fold Unions into |

Author: sayakpaul

src/diffusers/configuration_utils.py

@@ -24,7 +24,7 @@
 import re
 from collections import OrderedDict
 from pathlib import Path
-from typing import Any, Optional, Union
+from typing import Any, Optional
 
 import numpy as np
 from huggingface_hub import DDUFEntry, create_repo, hf_hub_download
@@ -143,7 +143,7 @@ def __getattr__(self, name: str) -> Any:
 
         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):
+    def save_config(self, save_directory: 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.
@@ -189,8 +189,8 @@ def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool
 
     @classmethod
     def from_config(
-        cls, config: Union[FrozenDict, dict[str, Any]] = None, return_unused_kwargs=False, **kwargs
-    ) -> Union[Self, tuple[Self, dict[str, Any]]]:
+        cls, config: FrozenDict | dict[str, Any] = None, return_unused_kwargs=False, **kwargs
+    ) -> Self | tuple[Self, dict[str, Any]]:
         r"""
         Instantiate a Python class from a config dictionary.
 
@@ -292,7 +292,7 @@ def get_config_dict(cls, *args, **kwargs):
     @validate_hf_hub_args
     def load_config(
         cls,
-        pretrained_model_name_or_path: Union[str, os.PathLike],
+        pretrained_model_name_or_path: str | os.PathLike,
         return_unused_kwargs=False,
         return_commit_hash=False,
         **kwargs,
@@ -563,9 +563,7 @@ def extract_init_dict(cls, config_dict, **kwargs):
         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
-    ):
+    def _dict_from_json_file(cls, json_file: str | os.PathLike, dduf_entries: Optional[dict[str, DDUFEntry]] = None):
         if dduf_entries:
             text = dduf_entries[json_file].read_text()
         else:
@@ -625,7 +623,7 @@ def to_json_saveable(value):
 
         return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
 
-    def to_json_file(self, json_file_path: Union[str, os.PathLike]):
+    def to_json_file(self, json_file_path: str | os.PathLike):
         """
         Save the configuration instance's parameters to a JSON file.
 
@@ -756,7 +754,7 @@ class LegacyConfigMixin(ConfigMixin):
     """
 
     @classmethod
-    def from_config(cls, config: Union[FrozenDict, dict[str, Any]] = None, return_unused_kwargs=False, **kwargs):
+    def from_config(cls, config: 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
 

src/diffusers/guiders/__init__.py

@@ -28,14 +28,14 @@
     from .smoothed_energy_guidance import SmoothedEnergyGuidance
     from .tangential_classifier_free_guidance import TangentialClassifierFreeGuidance
 
-    GuiderType = Union[
-        AdaptiveProjectedGuidance,
-        AutoGuidance,
-        ClassifierFreeGuidance,
-        ClassifierFreeZeroStarGuidance,
-        FrequencyDecoupledGuidance,
-        PerturbedAttentionGuidance,
-        SkipLayerGuidance,
-        SmoothedEnergyGuidance,
-        TangentialClassifierFreeGuidance,
-    ]
+    GuiderType = (
+        AdaptiveProjectedGuidance
+        | AutoGuidance
+        | ClassifierFreeGuidance
+        | ClassifierFreeZeroStarGuidance
+        | FrequencyDecoupledGuidance
+        | PerturbedAttentionGuidance
+        | SkipLayerGuidance
+        | SmoothedEnergyGuidance
+        | TangentialClassifierFreeGuidance
+    )

src/diffusers/guiders/adaptive_projected_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import TYPE_CHECKING, Optional, Union
+from typing import TYPE_CHECKING, Optional
 
 import torch
 
@@ -77,7 +77,7 @@ def __init__(
         self.momentum_buffer = None
 
     def prepare_inputs(
-        self, data: "BlockState", input_fields: Optional[dict[str, Union[str, tuple[str, str]]]] = None
+        self, data: "BlockState", input_fields: Optional[dict[str, str | tuple[str, str]]] = None
     ) -> list["BlockState"]:
         if input_fields is None:
             input_fields = self._input_fields

src/diffusers/guiders/auto_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import TYPE_CHECKING, Any, Optional, Union
+from typing import TYPE_CHECKING, Any, Optional
 
 import torch
 
@@ -65,8 +65,8 @@ class AutoGuidance(BaseGuidance):
     def __init__(
         self,
         guidance_scale: float = 7.5,
-        auto_guidance_layers: Optional[Union[int, list[int]]] = None,
-        auto_guidance_config: Union[LayerSkipConfig, list[LayerSkipConfig], dict[str, Any]] = None,
+        auto_guidance_layers: Optional[int | list[int]] = None,
+        auto_guidance_config: LayerSkipConfig | list[LayerSkipConfig] | dict[str, Any] = None,
         dropout: Optional[float] = None,
         guidance_rescale: float = 0.0,
         use_original_formulation: bool = False,
@@ -133,7 +133,7 @@ def cleanup_models(self, denoiser: torch.nn.Module) -> None:
                 registry.remove_hook(name, recurse=True)
 
     def prepare_inputs(
-        self, data: "BlockState", input_fields: Optional[dict[str, Union[str, tuple[str, str]]]] = None
+        self, data: "BlockState", input_fields: Optional[dict[str, str | tuple[str, str]]] = None
     ) -> list["BlockState"]:
         if input_fields is None:
             input_fields = self._input_fields

src/diffusers/guiders/classifier_free_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import TYPE_CHECKING, Optional, Union
+from typing import TYPE_CHECKING, Optional
 
 import torch
 
@@ -84,7 +84,7 @@ def __init__(
         self.use_original_formulation = use_original_formulation
 
     def prepare_inputs(
-        self, data: "BlockState", input_fields: Optional[dict[str, Union[str, tuple[str, str]]]] = None
+        self, data: "BlockState", input_fields: Optional[dict[str, str | tuple[str, str]]] = None
     ) -> list["BlockState"]:
         if input_fields is None:
             input_fields = self._input_fields

src/diffusers/guiders/classifier_free_zero_star_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import TYPE_CHECKING, Optional, Union
+from typing import TYPE_CHECKING, Optional
 
 import torch
 
@@ -77,7 +77,7 @@ def __init__(
         self.use_original_formulation = use_original_formulation
 
     def prepare_inputs(
-        self, data: "BlockState", input_fields: Optional[dict[str, Union[str, tuple[str, str]]]] = None
+        self, data: "BlockState", input_fields: Optional[dict[str, str | tuple[str, str]]] = None
     ) -> list["BlockState"]:
         if input_fields is None:
             input_fields = self._input_fields

src/diffusers/guiders/frequency_decoupled_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import TYPE_CHECKING, Optional, Union
+from typing import TYPE_CHECKING, Optional
 
 import torch
 
@@ -141,12 +141,12 @@ class FrequencyDecoupledGuidance(BaseGuidance):
     @register_to_config
     def __init__(
         self,
-        guidance_scales: Union[list[float], tuple[float]] = [10.0, 5.0],
-        guidance_rescale: Union[float, list[float], tuple[float]] = 0.0,
-        parallel_weights: Optional[Union[float, list[float], tuple[float]]] = None,
+        guidance_scales: list[float] | tuple[float] = [10.0, 5.0],
+        guidance_rescale: float | list[float] | tuple[float] = 0.0,
+        parallel_weights: Optional[float | list[float] | tuple[float]] = None,
         use_original_formulation: bool = False,
-        start: Union[float, list[float], tuple[float]] = 0.0,
-        stop: Union[float, list[float], tuple[float]] = 1.0,
+        start: float | list[float] | tuple[float] = 0.0,
+        stop: float | list[float] | tuple[float] = 1.0,
         guidance_rescale_space: str = "data",
         upcast_to_double: bool = True,
     ):
@@ -218,7 +218,7 @@ def __init__(
             )
 
     def prepare_inputs(
-        self, data: "BlockState", input_fields: Optional[dict[str, Union[str, tuple[str, str]]]] = None
+        self, data: "BlockState", input_fields: Optional[dict[str, str | tuple[str, str]]] = None
     ) -> list["BlockState"]:
         if input_fields is None:
             input_fields = self._input_fields

src/diffusers/guiders/guider_utils.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import os
-from typing import TYPE_CHECKING, Any, Optional, Union
+from typing import TYPE_CHECKING, Any, Optional
 
 import torch
 from huggingface_hub.utils import validate_hf_hub_args
@@ -47,7 +47,7 @@ def __init__(self, start: float = 0.0, stop: float = 1.0):
         self._num_inference_steps: int = None
         self._timestep: torch.LongTensor = None
         self._count_prepared = 0
-        self._input_fields: dict[str, Union[str, tuple[str, str]]] = None
+        self._input_fields: dict[str, str | tuple[str, str]] = None
         self._enabled = True
 
         if not (0.0 <= start < 1.0):
@@ -72,7 +72,7 @@ def set_state(self, step: int, num_inference_steps: int, timestep: torch.LongTen
         self._timestep = timestep
         self._count_prepared = 0
 
-    def set_input_fields(self, **kwargs: dict[str, Union[str, tuple[str, str]]]) -> None:
+    def set_input_fields(self, **kwargs: dict[str, str | tuple[str, str]]) -> None:
         """
         Set the input fields for the guidance technique. The input fields are used to specify the names of the returned
         attributes containing the prepared data after `prepare_inputs` is called. The prepared data is obtained from
@@ -155,7 +155,7 @@ def num_conditions(self) -> int:
     @classmethod
     def _prepare_batch(
         cls,
-        input_fields: dict[str, Union[str, tuple[str, str]]],
+        input_fields: dict[str, str | tuple[str, str]],
         data: "BlockState",
         tuple_index: int,
         identifier: str,
@@ -205,7 +205,7 @@ def _prepare_batch(
     @validate_hf_hub_args
     def from_pretrained(
         cls,
-        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None,
+        pretrained_model_name_or_path: Optional[str | os.PathLike] = None,
         subfolder: Optional[str] = None,
         return_unused_kwargs=False,
         **kwargs,
@@ -262,7 +262,7 @@ def from_pretrained(
         )
         return cls.from_config(config, return_unused_kwargs=return_unused_kwargs, **kwargs)
 
-    def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
+    def save_pretrained(self, save_directory: str | os.PathLike, push_to_hub: bool = False, **kwargs):
         """
         Save a guider configuration object to a directory so that it can be reloaded using the
         [`~BaseGuidance.from_pretrained`] class method.

src/diffusers/guiders/perturbed_attention_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import TYPE_CHECKING, Any, Optional, Union
+from typing import TYPE_CHECKING, Any, Optional
 
 import torch
 
@@ -92,8 +92,8 @@ def __init__(
         perturbed_guidance_scale: float = 2.8,
         perturbed_guidance_start: float = 0.01,
         perturbed_guidance_stop: float = 0.2,
-        perturbed_guidance_layers: Optional[Union[int, list[int]]] = None,
-        perturbed_guidance_config: Union[LayerSkipConfig, list[LayerSkipConfig], dict[str, Any]] = None,
+        perturbed_guidance_layers: Optional[int | list[int]] = None,
+        perturbed_guidance_config: LayerSkipConfig | list[LayerSkipConfig] | dict[str, Any] = None,
         guidance_rescale: float = 0.0,
         use_original_formulation: bool = False,
         start: float = 0.0,
@@ -169,7 +169,7 @@ def cleanup_models(self, denoiser: torch.nn.Module) -> None:
 
     # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance.prepare_inputs
     def prepare_inputs(
-        self, data: "BlockState", input_fields: Optional[dict[str, Union[str, tuple[str, str]]]] = None
+        self, data: "BlockState", input_fields: Optional[dict[str, str | tuple[str, str]]] = None
     ) -> list["BlockState"]:
         if input_fields is None:
             input_fields = self._input_fields

src/diffusers/guiders/skip_layer_guidance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import TYPE_CHECKING, Any, Optional, Union
+from typing import TYPE_CHECKING, Any, Optional
 
 import torch
 
@@ -94,8 +94,8 @@ def __init__(
         skip_layer_guidance_scale: float = 2.8,
         skip_layer_guidance_start: float = 0.01,
         skip_layer_guidance_stop: float = 0.2,
-        skip_layer_guidance_layers: Optional[Union[int, list[int]]] = None,
-        skip_layer_config: Union[LayerSkipConfig, list[LayerSkipConfig], dict[str, Any]] = None,
+        skip_layer_guidance_layers: Optional[int | list[int]] = None,
+        skip_layer_config: LayerSkipConfig | list[LayerSkipConfig] | dict[str, Any] = None,
         guidance_rescale: float = 0.0,
         use_original_formulation: bool = False,
         start: float = 0.0,
@@ -165,7 +165,7 @@ def cleanup_models(self, denoiser: torch.nn.Module) -> None:
                 registry.remove_hook(hook_name, recurse=True)
 
     def prepare_inputs(
-        self, data: "BlockState", input_fields: Optional[dict[str, Union[str, tuple[str, str]]]] = None
+        self, data: "BlockState", input_fields: Optional[dict[str, str | tuple[str, str]]] = None
     ) -> list["BlockState"]:
         if input_fields is None:
             input_fields = self._input_fields