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class FrozenDict(OrderedDict):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
for key, value in self.items():
setattr(self, key, value)
self.__frozen = True
def __delitem__(self, *args, **kwargs):
raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
def setdefault(self, *args, **kwargs):
raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
def pop(self, *args, **kwargs):
raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
def update(self, *args, **kwargs):
raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
def __setattr__(self, name, value):
if hasattr(self, "__frozen") and self.__frozen:
raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.")
super().__setattr__(name, value)
def __setitem__(self, name, value):
if hasattr(self, "__frozen") and self.__frozen:
raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.")
super().__setitem__(name, value) | class_definition | 1,471 | 2,729 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/configuration_utils.py | null | 0 |
class ConfigMixin:
r"""
Base class for all configuration classes. All configuration parameters are stored under `self.config`. Also
provides the [`~ConfigMixin.from_config`] and [`~ConfigMixin.save_config`] methods for loading, downloading, and
saving classes that inherit from [`ConfigMixin`].
Class attributes:
- **config_name** (`str`) -- A filename under which the config should stored when calling
[`~ConfigMixin.save_config`] (should be overridden by parent class).
- **ignore_for_config** (`List[str]`) -- A list of attributes that should not be saved in the config (should be
overridden by subclass).
- **has_compatibles** (`bool`) -- Whether the class has compatible classes (should be overridden by subclass).
- **_deprecated_kwargs** (`List[str]`) -- Keyword arguments that are deprecated. Note that the `init` function
should only have a `kwargs` argument if at least one argument is deprecated (should be overridden by
subclass).
"""
config_name = None
ignore_for_config = []
has_compatibles = False
_deprecated_kwargs = []
def register_to_config(self, **kwargs):
if self.config_name is None:
raise NotImplementedError(f"Make sure that {self.__class__} has defined a class name `config_name`")
# Special case for `kwargs` used in deprecation warning added to schedulers
# TODO: remove this when we remove the deprecation warning, and the `kwargs` argument,
# or solve in a more general way.
kwargs.pop("kwargs", None)
if not hasattr(self, "_internal_dict"):
internal_dict = kwargs
else:
previous_dict = dict(self._internal_dict)
internal_dict = {**self._internal_dict, **kwargs}
logger.debug(f"Updating config from {previous_dict} to {internal_dict}")
self._internal_dict = FrozenDict(internal_dict)
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
This function is mostly copied from PyTorch's __getattr__ overwrite:
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. 'scheduler.config.{name}'."
deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)
return self._internal_dict[name]
raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
"""
Save a configuration object to the directory specified in `save_directory` so that it can be reloaded using the
[`~ConfigMixin.from_config`] class method.
Args:
save_directory (`str` or `os.PathLike`):
Directory where the configuration JSON file is saved (will be created if it does not exist).
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):
raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
os.makedirs(save_directory, exist_ok=True)
# If we save using the predefined names, we can load using `from_config`
output_config_file = os.path.join(save_directory, self.config_name)
self.to_json_file(output_config_file)
logger.info(f"Configuration saved in {output_config_file}")
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
self._upload_folder(
save_directory,
repo_id,
token=token,
commit_message=commit_message,
create_pr=create_pr,
)
@classmethod
def from_config(cls, config: Union[FrozenDict, Dict[str, Any]] = None, return_unused_kwargs=False, **kwargs):
r"""
Instantiate a Python class from a config dictionary.
Parameters:
config (`Dict[str, Any]`):
A config dictionary from which the Python class is instantiated. Make sure to only load configuration
files of compatible classes.
return_unused_kwargs (`bool`, *optional*, defaults to `False`):
Whether kwargs that are not consumed by the Python class should be returned or not.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to update the configuration object (after it is loaded) and initiate the Python class.
`**kwargs` are passed directly to the underlying scheduler/model's `__init__` method and eventually
overwrite the same named arguments in `config`.
Returns:
[`ModelMixin`] or [`SchedulerMixin`]:
A model or scheduler object instantiated from a config dictionary.
Examples:
```python
>>> from diffusers import DDPMScheduler, DDIMScheduler, PNDMScheduler
>>> # Download scheduler from huggingface.co and cache.
>>> scheduler = DDPMScheduler.from_pretrained("google/ddpm-cifar10-32")
>>> # Instantiate DDIM scheduler class with same config as DDPM
>>> scheduler = DDIMScheduler.from_config(scheduler.config)
>>> # Instantiate PNDM scheduler class with same config as DDPM
>>> scheduler = PNDMScheduler.from_config(scheduler.config)
```
"""
# <===== TO BE REMOVED WITH DEPRECATION
# TODO(Patrick) - make sure to remove the following lines when config=="model_path" is deprecated
if "pretrained_model_name_or_path" in kwargs:
config = kwargs.pop("pretrained_model_name_or_path")
if config is None:
raise ValueError("Please make sure to provide a config as the first positional argument.")
# ======>
if not isinstance(config, dict):
deprecation_message = "It is deprecated to pass a pretrained model name or path to `from_config`."
if "Scheduler" in cls.__name__:
deprecation_message += (
f"If you were trying to load a scheduler, please use {cls}.from_pretrained(...) instead."
" Otherwise, please make sure to pass a configuration dictionary instead. This functionality will"
" be removed in v1.0.0."
)
elif "Model" in cls.__name__:
deprecation_message += (
f"If you were trying to load a model, please use {cls}.load_config(...) followed by"
f" {cls}.from_config(...) instead. Otherwise, please make sure to pass a configuration dictionary"
" instead. This functionality will be removed in v1.0.0."
)
deprecate("config-passed-as-path", "1.0.0", deprecation_message, standard_warn=False)
config, kwargs = cls.load_config(pretrained_model_name_or_path=config, return_unused_kwargs=True, **kwargs)
init_dict, unused_kwargs, hidden_dict = cls.extract_init_dict(config, **kwargs)
# Allow dtype to be specified on initialization
if "dtype" in unused_kwargs:
init_dict["dtype"] = unused_kwargs.pop("dtype")
# add possible deprecated kwargs
for deprecated_kwarg in cls._deprecated_kwargs:
if deprecated_kwarg in unused_kwargs:
init_dict[deprecated_kwarg] = unused_kwargs.pop(deprecated_kwarg)
# Return model and optionally state and/or unused_kwargs
model = cls(**init_dict)
# make sure to also save config parameters that might be used for compatible classes
# update _class_name
if "_class_name" in hidden_dict:
hidden_dict["_class_name"] = cls.__name__
model.register_to_config(**hidden_dict)
# add hidden kwargs of compatible classes to unused_kwargs
unused_kwargs = {**unused_kwargs, **hidden_dict}
if return_unused_kwargs:
return (model, unused_kwargs)
else:
return model
@classmethod
def get_config_dict(cls, *args, **kwargs):
deprecation_message = (
f" The function get_config_dict is deprecated. Please use {cls}.load_config instead. This function will be"
" removed in version v1.0.0"
)
deprecate("get_config_dict", "1.0.0", deprecation_message, standard_warn=False)
return cls.load_config(*args, **kwargs)
@classmethod
@validate_hf_hub_args
def load_config(
cls,
pretrained_model_name_or_path: Union[str, os.PathLike],
return_unused_kwargs=False,
return_commit_hash=False,
**kwargs,
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
r"""
Load a model or scheduler configuration.
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 model weights saved with
[`~ConfigMixin.save_config`].
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.
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.
subfolder (`str`, *optional*, defaults to `""`):
The subfolder location of a model file within a larger model repository on the Hub or locally.
return_unused_kwargs (`bool`, *optional*, defaults to `False):
Whether unused keyword arguments of the config are returned.
return_commit_hash (`bool`, *optional*, defaults to `False):
Whether the `commit_hash` of the loaded configuration are returned.
Returns:
`dict`:
A dictionary of all the parameters stored in a JSON configuration file.
"""
cache_dir = kwargs.pop("cache_dir", None)
local_dir = kwargs.pop("local_dir", None)
local_dir_use_symlinks = kwargs.pop("local_dir_use_symlinks", "auto")
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
token = kwargs.pop("token", None)
local_files_only = kwargs.pop("local_files_only", False)
revision = kwargs.pop("revision", None)
_ = kwargs.pop("mirror", None)
subfolder = kwargs.pop("subfolder", None)
user_agent = kwargs.pop("user_agent", {})
dduf_entries: Optional[Dict[str, DDUFEntry]] = kwargs.pop("dduf_entries", None)
user_agent = {**user_agent, "file_type": "config"}
user_agent = http_user_agent(user_agent)
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
if cls.config_name is None:
raise ValueError(
"`self.config_name` is not defined. Note that one should not load a config from "
"`ConfigMixin`. Please make sure to define `config_name` in a class inheriting from `ConfigMixin`"
)
# Custom path for now
if dduf_entries:
if subfolder is not None:
raise ValueError(
"DDUF file only allow for 1 level of directory (e.g transformer/model1/model.safetentors is not allowed). "
"Please check the DDUF structure"
)
config_file = cls._get_config_file_from_dduf(pretrained_model_name_or_path, dduf_entries)
elif os.path.isfile(pretrained_model_name_or_path):
config_file = pretrained_model_name_or_path
elif os.path.isdir(pretrained_model_name_or_path):
if subfolder is not None and os.path.isfile(
os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
):
config_file = os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
elif os.path.isfile(os.path.join(pretrained_model_name_or_path, cls.config_name)):
# Load from a PyTorch checkpoint
config_file = os.path.join(pretrained_model_name_or_path, cls.config_name)
else:
raise EnvironmentError(
f"Error no file named {cls.config_name} found in directory {pretrained_model_name_or_path}."
)
else:
try:
# Load from URL or cache if already cached
config_file = hf_hub_download(
pretrained_model_name_or_path,
filename=cls.config_name,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
user_agent=user_agent,
subfolder=subfolder,
revision=revision,
local_dir=local_dir,
local_dir_use_symlinks=local_dir_use_symlinks,
)
except RepositoryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier"
" listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a"
" token having permission to this repo with `token` or log in with `huggingface-cli login`."
)
except RevisionNotFoundError:
raise EnvironmentError(
f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for"
" this model name. Check the model page at"
f" 'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
)
except EntryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} does not appear to have a file named {cls.config_name}."
)
except HTTPError as err:
raise EnvironmentError(
"There was a specific connection error when trying to load"
f" {pretrained_model_name_or_path}:\n{err}"
)
except ValueError:
raise EnvironmentError(
f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
f" directory containing a {cls.config_name} file.\nCheckout your internet connection or see how to"
" run the library in offline mode at"
" 'https://huggingface.co/docs/diffusers/installation#offline-mode'."
)
except EnvironmentError:
raise EnvironmentError(
f"Can't load config for '{pretrained_model_name_or_path}'. If you were trying to load it from "
"'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
f"containing a {cls.config_name} file"
)
try:
config_dict = cls._dict_from_json_file(config_file, dduf_entries=dduf_entries)
commit_hash = extract_commit_hash(config_file)
except (json.JSONDecodeError, UnicodeDecodeError):
raise EnvironmentError(f"It looks like the config file at '{config_file}' is not a valid JSON file.")
if not (return_unused_kwargs or return_commit_hash):
return config_dict
outputs = (config_dict,)
if return_unused_kwargs:
outputs += (kwargs,)
if return_commit_hash:
outputs += (commit_hash,)
return outputs
@staticmethod
def _get_init_keys(input_class):
return set(dict(inspect.signature(input_class.__init__).parameters).keys())
@classmethod
def extract_init_dict(cls, config_dict, **kwargs):
# Skip keys that were not present in the original config, so default __init__ values were used
used_defaults = config_dict.get("_use_default_values", [])
config_dict = {k: v for k, v in config_dict.items() if k not in used_defaults and k != "_use_default_values"}
# 0. Copy origin config dict
original_dict = dict(config_dict.items())
# 1. Retrieve expected config attributes from __init__ signature
expected_keys = cls._get_init_keys(cls)
expected_keys.remove("self")
# remove general kwargs if present in dict
if "kwargs" in expected_keys:
expected_keys.remove("kwargs")
# remove flax internal keys
if hasattr(cls, "_flax_internal_args"):
for arg in cls._flax_internal_args:
expected_keys.remove(arg)
# 2. Remove attributes that cannot be expected from expected config attributes
# remove keys to be ignored
if len(cls.ignore_for_config) > 0:
expected_keys = expected_keys - set(cls.ignore_for_config)
# load diffusers library to import compatible and original scheduler
diffusers_library = importlib.import_module(__name__.split(".")[0])
if cls.has_compatibles:
compatible_classes = [c for c in cls._get_compatibles() if not isinstance(c, DummyObject)]
else:
compatible_classes = []
expected_keys_comp_cls = set()
for c in compatible_classes:
expected_keys_c = cls._get_init_keys(c)
expected_keys_comp_cls = expected_keys_comp_cls.union(expected_keys_c)
expected_keys_comp_cls = expected_keys_comp_cls - cls._get_init_keys(cls)
config_dict = {k: v for k, v in config_dict.items() if k not in expected_keys_comp_cls}
# remove attributes from orig class that cannot be expected
orig_cls_name = config_dict.pop("_class_name", cls.__name__)
if (
isinstance(orig_cls_name, str)
and orig_cls_name != cls.__name__
and hasattr(diffusers_library, orig_cls_name)
):
orig_cls = getattr(diffusers_library, orig_cls_name)
unexpected_keys_from_orig = cls._get_init_keys(orig_cls) - expected_keys
config_dict = {k: v for k, v in config_dict.items() if k not in unexpected_keys_from_orig}
elif not isinstance(orig_cls_name, str) and not isinstance(orig_cls_name, (list, tuple)):
raise ValueError(
"Make sure that the `_class_name` is of type string or list of string (for custom pipelines)."
)
# remove private attributes
config_dict = {k: v for k, v in config_dict.items() if not k.startswith("_")}
# remove quantization_config
config_dict = {k: v for k, v in config_dict.items() if k != "quantization_config"}
# 3. Create keyword arguments that will be passed to __init__ from expected keyword arguments
init_dict = {}
for key in expected_keys:
# if config param is passed to kwarg and is present in config dict
# it should overwrite existing config dict key
if key in kwargs and key in config_dict:
config_dict[key] = kwargs.pop(key)
if key in kwargs:
# overwrite key
init_dict[key] = kwargs.pop(key)
elif key in config_dict:
# use value from config dict
init_dict[key] = config_dict.pop(key)
# 4. Give nice warning if unexpected values have been passed
if len(config_dict) > 0:
logger.warning(
f"The config attributes {config_dict} were passed to {cls.__name__}, "
"but are not expected and will be ignored. Please verify your "
f"{cls.config_name} configuration file."
)
# 5. Give nice info if config attributes are initialized to default because they have not been passed
passed_keys = set(init_dict.keys())
if len(expected_keys - passed_keys) > 0:
logger.info(
f"{expected_keys - passed_keys} was not found in config. Values will be initialized to default values."
)
# 6. Define unused keyword arguments
unused_kwargs = {**config_dict, **kwargs}
# 7. Define "hidden" config parameters that were saved for compatible classes
hidden_config_dict = {k: v for k, v in original_dict.items() if k not in init_dict}
return init_dict, unused_kwargs, hidden_config_dict
@classmethod
def _dict_from_json_file(
cls, json_file: Union[str, os.PathLike], dduf_entries: Optional[Dict[str, DDUFEntry]] = None
):
if dduf_entries:
text = dduf_entries[json_file].read_text()
else:
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
return json.loads(text)
def __repr__(self):
return f"{self.__class__.__name__} {self.to_json_string()}"
@property
def config(self) -> Dict[str, Any]:
"""
Returns the config of the class as a frozen dictionary
Returns:
`Dict[str, Any]`: Config of the class.
"""
return self._internal_dict
def to_json_string(self) -> str:
"""
Serializes the configuration instance to a JSON string.
Returns:
`str`:
String containing all the attributes that make up the configuration instance in JSON format.
"""
config_dict = self._internal_dict if hasattr(self, "_internal_dict") else {}
config_dict["_class_name"] = self.__class__.__name__
config_dict["_diffusers_version"] = __version__
def to_json_saveable(value):
if isinstance(value, np.ndarray):
value = value.tolist()
elif isinstance(value, Path):
value = value.as_posix()
return value
if "quantization_config" in config_dict:
config_dict["quantization_config"] = (
config_dict.quantization_config.to_dict()
if not isinstance(config_dict.quantization_config, dict)
else config_dict.quantization_config
)
config_dict = {k: to_json_saveable(v) for k, v in config_dict.items()}
# Don't save "_ignore_files" or "_use_default_values"
config_dict.pop("_ignore_files", None)
config_dict.pop("_use_default_values", None)
# pop the `_pre_quantization_dtype` as torch.dtypes are not serializable.
_ = config_dict.pop("_pre_quantization_dtype", None)
return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
def to_json_file(self, json_file_path: Union[str, os.PathLike]):
"""
Save the configuration instance's parameters to a JSON file.
Args:
json_file_path (`str` or `os.PathLike`):
Path to the JSON file to save a configuration instance's parameters.
"""
with open(json_file_path, "w", encoding="utf-8") as writer:
writer.write(self.to_json_string())
@classmethod
def _get_config_file_from_dduf(cls, pretrained_model_name_or_path: str, dduf_entries: Dict[str, DDUFEntry]):
# paths inside a DDUF file must always be "/"
config_file = (
cls.config_name
if pretrained_model_name_or_path == ""
else "/".join([pretrained_model_name_or_path, cls.config_name])
)
if config_file not in dduf_entries:
raise ValueError(
f"We did not manage to find the file {config_file} in the dduf file. We only have the following files {dduf_entries.keys()}"
)
return config_file | class_definition | 2,732 | 29,807 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/configuration_utils.py | null | 1 |
class LegacyConfigMixin(ConfigMixin):
r"""
A subclass of `ConfigMixin` to resolve class mapping from legacy classes (like `Transformer2DModel`) to more
pipeline-specific classes (like `DiTTransformer2DModel`).
"""
@classmethod
def from_config(cls, config: Union[FrozenDict, Dict[str, Any]] = None, return_unused_kwargs=False, **kwargs):
# To prevent dependency import problem.
from .models.model_loading_utils import _fetch_remapped_cls_from_config
# resolve remapping
remapped_class = _fetch_remapped_cls_from_config(config, cls)
return remapped_class.from_config(config, return_unused_kwargs, **kwargs) | class_definition | 33,715 | 34,386 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/configuration_utils.py | null | 2 |
class VaeImageProcessor(ConfigMixin):
"""
Image processor for VAE.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. Can accept
`height` and `width` arguments from [`image_processor.VaeImageProcessor.preprocess`] method.
vae_scale_factor (`int`, *optional*, defaults to `8`):
VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
resample (`str`, *optional*, defaults to `lanczos`):
Resampling filter to use when resizing the image.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image to [-1,1].
do_binarize (`bool`, *optional*, defaults to `False`):
Whether to binarize the image to 0/1.
do_convert_rgb (`bool`, *optional*, defaults to be `False`):
Whether to convert the images to RGB format.
do_convert_grayscale (`bool`, *optional*, defaults to be `False`):
Whether to convert the images to grayscale format.
"""
config_name = CONFIG_NAME
@register_to_config
def __init__(
self,
do_resize: bool = True,
vae_scale_factor: int = 8,
vae_latent_channels: int = 4,
resample: str = "lanczos",
do_normalize: bool = True,
do_binarize: bool = False,
do_convert_rgb: bool = False,
do_convert_grayscale: bool = False,
):
super().__init__()
if do_convert_rgb and do_convert_grayscale:
raise ValueError(
"`do_convert_rgb` and `do_convert_grayscale` can not both be set to `True`,"
" if you intended to convert the image into RGB format, please set `do_convert_grayscale = False`.",
" if you intended to convert the image into grayscale format, please set `do_convert_rgb = False`",
)
@staticmethod
def numpy_to_pil(images: np.ndarray) -> List[PIL.Image.Image]:
r"""
Convert a numpy image or a batch of images to a PIL image.
Args:
images (`np.ndarray`):
The image array to convert to PIL format.
Returns:
`List[PIL.Image.Image]`:
A list of PIL images.
"""
if images.ndim == 3:
images = images[None, ...]
images = (images * 255).round().astype("uint8")
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
else:
pil_images = [Image.fromarray(image) for image in images]
return pil_images
@staticmethod
def pil_to_numpy(images: Union[List[PIL.Image.Image], PIL.Image.Image]) -> np.ndarray:
r"""
Convert a PIL image or a list of PIL images to NumPy arrays.
Args:
images (`PIL.Image.Image` or `List[PIL.Image.Image]`):
The PIL image or list of images to convert to NumPy format.
Returns:
`np.ndarray`:
A NumPy array representation of the images.
"""
if not isinstance(images, list):
images = [images]
images = [np.array(image).astype(np.float32) / 255.0 for image in images]
images = np.stack(images, axis=0)
return images
@staticmethod
def numpy_to_pt(images: np.ndarray) -> torch.Tensor:
r"""
Convert a NumPy image to a PyTorch tensor.
Args:
images (`np.ndarray`):
The NumPy image array to convert to PyTorch format.
Returns:
`torch.Tensor`:
A PyTorch tensor representation of the images.
"""
if images.ndim == 3:
images = images[..., None]
images = torch.from_numpy(images.transpose(0, 3, 1, 2))
return images
@staticmethod
def pt_to_numpy(images: torch.Tensor) -> np.ndarray:
r"""
Convert a PyTorch tensor to a NumPy image.
Args:
images (`torch.Tensor`):
The PyTorch tensor to convert to NumPy format.
Returns:
`np.ndarray`:
A NumPy array representation of the images.
"""
images = images.cpu().permute(0, 2, 3, 1).float().numpy()
return images
@staticmethod
def normalize(images: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:
r"""
Normalize an image array to [-1,1].
Args:
images (`np.ndarray` or `torch.Tensor`):
The image array to normalize.
Returns:
`np.ndarray` or `torch.Tensor`:
The normalized image array.
"""
return 2.0 * images - 1.0
@staticmethod
def denormalize(images: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:
r"""
Denormalize an image array to [0,1].
Args:
images (`np.ndarray` or `torch.Tensor`):
The image array to denormalize.
Returns:
`np.ndarray` or `torch.Tensor`:
The denormalized image array.
"""
return (images * 0.5 + 0.5).clamp(0, 1)
@staticmethod
def convert_to_rgb(image: PIL.Image.Image) -> PIL.Image.Image:
r"""
Converts a PIL image to RGB format.
Args:
image (`PIL.Image.Image`):
The PIL image to convert to RGB.
Returns:
`PIL.Image.Image`:
The RGB-converted PIL image.
"""
image = image.convert("RGB")
return image
@staticmethod
def convert_to_grayscale(image: PIL.Image.Image) -> PIL.Image.Image:
r"""
Converts a given PIL image to grayscale.
Args:
image (`PIL.Image.Image`):
The input image to convert.
Returns:
`PIL.Image.Image`:
The image converted to grayscale.
"""
image = image.convert("L")
return image
@staticmethod
def blur(image: PIL.Image.Image, blur_factor: int = 4) -> PIL.Image.Image:
r"""
Applies Gaussian blur to an image.
Args:
image (`PIL.Image.Image`):
The PIL image to convert to grayscale.
Returns:
`PIL.Image.Image`:
The grayscale-converted PIL image.
"""
image = image.filter(ImageFilter.GaussianBlur(blur_factor))
return image
@staticmethod
def get_crop_region(mask_image: PIL.Image.Image, width: int, height: int, pad=0):
r"""
Finds a rectangular region that contains all masked ares in an image, and expands region to match the aspect
ratio of the original image; for example, if user drew mask in a 128x32 region, and the dimensions for
processing are 512x512, the region will be expanded to 128x128.
Args:
mask_image (PIL.Image.Image): Mask image.
width (int): Width of the image to be processed.
height (int): Height of the image to be processed.
pad (int, optional): Padding to be added to the crop region. Defaults to 0.
Returns:
tuple: (x1, y1, x2, y2) represent a rectangular region that contains all masked ares in an image and
matches the original aspect ratio.
"""
mask_image = mask_image.convert("L")
mask = np.array(mask_image)
# 1. find a rectangular region that contains all masked ares in an image
h, w = mask.shape
crop_left = 0
for i in range(w):
if not (mask[:, i] == 0).all():
break
crop_left += 1
crop_right = 0
for i in reversed(range(w)):
if not (mask[:, i] == 0).all():
break
crop_right += 1
crop_top = 0
for i in range(h):
if not (mask[i] == 0).all():
break
crop_top += 1
crop_bottom = 0
for i in reversed(range(h)):
if not (mask[i] == 0).all():
break
crop_bottom += 1
# 2. add padding to the crop region
x1, y1, x2, y2 = (
int(max(crop_left - pad, 0)),
int(max(crop_top - pad, 0)),
int(min(w - crop_right + pad, w)),
int(min(h - crop_bottom + pad, h)),
)
# 3. expands crop region to match the aspect ratio of the image to be processed
ratio_crop_region = (x2 - x1) / (y2 - y1)
ratio_processing = width / height
if ratio_crop_region > ratio_processing:
desired_height = (x2 - x1) / ratio_processing
desired_height_diff = int(desired_height - (y2 - y1))
y1 -= desired_height_diff // 2
y2 += desired_height_diff - desired_height_diff // 2
if y2 >= mask_image.height:
diff = y2 - mask_image.height
y2 -= diff
y1 -= diff
if y1 < 0:
y2 -= y1
y1 -= y1
if y2 >= mask_image.height:
y2 = mask_image.height
else:
desired_width = (y2 - y1) * ratio_processing
desired_width_diff = int(desired_width - (x2 - x1))
x1 -= desired_width_diff // 2
x2 += desired_width_diff - desired_width_diff // 2
if x2 >= mask_image.width:
diff = x2 - mask_image.width
x2 -= diff
x1 -= diff
if x1 < 0:
x2 -= x1
x1 -= x1
if x2 >= mask_image.width:
x2 = mask_image.width
return x1, y1, x2, y2
def _resize_and_fill(
self,
image: PIL.Image.Image,
width: int,
height: int,
) -> PIL.Image.Image:
r"""
Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center
the image within the dimensions, filling empty with data from image.
Args:
image (`PIL.Image.Image`):
The image to resize and fill.
width (`int`):
The width to resize the image to.
height (`int`):
The height to resize the image to.
Returns:
`PIL.Image.Image`:
The resized and filled image.
"""
ratio = width / height
src_ratio = image.width / image.height
src_w = width if ratio < src_ratio else image.width * height // image.height
src_h = height if ratio >= src_ratio else image.height * width // image.width
resized = image.resize((src_w, src_h), resample=PIL_INTERPOLATION["lanczos"])
res = Image.new("RGB", (width, height))
res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
if ratio < src_ratio:
fill_height = height // 2 - src_h // 2
if fill_height > 0:
res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
res.paste(
resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)),
box=(0, fill_height + src_h),
)
elif ratio > src_ratio:
fill_width = width // 2 - src_w // 2
if fill_width > 0:
res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
res.paste(
resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)),
box=(fill_width + src_w, 0),
)
return res
def _resize_and_crop(
self,
image: PIL.Image.Image,
width: int,
height: int,
) -> PIL.Image.Image:
r"""
Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center
the image within the dimensions, cropping the excess.
Args:
image (`PIL.Image.Image`):
The image to resize and crop.
width (`int`):
The width to resize the image to.
height (`int`):
The height to resize the image to.
Returns:
`PIL.Image.Image`:
The resized and cropped image.
"""
ratio = width / height
src_ratio = image.width / image.height
src_w = width if ratio > src_ratio else image.width * height // image.height
src_h = height if ratio <= src_ratio else image.height * width // image.width
resized = image.resize((src_w, src_h), resample=PIL_INTERPOLATION["lanczos"])
res = Image.new("RGB", (width, height))
res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
return res
def resize(
self,
image: Union[PIL.Image.Image, np.ndarray, torch.Tensor],
height: int,
width: int,
resize_mode: str = "default", # "default", "fill", "crop"
) -> Union[PIL.Image.Image, np.ndarray, torch.Tensor]:
"""
Resize image.
Args:
image (`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`):
The image input, can be a PIL image, numpy array or pytorch tensor.
height (`int`):
The height to resize to.
width (`int`):
The width to resize to.
resize_mode (`str`, *optional*, defaults to `default`):
The resize mode to use, can be one of `default` or `fill`. If `default`, will resize the image to fit
within the specified width and height, and it may not maintaining the original aspect ratio. If `fill`,
will resize the image to fit within the specified width and height, maintaining the aspect ratio, and
then center the image within the dimensions, filling empty with data from image. If `crop`, will resize
the image to fit within the specified width and height, maintaining the aspect ratio, and then center
the image within the dimensions, cropping the excess. Note that resize_mode `fill` and `crop` are only
supported for PIL image input.
Returns:
`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`:
The resized image.
"""
if resize_mode != "default" and not isinstance(image, PIL.Image.Image):
raise ValueError(f"Only PIL image input is supported for resize_mode {resize_mode}")
if isinstance(image, PIL.Image.Image):
if resize_mode == "default":
image = image.resize((width, height), resample=PIL_INTERPOLATION[self.config.resample])
elif resize_mode == "fill":
image = self._resize_and_fill(image, width, height)
elif resize_mode == "crop":
image = self._resize_and_crop(image, width, height)
else:
raise ValueError(f"resize_mode {resize_mode} is not supported")
elif isinstance(image, torch.Tensor):
image = torch.nn.functional.interpolate(
image,
size=(height, width),
)
elif isinstance(image, np.ndarray):
image = self.numpy_to_pt(image)
image = torch.nn.functional.interpolate(
image,
size=(height, width),
)
image = self.pt_to_numpy(image)
return image
def binarize(self, image: PIL.Image.Image) -> PIL.Image.Image:
"""
Create a mask.
Args:
image (`PIL.Image.Image`):
The image input, should be a PIL image.
Returns:
`PIL.Image.Image`:
The binarized image. Values less than 0.5 are set to 0, values greater than 0.5 are set to 1.
"""
image[image < 0.5] = 0
image[image >= 0.5] = 1
return image
def _denormalize_conditionally(
self, images: torch.Tensor, do_denormalize: Optional[List[bool]] = None
) -> torch.Tensor:
r"""
Denormalize a batch of images based on a condition list.
Args:
images (`torch.Tensor`):
The input image tensor.
do_denormalize (`Optional[List[bool]`, *optional*, defaults to `None`):
A list of booleans indicating whether to denormalize each image in the batch. If `None`, will use the
value of `do_normalize` in the `VaeImageProcessor` config.
"""
if do_denormalize is None:
return self.denormalize(images) if self.config.do_normalize else images
return torch.stack(
[self.denormalize(images[i]) if do_denormalize[i] else images[i] for i in range(images.shape[0])]
)
def get_default_height_width(
self,
image: Union[PIL.Image.Image, np.ndarray, torch.Tensor],
height: Optional[int] = None,
width: Optional[int] = None,
) -> Tuple[int, int]:
r"""
Returns the height and width of the image, downscaled to the next integer multiple of `vae_scale_factor`.
Args:
image (`Union[PIL.Image.Image, np.ndarray, torch.Tensor]`):
The image input, which can be a PIL image, NumPy array, or PyTorch tensor. If it is a NumPy array, it
should have shape `[batch, height, width]` or `[batch, height, width, channels]`. If it is a PyTorch
tensor, it should have shape `[batch, channels, height, width]`.
height (`Optional[int]`, *optional*, defaults to `None`):
The height of the preprocessed image. If `None`, the height of the `image` input will be used.
width (`Optional[int]`, *optional*, defaults to `None`):
The width of the preprocessed image. If `None`, the width of the `image` input will be used.
Returns:
`Tuple[int, int]`:
A tuple containing the height and width, both resized to the nearest integer multiple of
`vae_scale_factor`.
"""
if height is None:
if isinstance(image, PIL.Image.Image):
height = image.height
elif isinstance(image, torch.Tensor):
height = image.shape[2]
else:
height = image.shape[1]
if width is None:
if isinstance(image, PIL.Image.Image):
width = image.width
elif isinstance(image, torch.Tensor):
width = image.shape[3]
else:
width = image.shape[2]
width, height = (
x - x % self.config.vae_scale_factor for x in (width, height)
) # resize to integer multiple of vae_scale_factor
return height, width
def preprocess(
self,
image: PipelineImageInput,
height: Optional[int] = None,
width: Optional[int] = None,
resize_mode: str = "default", # "default", "fill", "crop"
crops_coords: Optional[Tuple[int, int, int, int]] = None,
) -> torch.Tensor:
"""
Preprocess the image input.
Args:
image (`PipelineImageInput`):
The image input, accepted formats are PIL images, NumPy arrays, PyTorch tensors; Also accept list of
supported formats.
height (`int`, *optional*):
The height in preprocessed image. If `None`, will use the `get_default_height_width()` to get default
height.
width (`int`, *optional*):
The width in preprocessed. If `None`, will use get_default_height_width()` to get the default width.
resize_mode (`str`, *optional*, defaults to `default`):
The resize mode, can be one of `default` or `fill`. If `default`, will resize the image to fit within
the specified width and height, and it may not maintaining the original aspect ratio. If `fill`, will
resize the image to fit within the specified width and height, maintaining the aspect ratio, and then
center the image within the dimensions, filling empty with data from image. If `crop`, will resize the
image to fit within the specified width and height, maintaining the aspect ratio, and then center the
image within the dimensions, cropping the excess. Note that resize_mode `fill` and `crop` are only
supported for PIL image input.
crops_coords (`List[Tuple[int, int, int, int]]`, *optional*, defaults to `None`):
The crop coordinates for each image in the batch. If `None`, will not crop the image.
Returns:
`torch.Tensor`:
The preprocessed image.
"""
supported_formats = (PIL.Image.Image, np.ndarray, torch.Tensor)
# Expand the missing dimension for 3-dimensional pytorch tensor or numpy array that represents grayscale image
if self.config.do_convert_grayscale and isinstance(image, (torch.Tensor, np.ndarray)) and image.ndim == 3:
if isinstance(image, torch.Tensor):
# if image is a pytorch tensor could have 2 possible shapes:
# 1. batch x height x width: we should insert the channel dimension at position 1
# 2. channel x height x width: we should insert batch dimension at position 0,
# however, since both channel and batch dimension has same size 1, it is same to insert at position 1
# for simplicity, we insert a dimension of size 1 at position 1 for both cases
image = image.unsqueeze(1)
else:
# if it is a numpy array, it could have 2 possible shapes:
# 1. batch x height x width: insert channel dimension on last position
# 2. height x width x channel: insert batch dimension on first position
if image.shape[-1] == 1:
image = np.expand_dims(image, axis=0)
else:
image = np.expand_dims(image, axis=-1)
if isinstance(image, list) and isinstance(image[0], np.ndarray) and image[0].ndim == 4:
warnings.warn(
"Passing `image` as a list of 4d np.ndarray is deprecated."
"Please concatenate the list along the batch dimension and pass it as a single 4d np.ndarray",
FutureWarning,
)
image = np.concatenate(image, axis=0)
if isinstance(image, list) and isinstance(image[0], torch.Tensor) and image[0].ndim == 4:
warnings.warn(
"Passing `image` as a list of 4d torch.Tensor is deprecated."
"Please concatenate the list along the batch dimension and pass it as a single 4d torch.Tensor",
FutureWarning,
)
image = torch.cat(image, axis=0)
if not is_valid_image_imagelist(image):
raise ValueError(
f"Input is in incorrect format. Currently, we only support {', '.join(str(x) for x in supported_formats)}"
)
if not isinstance(image, list):
image = [image]
if isinstance(image[0], PIL.Image.Image):
if crops_coords is not None:
image = [i.crop(crops_coords) for i in image]
if self.config.do_resize:
height, width = self.get_default_height_width(image[0], height, width)
image = [self.resize(i, height, width, resize_mode=resize_mode) for i in image]
if self.config.do_convert_rgb:
image = [self.convert_to_rgb(i) for i in image]
elif self.config.do_convert_grayscale:
image = [self.convert_to_grayscale(i) for i in image]
image = self.pil_to_numpy(image) # to np
image = self.numpy_to_pt(image) # to pt
elif isinstance(image[0], np.ndarray):
image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)
image = self.numpy_to_pt(image)
height, width = self.get_default_height_width(image, height, width)
if self.config.do_resize:
image = self.resize(image, height, width)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)
if self.config.do_convert_grayscale and image.ndim == 3:
image = image.unsqueeze(1)
channel = image.shape[1]
# don't need any preprocess if the image is latents
if channel == self.config.vae_latent_channels:
return image
height, width = self.get_default_height_width(image, height, width)
if self.config.do_resize:
image = self.resize(image, height, width)
# expected range [0,1], normalize to [-1,1]
do_normalize = self.config.do_normalize
if do_normalize and image.min() < 0:
warnings.warn(
"Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]",
FutureWarning,
)
do_normalize = False
if do_normalize:
image = self.normalize(image)
if self.config.do_binarize:
image = self.binarize(image)
return image
def postprocess(
self,
image: torch.Tensor,
output_type: str = "pil",
do_denormalize: Optional[List[bool]] = None,
) -> Union[PIL.Image.Image, np.ndarray, torch.Tensor]:
"""
Postprocess the image output from tensor to `output_type`.
Args:
image (`torch.Tensor`):
The image input, should be a pytorch tensor with shape `B x C x H x W`.
output_type (`str`, *optional*, defaults to `pil`):
The output type of the image, can be one of `pil`, `np`, `pt`, `latent`.
do_denormalize (`List[bool]`, *optional*, defaults to `None`):
Whether to denormalize the image to [0,1]. If `None`, will use the value of `do_normalize` in the
`VaeImageProcessor` config.
Returns:
`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`:
The postprocessed image.
"""
if not isinstance(image, torch.Tensor):
raise ValueError(
f"Input for postprocessing is in incorrect format: {type(image)}. We only support pytorch tensor"
)
if output_type not in ["latent", "pt", "np", "pil"]:
deprecation_message = (
f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
"`pil`, `np`, `pt`, `latent`"
)
deprecate("Unsupported output_type", "1.0.0", deprecation_message, standard_warn=False)
output_type = "np"
if output_type == "latent":
return image
image = self._denormalize_conditionally(image, do_denormalize)
if output_type == "pt":
return image
image = self.pt_to_numpy(image)
if output_type == "np":
return image
if output_type == "pil":
return self.numpy_to_pil(image)
def apply_overlay(
self,
mask: PIL.Image.Image,
init_image: PIL.Image.Image,
image: PIL.Image.Image,
crop_coords: Optional[Tuple[int, int, int, int]] = None,
) -> PIL.Image.Image:
r"""
Applies an overlay of the mask and the inpainted image on the original image.
Args:
mask (`PIL.Image.Image`):
The mask image that highlights regions to overlay.
init_image (`PIL.Image.Image`):
The original image to which the overlay is applied.
image (`PIL.Image.Image`):
The image to overlay onto the original.
crop_coords (`Tuple[int, int, int, int]`, *optional*):
Coordinates to crop the image. If provided, the image will be cropped accordingly.
Returns:
`PIL.Image.Image`:
The final image with the overlay applied.
"""
width, height = init_image.width, init_image.height
init_image_masked = PIL.Image.new("RGBa", (width, height))
init_image_masked.paste(init_image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(mask.convert("L")))
init_image_masked = init_image_masked.convert("RGBA")
if crop_coords is not None:
x, y, x2, y2 = crop_coords
w = x2 - x
h = y2 - y
base_image = PIL.Image.new("RGBA", (width, height))
image = self.resize(image, height=h, width=w, resize_mode="crop")
base_image.paste(image, (x, y))
image = base_image.convert("RGB")
image = image.convert("RGBA")
image.alpha_composite(init_image_masked)
image = image.convert("RGB")
return image | class_definition | 2,722 | 32,473 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/image_processor.py | null | 3 |
class VaeImageProcessorLDM3D(VaeImageProcessor):
"""
Image processor for VAE LDM3D.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.
vae_scale_factor (`int`, *optional*, defaults to `8`):
VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
resample (`str`, *optional*, defaults to `lanczos`):
Resampling filter to use when resizing the image.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image to [-1,1].
"""
config_name = CONFIG_NAME
@register_to_config
def __init__(
self,
do_resize: bool = True,
vae_scale_factor: int = 8,
resample: str = "lanczos",
do_normalize: bool = True,
):
super().__init__()
@staticmethod
def numpy_to_pil(images: np.ndarray) -> List[PIL.Image.Image]:
r"""
Convert a NumPy image or a batch of images to a list of PIL images.
Args:
images (`np.ndarray`):
The input NumPy array of images, which can be a single image or a batch.
Returns:
`List[PIL.Image.Image]`:
A list of PIL images converted from the input NumPy array.
"""
if images.ndim == 3:
images = images[None, ...]
images = (images * 255).round().astype("uint8")
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
else:
pil_images = [Image.fromarray(image[:, :, :3]) for image in images]
return pil_images
@staticmethod
def depth_pil_to_numpy(images: Union[List[PIL.Image.Image], PIL.Image.Image]) -> np.ndarray:
r"""
Convert a PIL image or a list of PIL images to NumPy arrays.
Args:
images (`Union[List[PIL.Image.Image], PIL.Image.Image]`):
The input image or list of images to be converted.
Returns:
`np.ndarray`:
A NumPy array of the converted images.
"""
if not isinstance(images, list):
images = [images]
images = [np.array(image).astype(np.float32) / (2**16 - 1) for image in images]
images = np.stack(images, axis=0)
return images
@staticmethod
def rgblike_to_depthmap(image: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:
r"""
Convert an RGB-like depth image to a depth map.
Args:
image (`Union[np.ndarray, torch.Tensor]`):
The RGB-like depth image to convert.
Returns:
`Union[np.ndarray, torch.Tensor]`:
The corresponding depth map.
"""
return image[:, :, 1] * 2**8 + image[:, :, 2]
def numpy_to_depth(self, images: np.ndarray) -> List[PIL.Image.Image]:
r"""
Convert a NumPy depth image or a batch of images to a list of PIL images.
Args:
images (`np.ndarray`):
The input NumPy array of depth images, which can be a single image or a batch.
Returns:
`List[PIL.Image.Image]`:
A list of PIL images converted from the input NumPy depth images.
"""
if images.ndim == 3:
images = images[None, ...]
images_depth = images[:, :, :, 3:]
if images.shape[-1] == 6:
images_depth = (images_depth * 255).round().astype("uint8")
pil_images = [
Image.fromarray(self.rgblike_to_depthmap(image_depth), mode="I;16") for image_depth in images_depth
]
elif images.shape[-1] == 4:
images_depth = (images_depth * 65535.0).astype(np.uint16)
pil_images = [Image.fromarray(image_depth, mode="I;16") for image_depth in images_depth]
else:
raise Exception("Not supported")
return pil_images
def postprocess(
self,
image: torch.Tensor,
output_type: str = "pil",
do_denormalize: Optional[List[bool]] = None,
) -> Union[PIL.Image.Image, np.ndarray, torch.Tensor]:
"""
Postprocess the image output from tensor to `output_type`.
Args:
image (`torch.Tensor`):
The image input, should be a pytorch tensor with shape `B x C x H x W`.
output_type (`str`, *optional*, defaults to `pil`):
The output type of the image, can be one of `pil`, `np`, `pt`, `latent`.
do_denormalize (`List[bool]`, *optional*, defaults to `None`):
Whether to denormalize the image to [0,1]. If `None`, will use the value of `do_normalize` in the
`VaeImageProcessor` config.
Returns:
`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`:
The postprocessed image.
"""
if not isinstance(image, torch.Tensor):
raise ValueError(
f"Input for postprocessing is in incorrect format: {type(image)}. We only support pytorch tensor"
)
if output_type not in ["latent", "pt", "np", "pil"]:
deprecation_message = (
f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
"`pil`, `np`, `pt`, `latent`"
)
deprecate("Unsupported output_type", "1.0.0", deprecation_message, standard_warn=False)
output_type = "np"
image = self._denormalize_conditionally(image, do_denormalize)
image = self.pt_to_numpy(image)
if output_type == "np":
if image.shape[-1] == 6:
image_depth = np.stack([self.rgblike_to_depthmap(im[:, :, 3:]) for im in image], axis=0)
else:
image_depth = image[:, :, :, 3:]
return image[:, :, :, :3], image_depth
if output_type == "pil":
return self.numpy_to_pil(image), self.numpy_to_depth(image)
else:
raise Exception(f"This type {output_type} is not supported")
def preprocess(
self,
rgb: Union[torch.Tensor, PIL.Image.Image, np.ndarray],
depth: Union[torch.Tensor, PIL.Image.Image, np.ndarray],
height: Optional[int] = None,
width: Optional[int] = None,
target_res: Optional[int] = None,
) -> torch.Tensor:
r"""
Preprocess the image input. Accepted formats are PIL images, NumPy arrays, or PyTorch tensors.
Args:
rgb (`Union[torch.Tensor, PIL.Image.Image, np.ndarray]`):
The RGB input image, which can be a single image or a batch.
depth (`Union[torch.Tensor, PIL.Image.Image, np.ndarray]`):
The depth input image, which can be a single image or a batch.
height (`Optional[int]`, *optional*, defaults to `None`):
The desired height of the processed image. If `None`, defaults to the height of the input image.
width (`Optional[int]`, *optional*, defaults to `None`):
The desired width of the processed image. If `None`, defaults to the width of the input image.
target_res (`Optional[int]`, *optional*, defaults to `None`):
Target resolution for resizing the images. If specified, overrides height and width.
Returns:
`Tuple[torch.Tensor, torch.Tensor]`:
A tuple containing the processed RGB and depth images as PyTorch tensors.
"""
supported_formats = (PIL.Image.Image, np.ndarray, torch.Tensor)
# Expand the missing dimension for 3-dimensional pytorch tensor or numpy array that represents grayscale image
if self.config.do_convert_grayscale and isinstance(rgb, (torch.Tensor, np.ndarray)) and rgb.ndim == 3:
raise Exception("This is not yet supported")
if isinstance(rgb, supported_formats):
rgb = [rgb]
depth = [depth]
elif not (isinstance(rgb, list) and all(isinstance(i, supported_formats) for i in rgb)):
raise ValueError(
f"Input is in incorrect format: {[type(i) for i in rgb]}. Currently, we only support {', '.join(supported_formats)}"
)
if isinstance(rgb[0], PIL.Image.Image):
if self.config.do_convert_rgb:
raise Exception("This is not yet supported")
# rgb = [self.convert_to_rgb(i) for i in rgb]
# depth = [self.convert_to_depth(i) for i in depth] #TODO define convert_to_depth
if self.config.do_resize or target_res:
height, width = self.get_default_height_width(rgb[0], height, width) if not target_res else target_res
rgb = [self.resize(i, height, width) for i in rgb]
depth = [self.resize(i, height, width) for i in depth]
rgb = self.pil_to_numpy(rgb) # to np
rgb = self.numpy_to_pt(rgb) # to pt
depth = self.depth_pil_to_numpy(depth) # to np
depth = self.numpy_to_pt(depth) # to pt
elif isinstance(rgb[0], np.ndarray):
rgb = np.concatenate(rgb, axis=0) if rgb[0].ndim == 4 else np.stack(rgb, axis=0)
rgb = self.numpy_to_pt(rgb)
height, width = self.get_default_height_width(rgb, height, width)
if self.config.do_resize:
rgb = self.resize(rgb, height, width)
depth = np.concatenate(depth, axis=0) if rgb[0].ndim == 4 else np.stack(depth, axis=0)
depth = self.numpy_to_pt(depth)
height, width = self.get_default_height_width(depth, height, width)
if self.config.do_resize:
depth = self.resize(depth, height, width)
elif isinstance(rgb[0], torch.Tensor):
raise Exception("This is not yet supported")
# rgb = torch.cat(rgb, axis=0) if rgb[0].ndim == 4 else torch.stack(rgb, axis=0)
# if self.config.do_convert_grayscale and rgb.ndim == 3:
# rgb = rgb.unsqueeze(1)
# channel = rgb.shape[1]
# height, width = self.get_default_height_width(rgb, height, width)
# if self.config.do_resize:
# rgb = self.resize(rgb, height, width)
# depth = torch.cat(depth, axis=0) if depth[0].ndim == 4 else torch.stack(depth, axis=0)
# if self.config.do_convert_grayscale and depth.ndim == 3:
# depth = depth.unsqueeze(1)
# channel = depth.shape[1]
# # don't need any preprocess if the image is latents
# if depth == 4:
# return rgb, depth
# height, width = self.get_default_height_width(depth, height, width)
# if self.config.do_resize:
# depth = self.resize(depth, height, width)
# expected range [0,1], normalize to [-1,1]
do_normalize = self.config.do_normalize
if rgb.min() < 0 and do_normalize:
warnings.warn(
"Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{rgb.min()},{rgb.max()}]",
FutureWarning,
)
do_normalize = False
if do_normalize:
rgb = self.normalize(rgb)
depth = self.normalize(depth)
if self.config.do_binarize:
rgb = self.binarize(rgb)
depth = self.binarize(depth)
return rgb, depth | class_definition | 32,476 | 44,360 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/image_processor.py | null | 4 |
class IPAdapterMaskProcessor(VaeImageProcessor):
"""
Image processor for IP Adapter image masks.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.
vae_scale_factor (`int`, *optional*, defaults to `8`):
VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
resample (`str`, *optional*, defaults to `lanczos`):
Resampling filter to use when resizing the image.
do_normalize (`bool`, *optional*, defaults to `False`):
Whether to normalize the image to [-1,1].
do_binarize (`bool`, *optional*, defaults to `True`):
Whether to binarize the image to 0/1.
do_convert_grayscale (`bool`, *optional*, defaults to be `True`):
Whether to convert the images to grayscale format.
"""
config_name = CONFIG_NAME
@register_to_config
def __init__(
self,
do_resize: bool = True,
vae_scale_factor: int = 8,
resample: str = "lanczos",
do_normalize: bool = False,
do_binarize: bool = True,
do_convert_grayscale: bool = True,
):
super().__init__(
do_resize=do_resize,
vae_scale_factor=vae_scale_factor,
resample=resample,
do_normalize=do_normalize,
do_binarize=do_binarize,
do_convert_grayscale=do_convert_grayscale,
)
@staticmethod
def downsample(mask: torch.Tensor, batch_size: int, num_queries: int, value_embed_dim: int):
"""
Downsamples the provided mask tensor to match the expected dimensions for scaled dot-product attention. If the
aspect ratio of the mask does not match the aspect ratio of the output image, a warning is issued.
Args:
mask (`torch.Tensor`):
The input mask tensor generated with `IPAdapterMaskProcessor.preprocess()`.
batch_size (`int`):
The batch size.
num_queries (`int`):
The number of queries.
value_embed_dim (`int`):
The dimensionality of the value embeddings.
Returns:
`torch.Tensor`:
The downsampled mask tensor.
"""
o_h = mask.shape[1]
o_w = mask.shape[2]
ratio = o_w / o_h
mask_h = int(math.sqrt(num_queries / ratio))
mask_h = int(mask_h) + int((num_queries % int(mask_h)) != 0)
mask_w = num_queries // mask_h
mask_downsample = F.interpolate(mask.unsqueeze(0), size=(mask_h, mask_w), mode="bicubic").squeeze(0)
# Repeat batch_size times
if mask_downsample.shape[0] < batch_size:
mask_downsample = mask_downsample.repeat(batch_size, 1, 1)
mask_downsample = mask_downsample.view(mask_downsample.shape[0], -1)
downsampled_area = mask_h * mask_w
# If the output image and the mask do not have the same aspect ratio, tensor shapes will not match
# Pad tensor if downsampled_mask.shape[1] is smaller than num_queries
if downsampled_area < num_queries:
warnings.warn(
"The aspect ratio of the mask does not match the aspect ratio of the output image. "
"Please update your masks or adjust the output size for optimal performance.",
UserWarning,
)
mask_downsample = F.pad(mask_downsample, (0, num_queries - mask_downsample.shape[1]), value=0.0)
# Discard last embeddings if downsampled_mask.shape[1] is bigger than num_queries
if downsampled_area > num_queries:
warnings.warn(
"The aspect ratio of the mask does not match the aspect ratio of the output image. "
"Please update your masks or adjust the output size for optimal performance.",
UserWarning,
)
mask_downsample = mask_downsample[:, :num_queries]
# Repeat last dimension to match SDPA output shape
mask_downsample = mask_downsample.view(mask_downsample.shape[0], mask_downsample.shape[1], 1).repeat(
1, 1, value_embed_dim
)
return mask_downsample | class_definition | 44,363 | 48,691 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/image_processor.py | null | 5 |
class PixArtImageProcessor(VaeImageProcessor):
"""
Image processor for PixArt image resize and crop.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. Can accept
`height` and `width` arguments from [`image_processor.VaeImageProcessor.preprocess`] method.
vae_scale_factor (`int`, *optional*, defaults to `8`):
VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
resample (`str`, *optional*, defaults to `lanczos`):
Resampling filter to use when resizing the image.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image to [-1,1].
do_binarize (`bool`, *optional*, defaults to `False`):
Whether to binarize the image to 0/1.
do_convert_rgb (`bool`, *optional*, defaults to be `False`):
Whether to convert the images to RGB format.
do_convert_grayscale (`bool`, *optional*, defaults to be `False`):
Whether to convert the images to grayscale format.
"""
@register_to_config
def __init__(
self,
do_resize: bool = True,
vae_scale_factor: int = 8,
resample: str = "lanczos",
do_normalize: bool = True,
do_binarize: bool = False,
do_convert_grayscale: bool = False,
):
super().__init__(
do_resize=do_resize,
vae_scale_factor=vae_scale_factor,
resample=resample,
do_normalize=do_normalize,
do_binarize=do_binarize,
do_convert_grayscale=do_convert_grayscale,
)
@staticmethod
def classify_height_width_bin(height: int, width: int, ratios: dict) -> Tuple[int, int]:
r"""
Returns the binned height and width based on the aspect ratio.
Args:
height (`int`): The height of the image.
width (`int`): The width of the image.
ratios (`dict`): A dictionary where keys are aspect ratios and values are tuples of (height, width).
Returns:
`Tuple[int, int]`: The closest binned height and width.
"""
ar = float(height / width)
closest_ratio = min(ratios.keys(), key=lambda ratio: abs(float(ratio) - ar))
default_hw = ratios[closest_ratio]
return int(default_hw[0]), int(default_hw[1])
@staticmethod
def resize_and_crop_tensor(samples: torch.Tensor, new_width: int, new_height: int) -> torch.Tensor:
r"""
Resizes and crops a tensor of images to the specified dimensions.
Args:
samples (`torch.Tensor`):
A tensor of shape (N, C, H, W) where N is the batch size, C is the number of channels, H is the height,
and W is the width.
new_width (`int`): The desired width of the output images.
new_height (`int`): The desired height of the output images.
Returns:
`torch.Tensor`: A tensor containing the resized and cropped images.
"""
orig_height, orig_width = samples.shape[2], samples.shape[3]
# Check if resizing is needed
if orig_height != new_height or orig_width != new_width:
ratio = max(new_height / orig_height, new_width / orig_width)
resized_width = int(orig_width * ratio)
resized_height = int(orig_height * ratio)
# Resize
samples = F.interpolate(
samples, size=(resized_height, resized_width), mode="bilinear", align_corners=False
)
# Center Crop
start_x = (resized_width - new_width) // 2
end_x = start_x + new_width
start_y = (resized_height - new_height) // 2
end_y = start_y + new_height
samples = samples[:, :, start_y:end_y, start_x:end_x]
return samples | class_definition | 48,694 | 52,715 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/image_processor.py | null | 6 |
class VideoProcessor(VaeImageProcessor):
r"""Simple video processor."""
def preprocess_video(self, video, height: Optional[int] = None, width: Optional[int] = None) -> torch.Tensor:
r"""
Preprocesses input video(s).
Args:
video (`List[PIL.Image]`, `List[List[PIL.Image]]`, `torch.Tensor`, `np.array`, `List[torch.Tensor]`, `List[np.array]`):
The input video. It can be one of the following:
* List of the PIL images.
* List of list of PIL images.
* 4D Torch tensors (expected shape for each tensor `(num_frames, num_channels, height, width)`).
* 4D NumPy arrays (expected shape for each array `(num_frames, height, width, num_channels)`).
* List of 4D Torch tensors (expected shape for each tensor `(num_frames, num_channels, height,
width)`).
* List of 4D NumPy arrays (expected shape for each array `(num_frames, height, width, num_channels)`).
* 5D NumPy arrays: expected shape for each array `(batch_size, num_frames, height, width,
num_channels)`.
* 5D Torch tensors: expected shape for each array `(batch_size, num_frames, num_channels, height,
width)`.
height (`int`, *optional*, defaults to `None`):
The height in preprocessed frames of the video. If `None`, will use the `get_default_height_width()` to
get default height.
width (`int`, *optional*`, defaults to `None`):
The width in preprocessed frames of the video. If `None`, will use get_default_height_width()` to get
the default width.
"""
if isinstance(video, list) and isinstance(video[0], np.ndarray) and video[0].ndim == 5:
warnings.warn(
"Passing `video` as a list of 5d np.ndarray is deprecated."
"Please concatenate the list along the batch dimension and pass it as a single 5d np.ndarray",
FutureWarning,
)
video = np.concatenate(video, axis=0)
if isinstance(video, list) and isinstance(video[0], torch.Tensor) and video[0].ndim == 5:
warnings.warn(
"Passing `video` as a list of 5d torch.Tensor is deprecated."
"Please concatenate the list along the batch dimension and pass it as a single 5d torch.Tensor",
FutureWarning,
)
video = torch.cat(video, axis=0)
# ensure the input is a list of videos:
# - if it is a batch of videos (5d torch.Tensor or np.ndarray), it is converted to a list of videos (a list of 4d torch.Tensor or np.ndarray)
# - if it is is a single video, it is convereted to a list of one video.
if isinstance(video, (np.ndarray, torch.Tensor)) and video.ndim == 5:
video = list(video)
elif isinstance(video, list) and is_valid_image(video[0]) or is_valid_image_imagelist(video):
video = [video]
elif isinstance(video, list) and is_valid_image_imagelist(video[0]):
video = video
else:
raise ValueError(
"Input is in incorrect format. Currently, we only support numpy.ndarray, torch.Tensor, PIL.Image.Image"
)
video = torch.stack([self.preprocess(img, height=height, width=width) for img in video], dim=0)
# move the number of channels before the number of frames.
video = video.permute(0, 2, 1, 3, 4)
return video
def postprocess_video(
self, video: torch.Tensor, output_type: str = "np"
) -> Union[np.ndarray, torch.Tensor, List[PIL.Image.Image]]:
r"""
Converts a video tensor to a list of frames for export.
Args:
video (`torch.Tensor`): The video as a tensor.
output_type (`str`, defaults to `"np"`): Output type of the postprocessed `video` tensor.
"""
batch_size = video.shape[0]
outputs = []
for batch_idx in range(batch_size):
batch_vid = video[batch_idx].permute(1, 0, 2, 3)
batch_output = self.postprocess(batch_vid, output_type)
outputs.append(batch_output)
if output_type == "np":
outputs = np.stack(outputs)
elif output_type == "pt":
outputs = torch.stack(outputs)
elif not output_type == "pil":
raise ValueError(f"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil']")
return outputs | class_definition | 800 | 5,401 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/video_processor.py | null | 7 |
class SchedulerType(Enum):
LINEAR = "linear"
COSINE = "cosine"
COSINE_WITH_RESTARTS = "cosine_with_restarts"
POLYNOMIAL = "polynomial"
CONSTANT = "constant"
CONSTANT_WITH_WARMUP = "constant_with_warmup"
PIECEWISE_CONSTANT = "piecewise_constant" | class_definition | 875 | 1,147 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/optimization.py | null | 8 |
class EMAModel:
"""
Exponential Moving Average of models weights
"""
def __init__(
self,
parameters: Iterable[torch.nn.Parameter],
decay: float = 0.9999,
min_decay: float = 0.0,
update_after_step: int = 0,
use_ema_warmup: bool = False,
inv_gamma: Union[float, int] = 1.0,
power: Union[float, int] = 2 / 3,
foreach: bool = False,
model_cls: Optional[Any] = None,
model_config: Dict[str, Any] = None,
**kwargs,
):
"""
Args:
parameters (Iterable[torch.nn.Parameter]): The parameters to track.
decay (float): The decay factor for the exponential moving average.
min_decay (float): The minimum decay factor for the exponential moving average.
update_after_step (int): The number of steps to wait before starting to update the EMA weights.
use_ema_warmup (bool): Whether to use EMA warmup.
inv_gamma (float):
Inverse multiplicative factor of EMA warmup. Default: 1. Only used if `use_ema_warmup` is True.
power (float): Exponential factor of EMA warmup. Default: 2/3. Only used if `use_ema_warmup` is True.
foreach (bool): Use torch._foreach functions for updating shadow parameters. Should be faster.
device (Optional[Union[str, torch.device]]): The device to store the EMA weights on. If None, the EMA
weights will be stored on CPU.
@crowsonkb's notes on EMA Warmup:
If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan
to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps),
gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999
at 215.4k steps).
"""
if isinstance(parameters, torch.nn.Module):
deprecation_message = (
"Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
"Please pass the parameters of the module instead."
)
deprecate(
"passing a `torch.nn.Module` to `ExponentialMovingAverage`",
"1.0.0",
deprecation_message,
standard_warn=False,
)
parameters = parameters.parameters()
# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
use_ema_warmup = True
if kwargs.get("max_value", None) is not None:
deprecation_message = "The `max_value` argument is deprecated. Please use `decay` instead."
deprecate("max_value", "1.0.0", deprecation_message, standard_warn=False)
decay = kwargs["max_value"]
if kwargs.get("min_value", None) is not None:
deprecation_message = "The `min_value` argument is deprecated. Please use `min_decay` instead."
deprecate("min_value", "1.0.0", deprecation_message, standard_warn=False)
min_decay = kwargs["min_value"]
parameters = list(parameters)
self.shadow_params = [p.clone().detach() for p in parameters]
if kwargs.get("device", None) is not None:
deprecation_message = "The `device` argument is deprecated. Please use `to` instead."
deprecate("device", "1.0.0", deprecation_message, standard_warn=False)
self.to(device=kwargs["device"])
self.temp_stored_params = None
self.decay = decay
self.min_decay = min_decay
self.update_after_step = update_after_step
self.use_ema_warmup = use_ema_warmup
self.inv_gamma = inv_gamma
self.power = power
self.optimization_step = 0
self.cur_decay_value = None # set in `step()`
self.foreach = foreach
self.model_cls = model_cls
self.model_config = model_config
@classmethod
def from_pretrained(cls, path, model_cls, foreach=False) -> "EMAModel":
_, ema_kwargs = model_cls.from_config(path, return_unused_kwargs=True)
model = model_cls.from_pretrained(path)
ema_model = cls(model.parameters(), model_cls=model_cls, model_config=model.config, foreach=foreach)
ema_model.load_state_dict(ema_kwargs)
return ema_model
def save_pretrained(self, path):
if self.model_cls is None:
raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__.")
if self.model_config is None:
raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__.")
model = self.model_cls.from_config(self.model_config)
state_dict = self.state_dict()
state_dict.pop("shadow_params", None)
model.register_to_config(**state_dict)
self.copy_to(model.parameters())
model.save_pretrained(path)
def get_decay(self, optimization_step: int) -> float:
"""
Compute the decay factor for the exponential moving average.
"""
step = max(0, optimization_step - self.update_after_step - 1)
if step <= 0:
return 0.0
if self.use_ema_warmup:
cur_decay_value = 1 - (1 + step / self.inv_gamma) ** -self.power
else:
cur_decay_value = (1 + step) / (10 + step)
cur_decay_value = min(cur_decay_value, self.decay)
# make sure decay is not smaller than min_decay
cur_decay_value = max(cur_decay_value, self.min_decay)
return cur_decay_value
@torch.no_grad()
def step(self, parameters: Iterable[torch.nn.Parameter]):
if isinstance(parameters, torch.nn.Module):
deprecation_message = (
"Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
"Please pass the parameters of the module instead."
)
deprecate(
"passing a `torch.nn.Module` to `ExponentialMovingAverage.step`",
"1.0.0",
deprecation_message,
standard_warn=False,
)
parameters = parameters.parameters()
parameters = list(parameters)
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
decay = self.get_decay(self.optimization_step)
self.cur_decay_value = decay
one_minus_decay = 1 - decay
context_manager = contextlib.nullcontext()
if self.foreach:
if is_transformers_available() and transformers.integrations.deepspeed.is_deepspeed_zero3_enabled():
context_manager = deepspeed.zero.GatheredParameters(parameters, modifier_rank=None)
with context_manager:
params_grad = [param for param in parameters if param.requires_grad]
s_params_grad = [
s_param for s_param, param in zip(self.shadow_params, parameters) if param.requires_grad
]
if len(params_grad) < len(parameters):
torch._foreach_copy_(
[s_param for s_param, param in zip(self.shadow_params, parameters) if not param.requires_grad],
[param for param in parameters if not param.requires_grad],
non_blocking=True,
)
torch._foreach_sub_(
s_params_grad, torch._foreach_sub(s_params_grad, params_grad), alpha=one_minus_decay
)
else:
for s_param, param in zip(self.shadow_params, parameters):
if is_transformers_available() and transformers.integrations.deepspeed.is_deepspeed_zero3_enabled():
context_manager = deepspeed.zero.GatheredParameters(param, modifier_rank=None)
with context_manager:
if param.requires_grad:
s_param.sub_(one_minus_decay * (s_param - param))
else:
s_param.copy_(param)
def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None:
"""
Copy current averaged parameters into given collection of parameters.
Args:
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
updated with the stored moving averages. If `None`, the parameters with which this
`ExponentialMovingAverage` was initialized will be used.
"""
parameters = list(parameters)
if self.foreach:
torch._foreach_copy_(
[param.data for param in parameters],
[s_param.to(param.device).data for s_param, param in zip(self.shadow_params, parameters)],
)
else:
for s_param, param in zip(self.shadow_params, parameters):
param.data.copy_(s_param.to(param.device).data)
def pin_memory(self) -> None:
r"""
Move internal buffers of the ExponentialMovingAverage to pinned memory. Useful for non-blocking transfers for
offloading EMA params to the host.
"""
self.shadow_params = [p.pin_memory() for p in self.shadow_params]
def to(self, device=None, dtype=None, non_blocking=False) -> None:
r"""
Move internal buffers of the ExponentialMovingAverage to `device`.
Args:
device: like `device` argument to `torch.Tensor.to`
"""
# .to() on the tensors handles None correctly
self.shadow_params = [
p.to(device=device, dtype=dtype, non_blocking=non_blocking)
if p.is_floating_point()
else p.to(device=device, non_blocking=non_blocking)
for p in self.shadow_params
]
def state_dict(self) -> dict:
r"""
Returns the state of the ExponentialMovingAverage as a dict. This method is used by accelerate during
checkpointing to save the ema state dict.
"""
# Following PyTorch conventions, references to tensors are returned:
# "returns a reference to the state and not its copy!" -
# https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict
return {
"decay": self.decay,
"min_decay": self.min_decay,
"optimization_step": self.optimization_step,
"update_after_step": self.update_after_step,
"use_ema_warmup": self.use_ema_warmup,
"inv_gamma": self.inv_gamma,
"power": self.power,
"shadow_params": self.shadow_params,
}
def store(self, parameters: Iterable[torch.nn.Parameter]) -> None:
r"""
Saves the current parameters for restoring later.
Args:
parameters: Iterable of `torch.nn.Parameter`. The parameters to be temporarily stored.
"""
self.temp_stored_params = [param.detach().cpu().clone() for param in parameters]
def restore(self, parameters: Iterable[torch.nn.Parameter]) -> None:
r"""
Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters
without: affecting the original optimization process. Store the parameters before the `copy_to()` method. After
validation (or model saving), use this to restore the former parameters.
Args:
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
updated with the stored parameters. If `None`, the parameters with which this
`ExponentialMovingAverage` was initialized will be used.
"""
if self.temp_stored_params is None:
raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`")
if self.foreach:
torch._foreach_copy_(
[param.data for param in parameters], [c_param.data for c_param in self.temp_stored_params]
)
else:
for c_param, param in zip(self.temp_stored_params, parameters):
param.data.copy_(c_param.data)
# Better memory-wise.
self.temp_stored_params = None
def load_state_dict(self, state_dict: dict) -> None:
r"""
Loads the ExponentialMovingAverage state. This method is used by accelerate during checkpointing to save the
ema state dict.
Args:
state_dict (dict): EMA state. Should be an object returned
from a call to :meth:`state_dict`.
"""
# deepcopy, to be consistent with module API
state_dict = copy.deepcopy(state_dict)
self.decay = state_dict.get("decay", self.decay)
if self.decay < 0.0 or self.decay > 1.0:
raise ValueError("Decay must be between 0 and 1")
self.min_decay = state_dict.get("min_decay", self.min_decay)
if not isinstance(self.min_decay, float):
raise ValueError("Invalid min_decay")
self.optimization_step = state_dict.get("optimization_step", self.optimization_step)
if not isinstance(self.optimization_step, int):
raise ValueError("Invalid optimization_step")
self.update_after_step = state_dict.get("update_after_step", self.update_after_step)
if not isinstance(self.update_after_step, int):
raise ValueError("Invalid update_after_step")
self.use_ema_warmup = state_dict.get("use_ema_warmup", self.use_ema_warmup)
if not isinstance(self.use_ema_warmup, bool):
raise ValueError("Invalid use_ema_warmup")
self.inv_gamma = state_dict.get("inv_gamma", self.inv_gamma)
if not isinstance(self.inv_gamma, (float, int)):
raise ValueError("Invalid inv_gamma")
self.power = state_dict.get("power", self.power)
if not isinstance(self.power, (float, int)):
raise ValueError("Invalid power")
shadow_params = state_dict.get("shadow_params", None)
if shadow_params is not None:
self.shadow_params = shadow_params
if not isinstance(self.shadow_params, list):
raise ValueError("shadow_params must be a list")
if not all(isinstance(p, torch.Tensor) for p in self.shadow_params):
raise ValueError("shadow_params must all be Tensors") | class_definition | 11,693 | 26,213 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/training_utils.py | null | 9 |
class PipelineCallback(ConfigMixin):
"""
Base class for all the official callbacks used in a pipeline. This class provides a structure for implementing
custom callbacks and ensures that all callbacks have a consistent interface.
Please implement the following:
`tensor_inputs`: This should return a list of tensor inputs specific to your callback. You will only be able to
include
variables listed in the `._callback_tensor_inputs` attribute of your pipeline class.
`callback_fn`: This method defines the core functionality of your callback.
"""
config_name = CONFIG_NAME
@register_to_config
def __init__(self, cutoff_step_ratio=1.0, cutoff_step_index=None):
super().__init__()
if (cutoff_step_ratio is None and cutoff_step_index is None) or (
cutoff_step_ratio is not None and cutoff_step_index is not None
):
raise ValueError("Either cutoff_step_ratio or cutoff_step_index should be provided, not both or none.")
if cutoff_step_ratio is not None and (
not isinstance(cutoff_step_ratio, float) or not (0.0 <= cutoff_step_ratio <= 1.0)
):
raise ValueError("cutoff_step_ratio must be a float between 0.0 and 1.0.")
@property
def tensor_inputs(self) -> List[str]:
raise NotImplementedError(f"You need to set the attribute `tensor_inputs` for {self.__class__}")
def callback_fn(self, pipeline, step_index, timesteps, callback_kwargs) -> Dict[str, Any]:
raise NotImplementedError(f"You need to implement the method `callback_fn` for {self.__class__}")
def __call__(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
return self.callback_fn(pipeline, step_index, timestep, callback_kwargs) | class_definition | 134 | 1,945 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/callbacks.py | null | 10 |
class MultiPipelineCallbacks:
"""
This class is designed to handle multiple pipeline callbacks. It accepts a list of PipelineCallback objects and
provides a unified interface for calling all of them.
"""
def __init__(self, callbacks: List[PipelineCallback]):
self.callbacks = callbacks
@property
def tensor_inputs(self) -> List[str]:
return [input for callback in self.callbacks for input in callback.tensor_inputs]
def __call__(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
"""
Calls all the callbacks in order with the given arguments and returns the final callback_kwargs.
"""
for callback in self.callbacks:
callback_kwargs = callback(pipeline, step_index, timestep, callback_kwargs)
return callback_kwargs | class_definition | 1,948 | 2,790 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/callbacks.py | null | 11 |
class SDCFGCutoffCallback(PipelineCallback):
"""
Callback function for Stable Diffusion Pipelines. After certain number of steps (set by `cutoff_step_ratio` or
`cutoff_step_index`), this callback will disable the CFG.
Note: This callback mutates the pipeline by changing the `_guidance_scale` attribute to 0.0 after the cutoff step.
"""
tensor_inputs = ["prompt_embeds"]
def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
cutoff_step_ratio = self.config.cutoff_step_ratio
cutoff_step_index = self.config.cutoff_step_index
# Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio
cutoff_step = (
cutoff_step_index if cutoff_step_index is not None else int(pipeline.num_timesteps * cutoff_step_ratio)
)
if step_index == cutoff_step:
prompt_embeds = callback_kwargs[self.tensor_inputs[0]]
prompt_embeds = prompt_embeds[-1:] # "-1" denotes the embeddings for conditional text tokens.
pipeline._guidance_scale = 0.0
callback_kwargs[self.tensor_inputs[0]] = prompt_embeds
return callback_kwargs | class_definition | 2,793 | 3,989 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/callbacks.py | null | 12 |
class SDXLCFGCutoffCallback(PipelineCallback):
"""
Callback function for the base Stable Diffusion XL Pipelines. After certain number of steps (set by
`cutoff_step_ratio` or `cutoff_step_index`), this callback will disable the CFG.
Note: This callback mutates the pipeline by changing the `_guidance_scale` attribute to 0.0 after the cutoff step.
"""
tensor_inputs = [
"prompt_embeds",
"add_text_embeds",
"add_time_ids",
]
def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
cutoff_step_ratio = self.config.cutoff_step_ratio
cutoff_step_index = self.config.cutoff_step_index
# Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio
cutoff_step = (
cutoff_step_index if cutoff_step_index is not None else int(pipeline.num_timesteps * cutoff_step_ratio)
)
if step_index == cutoff_step:
prompt_embeds = callback_kwargs[self.tensor_inputs[0]]
prompt_embeds = prompt_embeds[-1:] # "-1" denotes the embeddings for conditional text tokens.
add_text_embeds = callback_kwargs[self.tensor_inputs[1]]
add_text_embeds = add_text_embeds[-1:] # "-1" denotes the embeddings for conditional pooled text tokens
add_time_ids = callback_kwargs[self.tensor_inputs[2]]
add_time_ids = add_time_ids[-1:] # "-1" denotes the embeddings for conditional added time vector
pipeline._guidance_scale = 0.0
callback_kwargs[self.tensor_inputs[0]] = prompt_embeds
callback_kwargs[self.tensor_inputs[1]] = add_text_embeds
callback_kwargs[self.tensor_inputs[2]] = add_time_ids
return callback_kwargs | class_definition | 3,992 | 5,768 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/callbacks.py | null | 13 |
class SDXLControlnetCFGCutoffCallback(PipelineCallback):
"""
Callback function for the Controlnet Stable Diffusion XL Pipelines. After certain number of steps (set by
`cutoff_step_ratio` or `cutoff_step_index`), this callback will disable the CFG.
Note: This callback mutates the pipeline by changing the `_guidance_scale` attribute to 0.0 after the cutoff step.
"""
tensor_inputs = [
"prompt_embeds",
"add_text_embeds",
"add_time_ids",
"image",
]
def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
cutoff_step_ratio = self.config.cutoff_step_ratio
cutoff_step_index = self.config.cutoff_step_index
# Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio
cutoff_step = (
cutoff_step_index if cutoff_step_index is not None else int(pipeline.num_timesteps * cutoff_step_ratio)
)
if step_index == cutoff_step:
prompt_embeds = callback_kwargs[self.tensor_inputs[0]]
prompt_embeds = prompt_embeds[-1:] # "-1" denotes the embeddings for conditional text tokens.
add_text_embeds = callback_kwargs[self.tensor_inputs[1]]
add_text_embeds = add_text_embeds[-1:] # "-1" denotes the embeddings for conditional pooled text tokens
add_time_ids = callback_kwargs[self.tensor_inputs[2]]
add_time_ids = add_time_ids[-1:] # "-1" denotes the embeddings for conditional added time vector
# For Controlnet
image = callback_kwargs[self.tensor_inputs[3]]
image = image[-1:]
pipeline._guidance_scale = 0.0
callback_kwargs[self.tensor_inputs[0]] = prompt_embeds
callback_kwargs[self.tensor_inputs[1]] = add_text_embeds
callback_kwargs[self.tensor_inputs[2]] = add_time_ids
callback_kwargs[self.tensor_inputs[3]] = image
return callback_kwargs | class_definition | 5,771 | 7,759 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/callbacks.py | null | 14 |
class IPAdapterScaleCutoffCallback(PipelineCallback):
"""
Callback function for any pipeline that inherits `IPAdapterMixin`. After certain number of steps (set by
`cutoff_step_ratio` or `cutoff_step_index`), this callback will set the IP Adapter scale to `0.0`.
Note: This callback mutates the IP Adapter attention processors by setting the scale to 0.0 after the cutoff step.
"""
tensor_inputs = []
def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
cutoff_step_ratio = self.config.cutoff_step_ratio
cutoff_step_index = self.config.cutoff_step_index
# Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio
cutoff_step = (
cutoff_step_index if cutoff_step_index is not None else int(pipeline.num_timesteps * cutoff_step_ratio)
)
if step_index == cutoff_step:
pipeline.set_ip_adapter_scale(0.0)
return callback_kwargs | class_definition | 7,762 | 8,748 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/callbacks.py | null | 15 |
class ValueGuidedRLPipeline(DiffusionPipeline):
r"""
Pipeline for value-guided sampling from a diffusion model trained to predict sequences of states.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
value_function ([`UNet1DModel`]):
A specialized UNet for fine-tuning trajectories base on reward.
unet ([`UNet1DModel`]):
UNet architecture to denoise the encoded trajectories.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded trajectories. Default for this
application is [`DDPMScheduler`].
env ():
An environment following the OpenAI gym API to act in. For now only Hopper has pretrained models.
"""
def __init__(
self,
value_function: UNet1DModel,
unet: UNet1DModel,
scheduler: DDPMScheduler,
env,
):
super().__init__()
self.register_modules(value_function=value_function, unet=unet, scheduler=scheduler, env=env)
self.data = env.get_dataset()
self.means = {}
for key in self.data.keys():
try:
self.means[key] = self.data[key].mean()
except: # noqa: E722
pass
self.stds = {}
for key in self.data.keys():
try:
self.stds[key] = self.data[key].std()
except: # noqa: E722
pass
self.state_dim = env.observation_space.shape[0]
self.action_dim = env.action_space.shape[0]
def normalize(self, x_in, key):
return (x_in - self.means[key]) / self.stds[key]
def de_normalize(self, x_in, key):
return x_in * self.stds[key] + self.means[key]
def to_torch(self, x_in):
if isinstance(x_in, dict):
return {k: self.to_torch(v) for k, v in x_in.items()}
elif torch.is_tensor(x_in):
return x_in.to(self.unet.device)
return torch.tensor(x_in, device=self.unet.device)
def reset_x0(self, x_in, cond, act_dim):
for key, val in cond.items():
x_in[:, key, act_dim:] = val.clone()
return x_in
def run_diffusion(self, x, conditions, n_guide_steps, scale):
batch_size = x.shape[0]
y = None
for i in tqdm.tqdm(self.scheduler.timesteps):
# create batch of timesteps to pass into model
timesteps = torch.full((batch_size,), i, device=self.unet.device, dtype=torch.long)
for _ in range(n_guide_steps):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
y = self.value_function(x.permute(0, 2, 1), timesteps).sample
grad = torch.autograd.grad([y.sum()], [x])[0]
posterior_variance = self.scheduler._get_variance(i)
model_std = torch.exp(0.5 * posterior_variance)
grad = model_std * grad
grad[timesteps < 2] = 0
x = x.detach()
x = x + scale * grad
x = self.reset_x0(x, conditions, self.action_dim)
prev_x = self.unet(x.permute(0, 2, 1), timesteps).sample.permute(0, 2, 1)
# TODO: verify deprecation of this kwarg
x = self.scheduler.step(prev_x, i, x)["prev_sample"]
# apply conditions to the trajectory (set the initial state)
x = self.reset_x0(x, conditions, self.action_dim)
x = self.to_torch(x)
return x, y
def __call__(self, obs, batch_size=64, planning_horizon=32, n_guide_steps=2, scale=0.1):
# normalize the observations and create batch dimension
obs = self.normalize(obs, "observations")
obs = obs[None].repeat(batch_size, axis=0)
conditions = {0: self.to_torch(obs)}
shape = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
x1 = randn_tensor(shape, device=self.unet.device)
x = self.reset_x0(x1, conditions, self.action_dim)
x = self.to_torch(x)
# run the diffusion process
x, y = self.run_diffusion(x, conditions, n_guide_steps, scale)
# sort output trajectories by value
sorted_idx = y.argsort(0, descending=True).squeeze()
sorted_values = x[sorted_idx]
actions = sorted_values[:, :, : self.action_dim]
actions = actions.detach().cpu().numpy()
denorm_actions = self.de_normalize(actions, key="actions")
# select the action with the highest value
if y is not None:
selected_index = 0
else:
# if we didn't run value guiding, select a random action
selected_index = np.random.randint(0, batch_size)
denorm_actions = denorm_actions[selected_index, 0]
return denorm_actions | class_definition | 843 | 6,032 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/experimental/rl/value_guided_sampling.py | null | 16 |
class QuantizationMethod(str, Enum):
BITS_AND_BYTES = "bitsandbytes"
GGUF = "gguf"
TORCHAO = "torchao" | class_definition | 1,208 | 1,322 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/quantization_config.py | null | 17 |
class QuantizationConfigMixin:
"""
Mixin class for quantization config
"""
quant_method: QuantizationMethod
_exclude_attributes_at_init = []
@classmethod
def from_dict(cls, config_dict, return_unused_kwargs=False, **kwargs):
"""
Instantiates a [`QuantizationConfigMixin`] from a Python dictionary of parameters.
Args:
config_dict (`Dict[str, Any]`):
Dictionary that will be used to instantiate the configuration object.
return_unused_kwargs (`bool`,*optional*, defaults to `False`):
Whether or not to return a list of unused keyword arguments. Used for `from_pretrained` method in
`PreTrainedModel`.
kwargs (`Dict[str, Any]`):
Additional parameters from which to initialize the configuration object.
Returns:
[`QuantizationConfigMixin`]: The configuration object instantiated from those parameters.
"""
config = cls(**config_dict)
to_remove = []
for key, value in kwargs.items():
if hasattr(config, key):
setattr(config, key, value)
to_remove.append(key)
for key in to_remove:
kwargs.pop(key, None)
if return_unused_kwargs:
return config, kwargs
else:
return config
def to_json_file(self, json_file_path: Union[str, os.PathLike]):
"""
Save this instance to a JSON file.
Args:
json_file_path (`str` or `os.PathLike`):
Path to the JSON file in which this configuration instance's parameters will be saved.
use_diff (`bool`, *optional*, defaults to `True`):
If set to `True`, only the difference between the config instance and the default
`QuantizationConfig()` is serialized to JSON file.
"""
with open(json_file_path, "w", encoding="utf-8") as writer:
config_dict = self.to_dict()
json_string = json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
writer.write(json_string)
def to_dict(self) -> Dict[str, Any]:
"""
Serializes this instance to a Python dictionary. Returns:
`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
"""
return copy.deepcopy(self.__dict__)
def __iter__(self):
"""allows `dict(obj)` for situations where obj may be a dict or QuantizationConfigMixin"""
for attr, value in copy.deepcopy(self.__dict__).items():
yield attr, value
def __repr__(self):
return f"{self.__class__.__name__} {self.to_json_string()}"
def to_json_string(self, use_diff: bool = True) -> str:
"""
Serializes this instance to a JSON string.
Args:
use_diff (`bool`, *optional*, defaults to `True`):
If set to `True`, only the difference between the config instance and the default `PretrainedConfig()`
is serialized to JSON string.
Returns:
`str`: String containing all the attributes that make up this configuration instance in JSON format.
"""
if use_diff is True:
config_dict = self.to_diff_dict()
else:
config_dict = self.to_dict()
return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
def update(self, **kwargs):
"""
Updates attributes of this class instance with attributes from `kwargs` if they match existing attributes,
returning all the unused kwargs.
Args:
kwargs (`Dict[str, Any]`):
Dictionary of attributes to tentatively update this class.
Returns:
`Dict[str, Any]`: Dictionary containing all the key-value pairs that were not used to update the instance.
"""
to_remove = []
for key, value in kwargs.items():
if hasattr(self, key):
setattr(self, key, value)
to_remove.append(key)
# Remove all the attributes that were updated, without modifying the input dict
unused_kwargs = {key: value for key, value in kwargs.items() if key not in to_remove}
return unused_kwargs | class_definition | 1,336 | 5,659 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/quantization_config.py | null | 18 |
class BitsAndBytesConfig(QuantizationConfigMixin):
"""
This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using `bitsandbytes`.
This replaces `load_in_8bit` or `load_in_4bit`therefore both options are mutually exclusive.
Currently only supports `LLM.int8()`, `FP4`, and `NF4` quantization. If more methods are added to `bitsandbytes`,
then more arguments will be added to this class.
Args:
load_in_8bit (`bool`, *optional*, defaults to `False`):
This flag is used to enable 8-bit quantization with LLM.int8().
load_in_4bit (`bool`, *optional*, defaults to `False`):
This flag is used to enable 4-bit quantization by replacing the Linear layers with FP4/NF4 layers from
`bitsandbytes`.
llm_int8_threshold (`float`, *optional*, defaults to 6.0):
This corresponds to the outlier threshold for outlier detection as described in `LLM.int8() : 8-bit Matrix
Multiplication for Transformers at Scale` paper: https://arxiv.org/abs/2208.07339 Any hidden states value
that is above this threshold will be considered an outlier and the operation on those values will be done
in fp16. Values are usually normally distributed, that is, most values are in the range [-3.5, 3.5], but
there are some exceptional systematic outliers that are very differently distributed for large models.
These outliers are often in the interval [-60, -6] or [6, 60]. Int8 quantization works well for values of
magnitude ~5, but beyond that, there is a significant performance penalty. A good default threshold is 6,
but a lower threshold might be needed for more unstable models (small models, fine-tuning).
llm_int8_skip_modules (`List[str]`, *optional*):
An explicit list of the modules that we do not want to convert in 8-bit. This is useful for models such as
Jukebox that has several heads in different places and not necessarily at the last position. For example
for `CausalLM` models, the last `lm_head` is typically kept in its original `dtype`.
llm_int8_enable_fp32_cpu_offload (`bool`, *optional*, defaults to `False`):
This flag is used for advanced use cases and users that are aware of this feature. If you want to split
your model in different parts and run some parts in int8 on GPU and some parts in fp32 on CPU, you can use
this flag. This is useful for offloading large models such as `google/flan-t5-xxl`. Note that the int8
operations will not be run on CPU.
llm_int8_has_fp16_weight (`bool`, *optional*, defaults to `False`):
This flag runs LLM.int8() with 16-bit main weights. This is useful for fine-tuning as the weights do not
have to be converted back and forth for the backward pass.
bnb_4bit_compute_dtype (`torch.dtype` or str, *optional*, defaults to `torch.float32`):
This sets the computational type which might be different than the input type. For example, inputs might be
fp32, but computation can be set to bf16 for speedups.
bnb_4bit_quant_type (`str`, *optional*, defaults to `"fp4"`):
This sets the quantization data type in the bnb.nn.Linear4Bit layers. Options are FP4 and NF4 data types
which are specified by `fp4` or `nf4`.
bnb_4bit_use_double_quant (`bool`, *optional*, defaults to `False`):
This flag is used for nested quantization where the quantization constants from the first quantization are
quantized again.
bnb_4bit_quant_storage (`torch.dtype` or str, *optional*, defaults to `torch.uint8`):
This sets the storage type to pack the quanitzed 4-bit prarams.
kwargs (`Dict[str, Any]`, *optional*):
Additional parameters from which to initialize the configuration object.
"""
_exclude_attributes_at_init = ["_load_in_4bit", "_load_in_8bit", "quant_method"]
def __init__(
self,
load_in_8bit=False,
load_in_4bit=False,
llm_int8_threshold=6.0,
llm_int8_skip_modules=None,
llm_int8_enable_fp32_cpu_offload=False,
llm_int8_has_fp16_weight=False,
bnb_4bit_compute_dtype=None,
bnb_4bit_quant_type="fp4",
bnb_4bit_use_double_quant=False,
bnb_4bit_quant_storage=None,
**kwargs,
):
self.quant_method = QuantizationMethod.BITS_AND_BYTES
if load_in_4bit and load_in_8bit:
raise ValueError("load_in_4bit and load_in_8bit are both True, but only one can be used at the same time")
self._load_in_8bit = load_in_8bit
self._load_in_4bit = load_in_4bit
self.llm_int8_threshold = llm_int8_threshold
self.llm_int8_skip_modules = llm_int8_skip_modules
self.llm_int8_enable_fp32_cpu_offload = llm_int8_enable_fp32_cpu_offload
self.llm_int8_has_fp16_weight = llm_int8_has_fp16_weight
self.bnb_4bit_quant_type = bnb_4bit_quant_type
self.bnb_4bit_use_double_quant = bnb_4bit_use_double_quant
if bnb_4bit_compute_dtype is None:
self.bnb_4bit_compute_dtype = torch.float32
elif isinstance(bnb_4bit_compute_dtype, str):
self.bnb_4bit_compute_dtype = getattr(torch, bnb_4bit_compute_dtype)
elif isinstance(bnb_4bit_compute_dtype, torch.dtype):
self.bnb_4bit_compute_dtype = bnb_4bit_compute_dtype
else:
raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype")
if bnb_4bit_quant_storage is None:
self.bnb_4bit_quant_storage = torch.uint8
elif isinstance(bnb_4bit_quant_storage, str):
if bnb_4bit_quant_storage not in ["float16", "float32", "int8", "uint8", "float64", "bfloat16"]:
raise ValueError(
"`bnb_4bit_quant_storage` must be a valid string (one of 'float16', 'float32', 'int8', 'uint8', 'float64', 'bfloat16') "
)
self.bnb_4bit_quant_storage = getattr(torch, bnb_4bit_quant_storage)
elif isinstance(bnb_4bit_quant_storage, torch.dtype):
self.bnb_4bit_quant_storage = bnb_4bit_quant_storage
else:
raise ValueError("bnb_4bit_quant_storage must be a string or a torch.dtype")
if kwargs and not all(k in self._exclude_attributes_at_init for k in kwargs):
logger.warning(f"Unused kwargs: {list(kwargs.keys())}. These kwargs are not used in {self.__class__}.")
self.post_init()
@property
def load_in_4bit(self):
return self._load_in_4bit
@load_in_4bit.setter
def load_in_4bit(self, value: bool):
if not isinstance(value, bool):
raise TypeError("load_in_4bit must be a boolean")
if self.load_in_8bit and value:
raise ValueError("load_in_4bit and load_in_8bit are both True, but only one can be used at the same time")
self._load_in_4bit = value
@property
def load_in_8bit(self):
return self._load_in_8bit
@load_in_8bit.setter
def load_in_8bit(self, value: bool):
if not isinstance(value, bool):
raise TypeError("load_in_8bit must be a boolean")
if self.load_in_4bit and value:
raise ValueError("load_in_4bit and load_in_8bit are both True, but only one can be used at the same time")
self._load_in_8bit = value
def post_init(self):
r"""
Safety checker that arguments are correct - also replaces some NoneType arguments with their default values.
"""
if not isinstance(self.load_in_4bit, bool):
raise TypeError("load_in_4bit must be a boolean")
if not isinstance(self.load_in_8bit, bool):
raise TypeError("load_in_8bit must be a boolean")
if not isinstance(self.llm_int8_threshold, float):
raise TypeError("llm_int8_threshold must be a float")
if self.llm_int8_skip_modules is not None and not isinstance(self.llm_int8_skip_modules, list):
raise TypeError("llm_int8_skip_modules must be a list of strings")
if not isinstance(self.llm_int8_enable_fp32_cpu_offload, bool):
raise TypeError("llm_int8_enable_fp32_cpu_offload must be a boolean")
if not isinstance(self.llm_int8_has_fp16_weight, bool):
raise TypeError("llm_int8_has_fp16_weight must be a boolean")
if self.bnb_4bit_compute_dtype is not None and not isinstance(self.bnb_4bit_compute_dtype, torch.dtype):
raise TypeError("bnb_4bit_compute_dtype must be torch.dtype")
if not isinstance(self.bnb_4bit_quant_type, str):
raise TypeError("bnb_4bit_quant_type must be a string")
if not isinstance(self.bnb_4bit_use_double_quant, bool):
raise TypeError("bnb_4bit_use_double_quant must be a boolean")
if self.load_in_4bit and not version.parse(importlib.metadata.version("bitsandbytes")) >= version.parse(
"0.39.0"
):
raise ValueError(
"4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version"
)
def is_quantizable(self):
r"""
Returns `True` if the model is quantizable, `False` otherwise.
"""
return self.load_in_8bit or self.load_in_4bit
def quantization_method(self):
r"""
This method returns the quantization method used for the model. If the model is not quantizable, it returns
`None`.
"""
if self.load_in_8bit:
return "llm_int8"
elif self.load_in_4bit and self.bnb_4bit_quant_type == "fp4":
return "fp4"
elif self.load_in_4bit and self.bnb_4bit_quant_type == "nf4":
return "nf4"
else:
return None
def to_dict(self) -> Dict[str, Any]:
"""
Serializes this instance to a Python dictionary. Returns:
`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
"""
output = copy.deepcopy(self.__dict__)
output["bnb_4bit_compute_dtype"] = str(output["bnb_4bit_compute_dtype"]).split(".")[1]
output["bnb_4bit_quant_storage"] = str(output["bnb_4bit_quant_storage"]).split(".")[1]
output["load_in_4bit"] = self.load_in_4bit
output["load_in_8bit"] = self.load_in_8bit
return output
def __repr__(self):
config_dict = self.to_dict()
return f"{self.__class__.__name__} {json.dumps(config_dict, indent=2, sort_keys=True)}\n"
def to_diff_dict(self) -> Dict[str, Any]:
"""
Removes all attributes from config which correspond to the default config attributes for better readability and
serializes to a Python dictionary.
Returns:
`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance,
"""
config_dict = self.to_dict()
# get the default config dict
default_config_dict = BitsAndBytesConfig().to_dict()
serializable_config_dict = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
serializable_config_dict[key] = value
return serializable_config_dict | class_definition | 5,673 | 17,200 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/quantization_config.py | null | 19 |
class GGUFQuantizationConfig(QuantizationConfigMixin):
"""This is a config class for GGUF Quantization techniques.
Args:
compute_dtype: (`torch.dtype`, defaults to `torch.float32`):
This sets the computational type which might be different than the input type. For example, inputs might be
fp32, but computation can be set to bf16 for speedups.
"""
def __init__(self, compute_dtype: Optional["torch.dtype"] = None):
self.quant_method = QuantizationMethod.GGUF
self.compute_dtype = compute_dtype
self.pre_quantized = True
# TODO: (Dhruv) Add this as an init argument when we can support loading unquantized checkpoints.
self.modules_to_not_convert = None
if self.compute_dtype is None:
self.compute_dtype = torch.float32 | class_definition | 17,214 | 18,046 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/quantization_config.py | null | 20 |
class TorchAoConfig(QuantizationConfigMixin):
"""This is a config class for torchao quantization/sparsity techniques.
Args:
quant_type (`str`):
The type of quantization we want to use, currently supporting:
- **Integer quantization:**
- Full function names: `int4_weight_only`, `int8_dynamic_activation_int4_weight`,
`int8_weight_only`, `int8_dynamic_activation_int8_weight`
- Shorthands: `int4wo`, `int4dq`, `int8wo`, `int8dq`
- **Floating point 8-bit quantization:**
- Full function names: `float8_weight_only`, `float8_dynamic_activation_float8_weight`,
`float8_static_activation_float8_weight`
- Shorthands: `float8wo`, `float8wo_e5m2`, `float8wo_e4m3`, `float8dq`, `float8dq_e4m3`,
`float8_e4m3_tensor`, `float8_e4m3_row`,
- **Floating point X-bit quantization:**
- Full function names: `fpx_weight_only`
- Shorthands: `fpX_eAwB`, where `X` is the number of bits (between `1` to `7`), `A` is the number
of exponent bits and `B` is the number of mantissa bits. The constraint of `X == A + B + 1` must
be satisfied for a given shorthand notation.
- **Unsigned Integer quantization:**
- Full function names: `uintx_weight_only`
- Shorthands: `uint1wo`, `uint2wo`, `uint3wo`, `uint4wo`, `uint5wo`, `uint6wo`, `uint7wo`
modules_to_not_convert (`List[str]`, *optional*, default to `None`):
The list of modules to not quantize, useful for quantizing models that explicitly require to have some
modules left in their original precision.
kwargs (`Dict[str, Any]`, *optional*):
The keyword arguments for the chosen type of quantization, for example, int4_weight_only quantization
supports two keyword arguments `group_size` and `inner_k_tiles` currently. More API examples and
documentation of arguments can be found in
https://github.com/pytorch/ao/tree/main/torchao/quantization#other-available-quantization-techniques
Example:
```python
from diffusers import FluxTransformer2DModel, TorchAoConfig
quantization_config = TorchAoConfig("int8wo")
transformer = FluxTransformer2DModel.from_pretrained(
"black-forest-labs/Flux.1-Dev",
subfolder="transformer",
quantization_config=quantization_config,
torch_dtype=torch.bfloat16,
)
```
"""
def __init__(self, quant_type: str, modules_to_not_convert: Optional[List[str]] = None, **kwargs) -> None:
self.quant_method = QuantizationMethod.TORCHAO
self.quant_type = quant_type
self.modules_to_not_convert = modules_to_not_convert
# When we load from serialized config, "quant_type_kwargs" will be the key
if "quant_type_kwargs" in kwargs:
self.quant_type_kwargs = kwargs["quant_type_kwargs"]
else:
self.quant_type_kwargs = kwargs
TORCHAO_QUANT_TYPE_METHODS = self._get_torchao_quant_type_to_method()
if self.quant_type not in TORCHAO_QUANT_TYPE_METHODS.keys():
raise ValueError(
f"Requested quantization type: {self.quant_type} is not supported yet or is incorrect. If you think the "
f"provided quantization type should be supported, please open an issue at https://github.com/huggingface/diffusers/issues."
)
method = TORCHAO_QUANT_TYPE_METHODS[self.quant_type]
signature = inspect.signature(method)
all_kwargs = {
param.name
for param in signature.parameters.values()
if param.kind in [inspect.Parameter.KEYWORD_ONLY, inspect.Parameter.POSITIONAL_OR_KEYWORD]
}
unsupported_kwargs = list(self.quant_type_kwargs.keys() - all_kwargs)
if len(unsupported_kwargs) > 0:
raise ValueError(
f'The quantization method "{quant_type}" does not support the following keyword arguments: '
f"{unsupported_kwargs}. The following keywords arguments are supported: {all_kwargs}."
)
@classmethod
def _get_torchao_quant_type_to_method(cls):
r"""
Returns supported torchao quantization types with all commonly used notations.
"""
if is_torchao_available():
# TODO(aryan): Support autoquant and sparsify
from torchao.quantization import (
float8_dynamic_activation_float8_weight,
float8_static_activation_float8_weight,
float8_weight_only,
fpx_weight_only,
int4_weight_only,
int8_dynamic_activation_int4_weight,
int8_dynamic_activation_int8_weight,
int8_weight_only,
uintx_weight_only,
)
# TODO(aryan): Add a note on how to use PerAxis and PerGroup observers
from torchao.quantization.observer import PerRow, PerTensor
def generate_float8dq_types(dtype: torch.dtype):
name = "e5m2" if dtype == torch.float8_e5m2 else "e4m3"
types = {}
for granularity_cls in [PerTensor, PerRow]:
# Note: Activation and Weights cannot have different granularities
granularity_name = "tensor" if granularity_cls is PerTensor else "row"
types[f"float8dq_{name}_{granularity_name}"] = partial(
float8_dynamic_activation_float8_weight,
activation_dtype=dtype,
weight_dtype=dtype,
granularity=(granularity_cls(), granularity_cls()),
)
return types
def generate_fpx_quantization_types(bits: int):
types = {}
for ebits in range(1, bits):
mbits = bits - ebits - 1
types[f"fp{bits}_e{ebits}m{mbits}"] = partial(fpx_weight_only, ebits=ebits, mbits=mbits)
non_sign_bits = bits - 1
default_ebits = (non_sign_bits + 1) // 2
default_mbits = non_sign_bits - default_ebits
types[f"fp{bits}"] = partial(fpx_weight_only, ebits=default_ebits, mbits=default_mbits)
return types
INT4_QUANTIZATION_TYPES = {
# int4 weight + bfloat16/float16 activation
"int4wo": int4_weight_only,
"int4_weight_only": int4_weight_only,
# int4 weight + int8 activation
"int4dq": int8_dynamic_activation_int4_weight,
"int8_dynamic_activation_int4_weight": int8_dynamic_activation_int4_weight,
}
INT8_QUANTIZATION_TYPES = {
# int8 weight + bfloat16/float16 activation
"int8wo": int8_weight_only,
"int8_weight_only": int8_weight_only,
# int8 weight + int8 activation
"int8dq": int8_dynamic_activation_int8_weight,
"int8_dynamic_activation_int8_weight": int8_dynamic_activation_int8_weight,
}
# TODO(aryan): handle torch 2.2/2.3
FLOATX_QUANTIZATION_TYPES = {
# float8_e5m2 weight + bfloat16/float16 activation
"float8wo": partial(float8_weight_only, weight_dtype=torch.float8_e5m2),
"float8_weight_only": float8_weight_only,
"float8wo_e5m2": partial(float8_weight_only, weight_dtype=torch.float8_e5m2),
# float8_e4m3 weight + bfloat16/float16 activation
"float8wo_e4m3": partial(float8_weight_only, weight_dtype=torch.float8_e4m3fn),
# float8_e5m2 weight + float8 activation (dynamic)
"float8dq": float8_dynamic_activation_float8_weight,
"float8_dynamic_activation_float8_weight": float8_dynamic_activation_float8_weight,
# ===== Matrix multiplication is not supported in float8_e5m2 so the following errors out.
# However, changing activation_dtype=torch.float8_e4m3 might work here =====
# "float8dq_e5m2": partial(
# float8_dynamic_activation_float8_weight,
# activation_dtype=torch.float8_e5m2,
# weight_dtype=torch.float8_e5m2,
# ),
# **generate_float8dq_types(torch.float8_e5m2),
# ===== =====
# float8_e4m3 weight + float8 activation (dynamic)
"float8dq_e4m3": partial(
float8_dynamic_activation_float8_weight,
activation_dtype=torch.float8_e4m3fn,
weight_dtype=torch.float8_e4m3fn,
),
**generate_float8dq_types(torch.float8_e4m3fn),
# float8 weight + float8 activation (static)
"float8_static_activation_float8_weight": float8_static_activation_float8_weight,
# For fpx, only x <= 8 is supported by default. Other dtypes can be explored by users directly
# fpx weight + bfloat16/float16 activation
**generate_fpx_quantization_types(3),
**generate_fpx_quantization_types(4),
**generate_fpx_quantization_types(5),
**generate_fpx_quantization_types(6),
**generate_fpx_quantization_types(7),
}
UINTX_QUANTIZATION_DTYPES = {
"uintx_weight_only": uintx_weight_only,
"uint1wo": partial(uintx_weight_only, dtype=torch.uint1),
"uint2wo": partial(uintx_weight_only, dtype=torch.uint2),
"uint3wo": partial(uintx_weight_only, dtype=torch.uint3),
"uint4wo": partial(uintx_weight_only, dtype=torch.uint4),
"uint5wo": partial(uintx_weight_only, dtype=torch.uint5),
"uint6wo": partial(uintx_weight_only, dtype=torch.uint6),
"uint7wo": partial(uintx_weight_only, dtype=torch.uint7),
# "uint8wo": partial(uintx_weight_only, dtype=torch.uint8), # uint8 quantization is not supported
}
QUANTIZATION_TYPES = {}
QUANTIZATION_TYPES.update(INT4_QUANTIZATION_TYPES)
QUANTIZATION_TYPES.update(INT8_QUANTIZATION_TYPES)
QUANTIZATION_TYPES.update(UINTX_QUANTIZATION_DTYPES)
if cls._is_cuda_capability_atleast_8_9():
QUANTIZATION_TYPES.update(FLOATX_QUANTIZATION_TYPES)
return QUANTIZATION_TYPES
else:
raise ValueError(
"TorchAoConfig requires torchao to be installed, please install with `pip install torchao`"
)
@staticmethod
def _is_cuda_capability_atleast_8_9() -> bool:
if not torch.cuda.is_available():
raise RuntimeError("TorchAO requires a CUDA compatible GPU and installation of PyTorch.")
major, minor = torch.cuda.get_device_capability()
if major == 8:
return minor >= 9
return major >= 9
def get_apply_tensor_subclass(self):
TORCHAO_QUANT_TYPE_METHODS = self._get_torchao_quant_type_to_method()
return TORCHAO_QUANT_TYPE_METHODS[self.quant_type](**self.quant_type_kwargs)
def __repr__(self):
r"""
Example of how this looks for `TorchAoConfig("uint_a16w4", group_size=32)`:
```
TorchAoConfig {
"modules_to_not_convert": null,
"quant_method": "torchao",
"quant_type": "uint_a16w4",
"quant_type_kwargs": {
"group_size": 32
}
}
```
"""
config_dict = self.to_dict()
return f"{self.__class__.__name__} {json.dumps(config_dict, indent=2, sort_keys=True)}\n" | class_definition | 18,060 | 30,174 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/quantization_config.py | null | 21 |
class DiffusersQuantizer(ABC):
"""
Abstract class of the HuggingFace quantizer. Supports for now quantizing HF diffusers models for inference and/or
quantization. This class is used only for diffusers.models.modeling_utils.ModelMixin.from_pretrained and cannot be
easily used outside the scope of that method yet.
Attributes
quantization_config (`diffusers.quantizers.quantization_config.QuantizationConfigMixin`):
The quantization config that defines the quantization parameters of your model that you want to quantize.
modules_to_not_convert (`List[str]`, *optional*):
The list of module names to not convert when quantizing the model.
required_packages (`List[str]`, *optional*):
The list of required pip packages to install prior to using the quantizer
requires_calibration (`bool`):
Whether the quantization method requires to calibrate the model before using it.
"""
requires_calibration = False
required_packages = None
def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs):
self.quantization_config = quantization_config
# -- Handle extra kwargs below --
self.modules_to_not_convert = kwargs.pop("modules_to_not_convert", [])
self.pre_quantized = kwargs.pop("pre_quantized", True)
if not self.pre_quantized and self.requires_calibration:
raise ValueError(
f"The quantization method {quantization_config.quant_method} does require the model to be pre-quantized."
f" You explicitly passed `pre_quantized=False` meaning your model weights are not quantized. Make sure to "
f"pass `pre_quantized=True` while knowing what you are doing."
)
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
"""
Some quantization methods require to explicitly set the dtype of the model to a target dtype. You need to
override this method in case you want to make sure that behavior is preserved
Args:
torch_dtype (`torch.dtype`):
The input dtype that is passed in `from_pretrained`
"""
return torch_dtype
def update_device_map(self, device_map: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
"""
Override this method if you want to pass a override the existing device map with a new one. E.g. for
bitsandbytes, since `accelerate` is a hard requirement, if no device_map is passed, the device_map is set to
`"auto"``
Args:
device_map (`Union[dict, str]`, *optional*):
The device_map that is passed through the `from_pretrained` method.
"""
return device_map
def adjust_target_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
"""
Override this method if you want to adjust the `target_dtype` variable used in `from_pretrained` to compute the
device_map in case the device_map is a `str`. E.g. for bitsandbytes we force-set `target_dtype` to `torch.int8`
and for 4-bit we pass a custom enum `accelerate.CustomDtype.int4`.
Args:
torch_dtype (`torch.dtype`, *optional*):
The torch_dtype that is used to compute the device_map.
"""
return torch_dtype
def update_missing_keys(self, model, missing_keys: List[str], prefix: str) -> List[str]:
"""
Override this method if you want to adjust the `missing_keys`.
Args:
missing_keys (`List[str]`, *optional*):
The list of missing keys in the checkpoint compared to the state dict of the model
"""
return missing_keys
def get_special_dtypes_update(self, model, torch_dtype: "torch.dtype") -> Dict[str, "torch.dtype"]:
"""
returns dtypes for modules that are not quantized - used for the computation of the device_map in case one
passes a str as a device_map. The method will use the `modules_to_not_convert` that is modified in
`_process_model_before_weight_loading`. `diffusers` models don't have any `modules_to_not_convert` attributes
yet but this can change soon in the future.
Args:
model (`~diffusers.models.modeling_utils.ModelMixin`):
The model to quantize
torch_dtype (`torch.dtype`):
The dtype passed in `from_pretrained` method.
"""
return {
name: torch_dtype
for name, _ in model.named_parameters()
if any(m in name for m in self.modules_to_not_convert)
}
def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
"""adjust max_memory argument for infer_auto_device_map() if extra memory is needed for quantization"""
return max_memory
def check_if_quantized_param(
self,
model: "ModelMixin",
param_value: "torch.Tensor",
param_name: str,
state_dict: Dict[str, Any],
**kwargs,
) -> bool:
"""
checks if a loaded state_dict component is part of quantized param + some validation; only defined for
quantization methods that require to create a new parameters for quantization.
"""
return False
def create_quantized_param(self, *args, **kwargs) -> "torch.nn.Parameter":
"""
takes needed components from state_dict and creates quantized param.
"""
return
def check_quantized_param_shape(self, *args, **kwargs):
"""
checks if the quantized param has expected shape.
"""
return True
def validate_environment(self, *args, **kwargs):
"""
This method is used to potentially check for potential conflicts with arguments that are passed in
`from_pretrained`. You need to define it for all future quantizers that are integrated with diffusers. If no
explicit check are needed, simply return nothing.
"""
return
def preprocess_model(self, model: "ModelMixin", **kwargs):
"""
Setting model attributes and/or converting model before weights loading. At this point the model should be
initialized on the meta device so you can freely manipulate the skeleton of the model in order to replace
modules in-place. Make sure to override the abstract method `_process_model_before_weight_loading`.
Args:
model (`~diffusers.models.modeling_utils.ModelMixin`):
The model to quantize
kwargs (`dict`, *optional*):
The keyword arguments that are passed along `_process_model_before_weight_loading`.
"""
model.is_quantized = True
model.quantization_method = self.quantization_config.quant_method
return self._process_model_before_weight_loading(model, **kwargs)
def postprocess_model(self, model: "ModelMixin", **kwargs):
"""
Post-process the model post weights loading. Make sure to override the abstract method
`_process_model_after_weight_loading`.
Args:
model (`~diffusers.models.modeling_utils.ModelMixin`):
The model to quantize
kwargs (`dict`, *optional*):
The keyword arguments that are passed along `_process_model_after_weight_loading`.
"""
return self._process_model_after_weight_loading(model, **kwargs)
def dequantize(self, model):
"""
Potentially dequantize the model to retrive the original model, with some loss in accuracy / performance. Note
not all quantization schemes support this.
"""
model = self._dequantize(model)
# Delete quantizer and quantization config
del model.hf_quantizer
return model
def _dequantize(self, model):
raise NotImplementedError(
f"{self.quantization_config.quant_method} has no implementation of `dequantize`, please raise an issue on GitHub."
)
@abstractmethod
def _process_model_before_weight_loading(self, model, **kwargs):
...
@abstractmethod
def _process_model_after_weight_loading(self, model, **kwargs):
...
@property
@abstractmethod
def is_serializable(self):
...
@property
@abstractmethod
def is_trainable(self):
... | class_definition | 1,076 | 9,564 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/base.py | null | 22 |
class DiffusersAutoQuantizer:
"""
The auto diffusers quantizer class that takes care of automatically instantiating to the correct
`DiffusersQuantizer` given the `QuantizationConfig`.
"""
@classmethod
def from_dict(cls, quantization_config_dict: Dict):
quant_method = quantization_config_dict.get("quant_method", None)
# We need a special care for bnb models to make sure everything is BC ..
if quantization_config_dict.get("load_in_8bit", False) or quantization_config_dict.get("load_in_4bit", False):
suffix = "_4bit" if quantization_config_dict.get("load_in_4bit", False) else "_8bit"
quant_method = QuantizationMethod.BITS_AND_BYTES + suffix
elif quant_method is None:
raise ValueError(
"The model's quantization config from the arguments has no `quant_method` attribute. Make sure that the model has been correctly quantized"
)
if quant_method not in AUTO_QUANTIZATION_CONFIG_MAPPING.keys():
raise ValueError(
f"Unknown quantization type, got {quant_method} - supported types are:"
f" {list(AUTO_QUANTIZER_MAPPING.keys())}"
)
target_cls = AUTO_QUANTIZATION_CONFIG_MAPPING[quant_method]
return target_cls.from_dict(quantization_config_dict)
@classmethod
def from_config(cls, quantization_config: Union[QuantizationConfigMixin, Dict], **kwargs):
# Convert it to a QuantizationConfig if the q_config is a dict
if isinstance(quantization_config, dict):
quantization_config = cls.from_dict(quantization_config)
quant_method = quantization_config.quant_method
# Again, we need a special care for bnb as we have a single quantization config
# class for both 4-bit and 8-bit quantization
if quant_method == QuantizationMethod.BITS_AND_BYTES:
if quantization_config.load_in_8bit:
quant_method += "_8bit"
else:
quant_method += "_4bit"
if quant_method not in AUTO_QUANTIZER_MAPPING.keys():
raise ValueError(
f"Unknown quantization type, got {quant_method} - supported types are:"
f" {list(AUTO_QUANTIZER_MAPPING.keys())}"
)
target_cls = AUTO_QUANTIZER_MAPPING[quant_method]
return target_cls(quantization_config, **kwargs)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
model_config = cls.load_config(pretrained_model_name_or_path, **kwargs)
if getattr(model_config, "quantization_config", None) is None:
raise ValueError(
f"Did not found a `quantization_config` in {pretrained_model_name_or_path}. Make sure that the model is correctly quantized."
)
quantization_config_dict = model_config.quantization_config
quantization_config = cls.from_dict(quantization_config_dict)
# Update with potential kwargs that are passed through from_pretrained.
quantization_config.update(kwargs)
return cls.from_config(quantization_config)
@classmethod
def merge_quantization_configs(
cls,
quantization_config: Union[dict, QuantizationConfigMixin],
quantization_config_from_args: Optional[QuantizationConfigMixin],
):
"""
handles situations where both quantization_config from args and quantization_config from model config are
present.
"""
if quantization_config_from_args is not None:
warning_msg = (
"You passed `quantization_config` or equivalent parameters to `from_pretrained` but the model you're loading"
" already has a `quantization_config` attribute. The `quantization_config` from the model will be used."
)
else:
warning_msg = ""
if isinstance(quantization_config, dict):
quantization_config = cls.from_dict(quantization_config)
if warning_msg != "":
warnings.warn(warning_msg)
return quantization_config | class_definition | 1,524 | 5,675 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/auto.py | null | 23 |
class GGUFQuantizer(DiffusersQuantizer):
use_keep_in_fp32_modules = True
def __init__(self, quantization_config, **kwargs):
super().__init__(quantization_config, **kwargs)
self.compute_dtype = quantization_config.compute_dtype
self.pre_quantized = quantization_config.pre_quantized
self.modules_to_not_convert = quantization_config.modules_to_not_convert
if not isinstance(self.modules_to_not_convert, list):
self.modules_to_not_convert = [self.modules_to_not_convert]
def validate_environment(self, *args, **kwargs):
if not is_accelerate_available() or is_accelerate_version("<", "0.26.0"):
raise ImportError(
"Loading GGUF Parameters requires `accelerate` installed in your enviroment: `pip install 'accelerate>=0.26.0'`"
)
if not is_gguf_available() or is_gguf_version("<", "0.10.0"):
raise ImportError(
"To load GGUF format files you must have `gguf` installed in your environment: `pip install gguf>=0.10.0`"
)
# Copied from diffusers.quantizers.bitsandbytes.bnb_quantizer.BnB4BitDiffusersQuantizer.adjust_max_memory
def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
# need more space for buffers that are created during quantization
max_memory = {key: val * 0.90 for key, val in max_memory.items()}
return max_memory
def adjust_target_dtype(self, target_dtype: "torch.dtype") -> "torch.dtype":
if target_dtype != torch.uint8:
logger.info(f"target_dtype {target_dtype} is replaced by `torch.uint8` for GGUF quantization")
return torch.uint8
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
if torch_dtype is None:
torch_dtype = self.compute_dtype
return torch_dtype
def check_quantized_param_shape(self, param_name, current_param, loaded_param):
loaded_param_shape = loaded_param.shape
current_param_shape = current_param.shape
quant_type = loaded_param.quant_type
block_size, type_size = GGML_QUANT_SIZES[quant_type]
inferred_shape = _quant_shape_from_byte_shape(loaded_param_shape, type_size, block_size)
if inferred_shape != current_param_shape:
raise ValueError(
f"{param_name} has an expected quantized shape of: {inferred_shape}, but receieved shape: {loaded_param_shape}"
)
return True
def check_if_quantized_param(
self,
model: "ModelMixin",
param_value: Union["GGUFParameter", "torch.Tensor"],
param_name: str,
state_dict: Dict[str, Any],
**kwargs,
) -> bool:
if isinstance(param_value, GGUFParameter):
return True
return False
def create_quantized_param(
self,
model: "ModelMixin",
param_value: Union["GGUFParameter", "torch.Tensor"],
param_name: str,
target_device: "torch.device",
state_dict: Optional[Dict[str, Any]] = None,
unexpected_keys: Optional[List[str]] = None,
):
module, tensor_name = get_module_from_name(model, param_name)
if tensor_name not in module._parameters and tensor_name not in module._buffers:
raise ValueError(f"{module} does not have a parameter or a buffer named {tensor_name}.")
if tensor_name in module._parameters:
module._parameters[tensor_name] = param_value.to(target_device)
if tensor_name in module._buffers:
module._buffers[tensor_name] = param_value.to(target_device)
def _process_model_before_weight_loading(
self,
model: "ModelMixin",
device_map,
keep_in_fp32_modules: List[str] = [],
**kwargs,
):
state_dict = kwargs.get("state_dict", None)
self.modules_to_not_convert.extend(keep_in_fp32_modules)
self.modules_to_not_convert = [module for module in self.modules_to_not_convert if module is not None]
_replace_with_gguf_linear(
model, self.compute_dtype, state_dict, modules_to_not_convert=self.modules_to_not_convert
)
def _process_model_after_weight_loading(self, model: "ModelMixin", **kwargs):
return model
@property
def is_serializable(self):
return False
@property
def is_trainable(self) -> bool:
return False
def _dequantize(self, model):
is_model_on_cpu = model.device.type == "cpu"
if is_model_on_cpu:
logger.info(
"Model was found to be on CPU (could happen as a result of `enable_model_cpu_offload()`). So, moving it to GPU. After dequantization, will move the model back to CPU again to preserve the previous device."
)
model.to(torch.cuda.current_device())
model = _dequantize_gguf_and_restore_linear(model, self.modules_to_not_convert)
if is_model_on_cpu:
model.to("cpu")
return model | class_definition | 677 | 5,753 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/gguf/gguf_quantizer.py | null | 24 |
class GGUFParameter(torch.nn.Parameter):
def __new__(cls, data, requires_grad=False, quant_type=None):
data = data if data is not None else torch.empty(0)
self = torch.Tensor._make_subclass(cls, data, requires_grad)
self.quant_type = quant_type
return self
def as_tensor(self):
return torch.Tensor._make_subclass(torch.Tensor, self, self.requires_grad)
@classmethod
def __torch_function__(cls, func, types, args=(), kwargs=None):
if kwargs is None:
kwargs = {}
result = super().__torch_function__(func, types, args, kwargs)
# When converting from original format checkpoints we often use splits, cats etc on tensors
# this method ensures that the returned tensor type from those operations remains GGUFParameter
# so that we preserve quant_type information
quant_type = None
for arg in args:
if isinstance(arg, list) and (arg[0], GGUFParameter):
quant_type = arg[0].quant_type
break
if isinstance(arg, GGUFParameter):
quant_type = arg.quant_type
break
if isinstance(result, torch.Tensor):
return cls(result, quant_type=quant_type)
# Handle tuples and lists
elif isinstance(result, (tuple, list)):
# Preserve the original type (tuple or list)
wrapped = [cls(x, quant_type=quant_type) if isinstance(x, torch.Tensor) else x for x in result]
return type(result)(wrapped)
else:
return result | class_definition | 13,739 | 15,334 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/gguf/utils.py | null | 25 |
class GGUFLinear(nn.Linear):
def __init__(
self,
in_features,
out_features,
bias=False,
compute_dtype=None,
device=None,
) -> None:
super().__init__(in_features, out_features, bias, device)
self.compute_dtype = compute_dtype
def forward(self, inputs):
weight = dequantize_gguf_tensor(self.weight)
weight = weight.to(self.compute_dtype)
bias = self.bias.to(self.compute_dtype) if self.bias is not None else None
output = torch.nn.functional.linear(inputs, weight, bias)
return output | class_definition | 15,337 | 15,937 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/gguf/utils.py | null | 26 |
class TorchAoHfQuantizer(DiffusersQuantizer):
r"""
Diffusers Quantizer for TorchAO: https://github.com/pytorch/ao/.
"""
requires_calibration = False
required_packages = ["torchao"]
def __init__(self, quantization_config, **kwargs):
super().__init__(quantization_config, **kwargs)
def validate_environment(self, *args, **kwargs):
if not is_torchao_available():
raise ImportError(
"Loading a TorchAO quantized model requires the torchao library. Please install with `pip install torchao`"
)
torchao_version = version.parse(importlib.metadata.version("torch"))
if torchao_version < version.parse("0.7.0"):
raise RuntimeError(
f"The minimum required version of `torchao` is 0.7.0, but the current version is {torchao_version}. Please upgrade with `pip install -U torchao`."
)
self.offload = False
device_map = kwargs.get("device_map", None)
if isinstance(device_map, dict):
if "cpu" in device_map.values() or "disk" in device_map.values():
if self.pre_quantized:
raise ValueError(
"You are attempting to perform cpu/disk offload with a pre-quantized torchao model "
"This is not supported yet. Please remove the CPU or disk device from the `device_map` argument."
)
else:
self.offload = True
if self.pre_quantized:
weights_only = kwargs.get("weights_only", None)
if weights_only:
torch_version = version.parse(importlib.metadata.version("torch"))
if torch_version < version.parse("2.5.0"):
# TODO(aryan): TorchAO is compatible with Pytorch >= 2.2 for certain quantization types. Try to see if we can support it in future
raise RuntimeError(
f"In order to use TorchAO pre-quantized model, you need to have torch>=2.5.0. However, the current version is {torch_version}."
)
def update_torch_dtype(self, torch_dtype):
quant_type = self.quantization_config.quant_type
if quant_type.startswith("int") or quant_type.startswith("uint"):
if torch_dtype is not None and torch_dtype != torch.bfloat16:
logger.warning(
f"You are trying to set torch_dtype to {torch_dtype} for int4/int8/uintx quantization, but "
f"only bfloat16 is supported right now. Please set `torch_dtype=torch.bfloat16`."
)
if torch_dtype is None:
# We need to set the torch_dtype, otherwise we have dtype mismatch when performing the quantized linear op
logger.warning(
"Overriding `torch_dtype` with `torch_dtype=torch.bfloat16` due to requirements of `torchao` "
"to enable model loading in different precisions. Pass your own `torch_dtype` to specify the "
"dtype of the remaining non-linear layers, or pass torch_dtype=torch.bfloat16, to remove this warning."
)
torch_dtype = torch.bfloat16
return torch_dtype
def adjust_target_dtype(self, target_dtype: "torch.dtype") -> "torch.dtype":
quant_type = self.quantization_config.quant_type
if quant_type.startswith("int8") or quant_type.startswith("int4"):
# Note that int4 weights are created by packing into torch.int8, but since there is no torch.int4, we use torch.int8
return torch.int8
elif quant_type == "uintx_weight_only":
return self.quantization_config.quant_type_kwargs.get("dtype", torch.uint8)
elif quant_type.startswith("uint"):
return {
1: torch.uint1,
2: torch.uint2,
3: torch.uint3,
4: torch.uint4,
5: torch.uint5,
6: torch.uint6,
7: torch.uint7,
}[int(quant_type[4])]
elif quant_type.startswith("float") or quant_type.startswith("fp"):
return torch.bfloat16
if isinstance(target_dtype, SUPPORTED_TORCH_DTYPES_FOR_QUANTIZATION):
return target_dtype
# We need one of the supported dtypes to be selected in order for accelerate to determine
# the total size of modules/parameters for auto device placement.
possible_device_maps = ["auto", "balanced", "balanced_low_0", "sequential"]
raise ValueError(
f"You have set `device_map` as one of {possible_device_maps} on a TorchAO quantized model but a suitable target dtype "
f"could not be inferred. The supported target_dtypes are: {SUPPORTED_TORCH_DTYPES_FOR_QUANTIZATION}. If you think the "
f"dtype you are using should be supported, please open an issue at https://github.com/huggingface/diffusers/issues."
)
def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
max_memory = {key: val * 0.9 for key, val in max_memory.items()}
return max_memory
def check_if_quantized_param(
self,
model: "ModelMixin",
param_value: "torch.Tensor",
param_name: str,
state_dict: Dict[str, Any],
**kwargs,
) -> bool:
param_device = kwargs.pop("param_device", None)
# Check if the param_name is not in self.modules_to_not_convert
if any((key + "." in param_name) or (key == param_name) for key in self.modules_to_not_convert):
return False
elif param_device == "cpu" and self.offload:
# We don't quantize weights that we offload
return False
else:
# We only quantize the weight of nn.Linear
module, tensor_name = get_module_from_name(model, param_name)
return isinstance(module, torch.nn.Linear) and (tensor_name == "weight")
def create_quantized_param(
self,
model: "ModelMixin",
param_value: "torch.Tensor",
param_name: str,
target_device: "torch.device",
state_dict: Dict[str, Any],
unexpected_keys: List[str],
):
r"""
Each nn.Linear layer that needs to be quantized is processsed here. First, we set the value the weight tensor,
then we move it to the target device. Finally, we quantize the module.
"""
module, tensor_name = get_module_from_name(model, param_name)
if self.pre_quantized:
# If we're loading pre-quantized weights, replace the repr of linear layers for pretty printing info
# about AffineQuantizedTensor
module._parameters[tensor_name] = torch.nn.Parameter(param_value.to(device=target_device))
if isinstance(module, nn.Linear):
module.extra_repr = types.MethodType(_linear_extra_repr, module)
else:
# As we perform quantization here, the repr of linear layers is that of AQT, so we don't have to do it ourselves
module._parameters[tensor_name] = torch.nn.Parameter(param_value).to(device=target_device)
quantize_(module, self.quantization_config.get_apply_tensor_subclass())
def _process_model_before_weight_loading(
self,
model: "ModelMixin",
device_map,
keep_in_fp32_modules: List[str] = [],
**kwargs,
):
self.modules_to_not_convert = self.quantization_config.modules_to_not_convert
if not isinstance(self.modules_to_not_convert, list):
self.modules_to_not_convert = [self.modules_to_not_convert]
self.modules_to_not_convert.extend(keep_in_fp32_modules)
# Extend `self.modules_to_not_convert` to keys that are supposed to be offloaded to `cpu` or `disk`
if isinstance(device_map, dict) and len(device_map.keys()) > 1:
keys_on_cpu = [key for key, value in device_map.items() if value in ["disk", "cpu"]]
self.modules_to_not_convert.extend(keys_on_cpu)
# Purge `None`.
# Unlike `transformers`, we don't know if we should always keep certain modules in FP32
# in case of diffusion transformer models. For language models and others alike, `lm_head`
# and tied modules are usually kept in FP32.
self.modules_to_not_convert = [module for module in self.modules_to_not_convert if module is not None]
model.config.quantization_config = self.quantization_config
def _process_model_after_weight_loading(self, model: "ModelMixin"):
return model
def is_serializable(self, safe_serialization=None):
# TODO(aryan): needs to be tested
if safe_serialization:
logger.warning(
"torchao quantized model does not support safe serialization, please set `safe_serialization` to False."
)
return False
_is_torchao_serializable = version.parse(importlib.metadata.version("huggingface_hub")) >= version.parse(
"0.25.0"
)
if not _is_torchao_serializable:
logger.warning("torchao quantized model is only serializable after huggingface_hub >= 0.25.0 ")
if self.offload and self.quantization_config.modules_to_not_convert is None:
logger.warning(
"The model contains offloaded modules and these modules are not quantized. We don't recommend saving the model as we won't be able to reload them."
"If you want to specify modules to not quantize, please specify modules_to_not_convert in the quantization_config."
)
return False
return _is_torchao_serializable
@property
def is_trainable(self):
return self.quantization_config.quant_type.startswith("int8") | class_definition | 2,926 | 12,820 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/torchao/torchao_quantizer.py | null | 27 |
class BnB4BitDiffusersQuantizer(DiffusersQuantizer):
"""
4-bit quantization from bitsandbytes.py quantization method:
before loading: converts transformer layers into Linear4bit during loading: load 16bit weight and pass to the
layer object after: quantizes individual weights in Linear4bit into 4bit at the first .cuda() call saving:
from state dict, as usual; saves weights and `quant_state` components
loading:
need to locate `quant_state` components and pass to Param4bit constructor
"""
use_keep_in_fp32_modules = True
requires_calibration = False
def __init__(self, quantization_config, **kwargs):
super().__init__(quantization_config, **kwargs)
if self.quantization_config.llm_int8_skip_modules is not None:
self.modules_to_not_convert = self.quantization_config.llm_int8_skip_modules
def validate_environment(self, *args, **kwargs):
if not torch.cuda.is_available():
raise RuntimeError("No GPU found. A GPU is needed for quantization.")
if not is_accelerate_available() or is_accelerate_version("<", "0.26.0"):
raise ImportError(
"Using `bitsandbytes` 4-bit quantization requires Accelerate: `pip install 'accelerate>=0.26.0'`"
)
if not is_bitsandbytes_available() or is_bitsandbytes_version("<", "0.43.3"):
raise ImportError(
"Using `bitsandbytes` 4-bit quantization requires the latest version of bitsandbytes: `pip install -U bitsandbytes`"
)
if kwargs.get("from_flax", False):
raise ValueError(
"Converting into 4-bit weights from flax weights is currently not supported, please make"
" sure the weights are in PyTorch format."
)
device_map = kwargs.get("device_map", None)
if (
device_map is not None
and isinstance(device_map, dict)
and not self.quantization_config.llm_int8_enable_fp32_cpu_offload
):
device_map_without_no_convert = {
key: device_map[key] for key in device_map.keys() if key not in self.modules_to_not_convert
}
if "cpu" in device_map_without_no_convert.values() or "disk" in device_map_without_no_convert.values():
raise ValueError(
"Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the "
"quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules "
"in 32-bit, you need to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom `device_map` to "
"`from_pretrained`. Check "
"https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu "
"for more details. "
)
def adjust_target_dtype(self, target_dtype: "torch.dtype") -> "torch.dtype":
if target_dtype != torch.int8:
from accelerate.utils import CustomDtype
logger.info("target_dtype {target_dtype} is replaced by `CustomDtype.INT4` for 4-bit BnB quantization")
return CustomDtype.INT4
else:
raise ValueError(f"Wrong `target_dtype` ({target_dtype}) provided.")
def check_if_quantized_param(
self,
model: "ModelMixin",
param_value: "torch.Tensor",
param_name: str,
state_dict: Dict[str, Any],
**kwargs,
) -> bool:
import bitsandbytes as bnb
module, tensor_name = get_module_from_name(model, param_name)
if isinstance(module._parameters.get(tensor_name, None), bnb.nn.Params4bit):
# Add here check for loaded components' dtypes once serialization is implemented
return True
elif isinstance(module, bnb.nn.Linear4bit) and tensor_name == "bias":
# bias could be loaded by regular set_module_tensor_to_device() from accelerate,
# but it would wrongly use uninitialized weight there.
return True
else:
return False
def create_quantized_param(
self,
model: "ModelMixin",
param_value: "torch.Tensor",
param_name: str,
target_device: "torch.device",
state_dict: Dict[str, Any],
unexpected_keys: Optional[List[str]] = None,
):
import bitsandbytes as bnb
module, tensor_name = get_module_from_name(model, param_name)
if tensor_name not in module._parameters:
raise ValueError(f"{module} does not have a parameter or a buffer named {tensor_name}.")
old_value = getattr(module, tensor_name)
if tensor_name == "bias":
if param_value is None:
new_value = old_value.to(target_device)
else:
new_value = param_value.to(target_device)
new_value = torch.nn.Parameter(new_value, requires_grad=old_value.requires_grad)
module._parameters[tensor_name] = new_value
return
if not isinstance(module._parameters[tensor_name], bnb.nn.Params4bit):
raise ValueError("this function only loads `Linear4bit components`")
if (
old_value.device == torch.device("meta")
and target_device not in ["meta", torch.device("meta")]
and param_value is None
):
raise ValueError(f"{tensor_name} is on the meta device, we need a `value` to put in on {target_device}.")
# construct `new_value` for the module._parameters[tensor_name]:
if self.pre_quantized:
# 4bit loading. Collecting components for restoring quantized weight
# This can be expanded to make a universal call for any quantized weight loading
if not self.is_serializable:
raise ValueError(
"Detected int4 weights but the version of bitsandbytes is not compatible with int4 serialization. "
"Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."
)
if (param_name + ".quant_state.bitsandbytes__fp4" not in state_dict) and (
param_name + ".quant_state.bitsandbytes__nf4" not in state_dict
):
raise ValueError(
f"Supplied state dict for {param_name} does not contain `bitsandbytes__*` and possibly other `quantized_stats` components."
)
quantized_stats = {}
for k, v in state_dict.items():
# `startswith` to counter for edge cases where `param_name`
# substring can be present in multiple places in the `state_dict`
if param_name + "." in k and k.startswith(param_name):
quantized_stats[k] = v
if unexpected_keys is not None and k in unexpected_keys:
unexpected_keys.remove(k)
new_value = bnb.nn.Params4bit.from_prequantized(
data=param_value,
quantized_stats=quantized_stats,
requires_grad=False,
device=target_device,
)
else:
new_value = param_value.to("cpu")
kwargs = old_value.__dict__
new_value = bnb.nn.Params4bit(new_value, requires_grad=False, **kwargs).to(target_device)
module._parameters[tensor_name] = new_value
def check_quantized_param_shape(self, param_name, current_param, loaded_param):
current_param_shape = current_param.shape
loaded_param_shape = loaded_param.shape
n = current_param_shape.numel()
inferred_shape = (n,) if "bias" in param_name else ((n + 1) // 2, 1)
if loaded_param_shape != inferred_shape:
raise ValueError(
f"Expected the flattened shape of the current param ({param_name}) to be {loaded_param_shape} but is {inferred_shape}."
)
else:
return True
def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
# need more space for buffers that are created during quantization
max_memory = {key: val * 0.90 for key, val in max_memory.items()}
return max_memory
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
if torch_dtype is None:
# We force the `dtype` to be float16, this is a requirement from `bitsandbytes`
logger.info(
"Overriding torch_dtype=%s with `torch_dtype=torch.float16` due to "
"requirements of `bitsandbytes` to enable model loading in 8-bit or 4-bit. "
"Pass your own torch_dtype to specify the dtype of the remaining non-linear layers or pass"
" torch_dtype=torch.float16 to remove this warning.",
torch_dtype,
)
torch_dtype = torch.float16
return torch_dtype
# (sayakpaul): I think it could be better to disable custom `device_map`s
# for the first phase of the integration in the interest of simplicity.
# Commenting this for discussions on the PR.
# def update_device_map(self, device_map):
# if device_map is None:
# device_map = {"": torch.cuda.current_device()}
# logger.info(
# "The device_map was not initialized. "
# "Setting device_map to {'':torch.cuda.current_device()}. "
# "If you want to use the model for inference, please set device_map ='auto' "
# )
# return device_map
def _process_model_before_weight_loading(
self,
model: "ModelMixin",
device_map,
keep_in_fp32_modules: List[str] = [],
**kwargs,
):
from .utils import replace_with_bnb_linear
load_in_8bit_fp32_cpu_offload = self.quantization_config.llm_int8_enable_fp32_cpu_offload
# We may keep some modules such as the `proj_out` in their original dtype for numerical stability reasons
self.modules_to_not_convert = self.quantization_config.llm_int8_skip_modules
if not isinstance(self.modules_to_not_convert, list):
self.modules_to_not_convert = [self.modules_to_not_convert]
self.modules_to_not_convert.extend(keep_in_fp32_modules)
# Extend `self.modules_to_not_convert` to keys that are supposed to be offloaded to `cpu` or `disk`
if isinstance(device_map, dict) and len(device_map.keys()) > 1:
keys_on_cpu = [key for key, value in device_map.items() if value in ["disk", "cpu"]]
if len(keys_on_cpu) > 0 and not load_in_8bit_fp32_cpu_offload:
raise ValueError(
"If you want to offload some keys to `cpu` or `disk`, you need to set "
"`llm_int8_enable_fp32_cpu_offload=True`. Note that these modules will not be "
" converted to 8-bit but kept in 32-bit."
)
self.modules_to_not_convert.extend(keys_on_cpu)
# Purge `None`.
# Unlike `transformers`, we don't know if we should always keep certain modules in FP32
# in case of diffusion transformer models. For language models and others alike, `lm_head`
# and tied modules are usually kept in FP32.
self.modules_to_not_convert = [module for module in self.modules_to_not_convert if module is not None]
model = replace_with_bnb_linear(
model, modules_to_not_convert=self.modules_to_not_convert, quantization_config=self.quantization_config
)
model.config.quantization_config = self.quantization_config
def _process_model_after_weight_loading(self, model: "ModelMixin", **kwargs):
model.is_loaded_in_4bit = True
model.is_4bit_serializable = self.is_serializable
return model
@property
def is_serializable(self):
# Because we're mandating `bitsandbytes` 0.43.3.
return True
@property
def is_trainable(self) -> bool:
# Because we're mandating `bitsandbytes` 0.43.3.
return True
def _dequantize(self, model):
from .utils import dequantize_and_replace
is_model_on_cpu = model.device.type == "cpu"
if is_model_on_cpu:
logger.info(
"Model was found to be on CPU (could happen as a result of `enable_model_cpu_offload()`). So, moving it to GPU. After dequantization, will move the model back to CPU again to preserve the previous device."
)
model.to(torch.cuda.current_device())
model = dequantize_and_replace(
model, self.modules_to_not_convert, quantization_config=self.quantization_config
)
if is_model_on_cpu:
model.to("cpu")
return model | class_definition | 1,257 | 14,257 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/quantizers/bitsandbytes/bnb_quantizer.py | null | 28 |
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