Custome Pipelines (#744)

* [Custom Pipelines]

* uP

* make style

* finish

* finish

* remove ipdb

* upload

* fix

* finish docs

* Apply suggestions from code review

Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: apolinario <joaopaulo.passos@gmail.com>

* finish

* final uploads

* remove unnecessary test

Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: apolinario <joaopaulo.passos@gmail.com>

docs/source/_toctree.yml

@@ -12,6 +12,8 @@
       title: "Loading Pipelines, Models, and Schedulers"
     - local: using-diffusers/configuration
       title: "Configuring Pipelines, Models, and Schedulers"
+    - local: using-diffusers/custom_pipelines
+      title: "Loading and Creating Custom Pipelines"
     title: "Loading"
   - sections:
     - local: using-diffusers/unconditional_image_generation

docs/source/using-diffusers/custom_pipelines.mdx

@@ -0,0 +1,121 @@
+<!--Copyright 2022 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Custom Pipelines
+
+Diffusers allows you to conveniently load any custom pipeline from the Hugging Face Hub as well as any [official community pipeline](https://github.com/huggingface/diffusers/tree/main/examples/community) 
+via the [`DiffusionPipeline`] class.
+
+## Loading custom pipelines from the Hub
+
+Custom pipelines can be easily loaded from any model repository on the Hub that defines a diffusion pipeline in a `pipeline.py` file.
+Let's load a dummy pipeline from [hf-internal-testing/diffusers-dummy-pipeline](https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline).
+
+All you need to do is pass the custom pipeline repo id with the `custom_pipeline` argument alongside the repo from where you wish to load the pipeline modules.
+
+```python
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
+)
+```
+
+This will load the custom pipeline as defined in the [model repository](https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py).
+
+<Tip warning={true} >
+
+By loading a custom pipeline from the Hugging Face Hub, you are trusting that the code you are loading 
+is safe 🔒. Make sure to check out the code online before loading & running it automatically.
+
+</Tip>
+
+## Loading official community pipelines
+
+Community pipelines are summarized in the [community examples folder](https://github.com/huggingface/diffusers/tree/main/examples/community) 
+
+Similarly, you need to pass both the *repo id* from where you wish to load the weights as well as the `custom_pipeline` argument. Here the `custom_pipeline` argument should consist simply of the filename of the community pipeline excluding the `.py` suffix, *e.g.* `clip_guided_stable_diffusion`.
+
+Since community pipelines are often more complex, one can mix loading weights from an official *repo id*
+and passing pipeline modules directly.
+
+```python
+from diffusers import DiffusionPipeline
+from transformers import CLIPFeatureExtractor, CLIPModel
+
+clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
+
+feature_extractor = CLIPFeatureExtractor.from_pretrained(clip_model_id)
+clip_model = CLIPModel.from_pretrained(clip_model_id)
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    custom_pipeline="clip_guided_stable_diffusion",
+    clip_model=clip_model,
+    feature_extractor=feature_extractor,
+)
+```
+
+## Adding custom pipelines to the Hub
+
+To add a custom pipeline to the Hub, all you need to do is to define a pipeline class that inherits 
+from [`DiffusionPipeline`] in a `pipeline.py` file.
+Make sure that the whole pipeline is encapsulated within a single class and that the `pipeline.py` file
+has only one such class.
+
+Let's quickly define an example pipeline.
+
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+
+
+class MyPipeline(DiffusionPipeline):
+    def __init__(self, unet, scheduler):
+        super().__init__()
+
+    self.register_modules(unet=unet, scheduler=scheduler)
+
+    @torch.no_grad()
+    def __call__(self, batch_size: int = 1, num_inference_steps: int = 50):
+        # Sample gaussian noise to begin loop
+        image = torch.randn((batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size))
+
+    image = image.to(self.device)
+
+    # set step values
+    self.scheduler.set_timesteps(num_inference_steps)
+
+    for t in self.progress_bar(self.scheduler.timesteps):
+        # 1. predict noise model_output
+        model_output = self.unet(image, t).sample
+
+        # 2. predict previous mean of image x_t-1 and add variance depending on eta
+        # eta corresponds to η in paper and should be between [0, 1]
+        # do x_t -> x_t-1
+        image = self.scheduler.step(model_output, t, image, eta).prev_sample
+
+    image = (image / 2 + 0.5).clamp(0, 1)
+    image = image.cpu().permute(0, 2, 3, 1).numpy()
+
+    return image
+```
+
+Now you can upload this short file under the name `pipeline.py` in your preferred [model repository](https://huggingface.co/docs/hub/models-uploading). For Stable Diffusion pipelines, you may also [join the community organisation for shared pipelines](https://huggingface.co/organizations/sd-diffusers-pipelines-library/share/BUPyDUuHcciGTOKaExlqtfFcyCZsVFdrjr) to upload yours.
+Finally, we can load the custom pipeline by passing the model repository name, *e.g.* `sd-diffusers-pipelines-library/my_custom_pipeline` alongside the model repository from where we want to load the `unet` and `scheduler` components.
+
+```python
+my_pipeline = DiffusionPipeline.from_pretrained(
+    "google/ddpm-cifar10-32", custom_pipeline="patrickvonplaten/my_custom_pipeline"
+)
+```

hf-internal-testing/diffusers-dummy-pipeline/models--hf-internal-testing--diffusers-dummy-pipeline/blobs/bbbcb9f65616524d6199fa3bc16dc0500fb2cbbb

@@ -0,0 +1,102 @@
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+
+# limitations under the License.
+
+
+from typing import Optional, Tuple, Union
+
+import torch
+
+from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+
+
+class CustomPipeline(DiffusionPipeline):
+    r"""
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Parameters:
+        unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
+            [`DDPMScheduler`], or [`DDIMScheduler`].
+    """
+
+    def __init__(self, unet, scheduler):
+        super().__init__()
+        self.register_modules(unet=unet, scheduler=scheduler)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        batch_size: int = 1,
+        generator: Optional[torch.Generator] = None,
+        eta: float = 0.0,
+        num_inference_steps: int = 50,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        **kwargs,
+    ) -> Union[ImagePipelineOutput, Tuple]:
+        r"""
+        Args:
+            batch_size (`int`, *optional*, defaults to 1):
+                The number of images to generate.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            eta (`float`, *optional*, defaults to 0.0):
+                The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
+            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
+            generated images.
+        """
+
+        # Sample gaussian noise to begin loop
+        image = torch.randn(
+            (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
+            generator=generator,
+        )
+        image = image.to(self.device)
+
+        # set step values
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        for t in self.progress_bar(self.scheduler.timesteps):
+            # 1. predict noise model_output
+            model_output = self.unet(image, t).sample
+
+            # 2. predict previous mean of image x_t-1 and add variance depending on eta
+            # eta corresponds to η in paper and should be between [0, 1]
+            # do x_t -> x_t-1
+            image = self.scheduler.step(model_output, t, image, eta).prev_sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image), "This is a test"

hf-internal-testing/diffusers-dummy-pipeline/models--hf-internal-testing--diffusers-dummy-pipeline/refs/main

@@ -0,0 +1 @@
+b8fa12635e53eebebc22f95ee863e7af4fc2fb07

hf-internal-testing/diffusers-dummy-pipeline/models--hf-internal-testing--diffusers-dummy-pipeline/snapshots/b8fa12635e53eebebc22f95ee863e7af4fc2fb07/pipeline.py

@@ -0,0 +1 @@
+../../blobs/bbbcb9f65616524d6199fa3bc16dc0500fb2cbbb

src/diffusers/dynamic_modules_utils.py

@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2021 The HuggingFace Inc. team.
+# Copyright 2022 The HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -15,6 +15,7 @@
 """Utilities to dynamically load objects from the Hub."""
 
 import importlib
+import inspect
 import os
 import re
 import shutil
@@ -22,11 +23,16 @@ import sys
 from pathlib import Path
 from typing import Dict, Optional, Union
 
-from huggingface_hub import cached_download
+from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
 
 from .utils import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
 
 
+COMMUNITY_PIPELINES_URL = (
+    "https://raw.githubusercontent.com/huggingface/diffusers/main/examples/community/{pipeline}.py"
+)
+
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -145,9 +151,35 @@ def get_class_in_module(class_name, module_path):
     """
     module_path = module_path.replace(os.path.sep, ".")
     module = importlib.import_module(module_path)
+
+    if class_name is None:
+        return find_pipeline_class(module)
     return getattr(module, class_name)
 
 
+def find_pipeline_class(loaded_module):
+    """
+    Retrieve pipeline class that inherits from `DiffusionPipeline`. Note that there has to be exactly one class
+    inheriting from `DiffusionPipeline`.
+    """
+    from .pipeline_utils import DiffusionPipeline
+
+    cls_members = dict(inspect.getmembers(loaded_module, inspect.isclass))
+
+    pipeline_class = None
+    for cls_name, cls in cls_members.items():
+        if cls_name != DiffusionPipeline.__name__ and issubclass(cls, DiffusionPipeline):
+            if pipeline_class is not None:
+                raise ValueError(
+                    f"Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"
+                    f" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"
+                    f" {loaded_module}."
+                )
+            pipeline_class = cls
+
+    return pipeline_class
+
+
 def get_cached_module_file(
     pretrained_model_name_or_path: Union[str, os.PathLike],
     module_file: str,
@@ -208,16 +240,35 @@ def get_cached_module_file(
     """
     # Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file.
     pretrained_model_name_or_path = str(pretrained_model_name_or_path)
+
     module_file_or_url = os.path.join(pretrained_model_name_or_path, module_file)
-    submodule = "local"
 
     if os.path.isfile(module_file_or_url):
         resolved_module_file = module_file_or_url
+        submodule = "local"
+    elif pretrained_model_name_or_path.count("/") == 0:
+        # community pipeline on GitHub
+        github_url = COMMUNITY_PIPELINES_URL.format(pipeline=pretrained_model_name_or_path)
+        try:
+            resolved_module_file = cached_download(
+                github_url,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                proxies=proxies,
+                resume_download=resume_download,
+                local_files_only=local_files_only,
+                use_auth_token=False,
+            )
+            submodule = "local"
+        except EnvironmentError:
+            logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
+            raise
     else:
         try:
             # Load from URL or cache if already cached
-            resolved_module_file = cached_download(
+            resolved_module_file = hf_hub_download(
-                module_file_or_url,
+                pretrained_model_name_or_path,
+                module_file,
                 cache_dir=cache_dir,
                 force_download=force_download,
                 proxies=proxies,
@@ -225,7 +276,7 @@ def get_cached_module_file(
                 local_files_only=local_files_only,
                 use_auth_token=use_auth_token,
             )
-
+            submodule = os.path.join("local", "--".join(pretrained_model_name_or_path.split("/")))
         except EnvironmentError:
             logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
             raise
@@ -237,20 +288,55 @@ def get_cached_module_file(
     full_submodule = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
     create_dynamic_module(full_submodule)
     submodule_path = Path(HF_MODULES_CACHE) / full_submodule
-    # We always copy local files (we could hash the file to see if there was a change, and give them the name of
+    if submodule == "local":
-    # that hash, to only copy when there is a modification but it seems overkill for now).
+        # We always copy local files (we could hash the file to see if there was a change, and give them the name of
-    # The only reason we do the copy is to avoid putting too many folders in sys.path.
+        # that hash, to only copy when there is a modification but it seems overkill for now).
-    shutil.copy(resolved_module_file, submodule_path / module_file)
+        # The only reason we do the copy is to avoid putting too many folders in sys.path.
-    for module_needed in modules_needed:
+        shutil.copy(resolved_module_file, submodule_path / module_file)
-        module_needed = f"{module_needed}.py"
+        for module_needed in modules_needed:
-        shutil.copy(os.path.join(pretrained_model_name_or_path, module_needed), submodule_path / module_needed)
+            module_needed = f"{module_needed}.py"
+            shutil.copy(os.path.join(pretrained_model_name_or_path, module_needed), submodule_path / module_needed)
+    else:
+        # Get the commit hash
+        # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
+        if isinstance(use_auth_token, str):
+            token = use_auth_token
+        elif use_auth_token is True:
+            token = HfFolder.get_token()
+        else:
+            token = None
+
+        commit_hash = model_info(pretrained_model_name_or_path, revision=revision, token=token).sha
+
+        # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
+        # benefit of versioning.
+        submodule_path = submodule_path / commit_hash
+        full_submodule = full_submodule + os.path.sep + commit_hash
+        create_dynamic_module(full_submodule)
+
+        if not (submodule_path / module_file).exists():
+            shutil.copy(resolved_module_file, submodule_path / module_file)
+        # Make sure we also have every file with relative
+        for module_needed in modules_needed:
+            if not (submodule_path / module_needed).exists():
+                get_cached_module_file(
+                    pretrained_model_name_or_path,
+                    f"{module_needed}.py",
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    resume_download=resume_download,
+                    proxies=proxies,
+                    use_auth_token=use_auth_token,
+                    revision=revision,
+                    local_files_only=local_files_only,
+                )
     return os.path.join(full_submodule, module_file)
 
 
 def get_class_from_dynamic_module(
     pretrained_model_name_or_path: Union[str, os.PathLike],
     module_file: str,
-    class_name: str,
+    class_name: Optional[str] = None,
     cache_dir: Optional[Union[str, os.PathLike]] = None,
     force_download: bool = False,
     resume_download: bool = False,

src/diffusers/pipeline_utils.py

@@ -30,11 +30,13 @@ from PIL import Image
 from tqdm.auto import tqdm
 
 from .configuration_utils import ConfigMixin
+from .dynamic_modules_utils import get_class_from_dynamic_module
 from .schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
 from .utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME, BaseOutput, logging
 
 
 INDEX_FILE = "diffusion_pytorch_model.bin"
+CUSTOM_PIPELINE_FILE_NAME = "pipeline.py"
 
 
 logger = logging.get_logger(__name__)
@@ -214,6 +216,52 @@ class DiffusionPipeline(ConfigMixin):
             torch_dtype (`str` or `torch.dtype`, *optional*):
                 Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
                 will be automatically derived from the model's weights.
+            custom_pipeline (`str`, *optional*):
+
+                <Tip warning={true}>
+
+                    This is an experimental feature and is likely to change in the future.
+
+                </Tip>
+
+                Can be either:
+
+                    - A string, the *repo id* of a custom pipeline hosted inside a model repo on
+                      https://huggingface.co/. Valid repo ids have to be located under a user or organization name,
+                      like `hf-internal-testing/diffusers-dummy-pipeline`.
+
+                        <Tip>
+
+                         It is required that the model repo has a file, called `pipeline.py` that defines the custom
+                         pipeline.
+
+                        </Tip>
+
+                    - A string, the *file name* of a community pipeline hosted on GitHub under
+                      https://github.com/huggingface/diffusers/tree/main/examples/community. Valid file names have to
+                      match exactly the file name without `.py` located under the above link, *e.g.*
+                      `clip_guided_stable_diffusion`.
+
+                        <Tip>
+
+                         Community pipelines are always loaded from the current `main` branch of GitHub.
+
+                        </Tip>
+
+                    - A path to a *directory* containing a custom pipeline, e.g., `./my_pipeline_directory/`.
+
+                        <Tip>
+
+                         It is required that the directory has a file, called `pipeline.py` that defines the custom
+                         pipeline.
+
+                        </Tip>
+
+                For more information on how to load and create custom pipelines, please have a look at [Loading and
+                Creating Custom
+                Pipelines](https://huggingface.co/docs/diffusers/main/en/using-diffusers/custom_pipelines)
+
+            torch_dtype (`str` or `torch.dtype`, *optional*):
             force_download (`bool`, *optional*, defaults to `False`):
                 Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                 cached versions if they exist.
@@ -285,6 +333,7 @@ class DiffusionPipeline(ConfigMixin):
         use_auth_token = kwargs.pop("use_auth_token", None)
         revision = kwargs.pop("revision", None)
         torch_dtype = kwargs.pop("torch_dtype", None)
+        custom_pipeline = kwargs.pop("custom_pipeline", None)
         provider = kwargs.pop("provider", None)
         sess_options = kwargs.pop("sess_options", None)
 
@@ -305,6 +354,9 @@ class DiffusionPipeline(ConfigMixin):
             allow_patterns = [os.path.join(k, "*") for k in folder_names]
             allow_patterns += [WEIGHTS_NAME, SCHEDULER_CONFIG_NAME, CONFIG_NAME, ONNX_WEIGHTS_NAME, cls.config_name]
 
+            if custom_pipeline is not None:
+                allow_patterns += [CUSTOM_PIPELINE_FILE_NAME]
+
             # download all allow_patterns
             cached_folder = snapshot_download(
                 pretrained_model_name_or_path,
@@ -323,7 +375,11 @@ class DiffusionPipeline(ConfigMixin):
 
         # 2. Load the pipeline class, if using custom module then load it from the hub
         # if we load from explicit class, let's use it
-        if cls != DiffusionPipeline:
+        if custom_pipeline is not None:
+            pipeline_class = get_class_from_dynamic_module(
+                custom_pipeline, module_file=CUSTOM_PIPELINE_FILE_NAME, cache_dir=custom_pipeline
+            )
+        elif cls != DiffusionPipeline:
             pipeline_class = cls
         else:
             diffusers_module = importlib.import_module(cls.__module__.split(".")[0])
@@ -332,7 +388,7 @@ class DiffusionPipeline(ConfigMixin):
         # some modules can be passed directly to the init
         # in this case they are already instantiated in `kwargs`
         # extract them here
-        expected_modules = set(inspect.signature(pipeline_class.__init__).parameters.keys())
+        expected_modules = set(inspect.signature(pipeline_class.__init__).parameters.keys()) - set(["self"])
         passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
 
         init_dict, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
@@ -414,7 +470,18 @@ class DiffusionPipeline(ConfigMixin):
 
             init_kwargs[name] = loaded_sub_model  # UNet(...), # DiffusionSchedule(...)
 
-        # 4. Instantiate the pipeline
+        # 4. Potentially add passed objects if expected
+        missing_modules = set(expected_modules) - set(init_kwargs.keys())
+        if len(missing_modules) > 0 and missing_modules <= set(passed_class_obj.keys()):
+            for module in missing_modules:
+                init_kwargs[module] = passed_class_obj[module]
+        elif len(missing_modules) > 0:
+            passed_modules = set(init_kwargs.keys() + passed_class_obj.keys())
+            raise ValueError(
+                f"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed."
+            )
+
+        # 5. Instantiate the pipeline
         model = pipeline_class(**init_kwargs)
         return model
 

src/diffusers/utils/testing_utils.py

@@ -1,3 +1,4 @@
+import inspect
 import os
 import random
 import re
@@ -22,6 +23,27 @@ if is_torch_higher_equal_than_1_12:
     torch_device = "mps" if torch.backends.mps.is_available() else torch_device
 
 
+def get_tests_dir(append_path=None):
+    """
+    Args:
+        append_path: optional path to append to the tests dir path
+    Return:
+        The full path to the `tests` dir, so that the tests can be invoked from anywhere. Optionally `append_path` is
+        joined after the `tests` dir the former is provided.
+    """
+    # this function caller's __file__
+    caller__file__ = inspect.stack()[1][1]
+    tests_dir = os.path.abspath(os.path.dirname(caller__file__))
+
+    while not tests_dir.endswith("tests"):
+        tests_dir = os.path.dirname(tests_dir)
+
+    if append_path:
+        return os.path.join(tests_dir, append_path)
+    else:
+        return tests_dir
+
+
 def parse_flag_from_env(key, default=False):
     try:
         value = os.environ[key]

tests/fixtures/custom_pipeline/pipeline.py

@@ -0,0 +1,102 @@
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+
+# limitations under the License.
+
+
+from typing import Optional, Tuple, Union
+
+import torch
+
+from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+
+
+class CustomLocalPipeline(DiffusionPipeline):
+    r"""
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Parameters:
+        unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
+            [`DDPMScheduler`], or [`DDIMScheduler`].
+    """
+
+    def __init__(self, unet, scheduler):
+        super().__init__()
+        self.register_modules(unet=unet, scheduler=scheduler)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        batch_size: int = 1,
+        generator: Optional[torch.Generator] = None,
+        eta: float = 0.0,
+        num_inference_steps: int = 50,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        **kwargs,
+    ) -> Union[ImagePipelineOutput, Tuple]:
+        r"""
+        Args:
+            batch_size (`int`, *optional*, defaults to 1):
+                The number of images to generate.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            eta (`float`, *optional*, defaults to 0.0):
+                The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
+            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
+            generated images.
+        """
+
+        # Sample gaussian noise to begin loop
+        image = torch.randn(
+            (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
+            generator=generator,
+        )
+        image = image.to(self.device)
+
+        # set step values
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        for t in self.progress_bar(self.scheduler.timesteps):
+            # 1. predict noise model_output
+            model_output = self.unet(image, t).sample
+
+            # 2. predict previous mean of image x_t-1 and add variance depending on eta
+            # eta corresponds to η in paper and should be between [0, 1]
+            # do x_t -> x_t-1
+            image = self.scheduler.step(model_output, t, image, eta).prev_sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image,), "This is a local test"
+
+        return ImagePipelineOutput(images=image), "This is a local test"

tests/test_pipelines.py

@@ -49,8 +49,9 @@ from diffusers import (
 from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
 from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, load_image, slow, torch_device
+from diffusers.utils.testing_utils import get_tests_dir
 from PIL import Image
-from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
@@ -79,6 +80,60 @@ def test_progress_bar(capsys):
     assert captured.err == "", "Progress bar should be disabled"
 
 
+class CustomPipelineTests(unittest.TestCase):
+    def test_load_custom_pipeline(self):
+        pipeline = DiffusionPipeline.from_pretrained(
+            "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
+        )
+        # NOTE that `"CustomPipeline"` is not a class that is defined in this library, but solely on the Hub
+        # under https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L24
+        assert pipeline.__class__.__name__ == "CustomPipeline"
+
+    def test_run_custom_pipeline(self):
+        pipeline = DiffusionPipeline.from_pretrained(
+            "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
+        )
+        images, output_str = pipeline(num_inference_steps=2, output_type="np")
+
+        assert images[0].shape == (1, 32, 32, 3)
+        # compare output to https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L102
+        assert output_str == "This is a test"
+
+    def test_local_custom_pipeline(self):
+        local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline")
+        pipeline = DiffusionPipeline.from_pretrained(
+            "google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path
+        )
+        images, output_str = pipeline(num_inference_steps=2, output_type="np")
+
+        assert pipeline.__class__.__name__ == "CustomLocalPipeline"
+        assert images[0].shape == (1, 32, 32, 3)
+        # compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102
+        assert output_str == "This is a local test"
+
+    @slow
+    def test_load_pipeline_from_git(self):
+        clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
+
+        feature_extractor = CLIPFeatureExtractor.from_pretrained(clip_model_id)
+        clip_model = CLIPModel.from_pretrained(clip_model_id)
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            "CompVis/stable-diffusion-v1-4",
+            custom_pipeline="clip_guided_stable_diffusion",
+            clip_model=clip_model,
+            feature_extractor=feature_extractor,
+        )
+        pipeline = pipeline.to(torch_device)
+
+        # NOTE that `"CLIPGuidedStableDiffusion"` is not a class that is defined in the pypi package of th e library, but solely on the community examples folder of GitHub under:
+        # https://github.com/huggingface/diffusers/blob/main/examples/community/clip_guided_stable_diffusion.py
+        assert pipeline.__class__.__name__ == "CLIPGuidedStableDiffusion"
+
+        image = pipeline("a prompt", num_inference_steps=2, output_type="np").images[0]
+        assert image.shape == (512, 512, 3)
+
+
 class PipelineFastTests(unittest.TestCase):
     def tearDown(self):
         # clean up the VRAM after each test