Sunset Python 3.8 & get rid of explicit typing exports where possible

.github/workflows/pr_dependency_test.yml

@@ -22,7 +22,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.10"
       - name: Install dependencies
         run: |
           pip install -e .

.github/workflows/pr_tests.yml

@@ -35,7 +35,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.10"
       - name: Install dependencies
         run: |
           pip install --upgrade pip
@@ -55,7 +55,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.10"
       - name: Install dependencies
         run: |
           pip install --upgrade pip

.github/workflows/pr_tests_gpu.yml

@@ -36,7 +36,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.10"
       - name: Install dependencies
         run: |
           pip install --upgrade pip
@@ -56,7 +56,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.10"
       - name: Install dependencies
         run: |
           pip install --upgrade pip

.github/workflows/pr_torch_dependency_test.yml

@@ -22,7 +22,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.10"
       - name: Install dependencies
         run: |
           pip install -e .

.github/workflows/pypi_publish.yaml

@@ -47,7 +47,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.10"
 
       - name: Install dependencies
         run: |

setup.py

@@ -122,7 +122,7 @@
     "pytest",
     "pytest-timeout",
     "pytest-xdist",
-    "python>=3.8.0",
+    "python>=3.9.0",
     "ruff==0.9.10",
     "safetensors>=0.3.1",
     "sentencepiece>=0.1.91,!=0.1.92",
@@ -287,7 +287,7 @@ def run(self):
     packages=find_packages("src"),
     package_data={"diffusers": ["py.typed"]},
     include_package_data=True,
-    python_requires=">=3.8.0",
+    python_requires=">=3.10.0",
     install_requires=list(install_requires),
     extras_require=extras,
     entry_points={"console_scripts": ["diffusers-cli=diffusers.commands.diffusers_cli:main"]},

src/diffusers/callbacks.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List
+from typing import Any
 
 from .configuration_utils import ConfigMixin, register_to_config
 from .utils import CONFIG_NAME
@@ -33,13 +33,13 @@ def __init__(self, cutoff_step_ratio=1.0, cutoff_step_index=None):
             raise ValueError("cutoff_step_ratio must be a float between 0.0 and 1.0.")
 
     @property
-    def tensor_inputs(self) -> List[str]:
+    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]:
+    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]:
+    def __call__(self, pipeline, step_index, timestep, callback_kwargs) -> dict[str, Any]:
         return self.callback_fn(pipeline, step_index, timestep, callback_kwargs)
 
 
@@ -49,14 +49,14 @@ class MultiPipelineCallbacks:
     provides a unified interface for calling all of them.
     """
 
-    def __init__(self, callbacks: List[PipelineCallback]):
+    def __init__(self, callbacks: list[PipelineCallback]):
         self.callbacks = callbacks
 
     @property
-    def tensor_inputs(self) -> List[str]:
+    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]:
+    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.
         """
@@ -76,7 +76,7 @@ class SDCFGCutoffCallback(PipelineCallback):
 
     tensor_inputs = ["prompt_embeds"]
 
-    def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
+    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
 
@@ -109,7 +109,7 @@ class SDXLCFGCutoffCallback(PipelineCallback):
         "add_time_ids",
     ]
 
-    def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
+    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
 
@@ -152,7 +152,7 @@ class SDXLControlnetCFGCutoffCallback(PipelineCallback):
         "image",
     ]
 
-    def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
+    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
 
@@ -195,7 +195,7 @@ class IPAdapterScaleCutoffCallback(PipelineCallback):
 
     tensor_inputs = []
 
-    def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
+    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
 
@@ -219,7 +219,7 @@ class SD3CFGCutoffCallback(PipelineCallback):
 
     tensor_inputs = ["prompt_embeds", "pooled_prompt_embeds"]
 
-    def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
+    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
 

src/diffusers/configuration_utils.py

@@ -24,7 +24,7 @@
 import re
 from collections import OrderedDict
 from pathlib import Path
-from typing import Any, Dict, Optional, Tuple, Union
+from typing import Any, Optional
 
 import numpy as np
 from huggingface_hub import DDUFEntry, create_repo, hf_hub_download
@@ -94,10 +94,10 @@ class 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
+        - **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
+        - **_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).
     """
@@ -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.
@@ -155,7 +155,7 @@ def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool
                 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*):
+            kwargs (`dict[str, Any]`, *optional*):
                 Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         if os.path.isfile(save_directory):
@@ -189,13 +189,13 @@ 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.
 
         Parameters:
-            config (`Dict[str, Any]`):
+            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`):
@@ -292,11 +292,11 @@ 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,
-    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+    ) -> tuple[dict[str, Any], dict[str, Any]]:
         r"""
         Load a model or scheduler configuration.
 
@@ -315,7 +315,7 @@ def load_config(
             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*):
+            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`):
@@ -352,7 +352,7 @@ def load_config(
         _ = 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)
+        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)
@@ -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:
@@ -577,12 +575,12 @@ def __repr__(self):
         return f"{self.__class__.__name__} {self.to_json_string()}"
 
     @property
-    def config(self) -> Dict[str, Any]:
+    def config(self) -> dict[str, Any]:
         """
         Returns the config of the class as a frozen dictionary
 
         Returns:
-            `Dict[str, Any]`: Config of the class.
+            `dict[str, Any]`: Config of the class.
         """
         return self._internal_dict
 
@@ -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.
 
@@ -637,7 +635,7 @@ def to_json_file(self, json_file_path: Union[str, os.PathLike]):
             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]):
+    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
@@ -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/dependency_versions_table.py

@@ -29,7 +29,7 @@
     "pytest": "pytest",
     "pytest-timeout": "pytest-timeout",
     "pytest-xdist": "pytest-xdist",
-    "python": "python>=3.8.0",
+    "python": "python>=3.9.0",
     "ruff": "ruff==0.9.10",
     "safetensors": "safetensors>=0.3.1",
     "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",

src/diffusers/guiders/adaptive_projected_guidance.py

@@ -12,8 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import math
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
+from typing import TYPE_CHECKING, Optional
 
 import torch
 
@@ -77,7 +79,7 @@ def __init__(
         self.use_original_formulation = use_original_formulation
         self.momentum_buffer = None
 
-    def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
+    def prepare_inputs(self, data: dict[str, tuple[torch.Tensor, torch.Tensor]]) -> list["BlockState"]:
         if self._step == 0:
             if self.adaptive_projected_guidance_momentum is not None:
                 self.momentum_buffer = MomentumBuffer(self.adaptive_projected_guidance_momentum)

src/diffusers/guiders/auto_guidance.py

@@ -12,8 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import math
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Optional
 
 import torch
 
@@ -36,10 +38,10 @@ class AutoGuidance(BaseGuidance):
             The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
             prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
             deterioration of image quality.
-        auto_guidance_layers (`int` or `List[int]`, *optional*):
+        auto_guidance_layers (`int` or `list[int]`, *optional*):
             The layer indices to apply skip layer guidance to. Can be a single integer or a list of integers. If not
             provided, `skip_layer_config` must be provided.
-        auto_guidance_config (`LayerSkipConfig` or `List[LayerSkipConfig]`, *optional*):
+        auto_guidance_config (`LayerSkipConfig` or `list[LayerSkipConfig]`, *optional*):
             The configuration for the skip layer guidance. Can be a single `LayerSkipConfig` or a list of
             `LayerSkipConfig`. If not provided, `skip_layer_guidance_layers` must be provided.
         dropout (`float`, *optional*):
@@ -65,8 +67,8 @@ 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 +135,7 @@ def cleanup_models(self, denoiser: torch.nn.Module) -> None:
                 registry = HookRegistry.check_if_exists_or_initialize(denoiser)
                 registry.remove_hook(name, recurse=True)
 
-    def prepare_inputs(self, data: Dict[str, Tuple[torch.Tensor, torch.Tensor]]) -> List["BlockState"]:
+    def prepare_inputs(self, data: dict[str, tuple[torch.Tensor, torch.Tensor]]) -> list["BlockState"]:
         tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
         data_batches = []
         for tuple_idx, input_prediction in zip(tuple_indices, self._input_predictions):