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Load weights from the specified dduf file. | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
<Tip>
To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
`huggingface-cli login`.
</Tip>
Examples:
```py
>>> from diffusers import DiffusionPipeline
>>> # Download pipeline from huggingface.co and cache.
>>> pipeline = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
>>> # Download pipeline that requires an authorization token
>>> # For more information on access tokens, please refer to this section
>>> # of the documentation](https://huggingface.co/docs/hub/security-tokens)
>>> pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
>>> # Use a different scheduler
>>> from diffusers import LMSDiscreteScheduler | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
>>> scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
>>> pipeline.scheduler = scheduler
```
"""
# Copy the kwargs to re-use during loading connected pipeline.
kwargs_copied = kwargs.copy() | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
cache_dir = kwargs.pop("cache_dir", None)
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) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
dduf_file = kwargs.pop("dduf_file", None)
use_safetensors = kwargs.pop("use_safetensors", None)
use_onnx = kwargs.pop("use_onnx", None)
load_connected_pipeline = kwargs.pop("load_connected_pipeline", False) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
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 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`."
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
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 device_map is not None and not is_accelerate_available():
raise NotImplementedError(
"Using `device_map` requires the `accelerate` library. Please install it using: `pip install accelerate`."
)
if device_map is not None and not isinstance(device_map, str):
raise ValueError("`device_map` must be a string.")
if device_map is not None and device_map not in SUPPORTED_DEVICE_MAP:
raise NotImplementedError(
f"{device_map} not supported. Supported strategies are: {', '.join(SUPPORTED_DEVICE_MAP)}"
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if device_map is not None and device_map in SUPPORTED_DEVICE_MAP:
if is_accelerate_version("<", "0.28.0"):
raise NotImplementedError("Device placement requires `accelerate` version `0.28.0` or later.")
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 dduf_file:
if custom_pipeline:
raise NotImplementedError("Custom pipelines are not supported with DDUF at the moment.")
if load_connected_pipeline:
raise NotImplementedError("Connected pipelines are not supported with DDUF at the moment.") | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# 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,
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, | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
custom_revision=custom_revision,
variant=variant,
dduf_file=dduf_file,
load_connected_pipeline=load_connected_pipeline,
**kwargs,
)
else:
cached_folder = pretrained_model_name_or_path | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# The variant filenames can have the legacy sharding checkpoint format that we check and throw
# a warning if detected.
if variant is not None and _check_legacy_sharding_variant_format(folder=cached_folder, variant=variant):
warn_msg = (
f"Warning: The repository contains sharded checkpoints for variant '{variant}' maybe in a deprecated format. "
"Please check your files carefully:\n\n"
"- Correct format example: diffusion_pytorch_model.fp16-00003-of-00003.safetensors\n"
"- Deprecated format example: diffusion_pytorch_model-00001-of-00002.fp16.safetensors\n\n"
"If you find any files in the deprecated format:\n"
"1. Remove all existing checkpoint files for this variant.\n"
"2. Re-obtain the correct files by running `save_pretrained()`.\n\n"
"This will ensure you're using the most up-to-date and compatible checkpoint format."
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
logger.warning(warn_msg) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
dduf_entries = None
if dduf_file:
dduf_file_path = os.path.join(cached_folder, dduf_file)
dduf_entries = read_dduf_file(dduf_file_path)
# The reader contains already all the files needed, no need to check it again
cached_folder = ""
config_dict = cls.load_config(cached_folder, dduf_entries=dduf_entries)
if dduf_file:
_maybe_raise_error_for_incorrect_transformers(config_dict)
# pop out "_ignore_files" as it is only needed for download
config_dict.pop("_ignore_files", None) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# 2. Define which model components should load variants
# We retrieve the information by matching whether variant model checkpoints exist in the subfolders.
# Example: `diffusion_pytorch_model.safetensors` -> `diffusion_pytorch_model.fp16.safetensors`
# with variant being `"fp16"`.
model_variants = _identify_model_variants(folder=cached_folder, variant=variant, config=config_dict)
if len(model_variants) == 0 and variant is not None:
error_message = f"You are trying to load the model files of the `variant={variant}`, but no such modeling files are available."
raise ValueError(error_message) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# 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_pipeline, custom_class_name = _resolve_custom_pipeline_and_cls(
folder=cached_folder, config=config_dict, custom_pipeline=custom_pipeline
)
pipeline_class = _get_pipeline_class(
cls,
config=config_dict,
load_connected_pipeline=load_connected_pipeline,
custom_pipeline=custom_pipeline,
class_name=custom_class_name,
cache_dir=cache_dir,
revision=custom_revision,
)
if device_map is not None and pipeline_class._load_connected_pipes:
raise NotImplementedError("`device_map` is not yet supported for connected pipelines.") | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# DEPRECATED: To be removed in 1.0.0
# we are deprecating the `StableDiffusionInpaintPipelineLegacy` pipeline which gets loaded
# when a user requests for a `StableDiffusionInpaintPipeline` with `diffusers` version being <= 0.5.1.
_maybe_raise_warning_for_inpainting(
pipeline_class=pipeline_class,
pretrained_model_name_or_path=pretrained_model_name_or_path,
config=config_dict,
)
# 4. Define expected modules given pipeline signature
# and define non-None initialized modules (=`init_kwargs`) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# 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)
expected_types = pipeline_class._get_signature_types()
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} | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# 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)}
for key in init_dict.keys():
if key not in passed_class_obj:
continue
if "scheduler" in key:
continue
class_obj = passed_class_obj[key]
_expected_class_types = []
for expected_type in expected_types[key]:
if isinstance(expected_type, enum.EnumMeta):
_expected_class_types.extend(expected_type.__members__.keys())
else:
_expected_class_types.append(expected_type.__name__) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
_is_valid_type = class_obj.__class__.__name__ in _expected_class_types
if not _is_valid_type:
logger.warning(
f"Expected types for {key}: {_expected_class_types}, got {class_obj.__class__.__name__}."
)
# 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."
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# 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 | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# 6. device map delegation
final_device_map = None
if device_map is not None:
final_device_map = _get_final_device_map(
device_map=device_map,
pipeline_class=pipeline_class,
passed_class_obj=passed_class_obj,
init_dict=init_dict,
library=library,
max_memory=max_memory,
torch_dtype=torch_dtype,
cached_folder=cached_folder,
force_download=force_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# 7. Load each module in the pipeline
current_device_map = None
for name, (library_name, class_name) in logging.tqdm(init_dict.items(), desc="Loading pipeline components..."):
# 7.1 device_map shenanigans
if final_device_map is not None and len(final_device_map) > 0:
component_device = final_device_map.get(name, None)
if component_device is not None:
current_device_map = {"": component_device}
else:
current_device_map = None
# 7.2 - 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
# 7.3 Define all importable classes
is_pipeline_module = hasattr(pipelines, library_name)
importable_classes = ALL_IMPORTABLE_CLASSES
loaded_sub_model = None | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# 7.4 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
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
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=current_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, | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
cached_folder=cached_folder,
use_safetensors=use_safetensors,
dduf_entries=dduf_entries,
)
logger.info(
f"Loaded {name} as {class_name} from `{name}` subfolder of {pretrained_model_name_or_path}."
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
init_kwargs[name] = loaded_sub_model # UNet(...), # DiffusionSchedule(...)
# 8. Handle connected pipelines.
if pipeline_class._load_connected_pipes and os.path.isfile(os.path.join(cached_folder, "README.md")):
init_kwargs = _update_init_kwargs_with_connected_pipeline(
init_kwargs=init_kwargs,
passed_pipe_kwargs=passed_pipe_kwargs,
passed_class_objs=passed_class_obj,
folder=cached_folder,
**kwargs_copied,
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# 9. 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."
)
# 10. Instantiate the pipeline
model = pipeline_class(**init_kwargs) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# 11. Save where the model was instantiated from
model.register_to_config(_name_or_path=pretrained_model_name_or_path)
if device_map is not None:
setattr(model, "hf_device_map", final_device_map)
return model
@property
def name_or_path(self) -> str:
return getattr(self.config, "_name_or_path", None)
@property
def _execution_device(self):
r"""
Returns the device on which the pipeline's models will be executed. After calling
[`~DiffusionPipeline.enable_sequential_cpu_offload`] the execution device can only be inferred from
Accelerate's module hooks.
"""
for name, model in self.components.items():
if not isinstance(model, torch.nn.Module) or name in self._exclude_from_cpu_offload:
continue | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if not hasattr(model, "_hf_hook"):
return self.device
for module in model.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
def remove_all_hooks(self):
r"""
Removes all hooks that were added when using `enable_sequential_cpu_offload` or `enable_model_cpu_offload`.
"""
for _, model in self.components.items():
if isinstance(model, torch.nn.Module) and hasattr(model, "_hf_hook"):
accelerate.hooks.remove_hook_from_module(model, recurse=True)
self._all_hooks = [] | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
Arguments:
gpu_id (`int`, *optional*):
The ID of the accelerator that shall be used in inference. If not specified, it will default to 0.
device (`torch.Device` or `str`, *optional*, defaults to "cuda"):
The PyTorch device type of the accelerator that shall be used in inference. If not specified, it will
default to "cuda".
"""
is_pipeline_device_mapped = self.hf_device_map is not None and len(self.hf_device_map) > 1
if is_pipeline_device_mapped:
raise ValueError(
"It seems like you have activated a device mapping strategy on the pipeline so calling `enable_model_cpu_offload() isn't allowed. You can call `reset_device_map()` first and then call `enable_model_cpu_offload()`."
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if self.model_cpu_offload_seq is None:
raise ValueError(
"Model CPU offload cannot be enabled because no `model_cpu_offload_seq` class attribute is set."
)
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
self.remove_all_hooks()
torch_device = torch.device(device)
device_index = torch_device.index
if gpu_id is not None and device_index is not None:
raise ValueError(
f"You have passed both `gpu_id`={gpu_id} and an index as part of the passed device `device`={device}"
f"Cannot pass both. Please make sure to either not define `gpu_id` or not pass the index as part of the device: `device`={torch_device.type}"
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# _offload_gpu_id should be set to passed gpu_id (or id in passed `device`) or default to previously set id or default to 0
self._offload_gpu_id = gpu_id or torch_device.index or getattr(self, "_offload_gpu_id", 0)
device_type = torch_device.type
device = torch.device(f"{device_type}:{self._offload_gpu_id}")
self._offload_device = device
self.to("cpu", silence_dtype_warnings=True)
device_mod = getattr(torch, device.type, None)
if hasattr(device_mod, "empty_cache") and device_mod.is_available():
device_mod.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
all_model_components = {k: v for k, v in self.components.items() if isinstance(v, torch.nn.Module)}
self._all_hooks = []
hook = None
for model_str in self.model_cpu_offload_seq.split("->"):
model = all_model_components.pop(model_str, None) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if not isinstance(model, torch.nn.Module):
continue
# This is because the model would already be placed on a CUDA device.
_, _, is_loaded_in_8bit_bnb = _check_bnb_status(model)
if is_loaded_in_8bit_bnb:
logger.info(
f"Skipping the hook placement for the {model.__class__.__name__} as it is loaded in `bitsandbytes` 8bit."
)
continue
_, hook = cpu_offload_with_hook(model, device, prev_module_hook=hook)
self._all_hooks.append(hook)
# CPU offload models that are not in the seq chain unless they are explicitly excluded
# these models will stay on CPU until maybe_free_model_hooks is called
# some models cannot be in the seq chain because they are iteratively called, such as controlnet
for name, model in all_model_components.items():
if not isinstance(model, torch.nn.Module):
continue | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if name in self._exclude_from_cpu_offload:
model.to(device)
else:
_, hook = cpu_offload_with_hook(model, device)
self._all_hooks.append(hook)
def maybe_free_model_hooks(self):
r"""
Function that offloads all components, removes all model hooks that were added when using
`enable_model_cpu_offload` and then applies them again. In case the model has not been offloaded this function
is a no-op. Make sure to add this function to the end of the `__call__` function of your pipeline so that it
functions correctly when applying enable_model_cpu_offload.
"""
if not hasattr(self, "_all_hooks") or len(self._all_hooks) == 0:
# `enable_model_cpu_offload` has not be called, so silently do nothing
return
# make sure the model is in the same state as before calling it
self.enable_model_cpu_offload(device=getattr(self, "_offload_device", "cuda")) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
def enable_sequential_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
r"""
Offloads all models to CPU using 🤗 Accelerate, significantly reducing memory usage. When called, the state
dicts of all `torch.nn.Module` components (except those in `self._exclude_from_cpu_offload`) are saved to CPU
and then moved to `torch.device('meta')` and loaded to GPU only when their specific submodule has its `forward`
method called. Offloading happens on a submodule basis. Memory savings are higher than with
`enable_model_cpu_offload`, but performance is lower. | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
Arguments:
gpu_id (`int`, *optional*):
The ID of the accelerator that shall be used in inference. If not specified, it will default to 0.
device (`torch.Device` or `str`, *optional*, defaults to "cuda"):
The PyTorch device type of the accelerator that shall be used in inference. If not specified, it will
default to "cuda".
"""
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
from accelerate import cpu_offload
else:
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
self.remove_all_hooks() | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
is_pipeline_device_mapped = self.hf_device_map is not None and len(self.hf_device_map) > 1
if is_pipeline_device_mapped:
raise ValueError(
"It seems like you have activated a device mapping strategy on the pipeline so calling `enable_sequential_cpu_offload() isn't allowed. You can call `reset_device_map()` first and then call `enable_sequential_cpu_offload()`."
)
torch_device = torch.device(device)
device_index = torch_device.index
if gpu_id is not None and device_index is not None:
raise ValueError(
f"You have passed both `gpu_id`={gpu_id} and an index as part of the passed device `device`={device}"
f"Cannot pass both. Please make sure to either not define `gpu_id` or not pass the index as part of the device: `device`={torch_device.type}"
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# _offload_gpu_id should be set to passed gpu_id (or id in passed `device`) or default to previously set id or default to 0
self._offload_gpu_id = gpu_id or torch_device.index or getattr(self, "_offload_gpu_id", 0)
device_type = torch_device.type
device = torch.device(f"{device_type}:{self._offload_gpu_id}")
self._offload_device = device
if self.device.type != "cpu":
self.to("cpu", silence_dtype_warnings=True)
device_mod = getattr(torch, self.device.type, None)
if hasattr(device_mod, "empty_cache") and device_mod.is_available():
device_mod.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
for name, model in self.components.items():
if not isinstance(model, torch.nn.Module):
continue | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if name in self._exclude_from_cpu_offload:
model.to(device)
else:
# make sure to offload buffers if not all high level weights
# are of type nn.Module
offload_buffers = len(model._parameters) > 0
cpu_offload(model, device, offload_buffers=offload_buffers)
def reset_device_map(self):
r"""
Resets the device maps (if any) to None.
"""
if self.hf_device_map is None:
return
else:
self.remove_all_hooks()
for name, component in self.components.items():
if isinstance(component, torch.nn.Module):
component.to("cpu")
self.hf_device_map = None
@classmethod
@validate_hf_hub_args
def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:
r"""
Download and cache a PyTorch diffusion pipeline from pretrained pipeline weights. | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
Parameters:
pretrained_model_name (`str` or `os.PathLike`, *optional*):
A string, the *repository id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline
hosted on the Hub.
custom_pipeline (`str`, *optional*):
Can be either:
- A string, the *repository id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained
pipeline hosted on the Hub. The repository must contain a file called `pipeline.py` that defines
the custom pipeline. | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
- A string, the *file name* of a community pipeline hosted on GitHub under
[Community](https://github.com/huggingface/diffusers/tree/main/examples/community). Valid file
names must match the file name and not the pipeline script (`clip_guided_stable_diffusion`
instead of `clip_guided_stable_diffusion.py`). Community pipelines are always loaded from the
current `main` branch of GitHub.
- A path to a *directory* (`./my_pipeline_directory/`) containing a custom pipeline. The directory
must contain a file called `pipeline.py` that defines the custom pipeline.
<Tip warning={true}>
🧪 This is an experimental feature and may change in the future.
</Tip> | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
For more information on how to load and create custom pipelines, take a look at [How to contribute a
community pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/contribute_pipeline).
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. | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
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"`): | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.
custom_revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
`revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
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.
variant (`str`, *optional*):
Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
loading `from_flax`.
dduf_file(`str`, *optional*):
Load weights from the specified DDUF file.
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.
use_onnx (`bool`, *optional*, defaults to `False`):
If set to `True`, ONNX weights will always be downloaded if present. If set to `False`, ONNX weights
will never be downloaded. By default `use_onnx` defaults to the `_is_onnx` class attribute which is
`False` for non-ONNX pipelines and `True` for ONNX pipelines. ONNX weights include both files ending
with `.onnx` and `.pb`. | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
trust_remote_code (`bool`, *optional*, defaults to `False`):
Whether or not to allow for custom pipelines and components defined on the Hub in their own files. This
option should only be set to `True` for repositories you trust and in which you have read the code, as
it will execute code present on the Hub on your local machine. | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
Returns:
`os.PathLike`:
A path to the downloaded pipeline.
<Tip>
To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with
`huggingface-cli login`.
</Tip> | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
"""
cache_dir = kwargs.pop("cache_dir", None)
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)
custom_pipeline = kwargs.pop("custom_pipeline", None)
custom_revision = kwargs.pop("custom_revision", None)
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)
trust_remote_code = kwargs.pop("trust_remote_code", False)
dduf_file: Optional[Dict[str, DDUFEntry]] = kwargs.pop("dduf_file", None) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if dduf_file:
if custom_pipeline:
raise NotImplementedError("Custom pipelines are not supported with DDUF at the moment.")
if load_connected_pipeline:
raise NotImplementedError("Connected pipelines are not supported with DDUF at the moment.")
return _download_dduf_file(
pretrained_model_name=pretrained_model_name,
dduf_file=dduf_file,
pipeline_class_name=cls.__name__,
cache_dir=cache_dir,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
)
allow_pickle = False
if use_safetensors is None:
use_safetensors = True
allow_pickle = True
allow_patterns = None
ignore_patterns = None | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
model_info_call_error: Optional[Exception] = None
if not local_files_only:
try:
info = model_info(pretrained_model_name, token=token, revision=revision)
except (HTTPError, OfflineModeIsEnabled, requests.ConnectionError) as e:
logger.warning(f"Couldn't connect to the Hub: {e}.\nWill try to load from local cache.")
local_files_only = True
model_info_call_error = e # save error to reraise it if model is not cached locally | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if not local_files_only:
filenames = {sibling.rfilename for sibling in info.siblings}
if variant is not None and _check_legacy_sharding_variant_format(filenames=filenames, variant=variant):
warn_msg = (
f"Warning: The repository contains sharded checkpoints for variant '{variant}' maybe in a deprecated format. "
"Please check your files carefully:\n\n"
"- Correct format example: diffusion_pytorch_model.fp16-00003-of-00003.safetensors\n"
"- Deprecated format example: diffusion_pytorch_model-00001-of-00002.fp16.safetensors\n\n"
"If you find any files in the deprecated format:\n"
"1. Remove all existing checkpoint files for this variant.\n"
"2. Re-obtain the correct files by running `save_pretrained()`.\n\n"
"This will ensure you're using the most up-to-date and compatible checkpoint format." | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
)
logger.warning(warn_msg) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
model_filenames, variant_filenames = variant_compatible_siblings(filenames, variant=variant)
config_file = hf_hub_download(
pretrained_model_name,
cls.config_name,
cache_dir=cache_dir,
revision=revision,
proxies=proxies,
force_download=force_download,
token=token,
)
config_dict = cls._dict_from_json_file(config_file)
ignore_filenames = config_dict.pop("_ignore_files", [])
# remove ignored filenames
model_filenames = set(model_filenames) - set(ignore_filenames)
variant_filenames = set(variant_filenames) - set(ignore_filenames) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if revision in DEPRECATED_REVISION_ARGS and version.parse(
version.parse(__version__).base_version
) >= version.parse("0.22.0"):
warn_deprecated_model_variant(pretrained_model_name, token, variant, revision, model_filenames)
custom_components, folder_names = _get_custom_components_and_folders(
pretrained_model_name, config_dict, filenames, variant_filenames, variant
)
model_folder_names = {os.path.split(f)[0] for f in model_filenames if os.path.split(f)[0] in folder_names}
custom_class_name = None
if custom_pipeline is None and isinstance(config_dict["_class_name"], (list, tuple)):
custom_pipeline = config_dict["_class_name"][0]
custom_class_name = config_dict["_class_name"][1]
# all filenames compatible with variant will be added
allow_patterns = list(model_filenames) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# allow all patterns from non-model folders
# this enables downloading schedulers, tokenizers, ...
allow_patterns += [f"{k}/*" for k in folder_names if k not in model_folder_names]
# add custom component files
allow_patterns += [f"{k}/{f}.py" for k, f in custom_components.items()]
# add custom pipeline file
allow_patterns += [f"{custom_pipeline}.py"] if f"{custom_pipeline}.py" in filenames else []
# also allow downloading config.json files with the model
allow_patterns += [os.path.join(k, "config.json") for k in model_folder_names]
allow_patterns += [
SCHEDULER_CONFIG_NAME,
CONFIG_NAME,
cls.config_name,
CUSTOM_PIPELINE_FILE_NAME,
]
load_pipe_from_hub = custom_pipeline is not None and f"{custom_pipeline}.py" in filenames
load_components_from_hub = len(custom_components) > 0 | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if load_pipe_from_hub and not trust_remote_code:
raise ValueError(
f"The repository for {pretrained_model_name} contains custom code in {custom_pipeline}.py which must be executed to correctly "
f"load the model. You can inspect the repository content at https://hf.co/{pretrained_model_name}/blob/main/{custom_pipeline}.py.\n"
f"Please pass the argument `trust_remote_code=True` to allow custom code to be run."
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if load_components_from_hub and not trust_remote_code:
raise ValueError(
f"The repository for {pretrained_model_name} contains custom code in {'.py, '.join([os.path.join(k, v) for k,v in custom_components.items()])} which must be executed to correctly "
f"load the model. You can inspect the repository content at {', '.join([f'https://hf.co/{pretrained_model_name}/{k}/{v}.py' for k,v in custom_components.items()])}.\n"
f"Please pass the argument `trust_remote_code=True` to allow custom code to be run."
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# retrieve passed components that should not be downloaded
pipeline_class = _get_pipeline_class(
cls,
config_dict,
load_connected_pipeline=load_connected_pipeline,
custom_pipeline=custom_pipeline,
repo_id=pretrained_model_name if load_pipe_from_hub else None,
hub_revision=revision,
class_name=custom_class_name,
cache_dir=cache_dir,
revision=custom_revision,
)
expected_components, _ = cls._get_signature_keys(pipeline_class)
passed_components = [k for k in expected_components if k in kwargs] | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# retrieve all patterns that should not be downloaded and error out when needed
ignore_patterns = _get_ignore_patterns(
passed_components,
model_folder_names,
model_filenames,
variant_filenames,
use_safetensors,
from_flax,
allow_pickle,
use_onnx,
pipeline_class._is_onnx,
variant,
)
# Don't download any objects that are passed
allow_patterns = [
p for p in allow_patterns if not (len(p.split("/")) == 2 and p.split("/")[0] in passed_components)
]
if pipeline_class._load_connected_pipes:
allow_patterns.append("README.md") | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# Don't download index files of forbidden patterns either
ignore_patterns = ignore_patterns + [f"{i}.index.*json" for i in ignore_patterns]
re_ignore_pattern = [re.compile(fnmatch.translate(p)) for p in ignore_patterns]
re_allow_pattern = [re.compile(fnmatch.translate(p)) for p in allow_patterns]
expected_files = [f for f in filenames if not any(p.match(f) for p in re_ignore_pattern)]
expected_files = [f for f in expected_files if any(p.match(f) for p in re_allow_pattern)]
snapshot_folder = Path(config_file).parent
pipeline_is_cached = all((snapshot_folder / f).is_file() for f in expected_files)
if pipeline_is_cached and not force_download:
# if the pipeline is cached, we can directly return it
# else call snapshot_download
return snapshot_folder | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
user_agent = {"pipeline_class": cls.__name__}
if custom_pipeline is not None and not custom_pipeline.endswith(".py"):
user_agent["custom_pipeline"] = custom_pipeline
# download all allow_patterns - ignore_patterns
try:
cached_folder = snapshot_download(
pretrained_model_name,
cache_dir=cache_dir,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
allow_patterns=allow_patterns,
ignore_patterns=ignore_patterns,
user_agent=user_agent,
)
cls_name = cls.load_config(os.path.join(cached_folder, "model_index.json")).get("_class_name", None)
cls_name = cls_name[4:] if isinstance(cls_name, str) and cls_name.startswith("Flax") else cls_name | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
diffusers_module = importlib.import_module(__name__.split(".")[0])
pipeline_class = getattr(diffusers_module, cls_name, None) if isinstance(cls_name, str) else None | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if pipeline_class is not None and pipeline_class._load_connected_pipes:
modelcard = ModelCard.load(os.path.join(cached_folder, "README.md"))
connected_pipes = sum([getattr(modelcard.data, k, []) for k in CONNECTED_PIPES_KEYS], [])
for connected_pipe_repo_id in connected_pipes:
download_kwargs = {
"cache_dir": cache_dir,
"force_download": force_download,
"proxies": proxies,
"local_files_only": local_files_only,
"token": token,
"variant": variant,
"use_safetensors": use_safetensors,
}
DiffusionPipeline.download(connected_pipe_repo_id, **download_kwargs)
return cached_folder | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
except FileNotFoundError:
# Means we tried to load pipeline with `local_files_only=True` but the files have not been found in local cache.
# This can happen in two cases:
# 1. If the user passed `local_files_only=True` => we raise the error directly
# 2. If we forced `local_files_only=True` when `model_info` failed => we raise the initial error
if model_info_call_error is None:
# 1. user passed `local_files_only=True`
raise
else:
# 2. we forced `local_files_only=True` when `model_info` failed
raise EnvironmentError(
f"Cannot load model {pretrained_model_name}: model is not cached locally and an error occurred"
" while trying to fetch metadata from the Hub. Please check out the root cause in the stacktrace"
" above."
) from model_info_call_error | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
@classmethod
def _get_signature_keys(cls, obj):
parameters = inspect.signature(obj.__init__).parameters
required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}
optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
expected_modules = set(required_parameters.keys()) - {"self"}
optional_names = list(optional_parameters)
for name in optional_names:
if name in cls._optional_components:
expected_modules.add(name)
optional_parameters.remove(name)
return expected_modules, optional_parameters | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
@classmethod
def _get_signature_types(cls):
signature_types = {}
for k, v in inspect.signature(cls.__init__).parameters.items():
if inspect.isclass(v.annotation):
signature_types[k] = (v.annotation,)
elif get_origin(v.annotation) == Union:
signature_types[k] = get_args(v.annotation)
else:
logger.warning(f"cannot get type annotation for Parameter {k} of {cls}.")
return signature_types
@property
def components(self) -> Dict[str, Any]:
r"""
The `self.components` property can be useful to run different pipelines with the same weights and
configurations without reallocating additional memory.
Returns (`dict`):
A dictionary containing all the modules needed to initialize the pipeline.
Examples: | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
```py
>>> from diffusers import (
... StableDiffusionPipeline,
... StableDiffusionImg2ImgPipeline,
... StableDiffusionInpaintPipeline,
... )
>>> text2img = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
>>> img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
>>> inpaint = StableDiffusionInpaintPipeline(**text2img.components)
```
"""
expected_modules, optional_parameters = self._get_signature_keys(self)
components = {
k: getattr(self, k) for k in self.config.keys() if not k.startswith("_") and k not in optional_parameters
} | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if set(components.keys()) != expected_modules:
raise ValueError(
f"{self} has been incorrectly initialized or {self.__class__} is incorrectly implemented. Expected"
f" {expected_modules} to be defined, but {components.keys()} are defined."
)
return components
@staticmethod
def numpy_to_pil(images):
"""
Convert a NumPy image or a batch of images to a PIL image.
"""
return numpy_to_pil(images)
@torch.compiler.disable
def progress_bar(self, iterable=None, total=None):
if not hasattr(self, "_progress_bar_config"):
self._progress_bar_config = {}
elif not isinstance(self._progress_bar_config, dict):
raise ValueError(
f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}."
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if iterable is not None:
return tqdm(iterable, **self._progress_bar_config)
elif total is not None:
return tqdm(total=total, **self._progress_bar_config)
else:
raise ValueError("Either `total` or `iterable` has to be defined.")
def set_progress_bar_config(self, **kwargs):
self._progress_bar_config = kwargs
def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = 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.
<Tip warning={true}>
⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.
</Tip> | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
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 DiffusionPipeline
>>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
>>> pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16)
>>> pipe = pipe.to("cuda")
>>> pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
>>> # Workaround for not accepting attention shape using VAE for Flash Attention
>>> pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None)
```
"""
self.set_use_memory_efficient_attention_xformers(True, attention_op)
def disable_xformers_memory_efficient_attention(self):
r"""
Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).
"""
self.set_use_memory_efficient_attention_xformers(False) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
def set_use_memory_efficient_attention_xformers(
self, valid: bool, attention_op: Optional[Callable] = 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)
module_names, _ = self._get_signature_keys(self)
modules = [getattr(self, n, None) for n in module_names]
modules = [m for m in modules if isinstance(m, torch.nn.Module)]
for module in modules:
fn_recursive_set_mem_eff(module) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor
in slices to compute attention in several steps. For more than one attention head, the computation is performed
sequentially over each head. This is useful to save some memory in exchange for a small speed decrease.
<Tip warning={true}>
⚠️ Don't enable attention slicing if you're already using `scaled_dot_product_attention` (SDPA) from PyTorch
2.0 or xFormers. These attention computations are already very memory efficient so you won't need to enable
this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious slow downs!
</Tip> | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
`"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
must be a multiple of `slice_size`.
Examples:
```py
>>> import torch
>>> from diffusers import StableDiffusionPipeline
>>> pipe = StableDiffusionPipeline.from_pretrained(
... "stable-diffusion-v1-5/stable-diffusion-v1-5",
... torch_dtype=torch.float16,
... use_safetensors=True,
... ) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
>>> prompt = "a photo of an astronaut riding a horse on mars"
>>> pipe.enable_attention_slicing()
>>> image = pipe(prompt).images[0]
```
"""
self.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously called, attention is
computed in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
def set_attention_slice(self, slice_size: Optional[int]):
module_names, _ = self._get_signature_keys(self)
modules = [getattr(self, n, None) for n in module_names]
modules = [m for m in modules if isinstance(m, torch.nn.Module) and hasattr(m, "set_attention_slice")]
for module in modules:
module.set_attention_slice(slice_size) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
@classmethod
def from_pipe(cls, pipeline, **kwargs):
r"""
Create a new pipeline from a given pipeline. This method is useful to create a new pipeline from the existing
pipeline components without reallocating additional memory.
Arguments:
pipeline (`DiffusionPipeline`):
The pipeline from which to create a new pipeline.
Returns:
`DiffusionPipeline`:
A new pipeline with the same weights and configurations as `pipeline`.
Examples:
```py
>>> from diffusers import StableDiffusionPipeline, StableDiffusionSAGPipeline
>>> pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
>>> new_pipe = StableDiffusionSAGPipeline.from_pipe(pipe)
```
"""
original_config = dict(pipeline.config)
torch_dtype = kwargs.pop("torch_dtype", None) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# derive the pipeline class to instantiate
custom_pipeline = kwargs.pop("custom_pipeline", None)
custom_revision = kwargs.pop("custom_revision", None)
if custom_pipeline is not None:
pipeline_class = _get_custom_pipeline_class(custom_pipeline, revision=custom_revision)
else:
pipeline_class = cls
expected_modules, optional_kwargs = cls._get_signature_keys(pipeline_class)
# true_optional_modules are optional components with default value in signature so it is ok not to pass them to `__init__`
# e.g. `image_encoder` for StableDiffusionPipeline
parameters = inspect.signature(cls.__init__).parameters
true_optional_modules = set(
{k for k, v in parameters.items() if v.default != inspect._empty and k in expected_modules}
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# get the class of each component based on its type hint
# e.g. {"unet": UNet2DConditionModel, "text_encoder": CLIPTextMode}
component_types = pipeline_class._get_signature_types()
pretrained_model_name_or_path = original_config.pop("_name_or_path", None)
# allow users pass modules in `kwargs` to override the original pipeline's components
passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs} | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
original_class_obj = {}
for name, component in pipeline.components.items():
if name in expected_modules and name not in passed_class_obj:
# for model components, we will not switch over if the class does not matches the type hint in the new pipeline's signature
if (
not isinstance(component, ModelMixin)
or type(component) in component_types[name]
or (component is None and name in cls._optional_components)
):
original_class_obj[name] = component
else:
logger.warning(
f"component {name} is not switched over to new pipeline because type does not match the expected."
f" {name} is {type(component)} while the new pipeline expect {component_types[name]}." | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
f" please pass the component of the correct type to the new pipeline. `from_pipe(..., {name}={name})`"
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# allow users pass optional kwargs to override the original pipelines config attribute
passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
original_pipe_kwargs = {
k: original_config[k]
for k in original_config.keys()
if k in optional_kwargs and k not in passed_pipe_kwargs
}
# config attribute that were not expected by pipeline is stored as its private attribute
# (i.e. when the original pipeline was also instantiated with `from_pipe` from another pipeline that has this config)
# in this case, we will pass them as optional arguments if they can be accepted by the new pipeline
additional_pipe_kwargs = [
k[1:]
for k in original_config.keys()
if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
]
for k in additional_pipe_kwargs:
original_pipe_kwargs[k] = original_config.pop(f"_{k}") | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
pipeline_kwargs = {
**passed_class_obj,
**original_class_obj,
**passed_pipe_kwargs,
**original_pipe_kwargs,
**kwargs,
}
# store unused config as private attribute in the new pipeline
unused_original_config = {
f"{'' if k.startswith('_') else '_'}{k}": v for k, v in original_config.items() if k not in pipeline_kwargs
}
missing_modules = (
set(expected_modules)
- set(pipeline._optional_components)
- set(pipeline_kwargs.keys())
- set(true_optional_modules)
)
if len(missing_modules) > 0:
raise ValueError(
f"Pipeline {pipeline_class} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed"
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
new_pipeline = pipeline_class(**pipeline_kwargs)
if pretrained_model_name_or_path is not None:
new_pipeline.register_to_config(_name_or_path=pretrained_model_name_or_path)
new_pipeline.register_to_config(**unused_original_config)
if torch_dtype is not None:
new_pipeline.to(dtype=torch_dtype)
return new_pipeline | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
class StableDiffusionMixin:
r"""
Helper for DiffusionPipeline with vae and unet.(mainly for LDM such as stable diffusion)
"""
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing() | 40 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
def enable_vae_tiling(self):
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.
"""
self.vae.enable_tiling()
def disable_vae_tiling(self):
r"""
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_tiling()
def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
The suffixes after the scaling factors represent the stages where they are being applied. | 40 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values
that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. | 40 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
Args:
s1 (`float`):
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
mitigate "oversmoothing effect" in the enhanced denoising process.
s2 (`float`):
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
mitigate "oversmoothing effect" in the enhanced denoising process.
b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
"""
if not hasattr(self, "unet"):
raise ValueError("The pipeline must have `unet` for using FreeU.")
self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)
def disable_freeu(self):
"""Disables the FreeU mechanism if enabled."""
self.unet.disable_freeu() | 40 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
def fuse_qkv_projections(self, unet: bool = True, vae: bool = True):
"""
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
are fused. For cross-attention modules, key and value projection matrices are fused.
<Tip warning={true}>
This API is 🧪 experimental.
</Tip>
Args:
unet (`bool`, defaults to `True`): To apply fusion on the UNet.
vae (`bool`, defaults to `True`): To apply fusion on the VAE.
"""
self.fusing_unet = False
self.fusing_vae = False
if unet:
self.fusing_unet = True
self.unet.fuse_qkv_projections()
self.unet.set_attn_processor(FusedAttnProcessor2_0())
if vae:
if not isinstance(self.vae, AutoencoderKL):
raise ValueError("`fuse_qkv_projections()` is only supported for the VAE of type `AutoencoderKL`.") | 40 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
self.fusing_vae = True
self.vae.fuse_qkv_projections()
self.vae.set_attn_processor(FusedAttnProcessor2_0())
def unfuse_qkv_projections(self, unet: bool = True, vae: bool = True):
"""Disable QKV projection fusion if enabled.
<Tip warning={true}>
This API is 🧪 experimental.
</Tip>
Args:
unet (`bool`, defaults to `True`): To apply fusion on the UNet.
vae (`bool`, defaults to `True`): To apply fusion on the VAE.
"""
if unet:
if not self.fusing_unet:
logger.warning("The UNet was not initially fused for QKV projections. Doing nothing.")
else:
self.unet.unfuse_qkv_projections()
self.fusing_unet = False | 40 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if vae:
if not self.fusing_vae:
logger.warning("The VAE was not initially fused for QKV projections. Doing nothing.")
else:
self.vae.unfuse_qkv_projections()
self.fusing_vae = False | 40 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
class SplitInferenceModule(nn.Module):
r"""
A wrapper module class that splits inputs along a specified dimension before performing a forward pass.
This module is useful when you need to perform inference on large tensors in a memory-efficient way by breaking
them into smaller chunks, processing each chunk separately, and then reassembling the results.
Args:
module (`nn.Module`):
The underlying PyTorch module that will be applied to each chunk of split inputs.
split_size (`int`, defaults to `1`):
The size of each chunk after splitting the input tensor.
split_dim (`int`, defaults to `0`):
The dimension along which the input tensors are split.
input_kwargs_to_split (`List[str]`, defaults to `["hidden_states"]`):
A list of keyword arguments (strings) that represent the input tensors to be split. | 41 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
Workflow:
1. The keyword arguments specified in `input_kwargs_to_split` are split into smaller chunks using
`torch.split()` along the dimension `split_dim` and with a chunk size of `split_size`.
2. The `module` is invoked once for each split with both the split inputs and any unchanged arguments
that were passed.
3. The output tensors from each split are concatenated back together along `split_dim` before returning.
Example:
```python
>>> import torch
>>> import torch.nn as nn
>>> model = nn.Linear(1000, 1000)
>>> split_module = SplitInferenceModule(model, split_size=2, split_dim=0, input_kwargs_to_split=["input"])
>>> input_tensor = torch.randn(42, 1000)
>>> # Will split the tensor into 21 slices of shape [2, 1000].
>>> output = split_module(input=input_tensor)
``` | 41 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
It is also possible to nest `SplitInferenceModule` across different split dimensions for more complex
multi-dimensional splitting.
"""
def __init__(
self,
module: nn.Module,
split_size: int = 1,
split_dim: int = 0,
input_kwargs_to_split: List[str] = ["hidden_states"],
) -> None:
super().__init__()
self.module = module
self.split_size = split_size
self.split_dim = split_dim
self.input_kwargs_to_split = set(input_kwargs_to_split)
def forward(self, *args, **kwargs) -> Union[torch.Tensor, Tuple[torch.Tensor]]:
r"""Forward method for the `SplitInferenceModule`.
This method processes the input by splitting specified keyword arguments along a given dimension, running the
underlying module on each split, and then concatenating the results. The splitting is controlled by the
`split_size` and `split_dim` parameters specified during initialization. | 41 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
Args:
*args (`Any`):
Positional arguments that are passed directly to the `module` without modification.
**kwargs (`Dict[str, torch.Tensor]`):
Keyword arguments passed to the underlying `module`. Only keyword arguments whose names match the
entries in `input_kwargs_to_split` and are of type `torch.Tensor` will be split. The remaining keyword
arguments are passed unchanged. | 41 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
Returns:
`Union[torch.Tensor, Tuple[torch.Tensor]]`:
The outputs obtained from `SplitInferenceModule` are the same as if the underlying module was inferred
without it.
- If the underlying module returns a single tensor, the result will be a single concatenated tensor
along the same `split_dim` after processing all splits.
- If the underlying module returns a tuple of tensors, each element of the tuple will be concatenated
along the `split_dim` across all splits, and the final result will be a tuple of concatenated tensors.
"""
split_inputs = {} | 41 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
# 1. Split inputs that were specified during initialization and also present in passed kwargs
for key in list(kwargs.keys()):
if key not in self.input_kwargs_to_split or not torch.is_tensor(kwargs[key]):
continue
split_inputs[key] = torch.split(kwargs[key], self.split_size, self.split_dim)
kwargs.pop(key)
# 2. Invoke forward pass across each split
results = []
for split_input in zip(*split_inputs.values()):
inputs = dict(zip(split_inputs.keys(), split_input))
inputs.update(kwargs)
intermediate_tensor_or_tensor_tuple = self.module(*args, **inputs)
results.append(intermediate_tensor_or_tensor_tuple) | 41 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
# 3. Concatenate split restuls to obtain final outputs
if isinstance(results[0], torch.Tensor):
return torch.cat(results, dim=self.split_dim)
elif isinstance(results[0], tuple):
return tuple([torch.cat(x, dim=self.split_dim) for x in zip(*results)])
else:
raise ValueError(
"In order to use the SplitInferenceModule, it is necessary for the underlying `module` to either return a torch.Tensor or a tuple of torch.Tensor's."
) | 41 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
class AnimateDiffFreeNoiseMixin:
r"""Mixin class for [FreeNoise](https://arxiv.org/abs/2310.15169)."""
def _enable_free_noise_in_block(self, block: Union[CrossAttnDownBlockMotion, DownBlockMotion, UpBlockMotion]):
r"""Helper function to enable FreeNoise in transformer blocks."""
for motion_module in block.motion_modules:
num_transformer_blocks = len(motion_module.transformer_blocks) | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
for i in range(num_transformer_blocks):
if isinstance(motion_module.transformer_blocks[i], FreeNoiseTransformerBlock):
motion_module.transformer_blocks[i].set_free_noise_properties(
self._free_noise_context_length,
self._free_noise_context_stride,
self._free_noise_weighting_scheme,
)
else:
assert isinstance(motion_module.transformer_blocks[i], BasicTransformerBlock)
basic_transfomer_block = motion_module.transformer_blocks[i] | 42 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_noise_utils.py |
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