resolve conflicts.

src/diffusers/loaders/autoencoder.py

@@ -143,4 +143,5 @@ class FromOriginalVAEMixin:
         if torch_dtype is not None:
             vae = vae.to(torch_dtype)
 
+        vae.eval()
         return vae

src/diffusers/loaders/controlnet.py

@@ -133,4 +133,5 @@ class FromOriginalControlNetMixin:
         if torch_dtype is not None:
             controlnet = controlnet.to(torch_dtype)
 
+        controlnet.eval()
         return controlnet

src/diffusers/models/modeling_utils.py

@@ -39,6 +39,7 @@ from ..utils import (
     _get_model_file,
     deprecate,
     is_accelerate_available,
+    is_single_file_checkpoint,
     is_torch_version,
     logging,
 )
@@ -48,6 +49,8 @@ from ..utils.hub_utils import PushToHubMixin, load_or_create_model_card, populat
 logger = logging.get_logger(__name__)
 
 
+SINGLE_FILE_LOADABLE_CLASSES = {"ControlNetModel", "AutoencoderKL"}
+
 if is_torch_version(">=", "1.9.0"):
     _LOW_CPU_MEM_USAGE_DEFAULT = True
 else:
@@ -497,102 +500,90 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
         ```
         """
-        cache_dir = kwargs.pop("cache_dir", None)
-        ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False)
-        force_download = kwargs.pop("force_download", False)
-        from_flax = kwargs.pop("from_flax", False)
-        resume_download = kwargs.pop("resume_download", False)
-        proxies = kwargs.pop("proxies", None)
-        output_loading_info = kwargs.pop("output_loading_info", False)
-        local_files_only = kwargs.pop("local_files_only", None)
-        token = kwargs.pop("token", None)
-        revision = kwargs.pop("revision", None)
-        torch_dtype = kwargs.pop("torch_dtype", None)
-        subfolder = kwargs.pop("subfolder", None)
-        device_map = kwargs.pop("device_map", None)
-        max_memory = kwargs.pop("max_memory", None)
-        offload_folder = kwargs.pop("offload_folder", None)
-        offload_state_dict = kwargs.pop("offload_state_dict", False)
-        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
-        variant = kwargs.pop("variant", None)
-        use_safetensors = kwargs.pop("use_safetensors", None)
+        if is_single_file_checkpoint(pretrained_model_name_or_path):
+            if cls.__name__ not in SINGLE_FILE_LOADABLE_CLASSES:
+                raise ValueError(
+                    f"{cls.__name__} is not supported. Supported classes are: {' '.join(list(SINGLE_FILE_LOADABLE_CLASSES))}."
+                )
+            logger.info("Single file checkpoint detected...")
+            model = cls.from_single_file(pretrained_model_name_or_path, **kwargs)
+            model = model.eval()
+            return model
+        else:
+            cache_dir = kwargs.pop("cache_dir", None)
+            ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False)
+            force_download = kwargs.pop("force_download", False)
+            from_flax = kwargs.pop("from_flax", False)
+            resume_download = kwargs.pop("resume_download", False)
+            proxies = kwargs.pop("proxies", None)
+            output_loading_info = kwargs.pop("output_loading_info", False)
+            local_files_only = kwargs.pop("local_files_only", None)
+            token = kwargs.pop("token", None)
+            revision = kwargs.pop("revision", None)
+            torch_dtype = kwargs.pop("torch_dtype", None)
+            subfolder = kwargs.pop("subfolder", None)
+            device_map = kwargs.pop("device_map", None)
+            max_memory = kwargs.pop("max_memory", None)
+            offload_folder = kwargs.pop("offload_folder", None)
+            offload_state_dict = kwargs.pop("offload_state_dict", False)
+            low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
+            variant = kwargs.pop("variant", None)
+            use_safetensors = kwargs.pop("use_safetensors", None)
 
-        allow_pickle = False
-        if use_safetensors is None:
-            use_safetensors = True
-            allow_pickle = True
+            allow_pickle = False
+            if use_safetensors is None:
+                use_safetensors = True
+                allow_pickle = True
 
-        if low_cpu_mem_usage and not is_accelerate_available():
-            low_cpu_mem_usage = False
-            logger.warning(
-                "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
-                " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
-                " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
-                " install accelerate\n```\n."
-            )
+            if low_cpu_mem_usage and not is_accelerate_available():
+                low_cpu_mem_usage = False
+                logger.warning(
+                    "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
+                    " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
+                    " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
+                    " install accelerate\n```\n."
+                )
 
-        if device_map is not None and not is_accelerate_available():
-            raise NotImplementedError(
-                "Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set"
-                " `device_map=None`. You can install accelerate with `pip install accelerate`."
-            )
+            if device_map is not None and not is_accelerate_available():
+                raise NotImplementedError(
+                    "Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set"
+                    " `device_map=None`. You can install accelerate with `pip install accelerate`."
+                )
 
-        # Check if we can handle device_map and dispatching the weights
-        if device_map is not None and not is_torch_version(">=", "1.9.0"):
-            raise NotImplementedError(
-                "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
-                " `device_map=None`."
-            )
+            # Check if we can handle device_map and dispatching the weights
+            if device_map is not None and not is_torch_version(">=", "1.9.0"):
+                raise NotImplementedError(
+                    "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
+                    " `device_map=None`."
+                )
 
-        if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
-            raise NotImplementedError(
-                "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
-                " `low_cpu_mem_usage=False`."
-            )
+            if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
+                raise NotImplementedError(
+                    "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
+                    " `low_cpu_mem_usage=False`."
+                )
 
-        if low_cpu_mem_usage is False and device_map is not None:
-            raise ValueError(
-                f"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and"
-                " dispatching. Please make sure to set `low_cpu_mem_usage=True`."
-            )
+            if low_cpu_mem_usage is False and device_map is not None:
+                raise ValueError(
+                    f"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and"
+                    " dispatching. Please make sure to set `low_cpu_mem_usage=True`."
+                )
 
-        # Load config if we don't provide a configuration
-        config_path = pretrained_model_name_or_path
+            # Load config if we don't provide a configuration
+            config_path = pretrained_model_name_or_path
 
-        user_agent = {
-            "diffusers": __version__,
-            "file_type": "model",
-            "framework": "pytorch",
-        }
+            user_agent = {
+                "diffusers": __version__,
+                "file_type": "model",
+                "framework": "pytorch",
+            }
 
-        # load config
-        config, unused_kwargs, commit_hash = cls.load_config(
-            config_path,
-            cache_dir=cache_dir,
-            return_unused_kwargs=True,
-            return_commit_hash=True,
-            force_download=force_download,
-            resume_download=resume_download,
-            proxies=proxies,
-            local_files_only=local_files_only,
-            token=token,
-            revision=revision,
-            subfolder=subfolder,
-            device_map=device_map,
-            max_memory=max_memory,
-            offload_folder=offload_folder,
-            offload_state_dict=offload_state_dict,
-            user_agent=user_agent,
-            **kwargs,
-        )
-
-        # load model
-        model_file = None
-        if from_flax:
-            model_file = _get_model_file(
-                pretrained_model_name_or_path,
-                weights_name=FLAX_WEIGHTS_NAME,
+            # load config
+            config, unused_kwargs, commit_hash = cls.load_config(
+                config_path,
                 cache_dir=cache_dir,
+                return_unused_kwargs=True,
+                return_commit_hash=True,
                 force_download=force_download,
                 resume_download=resume_download,
                 proxies=proxies,
@@ -600,40 +591,20 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                 token=token,
                 revision=revision,
                 subfolder=subfolder,
+                device_map=device_map,
+                max_memory=max_memory,
+                offload_folder=offload_folder,
+                offload_state_dict=offload_state_dict,
                 user_agent=user_agent,
-                commit_hash=commit_hash,
+                **kwargs,
             )
-            model = cls.from_config(config, **unused_kwargs)
 
-            # Convert the weights
-            from .modeling_pytorch_flax_utils import load_flax_checkpoint_in_pytorch_model
-
-            model = load_flax_checkpoint_in_pytorch_model(model, model_file)
-        else:
-            if use_safetensors:
-                try:
-                    model_file = _get_model_file(
-                        pretrained_model_name_or_path,
-                        weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant),
-                        cache_dir=cache_dir,
-                        force_download=force_download,
-                        resume_download=resume_download,
-                        proxies=proxies,
-                        local_files_only=local_files_only,
-                        token=token,
-                        revision=revision,
-                        subfolder=subfolder,
-                        user_agent=user_agent,
-                        commit_hash=commit_hash,
-                    )
-                except IOError as e:
-                    if not allow_pickle:
-                        raise e
-                    pass
-            if model_file is None:
+            # load model
+            model_file = None
+            if from_flax:
                 model_file = _get_model_file(
                     pretrained_model_name_or_path,
-                    weights_name=_add_variant(WEIGHTS_NAME, variant),
+                    weights_name=FLAX_WEIGHTS_NAME,
                     cache_dir=cache_dir,
                     force_download=force_download,
                     resume_download=resume_download,
@@ -645,76 +616,90 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                     user_agent=user_agent,
                     commit_hash=commit_hash,
                 )
+                model = cls.from_config(config, **unused_kwargs)
 
-            if low_cpu_mem_usage:
-                # Instantiate model with empty weights
-                with accelerate.init_empty_weights():
-                    model = cls.from_config(config, **unused_kwargs)
+                # Convert the weights
+                from .modeling_pytorch_flax_utils import load_flax_checkpoint_in_pytorch_model
 
-                # if device_map is None, load the state dict and move the params from meta device to the cpu
-                if device_map is None:
-                    param_device = "cpu"
-                    state_dict = load_state_dict(model_file, variant=variant)
-                    model._convert_deprecated_attention_blocks(state_dict)
-                    # move the params from meta device to cpu
-                    missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
-                    if len(missing_keys) > 0:
-                        raise ValueError(
-                            f"Cannot load {cls} from {pretrained_model_name_or_path} because the following keys are"
-                            f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass"
-                            " `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize"
-                            " those weights or else make sure your checkpoint file is correct."
+                model = load_flax_checkpoint_in_pytorch_model(model, model_file)
+            else:
+                if use_safetensors:
+                    try:
+                        model_file = _get_model_file(
+                            pretrained_model_name_or_path,
+                            weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant),
+                            cache_dir=cache_dir,
+                            force_download=force_download,
+                            resume_download=resume_download,
+                            proxies=proxies,
+                            local_files_only=local_files_only,
+                            token=token,
+                            revision=revision,
+                            subfolder=subfolder,
+                            user_agent=user_agent,
+                            commit_hash=commit_hash,
                         )
-
-                    unexpected_keys = load_model_dict_into_meta(
-                        model,
-                        state_dict,
-                        device=param_device,
-                        dtype=torch_dtype,
-                        model_name_or_path=pretrained_model_name_or_path,
+                    except IOError as e:
+                        if not allow_pickle:
+                            raise e
+                        pass
+                if model_file is None:
+                    model_file = _get_model_file(
+                        pretrained_model_name_or_path,
+                        weights_name=_add_variant(WEIGHTS_NAME, variant),
+                        cache_dir=cache_dir,
+                        force_download=force_download,
+                        resume_download=resume_download,
+                        proxies=proxies,
+                        local_files_only=local_files_only,
+                        token=token,
+                        revision=revision,
+                        subfolder=subfolder,
+                        user_agent=user_agent,
+                        commit_hash=commit_hash,
                     )
 
-                    if cls._keys_to_ignore_on_load_unexpected is not None:
-                        for pat in cls._keys_to_ignore_on_load_unexpected:
-                            unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
+                if low_cpu_mem_usage:
+                    # Instantiate model with empty weights
+                    with accelerate.init_empty_weights():
+                        model = cls.from_config(config, **unused_kwargs)
 
-                    if len(unexpected_keys) > 0:
-                        logger.warn(
-                            f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
-                        )
-
-                else:  # else let accelerate handle loading and dispatching.
-                    # Load weights and dispatch according to the device_map
-                    # by default the device_map is None and the weights are loaded on the CPU
-                    try:
-                        accelerate.load_checkpoint_and_dispatch(
-                            model,
-                            model_file,
-                            device_map,
-                            max_memory=max_memory,
-                            offload_folder=offload_folder,
-                            offload_state_dict=offload_state_dict,
-                            dtype=torch_dtype,
-                        )
-                    except AttributeError as e:
-                        # When using accelerate loading, we do not have the ability to load the state
-                        # dict and rename the weight names manually. Additionally, accelerate skips
-                        # torch loading conventions and directly writes into `module.{_buffers, _parameters}`
-                        # (which look like they should be private variables?), so we can't use the standard hooks
-                        # to rename parameters on load. We need to mimic the original weight names so the correct
-                        # attributes are available. After we have loaded the weights, we convert the deprecated
-                        # names to the new non-deprecated names. Then we _greatly encourage_ the user to convert
-                        # the weights so we don't have to do this again.
-
-                        if "'Attention' object has no attribute" in str(e):
-                            logger.warn(
-                                f"Taking `{str(e)}` while using `accelerate.load_checkpoint_and_dispatch` to mean {pretrained_model_name_or_path}"
-                                " was saved with deprecated attention block weight names. We will load it with the deprecated attention block"
-                                " names and convert them on the fly to the new attention block format. Please re-save the model after this conversion,"
-                                " so we don't have to do the on the fly renaming in the future. If the model is from a hub checkpoint,"
-                                " please also re-upload it or open a PR on the original repository."
+                    # if device_map is None, load the state dict and move the params from meta device to the cpu
+                    if device_map is None:
+                        param_device = "cpu"
+                        state_dict = load_state_dict(model_file, variant=variant)
+                        model._convert_deprecated_attention_blocks(state_dict)
+                        # move the params from meta device to cpu
+                        missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
+                        if len(missing_keys) > 0:
+                            raise ValueError(
+                                f"Cannot load {cls} from {pretrained_model_name_or_path} because the following keys are"
+                                f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass"
+                                " `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize"
+                                " those weights or else make sure your checkpoint file is correct."
                             )
-                            model._temp_convert_self_to_deprecated_attention_blocks()
+
+                        unexpected_keys = load_model_dict_into_meta(
+                            model,
+                            state_dict,
+                            device=param_device,
+                            dtype=torch_dtype,
+                            model_name_or_path=pretrained_model_name_or_path,
+                        )
+
+                        if cls._keys_to_ignore_on_load_unexpected is not None:
+                            for pat in cls._keys_to_ignore_on_load_unexpected:
+                                unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
+
+                        if len(unexpected_keys) > 0:
+                            logger.warn(
+                                f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
+                            )
+
+                    else:  # else let accelerate handle loading and dispatching.
+                        # Load weights and dispatch according to the device_map
+                        # by default the device_map is None and the weights are loaded on the CPU
+                        try:
                             accelerate.load_checkpoint_and_dispatch(
                                 model,
                                 model_file,
@@ -724,52 +709,80 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                                 offload_state_dict=offload_state_dict,
                                 dtype=torch_dtype,
                             )
-                            model._undo_temp_convert_self_to_deprecated_attention_blocks()
-                        else:
-                            raise e
+                        except AttributeError as e:
+                            # When using accelerate loading, we do not have the ability to load the state
+                            # dict and rename the weight names manually. Additionally, accelerate skips
+                            # torch loading conventions and directly writes into `module.{_buffers, _parameters}`
+                            # (which look like they should be private variables?), so we can't use the standard hooks
+                            # to rename parameters on load. We need to mimic the original weight names so the correct
+                            # attributes are available. After we have loaded the weights, we convert the deprecated
+                            # names to the new non-deprecated names. Then we _greatly encourage_ the user to convert
+                            # the weights so we don't have to do this again.
 
-                loading_info = {
-                    "missing_keys": [],
-                    "unexpected_keys": [],
-                    "mismatched_keys": [],
-                    "error_msgs": [],
-                }
-            else:
-                model = cls.from_config(config, **unused_kwargs)
+                            if "'Attention' object has no attribute" in str(e):
+                                logger.warn(
+                                    f"Taking `{str(e)}` while using `accelerate.load_checkpoint_and_dispatch` to mean {pretrained_model_name_or_path}"
+                                    " was saved with deprecated attention block weight names. We will load it with the deprecated attention block"
+                                    " names and convert them on the fly to the new attention block format. Please re-save the model after this conversion,"
+                                    " so we don't have to do the on the fly renaming in the future. If the model is from a hub checkpoint,"
+                                    " please also re-upload it or open a PR on the original repository."
+                                )
+                                model._temp_convert_self_to_deprecated_attention_blocks()
+                                accelerate.load_checkpoint_and_dispatch(
+                                    model,
+                                    model_file,
+                                    device_map,
+                                    max_memory=max_memory,
+                                    offload_folder=offload_folder,
+                                    offload_state_dict=offload_state_dict,
+                                    dtype=torch_dtype,
+                                )
+                                model._undo_temp_convert_self_to_deprecated_attention_blocks()
+                            else:
+                                raise e
 
-                state_dict = load_state_dict(model_file, variant=variant)
-                model._convert_deprecated_attention_blocks(state_dict)
+                    loading_info = {
+                        "missing_keys": [],
+                        "unexpected_keys": [],
+                        "mismatched_keys": [],
+                        "error_msgs": [],
+                    }
+                else:
+                    model = cls.from_config(config, **unused_kwargs)
 
-                model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(
-                    model,
-                    state_dict,
-                    model_file,
-                    pretrained_model_name_or_path,
-                    ignore_mismatched_sizes=ignore_mismatched_sizes,
+                    state_dict = load_state_dict(model_file, variant=variant)
+                    model._convert_deprecated_attention_blocks(state_dict)
+
+                    model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(
+                        model,
+                        state_dict,
+                        model_file,
+                        pretrained_model_name_or_path,
+                        ignore_mismatched_sizes=ignore_mismatched_sizes,
+                    )
+
+                    loading_info = {
+                        "missing_keys": missing_keys,
+                        "unexpected_keys": unexpected_keys,
+                        "mismatched_keys": mismatched_keys,
+                        "error_msgs": error_msgs,
+                    }
+
+            if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
+                raise ValueError(
+                    f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}."
                 )
+            elif torch_dtype is not None:
+                model = model.to(torch_dtype)
 
-                loading_info = {
-                    "missing_keys": missing_keys,
-                    "unexpected_keys": unexpected_keys,
-                    "mismatched_keys": mismatched_keys,
-                    "error_msgs": error_msgs,
-                }
+            model.register_to_config(_name_or_path=pretrained_model_name_or_path)
 
-        if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
-            raise ValueError(
-                f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}."
-            )
-        elif torch_dtype is not None:
-            model = model.to(torch_dtype)
+            # Set model in evaluation mode to deactivate DropOut modules by default
+            model.eval()
+            if output_loading_info:
+                return model, loading_info
 
-        model.register_to_config(_name_or_path=pretrained_model_name_or_path)
-
-        # Set model in evaluation mode to deactivate DropOut modules by default
-        model.eval()
-        if output_loading_info:
-            return model, loading_info
-
-        return model
+            return model
 
     @classmethod
     def _load_pretrained_model(

src/diffusers/pipelines/pipeline_utils.py

@@ -57,6 +57,7 @@ from ..utils import (
     is_accelerate_available,
     is_accelerate_version,
     is_peft_available,
+    is_single_file_checkpoint,
     is_torch_version,
     is_transformers_available,
     logging,
@@ -110,6 +111,20 @@ LOADABLE_CLASSES = {
     },
 }
 
+SINGLE_FILE_LOADABLE_CLASSES = {
+    "StableDiffusionPipeline",
+    "StableDiffusionImg2ImgPipeline",
+    "StableDiffusionInpaintPipeline",
+    "StableDiffusionUpscalePipeline",
+    "StableDiffusionControlNetPipeline",
+    "StableDiffusionControlNetImg2ImgPipeline",
+    "StableDiffusionControlNetInpaintPipeline",
+    "StableDiffusionXLPipeline",
+    "StableDiffusionXLImg2ImgPipeline",
+    "StableDiffusionXLInpaintPipeline",
+    "StableDiffusionXLControlNetImg2ImgPipeline",
+}
+
 ALL_IMPORTABLE_CLASSES = {}
 for library in LOADABLE_CLASSES:
     ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])
@@ -1056,308 +1071,334 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
         >>> pipeline.scheduler = scheduler
         ```
         """
-        cache_dir = kwargs.pop("cache_dir", None)
-        resume_download = kwargs.pop("resume_download", False)
-        force_download = kwargs.pop("force_download", False)
-        proxies = kwargs.pop("proxies", None)
-        local_files_only = kwargs.pop("local_files_only", None)
-        token = kwargs.pop("token", None)
-        revision = kwargs.pop("revision", None)
-        from_flax = kwargs.pop("from_flax", False)
-        torch_dtype = kwargs.pop("torch_dtype", None)
-        custom_pipeline = kwargs.pop("custom_pipeline", None)
-        custom_revision = kwargs.pop("custom_revision", None)
-        provider = kwargs.pop("provider", None)
-        sess_options = kwargs.pop("sess_options", None)
-        device_map = kwargs.pop("device_map", None)
-        max_memory = kwargs.pop("max_memory", None)
-        offload_folder = kwargs.pop("offload_folder", None)
-        offload_state_dict = kwargs.pop("offload_state_dict", False)
-        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
-        variant = kwargs.pop("variant", None)
-        use_safetensors = kwargs.pop("use_safetensors", None)
-        use_onnx = kwargs.pop("use_onnx", None)
-        load_connected_pipeline = kwargs.pop("load_connected_pipeline", False)
-
-        if low_cpu_mem_usage and not is_accelerate_available():
-            low_cpu_mem_usage = False
-            logger.warning(
-                "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
-                " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
-                " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
-                " install accelerate\n```\n."
-            )
-
-        if device_map is not None and not is_torch_version(">=", "1.9.0"):
-            raise NotImplementedError(
-                "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
-                " `device_map=None`."
-            )
-
-        if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
-            raise NotImplementedError(
-                "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
-                " `low_cpu_mem_usage=False`."
-            )
-
-        if low_cpu_mem_usage is False and device_map is not None:
-            raise ValueError(
-                f"You cannot set `low_cpu_mem_usage` to False while using device_map={device_map} for loading and"
-                " dispatching. Please make sure to set `low_cpu_mem_usage=True`."
-            )
-
-        # 1. Download the checkpoints and configs
-        # use snapshot download here to get it working from from_pretrained
-        if not os.path.isdir(pretrained_model_name_or_path):
-            if pretrained_model_name_or_path.count("/") > 1:
+        if is_single_file_checkpoint(pretrained_model_name_or_path):
+            if cls.__name__ not in SINGLE_FILE_LOADABLE_CLASSES:
                 raise ValueError(
                     f'The provided pretrained_model_name_or_path "{pretrained_model_name_or_path}"'
                     " is neither a valid local path nor a valid repo id. Please check the parameter."
+                    f"{cls.__name__} is not supported. Supported classes are: {' '.join(list(SINGLE_FILE_LOADABLE_CLASSES))}."
                 )
-            cached_folder = cls.download(
-                pretrained_model_name_or_path,
-                cache_dir=cache_dir,
-                resume_download=resume_download,
-                force_download=force_download,
-                proxies=proxies,
-                local_files_only=local_files_only,
-                token=token,
-                revision=revision,
-                from_flax=from_flax,
-                use_safetensors=use_safetensors,
-                use_onnx=use_onnx,
-                custom_pipeline=custom_pipeline,
-                custom_revision=custom_revision,
-                variant=variant,
-                load_connected_pipeline=load_connected_pipeline,
-                **kwargs,
-            )
+            logger.info("Single file checkpoint detected...")
+            model = cls.from_single_file(pretrained_model_name_or_path, **kwargs)
+            return model
+
         else:
-            cached_folder = pretrained_model_name_or_path
+            cache_dir = kwargs.pop("cache_dir", None)
+            resume_download = kwargs.pop("resume_download", False)
+            force_download = kwargs.pop("force_download", False)
+            proxies = kwargs.pop("proxies", None)
+            local_files_only = kwargs.pop("local_files_only", None)
+            token = kwargs.pop("token", None)
+            revision = kwargs.pop("revision", None)
+            from_flax = kwargs.pop("from_flax", False)
+            torch_dtype = kwargs.pop("torch_dtype", None)
+            custom_pipeline = kwargs.pop("custom_pipeline", None)
+            custom_revision = kwargs.pop("custom_revision", None)
+            provider = kwargs.pop("provider", None)
+            sess_options = kwargs.pop("sess_options", None)
+            device_map = kwargs.pop("device_map", None)
+            max_memory = kwargs.pop("max_memory", None)
+            offload_folder = kwargs.pop("offload_folder", None)
+            offload_state_dict = kwargs.pop("offload_state_dict", False)
+            low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
+            variant = kwargs.pop("variant", None)
+            use_safetensors = kwargs.pop("use_safetensors", None)
+            use_onnx = kwargs.pop("use_onnx", None)
+            load_connected_pipeline = kwargs.pop("load_connected_pipeline", False)
 
-        config_dict = cls.load_config(cached_folder)
-
-        # pop out "_ignore_files" as it is only needed for download
-        config_dict.pop("_ignore_files", None)
-
-        # 2. Define which model components should load variants
-        # We retrieve the information by matching whether variant
-        # model checkpoints exist in the subfolders
-        model_variants = {}
-        if variant is not None:
-            for folder in os.listdir(cached_folder):
-                folder_path = os.path.join(cached_folder, folder)
-                is_folder = os.path.isdir(folder_path) and folder in config_dict
-                variant_exists = is_folder and any(
-                    p.split(".")[1].startswith(variant) for p in os.listdir(folder_path)
-                )
-                if variant_exists:
-                    model_variants[folder] = variant
-
-        # 3. Load the pipeline class, if using custom module then load it from the hub
-        # if we load from explicit class, let's use it
-        custom_class_name = None
-        if os.path.isfile(os.path.join(cached_folder, f"{custom_pipeline}.py")):
-            custom_pipeline = os.path.join(cached_folder, f"{custom_pipeline}.py")
-        elif isinstance(config_dict["_class_name"], (list, tuple)) and os.path.isfile(
-            os.path.join(cached_folder, f"{config_dict['_class_name'][0]}.py")
-        ):
-            custom_pipeline = os.path.join(cached_folder, f"{config_dict['_class_name'][0]}.py")
-            custom_class_name = config_dict["_class_name"][1]
-
-        pipeline_class = _get_pipeline_class(
-            cls,
-            config_dict,
-            load_connected_pipeline=load_connected_pipeline,
-            custom_pipeline=custom_pipeline,
-            class_name=custom_class_name,
-            cache_dir=cache_dir,
-            revision=custom_revision,
-        )
-
-        # DEPRECATED: To be removed in 1.0.0
-        if pipeline_class.__name__ == "StableDiffusionInpaintPipeline" and version.parse(
-            version.parse(config_dict["_diffusers_version"]).base_version
-        ) <= version.parse("0.5.1"):
-            from diffusers import StableDiffusionInpaintPipeline, StableDiffusionInpaintPipelineLegacy
-
-            pipeline_class = StableDiffusionInpaintPipelineLegacy
-
-            deprecation_message = (
-                "You are using a legacy checkpoint for inpainting with Stable Diffusion, therefore we are loading the"
-                f" {StableDiffusionInpaintPipelineLegacy} class instead of {StableDiffusionInpaintPipeline}. For"
-                " better inpainting results, we strongly suggest using Stable Diffusion's official inpainting"
-                " checkpoint: https://huggingface.co/runwayml/stable-diffusion-inpainting instead or adapting your"
-                f" checkpoint {pretrained_model_name_or_path} to the format of"
-                " https://huggingface.co/runwayml/stable-diffusion-inpainting. Note that we do not actively maintain"
-                " the {StableDiffusionInpaintPipelineLegacy} class and will likely remove it in version 1.0.0."
-            )
-            deprecate("StableDiffusionInpaintPipelineLegacy", "1.0.0", deprecation_message, standard_warn=False)
-
-        # 4. Define expected modules given pipeline signature
-        # and define non-None initialized modules (=`init_kwargs`)
-
-        # some modules can be passed directly to the init
-        # in this case they are already instantiated in `kwargs`
-        # extract them here
-        expected_modules, optional_kwargs = cls._get_signature_keys(pipeline_class)
-        passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
-        passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
-
-        init_dict, unused_kwargs, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
-
-        # define init kwargs and make sure that optional component modules are filtered out
-        init_kwargs = {
-            k: init_dict.pop(k)
-            for k in optional_kwargs
-            if k in init_dict and k not in pipeline_class._optional_components
-        }
-        init_kwargs = {**init_kwargs, **passed_pipe_kwargs}
-
-        # remove `null` components
-        def load_module(name, value):
-            if value[0] is None:
-                return False
-            if name in passed_class_obj and passed_class_obj[name] is None:
-                return False
-            return True
-
-        init_dict = {k: v for k, v in init_dict.items() if load_module(k, v)}
-
-        # Special case: safety_checker must be loaded separately when using `from_flax`
-        if from_flax and "safety_checker" in init_dict and "safety_checker" not in passed_class_obj:
-            raise NotImplementedError(
-                "The safety checker cannot be automatically loaded when loading weights `from_flax`."
-                " Please, pass `safety_checker=None` to `from_pretrained`, and load the safety checker"
-                " separately if you need it."
-            )
-
-        # 5. Throw nice warnings / errors for fast accelerate loading
-        if len(unused_kwargs) > 0:
-            logger.warning(
-                f"Keyword arguments {unused_kwargs} are not expected by {pipeline_class.__name__} and will be ignored."
-            )
-
-        # import it here to avoid circular import
-        from diffusers import pipelines
-
-        # 6. Load each module in the pipeline
-        for name, (library_name, class_name) in logging.tqdm(init_dict.items(), desc="Loading pipeline components..."):
-            # 6.1 - now that JAX/Flax is an official framework of the library, we might load from Flax names
-            class_name = class_name[4:] if class_name.startswith("Flax") else class_name
-
-            # 6.2 Define all importable classes
-            is_pipeline_module = hasattr(pipelines, library_name)
-            importable_classes = ALL_IMPORTABLE_CLASSES
-            loaded_sub_model = None
-
-            # 6.3 Use passed sub model or load class_name from library_name
-            if name in passed_class_obj:
-                # if the model is in a pipeline module, then we load it from the pipeline
-                # check that passed_class_obj has correct parent class
-                maybe_raise_or_warn(
-                    library_name, library, class_name, importable_classes, passed_class_obj, name, is_pipeline_module
+            if low_cpu_mem_usage and not is_accelerate_available():
+                low_cpu_mem_usage = False
+                logger.warning(
+                    "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
+                    " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
+                    " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
+                    " install accelerate\n```\n."
                 )
 
-                loaded_sub_model = passed_class_obj[name]
-            else:
-                # load sub model
-                loaded_sub_model = load_sub_model(
-                    library_name=library_name,
-                    class_name=class_name,
-                    importable_classes=importable_classes,
-                    pipelines=pipelines,
-                    is_pipeline_module=is_pipeline_module,
-                    pipeline_class=pipeline_class,
-                    torch_dtype=torch_dtype,
-                    provider=provider,
-                    sess_options=sess_options,
-                    device_map=device_map,
-                    max_memory=max_memory,
-                    offload_folder=offload_folder,
-                    offload_state_dict=offload_state_dict,
-                    model_variants=model_variants,
-                    name=name,
+            if device_map is not None and not is_torch_version(">=", "1.9.0"):
+                raise NotImplementedError(
+                    "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
+                    " `device_map=None`."
+                )
+
+            if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
+                raise NotImplementedError(
+                    "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
+                    " `low_cpu_mem_usage=False`."
+                )
+
+            if low_cpu_mem_usage is False and device_map is not None:
+                raise ValueError(
+                    f"You cannot set `low_cpu_mem_usage` to False while using device_map={device_map} for loading and"
+                    " dispatching. Please make sure to set `low_cpu_mem_usage=True`."
+                )
+
+            # 1. Download the checkpoints and configs
+            # use snapshot download here to get it working from from_pretrained
+            if not os.path.isdir(pretrained_model_name_or_path):
+                if pretrained_model_name_or_path.count("/") > 1:
+                    raise ValueError(
+                        f'The provided pretrained_model_name_or_path "{pretrained_model_name_or_path}"'
+                        " is neither a valid local path nor a valid repo id. Please check the parameter."
+                    )
+                cached_folder = cls.download(
+                    pretrained_model_name_or_path,
+                    cache_dir=cache_dir,
+                    resume_download=resume_download,
+                    force_download=force_download,
+                    proxies=proxies,
+                    local_files_only=local_files_only,
+                    token=token,
+                    revision=revision,
                     from_flax=from_flax,
+                    use_safetensors=use_safetensors,
+                    use_onnx=use_onnx,
+                    custom_pipeline=custom_pipeline,
+                    custom_revision=custom_revision,
                     variant=variant,
-                    low_cpu_mem_usage=low_cpu_mem_usage,
-                    cached_folder=cached_folder,
-                )
-                logger.info(
-                    f"Loaded {name} as {class_name} from `{name}` subfolder of {pretrained_model_name_or_path}."
+                    load_connected_pipeline=load_connected_pipeline,
+                    **kwargs,
                 )
+            else:
+                cached_folder = pretrained_model_name_or_path
 
-            init_kwargs[name] = loaded_sub_model  # UNet(...), # DiffusionSchedule(...)
+            config_dict = cls.load_config(cached_folder)
 
-        if pipeline_class._load_connected_pipes and os.path.isfile(os.path.join(cached_folder, "README.md")):
-            modelcard = ModelCard.load(os.path.join(cached_folder, "README.md"))
-            connected_pipes = {prefix: getattr(modelcard.data, prefix, [None])[0] for prefix in CONNECTED_PIPES_KEYS}
-            load_kwargs = {
-                "cache_dir": cache_dir,
-                "resume_download": resume_download,
-                "force_download": force_download,
-                "proxies": proxies,
-                "local_files_only": local_files_only,
-                "token": token,
-                "revision": revision,
-                "torch_dtype": torch_dtype,
-                "custom_pipeline": custom_pipeline,
-                "custom_revision": custom_revision,
-                "provider": provider,
-                "sess_options": sess_options,
-                "device_map": device_map,
-                "max_memory": max_memory,
-                "offload_folder": offload_folder,
-                "offload_state_dict": offload_state_dict,
-                "low_cpu_mem_usage": low_cpu_mem_usage,
-                "variant": variant,
-                "use_safetensors": use_safetensors,
-            }
+            # pop out "_ignore_files" as it is only needed for download
+            config_dict.pop("_ignore_files", None)
 
-            def get_connected_passed_kwargs(prefix):
-                connected_passed_class_obj = {
-                    k.replace(f"{prefix}_", ""): w for k, w in passed_class_obj.items() if k.split("_")[0] == prefix
-                }
-                connected_passed_pipe_kwargs = {
-                    k.replace(f"{prefix}_", ""): w for k, w in passed_pipe_kwargs.items() if k.split("_")[0] == prefix
-                }
+            # 2. Define which model components should load variants
+            # We retrieve the information by matching whether variant
+            # model checkpoints exist in the subfolders
+            model_variants = {}
+            if variant is not None:
+                for folder in os.listdir(cached_folder):
+                    folder_path = os.path.join(cached_folder, folder)
+                    is_folder = os.path.isdir(folder_path) and folder in config_dict
+                    variant_exists = is_folder and any(
+                        p.split(".")[1].startswith(variant) for p in os.listdir(folder_path)
+                    )
+                    if variant_exists:
+                        model_variants[folder] = variant
 
-                connected_passed_kwargs = {**connected_passed_class_obj, **connected_passed_pipe_kwargs}
-                return connected_passed_kwargs
+            # 3. Load the pipeline class, if using custom module then load it from the hub
+            # if we load from explicit class, let's use it
+            custom_class_name = None
+            if os.path.isfile(os.path.join(cached_folder, f"{custom_pipeline}.py")):
+                custom_pipeline = os.path.join(cached_folder, f"{custom_pipeline}.py")
+            elif isinstance(config_dict["_class_name"], (list, tuple)) and os.path.isfile(
+                os.path.join(cached_folder, f"{config_dict['_class_name'][0]}.py")
+            ):
+                custom_pipeline = os.path.join(cached_folder, f"{config_dict['_class_name'][0]}.py")
+                custom_class_name = config_dict["_class_name"][1]
 
-            connected_pipes = {
-                prefix: DiffusionPipeline.from_pretrained(
-                    repo_id, **load_kwargs.copy(), **get_connected_passed_kwargs(prefix)
-                )
-                for prefix, repo_id in connected_pipes.items()
-                if repo_id is not None
-            }
-
-            for prefix, connected_pipe in connected_pipes.items():
-                # add connected pipes to `init_kwargs` with <prefix>_<component_name>, e.g. "prior_text_encoder"
-                init_kwargs.update(
-                    {"_".join([prefix, name]): component for name, component in connected_pipe.components.items()}
-                )
-
-        # 7. Potentially add passed objects if expected
-        missing_modules = set(expected_modules) - set(init_kwargs.keys())
-        passed_modules = list(passed_class_obj.keys())
-        optional_modules = pipeline_class._optional_components
-        if len(missing_modules) > 0 and missing_modules <= set(passed_modules + optional_modules):
-            for module in missing_modules:
-                init_kwargs[module] = passed_class_obj.get(module, None)
-        elif len(missing_modules) > 0:
-            passed_modules = set(list(init_kwargs.keys()) + list(passed_class_obj.keys())) - optional_kwargs
-            raise ValueError(
-                f"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed."
+            pipeline_class = _get_pipeline_class(
+                cls,
+                config_dict,
+                load_connected_pipeline=load_connected_pipeline,
+                custom_pipeline=custom_pipeline,
+                class_name=custom_class_name,
+                cache_dir=cache_dir,
+                revision=custom_revision,
             )
 
-        # 8. Instantiate the pipeline
-        model = pipeline_class(**init_kwargs)
+            # DEPRECATED: To be removed in 1.0.0
+            if pipeline_class.__name__ == "StableDiffusionInpaintPipeline" and version.parse(
+                version.parse(config_dict["_diffusers_version"]).base_version
+            ) <= version.parse("0.5.1"):
+                from diffusers import StableDiffusionInpaintPipeline, StableDiffusionInpaintPipelineLegacy
 
-        # 9. Save where the model was instantiated from
-        model.register_to_config(_name_or_path=pretrained_model_name_or_path)
-        return model
+                pipeline_class = StableDiffusionInpaintPipelineLegacy
+
+                deprecation_message = (
+                    "You are using a legacy checkpoint for inpainting with Stable Diffusion, therefore we are loading the"
+                    f" {StableDiffusionInpaintPipelineLegacy} class instead of {StableDiffusionInpaintPipeline}. For"
+                    " better inpainting results, we strongly suggest using Stable Diffusion's official inpainting"
+                    " checkpoint: https://huggingface.co/runwayml/stable-diffusion-inpainting instead or adapting your"
+                    f" checkpoint {pretrained_model_name_or_path} to the format of"
+                    " https://huggingface.co/runwayml/stable-diffusion-inpainting. Note that we do not actively maintain"
+                    " the {StableDiffusionInpaintPipelineLegacy} class and will likely remove it in version 1.0.0."
+                )
+                deprecate("StableDiffusionInpaintPipelineLegacy", "1.0.0", deprecation_message, standard_warn=False)
+
+            # 4. Define expected modules given pipeline signature
+            # and define non-None initialized modules (=`init_kwargs`)
+
+            # some modules can be passed directly to the init
+            # in this case they are already instantiated in `kwargs`
+            # extract them here
+            expected_modules, optional_kwargs = cls._get_signature_keys(pipeline_class)
+            passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
+            passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
+
+            init_dict, unused_kwargs, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
+
+            # define init kwargs and make sure that optional component modules are filtered out
+            init_kwargs = {
+                k: init_dict.pop(k)
+                for k in optional_kwargs
+                if k in init_dict and k not in pipeline_class._optional_components
+            }
+            init_kwargs = {**init_kwargs, **passed_pipe_kwargs}
+
+            # remove `null` components
+            def load_module(name, value):
+                if value[0] is None:
+                    return False
+                if name in passed_class_obj and passed_class_obj[name] is None:
+                    return False
+                return True
+
+            init_dict = {k: v for k, v in init_dict.items() if load_module(k, v)}
+
+            # Special case: safety_checker must be loaded separately when using `from_flax`
+            if from_flax and "safety_checker" in init_dict and "safety_checker" not in passed_class_obj:
+                raise NotImplementedError(
+                    "The safety checker cannot be automatically loaded when loading weights `from_flax`."
+                    " Please, pass `safety_checker=None` to `from_pretrained`, and load the safety checker"
+                    " separately if you need it."
+                )
+
+            # 5. Throw nice warnings / errors for fast accelerate loading
+            if len(unused_kwargs) > 0:
+                logger.warning(
+                    f"Keyword arguments {unused_kwargs} are not expected by {pipeline_class.__name__} and will be ignored."
+                )
+
+            # import it here to avoid circular import
+            from diffusers import pipelines
+
+            # 6. Load each module in the pipeline
+            for name, (library_name, class_name) in logging.tqdm(
+                init_dict.items(), desc="Loading pipeline components..."
+            ):
+                # 6.1 - now that JAX/Flax is an official framework of the library, we might load from Flax names
+                class_name = class_name[4:] if class_name.startswith("Flax") else class_name
+
+                # 6.2 Define all importable classes
+                is_pipeline_module = hasattr(pipelines, library_name)
+                importable_classes = ALL_IMPORTABLE_CLASSES
+                loaded_sub_model = None
+
+                # 6.3 Use passed sub model or load class_name from library_name
+                if name in passed_class_obj:
+                    # if the model is in a pipeline module, then we load it from the pipeline
+                    # check that passed_class_obj has correct parent class
+                    maybe_raise_or_warn(
+                        library_name,
+                        library,
+                        class_name,
+                        importable_classes,
+                        passed_class_obj,
+                        name,
+                        is_pipeline_module,
+                    )
+
+                    loaded_sub_model = passed_class_obj[name]
+                else:
+                    # load sub model
+                    loaded_sub_model = load_sub_model(
+                        library_name=library_name,
+                        class_name=class_name,
+                        importable_classes=importable_classes,
+                        pipelines=pipelines,
+                        is_pipeline_module=is_pipeline_module,
+                        pipeline_class=pipeline_class,
+                        torch_dtype=torch_dtype,
+                        provider=provider,
+                        sess_options=sess_options,
+                        device_map=device_map,
+                        max_memory=max_memory,
+                        offload_folder=offload_folder,
+                        offload_state_dict=offload_state_dict,
+                        model_variants=model_variants,
+                        name=name,
+                        from_flax=from_flax,
+                        variant=variant,
+                        low_cpu_mem_usage=low_cpu_mem_usage,
+                        cached_folder=cached_folder,
+                    )
+                    logger.info(
+                        f"Loaded {name} as {class_name} from `{name}` subfolder of {pretrained_model_name_or_path}."
+                    )
+
+                init_kwargs[name] = loaded_sub_model  # UNet(...), # DiffusionSchedule(...)
+
+            if pipeline_class._load_connected_pipes and os.path.isfile(os.path.join(cached_folder, "README.md")):
+                modelcard = ModelCard.load(os.path.join(cached_folder, "README.md"))
+                connected_pipes = {
+                    prefix: getattr(modelcard.data, prefix, [None])[0] for prefix in CONNECTED_PIPES_KEYS
+                }
+                load_kwargs = {
+                    "cache_dir": cache_dir,
+                    "resume_download": resume_download,
+                    "force_download": force_download,
+                    "proxies": proxies,
+                    "local_files_only": local_files_only,
+                    "token": token,
+                    "revision": revision,
+                    "torch_dtype": torch_dtype,
+                    "custom_pipeline": custom_pipeline,
+                    "custom_revision": custom_revision,
+                    "provider": provider,
+                    "sess_options": sess_options,
+                    "device_map": device_map,
+                    "max_memory": max_memory,
+                    "offload_folder": offload_folder,
+                    "offload_state_dict": offload_state_dict,
+                    "low_cpu_mem_usage": low_cpu_mem_usage,
+                    "variant": variant,
+                    "use_safetensors": use_safetensors,
+                }
+
+                def get_connected_passed_kwargs(prefix):
+                    connected_passed_class_obj = {
+                        k.replace(f"{prefix}_", ""): w
+                        for k, w in passed_class_obj.items()
+                        if k.split("_")[0] == prefix
+                    }
+                    connected_passed_pipe_kwargs = {
+                        k.replace(f"{prefix}_", ""): w
+                        for k, w in passed_pipe_kwargs.items()
+                        if k.split("_")[0] == prefix
+                    }
+
+                    connected_passed_kwargs = {**connected_passed_class_obj, **connected_passed_pipe_kwargs}
+                    return connected_passed_kwargs
+
+                connected_pipes = {
+                    prefix: DiffusionPipeline.from_pretrained(
+                        repo_id, **load_kwargs.copy(), **get_connected_passed_kwargs(prefix)
+                    )
+                    for prefix, repo_id in connected_pipes.items()
+                    if repo_id is not None
+                }
+
+                for prefix, connected_pipe in connected_pipes.items():
+                    # add connected pipes to `init_kwargs` with <prefix>_<component_name>, e.g. "prior_text_encoder"
+                    init_kwargs.update(
+                        {"_".join([prefix, name]): component for name, component in connected_pipe.components.items()}
+                    )
+
+            # 7. Potentially add passed objects if expected
+            missing_modules = set(expected_modules) - set(init_kwargs.keys())
+            passed_modules = list(passed_class_obj.keys())
+            optional_modules = pipeline_class._optional_components
+            if len(missing_modules) > 0 and missing_modules <= set(passed_modules + optional_modules):
+                for module in missing_modules:
+                    init_kwargs[module] = passed_class_obj.get(module, None)
+            elif len(missing_modules) > 0:
+                passed_modules = set(list(init_kwargs.keys()) + list(passed_class_obj.keys())) - optional_kwargs
+                raise ValueError(
+                    f"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed."
+                )
+
+            # 8. Instantiate the pipeline
+            model = pipeline_class(**init_kwargs)
+
+            # 9. Save where the model was instantiated from
+            model.register_to_config(_name_or_path=pretrained_model_name_or_path)
+            return model
 
     @property
     def name_or_path(self) -> str:

src/diffusers/utils/__init__.py

@@ -19,6 +19,7 @@ from packaging import version
 
 from .. import __version__
 from .constants import (
+    _ACCEPTED_SINGLE_FILE_FORMATS,
     CONFIG_NAME,
     DEPRECATED_REVISION_ARGS,
     DIFFUSERS_DYNAMIC_MODULE_NAME,
@@ -83,7 +84,7 @@ from .import_utils import (
     is_xformers_available,
     requires_backends,
 )
-from .loading_utils import load_image
+from .loading_utils import is_single_file_checkpoint, load_image
 from .logging import get_logger
 from .outputs import BaseOutput
 from .peft_utils import (

src/diffusers/utils/constants.py

@@ -37,6 +37,7 @@ HUGGINGFACE_CO_RESOLVE_ENDPOINT = os.environ.get("HF_ENDPOINT", "https://hugging
 DIFFUSERS_DYNAMIC_MODULE_NAME = "diffusers_modules"
 HF_MODULES_CACHE = os.getenv("HF_MODULES_CACHE", os.path.join(HF_HOME, "modules"))
 DEPRECATED_REVISION_ARGS = ["fp16", "non-ema"]
+_ACCEPTED_SINGLE_FILE_FORMATS = (".safetensors", ".ckpt", ".bin", ".pth", ".pt")
 
 # Below should be `True` if the current version of `peft` and `transformers` are compatible with
 # PEFT backend. Will automatically fall back to PEFT backend if the correct versions of the libraries are

src/diffusers/utils/loading_utils.py

@@ -1,10 +1,28 @@
 import os
 from typing import Callable, Union
+from urllib.parse import urlparse
 
 import PIL.Image
 import PIL.ImageOps
 import requests
 
+from ..utils.constants import _ACCEPTED_SINGLE_FILE_FORMATS
+
+
+def is_single_file_checkpoint(filepath):
+    def is_valid_url(url):
+        result = urlparse(url)
+        if result.scheme and result.netloc:
+            return True
+
+    filepath = str(filepath)
+    if filepath.endswith(_ACCEPTED_SINGLE_FILE_FORMATS):
+        if is_valid_url(filepath):
+            return True
+        elif os.path.isfile(filepath):
+            return True
+    return False
+
 
 def load_image(
     image: Union[str, PIL.Image.Image], convert_method: Callable[[PIL.Image.Image], PIL.Image.Image] = None