diff --git a/src/diffusers/pipelines/components_manager.py b/src/diffusers/pipelines/components_manager.py
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+# Copyright 2024 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 collections import OrderedDict
+from itertools import combinations
+from typing import List, Optional, Union
+
+import torch
+
+from ..utils import (
+    is_accelerate_available,
+    logging,
+)
+
+
+if is_accelerate_available():
+    from accelerate.hooks import ModelHook, add_hook_to_module, remove_hook_from_module
+    from accelerate.state import PartialState
+    from accelerate.utils import send_to_device
+    from accelerate.utils.memory import clear_device_cache
+    from accelerate.utils.modeling import convert_file_size_to_int
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+# YiYi Notes: copied from modeling_utils.py (decide later where to put this)
+def get_memory_footprint(self, return_buffers=True):
+    r"""
+    Get the memory footprint of a model. This will return the memory footprint of the current model in bytes. Useful to
+    benchmark the memory footprint of the current model and design some tests. Solution inspired from the PyTorch
+    discussions: https://discuss.pytorch.org/t/gpu-memory-that-model-uses/56822/2
+
+    Arguments:
+        return_buffers (`bool`, *optional*, defaults to `True`):
+            Whether to return the size of the buffer tensors in the computation of the memory footprint. Buffers are
+            tensors that do not require gradients and not registered as parameters. E.g. mean and std in batch norm
+            layers. Please see: https://discuss.pytorch.org/t/what-pytorch-means-by-buffers/120266/2
+    """
+    mem = sum([param.nelement() * param.element_size() for param in self.parameters()])
+    if return_buffers:
+        mem_bufs = sum([buf.nelement() * buf.element_size() for buf in self.buffers()])
+        mem = mem + mem_bufs
+    return mem
+
+
+class CustomOffloadHook(ModelHook):
+    """
+    A hook that offloads a model on the CPU until its forward pass is called. It ensures the model and its inputs are
+    on the given device. Optionally offloads other models to the CPU before the forward pass is called.
+
+    Args:
+        execution_device(`str`, `int` or `torch.device`, *optional*):
+            The device on which the model should be executed. Will default to the MPS device if it's available, then
+            GPU 0 if there is a GPU, and finally to the CPU.
+    """
+
+    def __init__(
+        self,
+        execution_device: Optional[Union[str, int, torch.device]] = None,
+        other_hooks: Optional[List["UserCustomOffloadHook"]] = None,
+        offload_strategy: Optional["AutoOffloadStrategy"] = None,
+    ):
+        self.execution_device = execution_device if execution_device is not None else PartialState().default_device
+        self.other_hooks = other_hooks
+        self.offload_strategy = offload_strategy
+        self.model_id = None
+
+    def set_strategy(self, offload_strategy: "AutoOffloadStrategy"):
+        self.offload_strategy = offload_strategy
+
+    def add_other_hook(self, hook: "UserCustomOffloadHook"):
+        """
+        Add a hook to the list of hooks to consider for offloading.
+        """
+        if self.other_hooks is None:
+            self.other_hooks = []
+        self.other_hooks.append(hook)
+
+    def init_hook(self, module):
+        return module.to("cpu")
+
+    def pre_forward(self, module, *args, **kwargs):
+        if module.device != self.execution_device:
+            if self.other_hooks is not None:
+                hooks_to_offload = [hook for hook in self.other_hooks if hook.model.device == self.execution_device]
+                # offload all other hooks
+                import time
+
+                # YiYi Notes: only logging time for now to monitor the overhead of offloading strategy (remove later)
+                start_time = time.perf_counter()
+                if self.offload_strategy is not None:
+                    hooks_to_offload = self.offload_strategy(
+                        hooks=hooks_to_offload,
+                        model_id=self.model_id,
+                        model=module,
+                        execution_device=self.execution_device,
+                    )
+                end_time = time.perf_counter()
+                logger.info(
+                    f" time taken to apply offload strategy for {self.model_id}: {(end_time - start_time):.2f} seconds"
+                )
+
+                for hook in hooks_to_offload:
+                    logger.info(
+                        f"moving {self.model_id} to {self.execution_device}, offloading {hook.model_id} to cpu"
+                    )
+                    hook.offload()
+
+                if hooks_to_offload:
+                    clear_device_cache()
+            module.to(self.execution_device)
+        return send_to_device(args, self.execution_device), send_to_device(kwargs, self.execution_device)
+
+
+class UserCustomOffloadHook:
+    """
+    A simple hook grouping a model and a `CustomOffloadHook`, which provides easy APIs for to call the init method of
+    the hook or remove it entirely.
+    """
+
+    def __init__(self, model_id, model, hook):
+        self.model_id = model_id
+        self.model = model
+        self.hook = hook
+
+    def offload(self):
+        self.hook.init_hook(self.model)
+
+    def attach(self):
+        add_hook_to_module(self.model, self.hook)
+        self.hook.model_id = self.model_id
+
+    def remove(self):
+        remove_hook_from_module(self.model)
+        self.hook.model_id = None
+
+    def add_other_hook(self, hook: "UserCustomOffloadHook"):
+        self.hook.add_other_hook(hook)
+
+
+def custom_offload_with_hook(
+    model_id: str,
+    model: torch.nn.Module,
+    execution_device: Union[str, int, torch.device] = None,
+    offload_strategy: Optional["AutoOffloadStrategy"] = None,
+):
+    hook = CustomOffloadHook(execution_device=execution_device, offload_strategy=offload_strategy)
+    user_hook = UserCustomOffloadHook(model_id=model_id, model=model, hook=hook)
+    user_hook.attach()
+    return user_hook
+
+
+class AutoOffloadStrategy:
+    """
+    Offload strategy that should be used with `CustomOffloadHook` to automatically offload models to the CPU based on
+    the available memory on the device.
+    """
+
+    def __init__(self, memory_reserve_margin="3GB"):
+        self.memory_reserve_margin = convert_file_size_to_int(memory_reserve_margin)
+
+    def __call__(self, hooks, model_id, model, execution_device):
+        if len(hooks) == 0:
+            return []
+
+        current_module_size = get_memory_footprint(model)
+
+        mem_on_device = torch.cuda.mem_get_info(execution_device.index)[0]
+        mem_on_device = mem_on_device - self.memory_reserve_margin
+        if current_module_size < mem_on_device:
+            return []
+
+        min_memory_offload = current_module_size - mem_on_device
+        logger.info(f" search for models to offload in order to free up {min_memory_offload / 1024**3:.2f} GB memory")
+
+        # exlucde models that's not currently loaded on the device
+        module_sizes = dict(
+            sorted(
+                {hook.model_id: get_memory_footprint(hook.model) for hook in hooks}.items(),
+                key=lambda x: x[1],
+                reverse=True,
+            )
+        )
+
+        def search_best_candidate(module_sizes, min_memory_offload):
+            """
+            search the optimal combination of models to offload to cpu, given a dictionary of module sizes and a
+            minimum memory offload size. the combination of models should add up to the smallest modulesize that is
+            larger than `min_memory_offload`
+            """
+            model_ids = list(module_sizes.keys())
+            best_candidate = None
+            best_size = float("inf")
+            for r in range(1, len(model_ids) + 1):
+                for candidate_model_ids in combinations(model_ids, r):
+                    candidate_size = sum(
+                        module_sizes[candidate_model_id] for candidate_model_id in candidate_model_ids
+                    )
+                    if candidate_size < min_memory_offload:
+                        continue
+                    else:
+                        if best_candidate is None or candidate_size < best_size:
+                            best_candidate = candidate_model_ids
+                            best_size = candidate_size
+
+            return best_candidate
+
+        best_offload_model_ids = search_best_candidate(module_sizes, min_memory_offload)
+
+        if best_offload_model_ids is None:
+            # if no combination is found, meaning that we cannot meet the memory requirement, offload all models
+            logger.warning("no combination of models to offload to cpu is found, offloading all models")
+            hooks_to_offload = hooks
+        else:
+            hooks_to_offload = [hook for hook in hooks if hook.model_id in best_offload_model_ids]
+
+        return hooks_to_offload
+
+
+class ComponentsManager:
+    def __init__(self):
+        self.components = OrderedDict()
+        self.model_hooks = None
+        self._auto_offload_enabled = False
+
+    def add(self, name, component):
+        if name not in self.components:
+            self.components[name] = component
+            if self._auto_offload_enabled:
+                self.enable_auto_cpu_offload(self._auto_offload_device)
+
+    def remove(self, name):
+        self.components.pop(name)
+        if self._auto_offload_enabled:
+            self.enable_auto_cpu_offload(self._auto_offload_device)
+
+    def get(self, names: Union[str, List[str]]):
+        if isinstance(names, str):
+            if names not in self.components:
+                raise ValueError(f"Component '{names}' not found in ComponentsManager")
+            return self.components[names]
+        elif isinstance(names, list):
+            return {n: self.components[n] for n in names}
+        else:
+            raise ValueError(f"Invalid type for names: {type(names)}")
+
+    def enable_auto_cpu_offload(self, device, memory_reserve_margin="3GB"):
+        for name, component in self.components.items():
+            if isinstance(component, torch.nn.Module) and hasattr(component, "_hf_hook"):
+                remove_hook_from_module(component, recurse=True)
+
+        self.disable_auto_cpu_offload()
+        offload_strategy = AutoOffloadStrategy(memory_reserve_margin=memory_reserve_margin)
+        device = torch.device(device)
+        if device.index is None:
+            device = torch.device(f"{device.type}:{0}")
+        all_hooks = []
+        for name, component in self.components.items():
+            if isinstance(component, torch.nn.Module):
+                hook = custom_offload_with_hook(name, component, device, offload_strategy=offload_strategy)
+                all_hooks.append(hook)
+
+        for hook in all_hooks:
+            other_hooks = [h for h in all_hooks if h is not hook]
+            for other_hook in other_hooks:
+                if other_hook.hook.execution_device == hook.hook.execution_device:
+                    hook.add_other_hook(other_hook)
+
+        self.model_hooks = all_hooks
+        self._auto_offload_enabled = True
+        self._auto_offload_device = device
+
+    def disable_auto_cpu_offload(self):
+        if self.model_hooks is None:
+            self._auto_offload_enabled = False
+            return
+
+        for hook in self.model_hooks:
+            hook.offload()
+            hook.remove()
+        if self.model_hooks:
+            clear_device_cache()
+        self.model_hooks = None
+        self._auto_offload_enabled = False
+
+    def __repr__(self):
+        col_widths = {
+            "id": max(15, max(len(id) for id in self.components.keys())),
+            "class": max(25, max(len(component.__class__.__name__) for component in self.components.values())),
+            "device": 10,
+            "dtype": 15,
+            "size": 10,
+        }
+
+        # Create the header lines
+        sep_line = "=" * (sum(col_widths.values()) + len(col_widths) * 3 - 1) + "\n"
+        dash_line = "-" * (sum(col_widths.values()) + len(col_widths) * 3 - 1) + "\n"
+
+        output = "Components:\n" + sep_line
+
+        # Separate components into models and others
+        models = {k: v for k, v in self.components.items() if isinstance(v, torch.nn.Module)}
+        others = {k: v for k, v in self.components.items() if not isinstance(v, torch.nn.Module)}
+
+        # Models section
+        if models:
+            output += "Models:\n" + dash_line
+            # Column headers
+            output += f"{'Model ID':<{col_widths['id']}} | {'Class':<{col_widths['class']}} | "
+            output += f"{'Device':<{col_widths['device']}} | {'Dtype':<{col_widths['dtype']}} | Size (GB) \n"
+            output += dash_line
+
+            # Model entries
+            for name, component in models.items():
+                device = component.device
+                dtype = component.dtype
+                size_bytes = get_memory_footprint(component)
+                size_gb = size_bytes / (1024**3)
+
+                output += f"{name:<{col_widths['id']}} | {component.__class__.__name__:<{col_widths['class']}} | "
+                output += (
+                    f"{str(device):<{col_widths['device']}} | {str(dtype):<{col_widths['dtype']}} | {size_gb:.2f}\n"
+                )
+            output += dash_line
+
+        # Other components section
+        if others:
+            if models:  # Add extra newline if we had models section
+                output += "\n"
+            output += "Other Components:\n" + dash_line
+            # Column headers for other components
+            output += f"{'Component ID':<{col_widths['id']}} | {'Class':<{col_widths['class']}}\n"
+            output += dash_line
+
+            # Other component entries
+            for name, component in others.items():
+                output += f"{name:<{col_widths['id']}} | {component.__class__.__name__:<{col_widths['class']}}\n"
+            output += dash_line
+
+        return output
+
+    def add_from_pretrained(self, pretrained_model_name_or_path, **kwargs):
+        from ..pipelines.pipeline_utils import DiffusionPipeline
+
+        pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        for name, component in pipe.components.items():
+            if name not in self.components and component is not None:
+                self.add(name, component)
+            elif name in self.components:
+                logger.warning(
+                    f"Component '{name}' already exists in ComponentsManager and will not be added. To add it, either:\n"
+                    f"1. remove the existing component with remove('{name}')\n"
+                    f"2. Use a different name: add('{name}_2', component)"
+                )