make from hub import work

make from hub import work

src/diffusers/configuration_utils.py

@@ -89,6 +89,7 @@ class ConfigMixin:
 
         self.to_json_file(output_config_file)
         logger.info(f"ConfigMixinuration saved in {output_config_file}")
+    
 
     @classmethod
     def get_config_dict(
@@ -182,35 +183,42 @@ class ConfigMixin:
             logger.info(f"loading configuration file {config_file}")
         else:
             logger.info(f"loading configuration file {config_file} from cache at {resolved_config_file}")
+        
+        return config_dict
 
+    @classmethod
+    def extract_init_dict(cls, config_dict, **kwargs):
         expected_keys = set(dict(inspect.signature(cls.__init__).parameters).keys())
         expected_keys.remove("self")
-
+        init_dict = {}
         for key in expected_keys:
             if key in kwargs:
                 # overwrite key
-                config_dict[key] = kwargs.pop(key)
+                init_dict[key] = kwargs.pop(key)
+            elif key in config_dict:
+                # use value from config dict
+                init_dict[key] = config_dict.pop(key)
 
-        passed_keys = set(config_dict.keys())
-
-        unused_kwargs = kwargs
-        for key in passed_keys - expected_keys:
-            unused_kwargs[key] = config_dict.pop(key)
 
+        unused_kwargs = config_dict.update(kwargs)
+        
+        passed_keys = set(init_dict.keys())
         if len(expected_keys - passed_keys) > 0:
             logger.warn(
                 f"{expected_keys - passed_keys} was not found in config. Values will be initialized to default values."
             )
 
-        return config_dict, unused_kwargs
+        return init_dict, unused_kwargs
 
     @classmethod
     def from_config(cls, pretrained_model_name_or_path: Union[str, os.PathLike], return_unused_kwargs=False, **kwargs):
-        config_dict, unused_kwargs = cls.get_config_dict(
+        config_dict = cls.get_config_dict(
             pretrained_model_name_or_path=pretrained_model_name_or_path, **kwargs
         )
 
-        model = cls(**config_dict)
+        init_dict, unused_kwargs = cls.extract_init_dict(config_dict, **kwargs)
+
+        model = cls(**init_dict)
 
         if return_unused_kwargs:
             return model, unused_kwargs

src/diffusers/dynamic_modules_utils.py

+# coding=utf-8
+# Copyright 2021 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.
+# 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.
+"""Utilities to dynamically load objects from the Hub."""
+
+import importlib
+import os
+import re
+import shutil
+import sys
+from pathlib import Path
+from typing import Dict, Optional, Union
+
+from huggingface_hub import HfFolder, model_info
+
+from transformers.utils import (
+    HF_MODULES_CACHE,
+    TRANSFORMERS_DYNAMIC_MODULE_NAME,
+    cached_path,
+    hf_bucket_url,
+    is_offline_mode,
+    logging,
+)
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def init_hf_modules():
+    """
+    Creates the cache directory for modules with an init, and adds it to the Python path.
+    """
+    # This function has already been executed if HF_MODULES_CACHE already is in the Python path.
+    if HF_MODULES_CACHE in sys.path:
+        return
+
+    sys.path.append(HF_MODULES_CACHE)
+    os.makedirs(HF_MODULES_CACHE, exist_ok=True)
+    init_path = Path(HF_MODULES_CACHE) / "__init__.py"
+    if not init_path.exists():
+        init_path.touch()
+
+
+def create_dynamic_module(name: Union[str, os.PathLike]):
+    """
+    Creates a dynamic module in the cache directory for modules.
+    """
+    init_hf_modules()
+    dynamic_module_path = Path(HF_MODULES_CACHE) / name
+    # If the parent module does not exist yet, recursively create it.
+    if not dynamic_module_path.parent.exists():
+        create_dynamic_module(dynamic_module_path.parent)
+    os.makedirs(dynamic_module_path, exist_ok=True)
+    init_path = dynamic_module_path / "__init__.py"
+    if not init_path.exists():
+        init_path.touch()
+
+
+def get_relative_imports(module_file):
+    """
+    Get the list of modules that are relatively imported in a module file.
+
+    Args:
+        module_file (`str` or `os.PathLike`): The module file to inspect.
+    """
+    with open(module_file, "r", encoding="utf-8") as f:
+        content = f.read()
+
+    # Imports of the form `import .xxx`
+    relative_imports = re.findall("^\s*import\s+\.(\S+)\s*$", content, flags=re.MULTILINE)
+    # Imports of the form `from .xxx import yyy`
+    relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import", content, flags=re.MULTILINE)
+    # Unique-ify
+    return list(set(relative_imports))
+
+
+def get_relative_import_files(module_file):
+    """
+    Get the list of all files that are needed for a given module. Note that this function recurses through the relative
+    imports (if a imports b and b imports c, it will return module files for b and c).
+
+    Args:
+        module_file (`str` or `os.PathLike`): The module file to inspect.
+    """
+    no_change = False
+    files_to_check = [module_file]
+    all_relative_imports = []
+
+    # Let's recurse through all relative imports
+    while not no_change:
+        new_imports = []
+        for f in files_to_check:
+            new_imports.extend(get_relative_imports(f))
+
+        module_path = Path(module_file).parent
+        new_import_files = [str(module_path / m) for m in new_imports]
+        new_import_files = [f for f in new_import_files if f not in all_relative_imports]
+        files_to_check = [f"{f}.py" for f in new_import_files]
+
+        no_change = len(new_import_files) == 0
+        all_relative_imports.extend(files_to_check)
+
+    return all_relative_imports
+
+
+def check_imports(filename):
+    """
+    Check if the current Python environment contains all the libraries that are imported in a file.
+    """
+    with open(filename, "r", encoding="utf-8") as f:
+        content = f.read()
+
+    # Imports of the form `import xxx`
+    imports = re.findall("^\s*import\s+(\S+)\s*$", content, flags=re.MULTILINE)
+    # Imports of the form `from xxx import yyy`
+    imports += re.findall("^\s*from\s+(\S+)\s+import", content, flags=re.MULTILINE)
+    # Only keep the top-level module
+    imports = [imp.split(".")[0] for imp in imports if not imp.startswith(".")]
+
+    # Unique-ify and test we got them all
+    imports = list(set(imports))
+    missing_packages = []
+    for imp in imports:
+        try:
+            importlib.import_module(imp)
+        except ImportError:
+            missing_packages.append(imp)
+
+    if len(missing_packages) > 0:
+        raise ImportError(
+            "This modeling file requires the following packages that were not found in your environment: "
+            f"{', '.join(missing_packages)}. Run `pip install {' '.join(missing_packages)}`"
+        )
+
+    return get_relative_imports(filename)
+
+
+def get_class_in_module(class_name, module_path):
+    """
+    Import a module on the cache directory for modules and extract a class from it.
+    """
+    module_path = module_path.replace(os.path.sep, ".")
+    module = importlib.import_module(module_path)
+    return getattr(module, class_name)
+
+
+def get_cached_module_file(
+    pretrained_model_name_or_path: Union[str, os.PathLike],
+    module_file: str,
+    cache_dir: Optional[Union[str, os.PathLike]] = None,
+    force_download: bool = False,
+    resume_download: bool = False,
+    proxies: Optional[Dict[str, str]] = None,
+    use_auth_token: Optional[Union[bool, str]] = None,
+    revision: Optional[str] = None,
+    local_files_only: bool = False,
+):
+    """
+    Prepares Downloads a module from a local folder or a distant repo and returns its path inside the cached
+    Transformers module.
+
+    Args:
+        pretrained_model_name_or_path (`str` or `os.PathLike`):
+            This can be either:
+
+            - a string, the *model id* of a pretrained model configuration hosted inside a model repo on
+              huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
+              under a user or organization name, like `dbmdz/bert-base-german-cased`.
+            - a path to a *directory* containing a configuration file saved using the
+              [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
+
+        module_file (`str`):
+            The name of the module file containing the class to look for.
+        cache_dir (`str` or `os.PathLike`, *optional*):
+            Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
+            cache should not be used.
+        force_download (`bool`, *optional*, defaults to `False`):
+            Whether or not to force to (re-)download the configuration files and override the cached versions if they
+            exist.
+        resume_download (`bool`, *optional*, defaults to `False`):
+            Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
+        proxies (`Dict[str, str]`, *optional*):
+            A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
+            'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
+        use_auth_token (`str` or *bool*, *optional*):
+            The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
+            when running `transformers-cli login` (stored in `~/.huggingface`).
+        revision (`str`, *optional*, defaults to `"main"`):
+            The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
+            git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
+            identifier allowed by git.
+        local_files_only (`bool`, *optional*, defaults to `False`):
+            If `True`, will only try to load the tokenizer configuration from local files.
+
+    <Tip>
+
+    Passing `use_auth_token=True` is required when you want to use a private model.
+
+    </Tip>
+
+    Returns:
+        `str`: The path to the module inside the cache.
+    """
+    # 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"
+
+    try:
+        # Load from URL or cache if already cached
+        resolved_module_file = cached_path(
+            module_file_or_url,
+            cache_dir=cache_dir,
+            force_download=force_download,
+            proxies=proxies,
+            resume_download=resume_download,
+            local_files_only=local_files_only,
+            use_auth_token=use_auth_token,
+        )
+
+    except EnvironmentError:
+        logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
+        raise
+
+    # Check we have all the requirements in our environment
+    modules_needed = check_imports(resolved_module_file)
+
+    # Now we move the module inside our cached dynamic modules.
+    full_submodule = TRANSFORMERS_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
+    # that hash, to only copy when there is a modification but it seems overkill for now).
+    # The only reason we do the copy is to avoid putting too many folders in sys.path.
+    shutil.copy(resolved_module_file, submodule_path / module_file)
+    for module_needed in modules_needed:
+        module_needed = f"{module_needed}.py"
+        shutil.copy(os.path.join(pretrained_model_name_or_path, module_needed), submodule_path / module_needed)
+    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,
+    cache_dir: Optional[Union[str, os.PathLike]] = None,
+    force_download: bool = False,
+    resume_download: bool = False,
+    proxies: Optional[Dict[str, str]] = None,
+    use_auth_token: Optional[Union[bool, str]] = None,
+    revision: Optional[str] = None,
+    local_files_only: bool = False,
+    **kwargs,
+):
+    """
+    Extracts a class from a module file, present in the local folder or repository of a model.
+
+    <Tip warning={true}>
+
+    Calling this function will execute the code in the module file found locally or downloaded from the Hub. It should
+    therefore only be called on trusted repos.
+
+    </Tip>
+
+    Args:
+        pretrained_model_name_or_path (`str` or `os.PathLike`):
+            This can be either:
+
+            - a string, the *model id* of a pretrained model configuration hosted inside a model repo on
+              huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
+              under a user or organization name, like `dbmdz/bert-base-german-cased`.
+            - a path to a *directory* containing a configuration file saved using the
+              [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
+
+        module_file (`str`):
+            The name of the module file containing the class to look for.
+        class_name (`str`):
+            The name of the class to import in the module.
+        cache_dir (`str` or `os.PathLike`, *optional*):
+            Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
+            cache should not be used.
+        force_download (`bool`, *optional*, defaults to `False`):
+            Whether or not to force to (re-)download the configuration files and override the cached versions if they
+            exist.
+        resume_download (`bool`, *optional*, defaults to `False`):
+            Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
+        proxies (`Dict[str, str]`, *optional*):
+            A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
+            'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
+        use_auth_token (`str` or `bool`, *optional*):
+            The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
+            when running `transformers-cli login` (stored in `~/.huggingface`).
+        revision (`str`, *optional*, defaults to `"main"`):
+            The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
+            git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
+            identifier allowed by git.
+        local_files_only (`bool`, *optional*, defaults to `False`):
+            If `True`, will only try to load the tokenizer configuration from local files.
+
+    <Tip>
+
+    Passing `use_auth_token=True` is required when you want to use a private model.
+
+    </Tip>
+
+    Returns:
+        `type`: The class, dynamically imported from the module.
+
+    Examples:
+
+    ```python
+    # Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this
+    # module.
+    cls = get_class_from_dynamic_module("sgugger/my-bert-model", "modeling.py", "MyBertModel")
+    ```"""
+    # And lastly we get the class inside our newly created module
+    final_module = get_cached_module_file(
+        pretrained_model_name_or_path,
+        module_file,
+        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 get_class_in_module(class_name, final_module.replace(".py", ""))

src/diffusers/pipeline_utils.py

@@ -16,6 +16,7 @@
 
 import importlib
 import os
+from pathlib import Path
 from typing import Optional, Union
 from huggingface_hub import snapshot_download
 
@@ -23,6 +24,7 @@ from huggingface_hub import snapshot_download
 from transformers.utils import logging
 
 from .configuration_utils import ConfigMixin
+from .dynamic_modules_utils import get_class_from_dynamic_module
 
 
 INDEX_FILE = "diffusion_model.pt"
@@ -54,19 +56,23 @@ class DiffusionPipeline(ConfigMixin):
             class_name = module.__class__.__name__
 
             register_dict = {name: (library, class_name)}
-            register_dict["_module"] = self.__module__
+            
 
             # save model index config
             self.register(**register_dict)
 
             # set models
             setattr(self, name, module)
+        
+        register_dict = {"_module" : self.__module__.split(".")[-1] + ".py"}
+        self.register(**register_dict)
 
     def save_pretrained(self, save_directory: Union[str, os.PathLike]):
         self.save_config(save_directory)
 
         model_index_dict = self._dict_to_save
         model_index_dict.pop("_class_name")
+        model_index_dict.pop("_module")
 
         for name, (library_name, class_name) in self._dict_to_save.items():
             importable_classes = LOADABLE_CLASSES[library_name]
@@ -95,16 +101,22 @@ class DiffusionPipeline(ConfigMixin):
         else:
             cached_folder = pretrained_model_name_or_path
 
-        config_dict, pipeline_kwargs = cls.get_config_dict(cached_folder)
+        config_dict = cls.get_config_dict(cached_folder)
+        
+        module = config_dict["_module"]
+        class_name_ = config_dict["_class_name"]
+        
+        if class_name_ == cls.__name__:
+            pipeline_class = cls
+        else:
+            pipeline_class = get_class_from_dynamic_module(cached_folder, module, class_name_, cached_folder)
+        
 
-        module = pipeline_kwargs.pop("_module", None)
-        # TODO(Suraj) - make from hub import work
-        # Make `ddpm = DiffusionPipeline.from_pretrained("fusing/ddpm-lsun-bedroom-pipe")` work
-        # Add Sylvains code from transformers
+        init_dict, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
 
         init_kwargs = {}
 
-        for name, (library_name, class_name) in config_dict.items():
+        for name, (library_name, class_name) in init_dict.items():
             importable_classes = LOADABLE_CLASSES[library_name]
 
             if library_name == module:
@@ -129,5 +141,5 @@ class DiffusionPipeline(ConfigMixin):
 
             init_kwargs[name] = loaded_sub_model  # UNet(...), # DiffusionSchedule(...)
 
-        model = cls(**init_kwargs)
+        model = pipeline_class(**init_kwargs)
         return model

tests/test_modeling_utils.py

@@ -22,6 +22,8 @@ from distutils.util import strtobool
 import torch
 
 from diffusers import GaussianDDPMScheduler, UNetModel
+from diffusers.pipeline_utils import DiffusionPipeline
+from models.vision.ddpm.modeling_ddpm import DDPM
 
 
 global_rng = random.Random()
@@ -199,3 +201,46 @@ class SamplerTesterMixin(unittest.TestCase):
         assert image.shape == (1, 3, 256, 256)
         image_slice = image[0, -1, -3:, -3:].cpu()
         assert (image_slice - torch.tensor([[0.1746, 0.5125, -0.7920], [-0.5734, -0.2910, -0.1984], [0.4090, -0.7740, -0.3941]])).abs().sum() < 1e-3
+
+
+class PipelineTesterMixin(unittest.TestCase):
+    def test_from_pretrained_save_pretrained(self):
+        # 1. Load models
+        model = UNetModel(ch=32, ch_mult=(1, 2), num_res_blocks=2, attn_resolutions=(16,), resolution=32)
+        schedular = GaussianDDPMScheduler(timesteps=10)
+
+        ddpm = DDPM(model, schedular)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            ddpm.save_pretrained(tmpdirname)
+            new_ddpm = DDPM.from_pretrained(tmpdirname)
+        
+        generator = torch.Generator()
+        generator = generator.manual_seed(669472945848556)
+
+        image = ddpm(generator=generator)
+        generator = generator.manual_seed(669472945848556)
+        new_image = new_ddpm(generator=generator)
+
+        assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass"
+    
+
+    @slow
+    def test_from_pretrained_hub(self):
+        model_path = "fusing/ddpm-cifar10"
+
+        ddpm = DDPM.from_pretrained(model_path)
+        ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path)
+
+        ddpm.noise_scheduler.num_timesteps = 10
+        ddpm_from_hub.noise_scheduler.num_timesteps = 10
+
+
+        generator = torch.Generator(device=torch_device)
+        generator = generator.manual_seed(669472945848556)
+
+        image = ddpm(generator=generator)
+        generator = generator.manual_seed(669472945848556)
+        new_image = ddpm_from_hub(generator=generator)
+
+        assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass"