dict, unflatten_dict
-from huggingface_hub import hf_hub_download
+from huggingface_hub import create_repo, hf_hub_download
 from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
 from requests import HTTPError
 
@@ -34,6 +34,7 @@
     FLAX_WEIGHTS_NAME,
     HUGGINGFACE_CO_RESOLVE_ENDPOINT,
     WEIGHTS_NAME,
+    PushToHubMixin,
     logging,
 )
 from .modeling_flax_pytorch_utils import convert_pytorch_state_dict_to_flax
@@ -42,7 +43,7 @@
 logger = logging.get_logger(__name__)
 
 
-class FlaxModelMixin:
+class FlaxModelMixin(PushToHubMixin):
     r"""
     Base class for all Flax models.
 
@@ -497,6 +498,8 @@ def save_pretrained(
         save_directory: Union[str, os.PathLike],
         params: Union[Dict, FrozenDict],
         is_main_process: bool = True,
+        push_to_hub: bool = False,
+        **kwargs,
     ):
         """
         Save a model and its configuration file to a directory so that it can be reloaded using the
@@ -511,13 +514,27 @@ def save_pretrained(
                 Whether the process calling this is the main process or not. Useful during distributed training and you
                 need to call this function on all processes. In this case, set `is_main_process=True` only on the main
                 process to avoid race conditions.
+            push_to_hub (`bool`, *optional*, defaults to `False`):
+                Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
+                repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
+                namespace).
+            kwargs (`Dict[str, Any]`, *optional*):
+                Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return
 
         os.makedirs(save_directory, exist_ok=True)
 
+        if push_to_hub:
+            commit_message = kwargs.pop("commit_message", None)
+            private = kwargs.pop("private", False)
+            create_pr = kwargs.pop("create_pr", False)
+            token = kwargs.pop("token", None)
+            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
+            repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id
+
         model_to_save = self
 
         # Attach architecture to the config
@@ -532,3 +549,12 @@ def save_pretrained(
             f.write(model_bytes)
 
         logger.info(f"Model weights saved in {output_model_file}")
+
+        if push_to_hub:
+            self._upload_folder(
+                save_directory,
+                repo_id,
+                token=token,
+                commit_message=commit_message,
+                create_pr=create_pr,
+            )
@@ -23,6 +23,7 @@
 
 import safetensors
 import torch
+from huggingface_hub import create_repo
 from torch import Tensor, device, nn
 
 from .. import __version__
@@ -40,6 +41,7 @@
     is_torch_version,
     logging,
 )
+from ..utils.hub_utils import PushToHubMixin
 
 
 logger = logging.get_logger(__name__)
@@ -147,7 +149,7 @@ def load(module: torch.nn.Module, prefix=""):
     return error_msgs
 
 
-class ModelMixin(torch.nn.Module):
+class ModelMixin(torch.nn.Module, PushToHubMixin):
     r"""
     Base class for all models.
 
@@ -272,6 +274,8 @@ def save_pretrained(
         save_function: Callable = None,
         safe_serialization: bool = False,
         variant: Optional[str] = None,
+        push_to_hub: bool = False,
+        **kwargs,
     ):
         """
         Save a model and its configuration file to a directory so that it can be reloaded using the
@@ -292,13 +296,28 @@ def save_pretrained(
                 Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
             variant (`str`, *optional*):
                 If specified, weights are saved in the format `pytorch_model.<variant>.bin`.
+            push_to_hub (`bool`, *optional*, defaults to `False`):
+                Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the
+                repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
+                namespace).
+            kwargs (`Dict[str, Any]`, *optional*):
+                Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return
 
         os.makedirs(save_directory, exist_ok=True)
 
+        if push_to_hub:
+            commit_message = kwargs.pop("commit_message", None)
+            private = kwargs.pop("private", False)
+            create_pr = kwargs.pop("create_pr", False)
+            token = kwargs.pop("token", None)
+            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
+            repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id
+
+        # Only save the model itself if we are using distributed training
         model_to_save = self
 
         # Attach architecture to the config
@@ -322,6 +341,15 @@ def save_pretrained(
 
         logger.info(f"Model weights saved in {os.path.join(save_directory, weights_name)}")
 
+        if push_to_hub:
+            self._upload_folder(
+                save_directory,
+                repo_id,
+                token=token,
+                commit_message=commit_message,
+                create_pr=create_pr,
+            )
+
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
         r"""
 
@@ -23,14 +23,22 @@
 import numpy as np
 import PIL
 from flax.core.frozen_dict import FrozenDict
-from huggingface_hub import snapshot_download
+from huggingface_hub import create_repo, snapshot_download
 from PIL import Image
 from tqdm.auto import tqdm
 
 from ..configuration_utils import ConfigMixin
 from ..models.modeling_flax_utils import FLAX_WEIGHTS_NAME, FlaxModelMixin
 from ..schedulers.scheduling_utils_flax import SCHEDULER_CONFIG_NAME, FlaxSchedulerMixin
-from ..utils import CONFIG_NAME, DIFFUSERS_CACHE, BaseOutput, http_user_agent, is_transformers_available, logging
+from ..utils import (
+    CONFIG_NAME,
+    DIFFUSERS_CACHE,
+    BaseOutput,
+    PushToHubMixin,
+    http_user_agent,
+    is_transformers_available,
+    logging,
+)
 
 
 if is_transformers_available():
@@ -90,7 +98,7 @@ class FlaxImagePipelineOutput(BaseOutput):
     images: Union[List[PIL.Image.Image], np.ndarray]
 
 
-class FlaxDiffusionPipeline(ConfigMixin):
+class FlaxDiffusionPipeline(ConfigMixin, PushToHubMixin):
     r"""
     Base class for Flax-based pipelines.
 
@@ -139,7 +147,13 @@ def register_modules(self, **kwargs):
             # set models
             setattr(self, name, module)
 
-    def save_pretrained(self, save_directory: Union[str, os.PathLike], params: Union[Dict, FrozenDict]):
+    def save_pretrained(
+        self,
+        save_directory: Union[str, os.PathLike],
+        params: Union[Dict, FrozenDict],
+        push_to_hub: bool = False,
+        **kwargs,
+    ):
         # TODO: handle inference_state
         """
         Save all saveable variables of the pipeline to a directory. A pipeline variable can be saved and loaded if its
@@ -149,6 +163,12 @@ class implements both a save and loading method. The pipeline is easily reloaded
         Arguments:
             save_directory (`str` or `os.PathLike`):
                 Directory to which to save. Will be created if it doesn't exist.
+            push_to_hub (`bool`, *optional*, defaults to `False`):
+                Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
+                repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
+                namespace).
+            kwargs (`Dict[str, Any]`, *optional*):
+                Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         self.save_config(save_directory)
 
@@ -157,6 +177,14 @@ class implements both a save and loading method. The pipeline is easily reloaded
         model_index_dict.pop("_diffusers_version")
         model_index_dict.pop("_module", None)
 
+        if push_to_hub:
+            commit_message = kwargs.pop("commit_message", None)
+            private = kwargs.pop("private", False)
+            create_pr = kwargs.pop("create_pr", False)
+            token = kwargs.pop("token", None)
+            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
+            repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id
+
         for pipeline_component_name in model_index_dict.keys():
             sub_model = getattr(self, pipeline_component_name)
             if sub_model is None:
@@ -188,6 +216,15 @@ class implements both a save and loading method. The pipeline is easily reloaded
             else:
                 save_method(os.path.join(save_directory, pipeline_component_name))
 
+            if push_to_hub:
+                self._upload_folder(
+                    save_directory,
+                    repo_id,
+                    token=token,
+                    commit_message=commit_message,
+                    create_pr=create_pr,
+                )
+
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
         r"""