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2122 lines (1824 loc) 路 97.1 KB
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# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. 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.
import copy
import functools
import inspect
import itertools
import json
import os
import re
import shutil
import tempfile
from collections import OrderedDict
from contextlib import ExitStack, contextmanager
from functools import wraps
from pathlib import Path
from typing import Any, Callable, ContextManager, Type
import safetensors
import torch
import torch.utils.checkpoint
from huggingface_hub import DDUFEntry, create_repo, split_torch_state_dict_into_shards
from huggingface_hub.utils import validate_hf_hub_args
from torch import Tensor, nn
from typing_extensions import Self
from .. import __version__
from ..quantizers import DiffusersAutoQuantizer, DiffusersQuantizer
from ..quantizers.quantization_config import QuantizationMethod
from ..utils import (
CONFIG_NAME,
FLASHPACK_WEIGHTS_NAME,
FLAX_WEIGHTS_NAME,
HF_ENABLE_PARALLEL_LOADING,
SAFE_WEIGHTS_INDEX_NAME,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
_add_variant,
_get_checkpoint_shard_files,
_get_model_file,
deprecate,
is_accelerate_available,
is_bitsandbytes_available,
is_bitsandbytes_version,
is_flashpack_available,
is_peft_available,
is_torch_version,
logging,
)
from ..utils.distributed_utils import is_torch_dist_rank_zero
from ..utils.hub_utils import PushToHubMixin, load_or_create_model_card, populate_model_card
from ..utils.torch_utils import empty_device_cache
from ._modeling_parallel import ContextParallelConfig, ContextParallelModelPlan, ParallelConfig
from .model_loading_utils import (
_caching_allocator_warmup,
_determine_device_map,
_expand_device_map,
_fetch_index_file,
_fetch_index_file_legacy,
_load_shard_file,
_load_shard_files_with_threadpool,
load_state_dict,
)
class ContextManagers:
"""
Wrapper for `contextlib.ExitStack` which enters a collection of context managers. Adaptation of `ContextManagers`
in the `fastcore` library.
"""
def __init__(self, context_managers: list[ContextManager]):
self.context_managers = context_managers
self.stack = ExitStack()
def __enter__(self):
for context_manager in self.context_managers:
self.stack.enter_context(context_manager)
def __exit__(self, *args, **kwargs):
self.stack.__exit__(*args, **kwargs)
logger = logging.get_logger(__name__)
_REGEX_SHARD = re.compile(r"(.*?)-\d{5}-of-\d{5}")
TORCH_INIT_FUNCTIONS = {
"uniform_": nn.init.uniform_,
"normal_": nn.init.normal_,
"trunc_normal_": nn.init.trunc_normal_,
"constant_": nn.init.constant_,
"xavier_uniform_": nn.init.xavier_uniform_,
"xavier_normal_": nn.init.xavier_normal_,
"kaiming_uniform_": nn.init.kaiming_uniform_,
"kaiming_normal_": nn.init.kaiming_normal_,
"uniform": nn.init.uniform,
"normal": nn.init.normal,
"xavier_uniform": nn.init.xavier_uniform,
"xavier_normal": nn.init.xavier_normal,
"kaiming_uniform": nn.init.kaiming_uniform,
"kaiming_normal": nn.init.kaiming_normal,
}
if is_torch_version(">=", "1.9.0"):
_LOW_CPU_MEM_USAGE_DEFAULT = True
else:
_LOW_CPU_MEM_USAGE_DEFAULT = False
if is_accelerate_available():
import accelerate
from accelerate import dispatch_model
from accelerate.utils import load_offloaded_weights, save_offload_index
def get_parameter_device(parameter: torch.nn.Module) -> torch.device:
from ..hooks.group_offloading import _get_group_onload_device
try:
# Try to get the onload device from the group offloading hook
return _get_group_onload_device(parameter)
except ValueError:
pass
try:
# If the onload device is not available due to no group offloading hooks, try to get the device
# from the first parameter or buffer
parameters_and_buffers = itertools.chain(parameter.parameters(), parameter.buffers())
return next(parameters_and_buffers).device
except StopIteration:
# For torch.nn.DataParallel compatibility in PyTorch 1.5
def find_tensor_attributes(module: torch.nn.Module) -> list[tuple[str, Tensor]]:
tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
return tuples
gen = parameter._named_members(get_members_fn=find_tensor_attributes)
first_tuple = next(gen)
return first_tuple[1].device
def get_parameter_dtype(parameter: torch.nn.Module) -> torch.dtype:
"""
Returns the first found floating dtype in parameters if there is one, otherwise returns the last dtype it found.
"""
# 1. Check if we have attached any dtype modifying hooks (eg. layerwise casting)
if isinstance(parameter, nn.Module):
for name, submodule in parameter.named_modules():
if not hasattr(submodule, "_diffusers_hook"):
continue
registry = submodule._diffusers_hook
hook = registry.get_hook("layerwise_casting")
if hook is not None:
return hook.compute_dtype
# 2. If no dtype modifying hooks are attached, return the dtype of the first floating point parameter/buffer
last_dtype = None
for name, param in parameter.named_parameters():
last_dtype = param.dtype
if (
hasattr(parameter, "_keep_in_fp32_modules")
and parameter._keep_in_fp32_modules
and any(m in name for m in parameter._keep_in_fp32_modules)
):
continue
if param.is_floating_point():
return param.dtype
for buffer in parameter.buffers():
last_dtype = buffer.dtype
if buffer.is_floating_point():
return buffer.dtype
if last_dtype is not None:
# if no floating dtype was found return whatever the first dtype is
return last_dtype
# For nn.DataParallel compatibility in PyTorch > 1.5
def find_tensor_attributes(module: nn.Module) -> list[tuple[str, Tensor]]:
tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
return tuples
gen = parameter._named_members(get_members_fn=find_tensor_attributes)
last_tuple = None
for tuple in gen:
last_tuple = tuple
if tuple[1].is_floating_point():
return tuple[1].dtype
if last_tuple is not None:
# fallback to the last dtype
return last_tuple[1].dtype
@contextmanager
def no_init_weights():
"""
Context manager to globally disable weight initialization to speed up loading large models. To do that, all the
torch.nn.init function are all replaced with skip.
"""
def _skip_init(*args, **kwargs):
pass
for name, init_func in TORCH_INIT_FUNCTIONS.items():
setattr(torch.nn.init, name, _skip_init)
try:
yield
finally:
# Restore the original initialization functions
for name, init_func in TORCH_INIT_FUNCTIONS.items():
setattr(torch.nn.init, name, init_func)
class ModelMixin(torch.nn.Module, PushToHubMixin):
r"""
Base class for all models.
[`ModelMixin`] takes care of storing the model configuration and provides methods for loading, downloading and
saving models.
- **config_name** ([`str`]) -- Filename to save a model to when calling [`~models.ModelMixin.save_pretrained`].
"""
config_name = CONFIG_NAME
_automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"]
_supports_gradient_checkpointing = False
_keys_to_ignore_on_load_unexpected = None
_no_split_modules = None
_keep_in_fp32_modules = None
_skip_layerwise_casting_patterns = None
_supports_group_offloading = True
_repeated_blocks = []
_parallel_config = None
_cp_plan = None
_skip_keys = None
def __init__(self):
super().__init__()
self._gradient_checkpointing_func = None
def __getattr__(self, name: str) -> Any:
"""The only reason we overwrite `getattr` here is to gracefully deprecate accessing
config attributes directly. See https://github.com/huggingface/diffusers/pull/3129 We need to overwrite
__getattr__ here in addition so that we don't trigger `torch.nn.Module`'s __getattr__':
https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module
"""
is_in_config = "_internal_dict" in self.__dict__ and hasattr(self.__dict__["_internal_dict"], name)
is_attribute = name in self.__dict__
if is_in_config and not is_attribute:
deprecation_message = f"Accessing config attribute `{name}` directly via '{type(self).__name__}' object attribute is deprecated. Please access '{name}' over '{type(self).__name__}'s config object instead, e.g. 'unet.config.{name}'."
deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False, stacklevel=3)
return self._internal_dict[name]
# call PyTorch's https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module
return super().__getattr__(name)
@property
def is_gradient_checkpointing(self) -> bool:
"""
Whether gradient checkpointing is activated for this model or not.
"""
return any(hasattr(m, "gradient_checkpointing") and m.gradient_checkpointing for m in self.modules())
def enable_gradient_checkpointing(self, gradient_checkpointing_func: Callable | None = None) -> None:
"""
Activates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or
*checkpoint activations* in other frameworks).
Args:
gradient_checkpointing_func (`Callable`, *optional*):
The function to use for gradient checkpointing. If `None`, the default PyTorch checkpointing function
is used (`torch.utils.checkpoint.checkpoint`).
"""
if not self._supports_gradient_checkpointing:
raise ValueError(
f"{self.__class__.__name__} does not support gradient checkpointing. Please make sure to set the boolean attribute "
f"`_supports_gradient_checkpointing` to `True` in the class definition."
)
if gradient_checkpointing_func is None:
def _gradient_checkpointing_func(module, *args):
ckpt_kwargs = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
return torch.utils.checkpoint.checkpoint(
module.__call__,
*args,
**ckpt_kwargs,
)
gradient_checkpointing_func = _gradient_checkpointing_func
self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=gradient_checkpointing_func)
def disable_gradient_checkpointing(self) -> None:
"""
Deactivates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or
*checkpoint activations* in other frameworks).
"""
if self._supports_gradient_checkpointing:
self._set_gradient_checkpointing(enable=False)
def set_use_npu_flash_attention(self, valid: bool) -> None:
r"""
Set the switch for the npu flash attention.
"""
def fn_recursive_set_npu_flash_attention(module: torch.nn.Module):
if hasattr(module, "set_use_npu_flash_attention"):
module.set_use_npu_flash_attention(valid)
for child in module.children():
fn_recursive_set_npu_flash_attention(child)
for module in self.children():
if isinstance(module, torch.nn.Module):
fn_recursive_set_npu_flash_attention(module)
def enable_npu_flash_attention(self) -> None:
r"""
Enable npu flash attention from torch_npu
"""
self.set_use_npu_flash_attention(True)
def disable_npu_flash_attention(self) -> None:
r"""
disable npu flash attention from torch_npu
"""
self.set_use_npu_flash_attention(False)
def set_use_xla_flash_attention(
self, use_xla_flash_attention: bool, partition_spec: Callable | None = None, **kwargs
) -> None:
# Recursively walk through all the children.
# Any children which exposes the set_use_xla_flash_attention method
# gets the message
def fn_recursive_set_flash_attention(module: torch.nn.Module):
if hasattr(module, "set_use_xla_flash_attention"):
module.set_use_xla_flash_attention(use_xla_flash_attention, partition_spec, **kwargs)
for child in module.children():
fn_recursive_set_flash_attention(child)
for module in self.children():
if isinstance(module, torch.nn.Module):
fn_recursive_set_flash_attention(module)
def enable_xla_flash_attention(self, partition_spec: Callable | None = None, **kwargs):
r"""
Enable the flash attention pallals kernel for torch_xla.
"""
self.set_use_xla_flash_attention(True, partition_spec, **kwargs)
def disable_xla_flash_attention(self):
r"""
Disable the flash attention pallals kernel for torch_xla.
"""
self.set_use_xla_flash_attention(False)
def set_use_memory_efficient_attention_xformers(self, valid: bool, attention_op: Callable | None = None) -> None:
# Recursively walk through all the children.
# Any children which exposes the set_use_memory_efficient_attention_xformers method
# gets the message
def fn_recursive_set_mem_eff(module: torch.nn.Module):
if hasattr(module, "set_use_memory_efficient_attention_xformers"):
module.set_use_memory_efficient_attention_xformers(valid, attention_op)
for child in module.children():
fn_recursive_set_mem_eff(child)
for module in self.children():
if isinstance(module, torch.nn.Module):
fn_recursive_set_mem_eff(module)
def enable_xformers_memory_efficient_attention(self, attention_op: Callable | None = None) -> None:
r"""
Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).
When this option is enabled, you should observe lower GPU memory usage and a potential speed up during
inference. Speed up during training is not guaranteed.
> [!WARNING] > 鈿狅笍 When memory efficient attention and sliced attention are both enabled, memory efficient
attention takes > precedent.
Parameters:
attention_op (`Callable`, *optional*):
Override the default `None` operator for use as `op` argument to the
[`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)
function of xFormers.
Examples:
```py
>>> import torch
>>> from diffusers import UNet2DConditionModel
>>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp
>>> model = UNet2DConditionModel.from_pretrained(
... "stabilityai/stable-diffusion-2-1", subfolder="unet", torch_dtype=torch.float16
... )
>>> model = model.to("cuda")
>>> model.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
```
"""
self.set_use_memory_efficient_attention_xformers(True, attention_op)
def disable_xformers_memory_efficient_attention(self) -> None:
r"""
Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).
"""
self.set_use_memory_efficient_attention_xformers(False)
def enable_layerwise_casting(
self,
storage_dtype: torch.dtype = torch.float8_e4m3fn,
compute_dtype: torch.dtype | None = None,
skip_modules_pattern: tuple[str, ...] | None = None,
skip_modules_classes: tuple[Type[torch.nn.Module], ...] | None = None,
non_blocking: bool = False,
) -> None:
r"""
Activates layerwise casting for the current model.
Layerwise casting is a technique that casts the model weights to a lower precision dtype for storage but
upcasts them on-the-fly to a higher precision dtype for computation. This process can significantly reduce the
memory footprint from model weights, but may lead to some quality degradation in the outputs. Most degradations
are negligible, mostly stemming from weight casting in normalization and modulation layers.
By default, most models in diffusers set the `_skip_layerwise_casting_patterns` attribute to ignore patch
embedding, positional embedding and normalization layers. This is because these layers are most likely
precision-critical for quality. If you wish to change this behavior, you can set the
`_skip_layerwise_casting_patterns` attribute to `None`, or call
[`~hooks.layerwise_casting.apply_layerwise_casting`] with custom arguments.
Example:
Using [`~models.ModelMixin.enable_layerwise_casting`]:
```python
>>> from diffusers import CogVideoXTransformer3DModel
>>> transformer = CogVideoXTransformer3DModel.from_pretrained(
... "THUDM/CogVideoX-5b", subfolder="transformer", torch_dtype=torch.bfloat16
... )
>>> # Enable layerwise casting via the model, which ignores certain modules by default
>>> transformer.enable_layerwise_casting(storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.bfloat16)
```
Args:
storage_dtype (`torch.dtype`):
The dtype to which the model should be cast for storage.
compute_dtype (`torch.dtype`):
The dtype to which the model weights should be cast during the forward pass.
skip_modules_pattern (`tuple[str, ...]`, *optional*):
A list of patterns to match the names of the modules to skip during the layerwise casting process. If
set to `None`, default skip patterns are used to ignore certain internal layers of modules and PEFT
layers.
skip_modules_classes (`tuple[Type[torch.nn.Module], ...]`, *optional*):
A list of module classes to skip during the layerwise casting process.
non_blocking (`bool`, *optional*, defaults to `False`):
If `True`, the weight casting operations are non-blocking.
"""
from ..hooks import apply_layerwise_casting
user_provided_patterns = True
if skip_modules_pattern is None:
from ..hooks.layerwise_casting import DEFAULT_SKIP_MODULES_PATTERN
skip_modules_pattern = DEFAULT_SKIP_MODULES_PATTERN
user_provided_patterns = False
if self._keep_in_fp32_modules is not None:
skip_modules_pattern += tuple(self._keep_in_fp32_modules)
if self._skip_layerwise_casting_patterns is not None:
skip_modules_pattern += tuple(self._skip_layerwise_casting_patterns)
skip_modules_pattern = tuple(set(skip_modules_pattern))
if is_peft_available() and not user_provided_patterns:
# By default, we want to skip all peft layers because they have a very low memory footprint.
# If users want to apply layerwise casting on peft layers as well, they can utilize the
# `~diffusers.hooks.layerwise_casting.apply_layerwise_casting` function which provides
# them with more flexibility and control.
from peft.tuners.loha.layer import LoHaLayer
from peft.tuners.lokr.layer import LoKrLayer
from peft.tuners.lora.layer import LoraLayer
for layer in (LoHaLayer, LoKrLayer, LoraLayer):
skip_modules_pattern += tuple(layer.adapter_layer_names)
if compute_dtype is None:
logger.info("`compute_dtype` not provided when enabling layerwise casting. Using dtype of the model.")
compute_dtype = self.dtype
apply_layerwise_casting(
self, storage_dtype, compute_dtype, skip_modules_pattern, skip_modules_classes, non_blocking
)
def enable_group_offload(
self,
onload_device: torch.device,
offload_device: torch.device = torch.device("cpu"),
offload_type: str = "block_level",
num_blocks_per_group: int | None = None,
non_blocking: bool = False,
use_stream: bool = False,
record_stream: bool = False,
low_cpu_mem_usage=False,
offload_to_disk_path: str | None = None,
block_modules: str | None = None,
exclude_kwargs: str | None = None,
) -> None:
r"""
Activates group offloading for the current model.
See [`~hooks.group_offloading.apply_group_offloading`] for more information.
Example:
```python
>>> from diffusers import CogVideoXTransformer3DModel
>>> transformer = CogVideoXTransformer3DModel.from_pretrained(
... "THUDM/CogVideoX-5b", subfolder="transformer", torch_dtype=torch.bfloat16
... )
>>> transformer.enable_group_offload(
... onload_device=torch.device("cuda"),
... offload_device=torch.device("cpu"),
... offload_type="leaf_level",
... use_stream=True,
... )
```
"""
from ..hooks import apply_group_offloading
if getattr(self, "enable_tiling", None) is not None and getattr(self, "use_tiling", False) and use_stream:
msg = (
"Applying group offloading on autoencoders, with CUDA streams, may not work as expected if the first "
"forward pass is executed with tiling enabled. Please make sure to either:\n"
"1. Run a forward pass with small input shapes.\n"
"2. Or, run a forward pass with tiling disabled (can still use small dummy inputs)."
)
logger.warning(msg)
if not self._supports_group_offloading:
raise ValueError(
f"{self.__class__.__name__} does not support group offloading. Please make sure to set the boolean attribute "
f"`_supports_group_offloading` to `True` in the class definition. If you believe this is a mistake, please "
f"open an issue at https://github.com/huggingface/diffusers/issues."
)
apply_group_offloading(
module=self,
onload_device=onload_device,
offload_device=offload_device,
offload_type=offload_type,
num_blocks_per_group=num_blocks_per_group,
non_blocking=non_blocking,
use_stream=use_stream,
record_stream=record_stream,
low_cpu_mem_usage=low_cpu_mem_usage,
offload_to_disk_path=offload_to_disk_path,
block_modules=block_modules,
exclude_kwargs=exclude_kwargs,
)
def set_attention_backend(self, backend: str) -> None:
"""
Set the attention backend for the model.
Args:
backend (`str`):
The name of the backend to set. Must be one of the available backends defined in
`AttentionBackendName`. Available backends can be found in
`diffusers.attention_dispatch.AttentionBackendName`. Defaults to torch native scaled dot product
attention as backend.
"""
from .attention import AttentionModuleMixin
from .attention_dispatch import (
AttentionBackendName,
_AttentionBackendRegistry,
_check_attention_backend_requirements,
_maybe_download_kernel_for_backend,
)
# TODO: the following will not be required when everything is refactored to AttentionModuleMixin
from .attention_processor import Attention, MochiAttention
logger.warning("Attention backends are an experimental feature and the API may be subject to change.")
attention_classes = (Attention, MochiAttention, AttentionModuleMixin)
parallel_config_set = False
for module in self.modules():
if not isinstance(module, attention_classes):
continue
processor = module.processor
if getattr(processor, "_parallel_config", None) is not None:
parallel_config_set = True
break
backend = backend.lower()
available_backends = {x.value for x in AttentionBackendName.__members__.values()}
if backend not in available_backends:
raise ValueError(f"`{backend=}` must be one of the following: " + ", ".join(available_backends))
backend = AttentionBackendName(backend)
if parallel_config_set and not _AttentionBackendRegistry._is_context_parallel_available(backend):
compatible_backends = sorted(_AttentionBackendRegistry._supports_context_parallel)
raise ValueError(
f"Context parallelism is enabled but current attention backend '{backend.value}' "
f"does not support context parallelism. "
f"Please set a compatible attention backend: {compatible_backends} using `model.set_attention_backend()`."
)
_check_attention_backend_requirements(backend)
_maybe_download_kernel_for_backend(backend)
for module in self.modules():
if not isinstance(module, attention_classes):
continue
processor = module.processor
if processor is None or not hasattr(processor, "_attention_backend"):
continue
processor._attention_backend = backend
# Important to set the active backend so that it propagates gracefully throughout.
_AttentionBackendRegistry.set_active_backend(backend)
def reset_attention_backend(self) -> None:
"""
Resets the attention backend for the model. Following calls to `forward` will use the environment default, if
set, or the torch native scaled dot product attention.
"""
from .attention import AttentionModuleMixin
from .attention_processor import Attention, MochiAttention
logger.warning("Attention backends are an experimental feature and the API may be subject to change.")
attention_classes = (Attention, MochiAttention, AttentionModuleMixin)
for module in self.modules():
if not isinstance(module, attention_classes):
continue
processor = module.processor
if processor is None or not hasattr(processor, "_attention_backend"):
continue
processor._attention_backend = None
def save_pretrained(
self,
save_directory: str | os.PathLike,
is_main_process: bool = True,
save_function: Callable | None = None,
safe_serialization: bool = True,
variant: str | None = None,
max_shard_size: int | str = "10GB",
push_to_hub: bool = False,
use_flashpack: bool = False,
**kwargs,
):
"""
Save a model and its configuration file to a directory so that it can be reloaded using the
[`~models.ModelMixin.from_pretrained`] class method.
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to save a model and its configuration file to. Will be created if it doesn't exist.
is_main_process (`bool`, *optional*, defaults to `True`):
Whether the process calling this is the main process or not. Useful during distributed training and you
need to call this function on all processes. In this case, set `is_main_process=True` only on the main
process to avoid race conditions.
save_function (`Callable`):
The function to use to save the state dictionary. Useful during distributed training when you need to
replace `torch.save` with another method. Can be configured with the environment variable
`DIFFUSERS_SAVE_MODE`.
safe_serialization (`bool`, *optional*, defaults to `True`):
Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
variant (`str`, *optional*):
If specified, weights are saved in the format `pytorch_model.<variant>.bin`.
max_shard_size (`int` or `str`, defaults to `"10GB"`):
The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size
lower than this size. If expressed as a string, needs to be digits followed by a unit (like `"5GB"`).
If expressed as an integer, the unit is bytes. Note that this limit will be decreased after a certain
period of time (starting from Oct 2024) to allow users to upgrade to the latest version of `diffusers`.
This is to establish a common default size for this argument across different libraries in the Hugging
Face ecosystem (`transformers`, and `accelerate`, for example).
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the
repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
namespace).
kwargs (`dict[str, Any]`, *optional*):
Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
"""
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return
hf_quantizer = getattr(self, "hf_quantizer", None)
if hf_quantizer is not None:
quantization_serializable = (
hf_quantizer is not None
and isinstance(hf_quantizer, DiffusersQuantizer)
and hf_quantizer.is_serializable
)
if not quantization_serializable:
raise ValueError(
f"The model is quantized with {hf_quantizer.quantization_config.quant_method} and is not serializable - check out the warnings from"
" the logger on the traceback to understand the reason why the quantized model is not serializable."
)
weights_name = WEIGHTS_NAME
if use_flashpack:
weights_name = FLASHPACK_WEIGHTS_NAME
elif safe_serialization:
weights_name = SAFETENSORS_WEIGHTS_NAME
weights_name = _add_variant(weights_name, variant)
weights_name_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(
".safetensors", "{suffix}.safetensors"
)
os.makedirs(save_directory, exist_ok=True)
if push_to_hub:
commit_message = kwargs.pop("commit_message", None)
private = kwargs.pop("private", None)
create_pr = kwargs.pop("create_pr", False)
token = kwargs.pop("token", None)
repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id
# Only save the model itself if we are using distributed training
model_to_save = self
# Attach architecture to the config
# Save the config
if is_main_process:
model_to_save.save_config(save_directory)
# Save the model
state_dict = model_to_save.state_dict()
if use_flashpack:
if is_flashpack_available():
import flashpack
else:
logger.error(
"Saving a FlashPack checkpoint in PyTorch, requires both PyTorch and flashpack to be installed. Please see "
"https://pytorch.org/ and https://github.com/fal-ai/flashpack for installation instructions."
)
raise ImportError("Please install torch and flashpack to save a FlashPack checkpoint in PyTorch.")
flashpack.serialization.pack_to_file(
state_dict_or_model=state_dict,
destination_path=os.path.join(save_directory, weights_name),
target_dtype=self.dtype,
)
else:
# Save the model
state_dict_split = split_torch_state_dict_into_shards(
state_dict, max_shard_size=max_shard_size, filename_pattern=weights_name_pattern
)
# Clean the folder from a previous save
if is_main_process:
for filename in os.listdir(save_directory):
if filename in state_dict_split.filename_to_tensors.keys():
continue
full_filename = os.path.join(save_directory, filename)
if not os.path.isfile(full_filename):
continue
weights_without_ext = weights_name_pattern.replace(".bin", "").replace(".safetensors", "")
weights_without_ext = weights_without_ext.replace("{suffix}", "")
filename_without_ext = filename.replace(".bin", "").replace(".safetensors", "")
# make sure that file to be deleted matches format of sharded file, e.g. pytorch_model-00001-of-00005
if (
filename.startswith(weights_without_ext)
and _REGEX_SHARD.fullmatch(filename_without_ext) is not None
):
os.remove(full_filename)
for filename, tensors in state_dict_split.filename_to_tensors.items():
shard = {tensor: state_dict[tensor].contiguous() for tensor in tensors}
filepath = os.path.join(save_directory, filename)
if safe_serialization:
# At some point we will need to deal better with save_function (used for TPU and other distributed
# joyfulness), but for now this enough.
safetensors.torch.save_file(shard, filepath, metadata={"format": "pt"})
else:
torch.save(shard, filepath)
if state_dict_split.is_sharded:
index = {
"metadata": state_dict_split.metadata,
"weight_map": state_dict_split.tensor_to_filename,
}
save_index_file = SAFE_WEIGHTS_INDEX_NAME if safe_serialization else WEIGHTS_INDEX_NAME
save_index_file = os.path.join(save_directory, _add_variant(save_index_file, variant))
# Save the index as well
with open(save_index_file, "w", encoding="utf-8") as f:
content = json.dumps(index, indent=2, sort_keys=True) + "\n"
f.write(content)
logger.info(
f"The model is bigger than the maximum size per checkpoint ({max_shard_size}) and is going to be "
f"split in {len(state_dict_split.filename_to_tensors)} checkpoint shards. You can find where each parameters has been saved in the "
f"index located at {save_index_file}."
)
else:
path_to_weights = os.path.join(save_directory, weights_name)
logger.info(f"Model weights saved in {path_to_weights}")
if push_to_hub:
# Create a new empty model card and eventually tag it
model_card = load_or_create_model_card(repo_id, token=token)
model_card = populate_model_card(model_card)
model_card.save(Path(save_directory, "README.md").as_posix())
self._upload_folder(
save_directory,
repo_id,
token=token,
commit_message=commit_message,
create_pr=create_pr,
)
def dequantize(self):
"""
Potentially dequantize the model in case it has been quantized by a quantization method that support
dequantization.
"""
hf_quantizer = getattr(self, "hf_quantizer", None)
if hf_quantizer is None:
raise ValueError("You need to first quantize your model in order to dequantize it")
return hf_quantizer.dequantize(self)
@classmethod
@validate_hf_hub_args
def from_pretrained(cls, pretrained_model_name_or_path: str | os.PathLike | None, **kwargs) -> Self:
r"""
Instantiate a pretrained PyTorch model from a pretrained model configuration.
The model is set in evaluation mode - `model.eval()` - by default, and dropout modules are deactivated. To
train the model, set it back in training mode with `model.train()`.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
the Hub.
- A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
with [`~ModelMixin.save_pretrained`].
cache_dir (`str | os.PathLike`, *optional*):
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.
torch_dtype (`torch.dtype`, *optional*):
Override the default `torch.dtype` and load the model with another dtype.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
proxies (`dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info (`bool`, *optional*, defaults to `False`):
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether to only load local model weights and configuration files or not. If set to `True`, the model
won't be downloaded from the Hub.
token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
`diffusers-cli login` (stored in `~/.huggingface`) is used.
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.
from_flax (`bool`, *optional*, defaults to `False`):
Load the model weights from a Flax checkpoint save file.
subfolder (`str`, *optional*, defaults to `""`):
The subfolder location of a model file within a larger model repository on the Hub or locally.
mirror (`str`, *optional*):
Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
information.
device_map (`int | str | torch.device` or `dict[str, int | str | torch.device]`, *optional*):
A map that specifies where each submodule should go. It doesn't need to be defined for each
parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
same device. Defaults to `None`, meaning that the model will be loaded on CPU.
Examples:
```py
>>> from diffusers import AutoModel
>>> import torch
>>> # This works.
>>> model = AutoModel.from_pretrained(
... "stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet", device_map="cuda"
... )
>>> # This also works (integer accelerator device ID).
>>> model = AutoModel.from_pretrained(
... "stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet", device_map=0
... )
>>> # Specifying a supported offloading strategy like "auto" also works.
>>> model = AutoModel.from_pretrained(
... "stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet", device_map="auto"
... )
>>> # Specifying a dictionary as `device_map` also works.
>>> model = AutoModel.from_pretrained(
... "stabilityai/stable-diffusion-xl-base-1.0",
... subfolder="unet",
... device_map={"": torch.device("cuda")},
... )
```
Set `device_map="auto"` to have 馃 Accelerate automatically compute the most optimized `device_map`. For
more information about each option see [designing a device
map](https://huggingface.co/docs/accelerate/en/concept_guides/big_model_inference#the-devicemap). You
can also refer to the [Diffusers-specific
documentation](https://huggingface.co/docs/diffusers/main/en/training/distributed_inference#model-sharding)
for more concrete examples.
max_memory (`Dict`, *optional*):
A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
each GPU and the available CPU RAM if unset.
offload_folder (`str` or `os.PathLike`, *optional*):
The path to offload weights if `device_map` contains the value `"disk"`.
offload_state_dict (`bool`, *optional*):
If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
when there is some disk offload.
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
Speed up model loading only loading the pretrained weights and not initializing the weights. This also
tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
argument to `True` will raise an error.
variant (`str`, *optional*):
Load weights from a specified `variant` filename such as `"fp16"` or `"ema"`. This is ignored when
loading `from_flax`.
use_safetensors (`bool`, *optional*, defaults to `None`):
If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the
`safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors`
weights. If set to `False`, `safetensors` weights are not loaded.
disable_mmap ('bool', *optional*, defaults to 'False'):
Whether to disable mmap when loading a Safetensors model. This option can perform better when the model
is on a network mount or hard drive, which may not handle the seeky-ness of mmap very well.
use_flashpack (`bool`, *optional*, defaults to `False`):
If set to `True`, the model is loaded from `flashpack` weights.
flashpack_kwargs(`dict[str, Any]`, *optional*, defaults to `{}`):
Kwargs passed to
[`flashpack.deserialization.assign_from_file`](https://github.com/fal-ai/flashpack/blob/f1aa91c5cd9532a3dbf5bcc707ab9b01c274b76c/src/flashpack/deserialization.py#L408-L422)
> [!TIP] > To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in
with `hf > auth login`. You can also activate the special >
["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a >
firewalled environment.
Example:
```py
from diffusers import UNet2DConditionModel
unet = UNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet")
```
If you get the error message below, you need to finetune the weights for your downstream task:
```bash
Some weights of UNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
- conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
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)
proxies = kwargs.pop("proxies", None)
output_loading_info = kwargs.pop("output_loading_info", False)