[Utils] Add deprecate function and move testing_utils under utils (#659)

* [Utils] Add deprecate function

* up

* up

* uP

* up

* up

* up

* up

* uP

* up

* fix

* up

* move to deprecation utils file

* fix

* fix

* fix more

examples/conftest.py

@@ -32,13 +32,13 @@ warnings.simplefilter(action="ignore", category=FutureWarning)
 
 
 def pytest_addoption(parser):
-    from diffusers.testing_utils import pytest_addoption_shared
+    from diffusers.utils.testing_utils import pytest_addoption_shared
 
     pytest_addoption_shared(parser)
 
 
 def pytest_terminal_summary(terminalreporter):
-    from diffusers.testing_utils import pytest_terminal_summary_main
+    from diffusers.utils.testing_utils import pytest_terminal_summary_main
 
     make_reports = terminalreporter.config.getoption("--make-reports")
     if make_reports:

examples/test_examples.py

@@ -24,7 +24,7 @@ import unittest
 from typing import List
 
 from accelerate.utils import write_basic_config
-from diffusers.testing_utils import slow
+from diffusers.utils import slow
 
 
 logging.basicConfig(level=logging.DEBUG)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

 import inspect
-import warnings
 from typing import Callable, List, Optional, Union
 
 import torch
@@ -10,7 +9,7 @@ from ...configuration_utils import FrozenDict
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...pipeline_utils import DiffusionPipeline
 from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from ...utils import logging
+from ...utils import deprecate, logging
 from . import StableDiffusionPipelineOutput
 from .safety_checker import StableDiffusionSafetyChecker
 
@@ -59,15 +58,15 @@ class StableDiffusionPipeline(DiffusionPipeline):
         super().__init__()
 
         if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            warnings.warn(
+            deprecation_message = (
                 f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                 f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                 "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                 " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                 " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file",
-                DeprecationWarning,
+                " file"
             )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
             new_config = dict(scheduler.config)
             new_config["steps_offset"] = 1
             scheduler._internal_dict = FrozenDict(new_config)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

 import inspect
-import warnings
 from typing import Callable, List, Optional, Union
 
 import numpy as np
@@ -12,7 +11,7 @@ from ...configuration_utils import FrozenDict
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...pipeline_utils import DiffusionPipeline
 from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from ...utils import logging
+from ...utils import deprecate, logging
 from . import StableDiffusionPipelineOutput
 from .safety_checker import StableDiffusionSafetyChecker
 
@@ -71,15 +70,15 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
         super().__init__()
 
         if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            warnings.warn(
+            deprecation_message = (
                 f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                 f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                 "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                 " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                 " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file",
-                DeprecationWarning,
+                " file"
             )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
             new_config = dict(scheduler.config)
             new_config["steps_offset"] = 1
             scheduler._internal_dict = FrozenDict(new_config)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

 import inspect
-import warnings
 from typing import Callable, List, Optional, Union
 
 import numpy as np
@@ -13,7 +12,7 @@ from ...configuration_utils import FrozenDict
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...pipeline_utils import DiffusionPipeline
 from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from ...utils import logging
+from ...utils import deprecate, logging
 from . import StableDiffusionPipelineOutput
 from .safety_checker import StableDiffusionSafetyChecker
 
@@ -86,15 +85,15 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
         logger.info("`StableDiffusionInpaintPipeline` is experimental and will very likely change in the future.")
 
         if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            warnings.warn(
+            deprecation_message = (
                 f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                 f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                 "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                 " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                 " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file",
-                DeprecationWarning,
+                " file"
             )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
             new_config = dict(scheduler.config)
             new_config["steps_offset"] = 1
             scheduler._internal_dict = FrozenDict(new_config)

src/diffusers/schedulers/scheduling_ddim.py

@@ -16,7 +16,6 @@
 # and https://github.com/hojonathanho/diffusion
 
 import math
-import warnings
 from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
@@ -24,7 +23,7 @@ import numpy as np
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
+from ..utils import BaseOutput, deprecate
 from .scheduling_utils import SchedulerMixin
 
 
@@ -122,12 +121,12 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
         steps_offset: int = 0,
         **kwargs,
     ):
-        if "tensor_format" in kwargs:
-            warnings.warn(
-                "`tensor_format` is deprecated as an argument and will be removed in version `0.5.0`."
-                "If you're running your code in PyTorch, you can safely remove this argument.",
-                DeprecationWarning,
-            )
+        deprecate(
+            "tensor_format",
+            "0.5.0",
+            "If you're running your code in PyTorch, you can safely remove this argument.",
+            take_from=kwargs,
+        )
 
         if trained_betas is not None:
             self.betas = torch.from_numpy(trained_betas)
@@ -175,17 +174,10 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
             num_inference_steps (`int`):
                 the number of diffusion steps used when generating samples with a pre-trained model.
         """
-
-        offset = self.config.steps_offset
-
-        if "offset" in kwargs:
-            warnings.warn(
-                "`offset` is deprecated as an input argument to `set_timesteps` and will be removed in v0.4.0."
-                " Please pass `steps_offset` to `__init__` instead.",
-                DeprecationWarning,
-            )
-
-            offset = kwargs["offset"]
+        deprecated_offset = deprecate(
+            "offset", "0.5.0", "Please pass `steps_offset` to `__init__` instead.", take_from=kwargs
+        )
+        offset = deprecated_offset or self.config.steps_offset
 
         self.num_inference_steps = num_inference_steps
         step_ratio = self.config.num_train_timesteps // self.num_inference_steps

src/diffusers/schedulers/scheduling_ddpm.py

@@ -15,7 +15,6 @@
 # DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
 
 import math
-import warnings
 from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
@@ -23,7 +22,7 @@ import numpy as np
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
+from ..utils import BaseOutput, deprecate
 from .scheduling_utils import SchedulerMixin
 
 
@@ -115,12 +114,12 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         clip_sample: bool = True,
         **kwargs,
     ):
-        if "tensor_format" in kwargs:
-            warnings.warn(
-                "`tensor_format` is deprecated as an argument and will be removed in version `0.5.0`."
-                "If you're running your code in PyTorch, you can safely remove this argument.",
-                DeprecationWarning,
-            )
+        deprecate(
+            "tensor_format",
+            "0.5.0",
+            "If you're running your code in PyTorch, you can safely remove this argument.",
+            take_from=kwargs,
+        )
 
         if trained_betas is not None:
             self.betas = torch.from_numpy(trained_betas)

src/diffusers/schedulers/scheduling_karras_ve.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 
-import warnings
 from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
@@ -21,7 +20,7 @@ import numpy as np
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
+from ..utils import BaseOutput, deprecate
 from .scheduling_utils import SchedulerMixin
 
 
@@ -89,12 +88,12 @@ class KarrasVeScheduler(SchedulerMixin, ConfigMixin):
         s_max: float = 50,
         **kwargs,
     ):
-        if "tensor_format" in kwargs:
-            warnings.warn(
-                "`tensor_format` is deprecated as an argument and will be removed in version `0.5.0`."
-                "If you're running your code in PyTorch, you can safely remove this argument.",
-                DeprecationWarning,
-            )
+        deprecate(
+            "tensor_format",
+            "0.5.0",
+            "If you're running your code in PyTorch, you can safely remove this argument.",
+            take_from=kwargs,
+        )
 
         # setable values
         self.num_inference_steps: int = None

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import warnings
 from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
@@ -22,7 +21,7 @@ import torch
 from scipy import integrate
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
+from ..utils import BaseOutput, deprecate
 from .scheduling_utils import SchedulerMixin
 
 
@@ -77,12 +76,12 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
         trained_betas: Optional[np.ndarray] = None,
         **kwargs,
     ):
-        if "tensor_format" in kwargs:
-            warnings.warn(
-                "`tensor_format` is deprecated as an argument and will be removed in version `0.5.0`."
-                "If you're running your code in PyTorch, you can safely remove this argument.",
-                DeprecationWarning,
-            )
+        deprecate(
+            "tensor_format",
+            "0.5.0",
+            "If you're running your code in PyTorch, you can safely remove this argument.",
+            take_from=kwargs,
+        )
 
         if trained_betas is not None:
             self.betas = torch.from_numpy(trained_betas)

src/diffusers/schedulers/scheduling_pndm.py

@@ -15,13 +15,13 @@
 # DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
 
 import math
-import warnings
 from typing import Optional, Tuple, Union
 
 import numpy as np
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
+from ..utils import deprecate
 from .scheduling_utils import SchedulerMixin, SchedulerOutput
 
 
@@ -102,12 +102,12 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
         steps_offset: int = 0,
         **kwargs,
     ):
-        if "tensor_format" in kwargs:
-            warnings.warn(
-                "`tensor_format` is deprecated as an argument and will be removed in version `0.5.0`."
-                "If you're running your code in PyTorch, you can safely remove this argument.",
-                DeprecationWarning,
-            )
+        deprecate(
+            "tensor_format",
+            "0.5.0",
+            "If you're running your code in PyTorch, you can safely remove this argument.",
+            take_from=kwargs,
+        )
 
         if trained_betas is not None:
             self.betas = torch.from_numpy(trained_betas)
@@ -155,16 +155,10 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
             num_inference_steps (`int`):
                 the number of diffusion steps used when generating samples with a pre-trained model.
         """
-
-        offset = self.config.steps_offset
-
-        if "offset" in kwargs:
-            warnings.warn(
-                "`offset` is deprecated as an input argument to `set_timesteps` and will be removed in v0.4.0."
-                " Please pass `steps_offset` to `__init__` instead."
-            )
-
-            offset = kwargs["offset"]
+        deprecated_offset = deprecate(
+            "offset", "0.5.0", "Please pass `steps_offset` to `__init__` instead.", take_from=kwargs
+        )
+        offset = deprecated_offset or self.config.steps_offset
 
         self.num_inference_steps = num_inference_steps
         step_ratio = self.config.num_train_timesteps // self.num_inference_steps

src/diffusers/schedulers/scheduling_sde_ve.py

@@ -15,14 +15,13 @@
 # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
 
 import math
-import warnings
 from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
+from ..utils import BaseOutput, deprecate
 from .scheduling_utils import SchedulerMixin, SchedulerOutput
 
 
@@ -78,12 +77,12 @@ class ScoreSdeVeScheduler(SchedulerMixin, ConfigMixin):
         correct_steps: int = 1,
         **kwargs,
     ):
-        if "tensor_format" in kwargs:
-            warnings.warn(
-                "`tensor_format` is deprecated as an argument and will be removed in version `0.5.0`."
-                "If you're running your code in PyTorch, you can safely remove this argument.",
-                DeprecationWarning,
-            )
+        deprecate(
+            "tensor_format",
+            "0.5.0",
+            "If you're running your code in PyTorch, you can safely remove this argument.",
+            take_from=kwargs,
+        )
 
         # setable values
         self.timesteps = None
@@ -139,11 +138,7 @@ class ScoreSdeVeScheduler(SchedulerMixin, ConfigMixin):
         )
 
     def set_seed(self, seed):
-        warnings.warn(
-            "The method `set_seed` is deprecated and will be removed in version `0.4.0`. Please consider passing a"
-            " generator instead.",
-            DeprecationWarning,
-        )
+        deprecate("set_seed", "0.5.0", "Please consider passing a generator instead.")
         torch.manual_seed(seed)
 
     def step_pred(

src/diffusers/schedulers/scheduling_sde_vp.py

@@ -17,11 +17,11 @@
 # TODO(Patrick, Anton, Suraj) - make scheduler framework independent and clean-up a bit
 
 import math
-import warnings
 
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
+from ..utils import deprecate
 from .scheduling_utils import SchedulerMixin
 
 
@@ -42,12 +42,12 @@ class ScoreSdeVpScheduler(SchedulerMixin, ConfigMixin):
 
     @register_to_config
     def __init__(self, num_train_timesteps=2000, beta_min=0.1, beta_max=20, sampling_eps=1e-3, **kwargs):
-        if "tensor_format" in kwargs:
-            warnings.warn(
-                "`tensor_format` is deprecated as an argument and will be removed in version `0.5.0`."
-                "If you're running your code in PyTorch, you can safely remove this argument.",
-                DeprecationWarning,
-            )
+        deprecate(
+            "tensor_format",
+            "0.5.0",
+            "If you're running your code in PyTorch, you can safely remove this argument.",
+            take_from=kwargs,
+        )
         self.sigmas = None
         self.discrete_sigmas = None
         self.timesteps = None

src/diffusers/schedulers/scheduling_utils.py

@@ -11,12 +11,11 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import warnings
 from dataclasses import dataclass
 
 import torch
 
-from ..utils import BaseOutput
+from ..utils import BaseOutput, deprecate
 
 
 SCHEDULER_CONFIG_NAME = "scheduler_config.json"
@@ -44,10 +43,10 @@ class SchedulerMixin:
     config_name = SCHEDULER_CONFIG_NAME
 
     def set_format(self, tensor_format="pt"):
-        warnings.warn(
-            "The method `set_format` is deprecated and will be removed in version `0.5.0`."
-            "If you're running your code in PyTorch, you can safely remove this function as the schedulers"
-            "are always in Pytorch",
-            DeprecationWarning,
+        deprecate(
+            "set_format",
+            "0.5.0",
+            "If you're running your code in PyTorch, you can safely remove this function as the schedulers are always"
+            " in Pytorch",
         )
         return self

src/diffusers/utils/__init__.py

@@ -15,6 +15,7 @@
 
 import os
 
+from .deprecation_utils import deprecate
 from .import_utils import (
     ENV_VARS_TRUE_AND_AUTO_VALUES,
     ENV_VARS_TRUE_VALUES,
@@ -35,6 +36,7 @@ from .import_utils import (
 )
 from .logging import get_logger
 from .outputs import BaseOutput
+from .testing_utils import floats_tensor, load_image, parse_flag_from_env, slow, torch_device
 
 
 logger = get_logger(__name__)

src/diffusers/utils/deprecation_utils.py

+import inspect
+import warnings
+from typing import Any, Dict, Optional, Union
+
+from packaging import version
+
+
+def deprecate(*args, take_from: Optional[Union[Dict, Any]] = None, standard_warn=True):
+    from .. import __version__
+
+    deprecated_kwargs = take_from
+    values = ()
+    if not isinstance(args[0], tuple):
+        args = (args,)
+
+    for attribute, version_name, message in args:
+        if version.parse(version.parse(__version__).base_version) >= version.parse(version_name):
+            raise ValueError(
+                f"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
+                f" version {__version__} is >= {version_name}"
+            )
+
+        warning = None
+        if isinstance(deprecated_kwargs, dict) and attribute in deprecated_kwargs:
+            values += (deprecated_kwargs.pop(attribute),)
+            warning = f"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
+        elif hasattr(deprecated_kwargs, attribute):
+            values += (getattr(deprecated_kwargs, attribute),)
+            warning = f"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
+        elif deprecated_kwargs is None:
+            warning = f"`{attribute}` is deprecated and will be removed in version {version_name}."
+
+        if warning is not None:
+            warning = warning + " " if standard_warn else ""
+            warnings.warn(warning + message, DeprecationWarning)
+
+    if isinstance(deprecated_kwargs, dict) and len(deprecated_kwargs) > 0:
+        call_frame = inspect.getouterframes(inspect.currentframe())[1]
+        filename = call_frame.filename
+        line_number = call_frame.lineno
+        function = call_frame.function
+        key, value = next(iter(deprecated_kwargs.items()))
+        raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`")
+
+    if len(values) == 0:
+        return
+    elif len(values) == 1:
+        return values[0]
+    return values

src/diffusers/utils/outputs.py

@@ -15,13 +15,13 @@
 Generic utilities
 """
 
-import warnings
 from collections import OrderedDict
 from dataclasses import fields
 from typing import Any, Tuple
 
 import numpy as np
 
+from .deprecation_utils import deprecate
 from .import_utils import is_torch_available
 
 
@@ -87,11 +87,7 @@ class BaseOutput(OrderedDict):
         if isinstance(k, str):
             inner_dict = {k: v for (k, v) in self.items()}
             if self.__class__.__name__ in ["StableDiffusionPipelineOutput", "ImagePipelineOutput"] and k == "sample":
-                warnings.warn(
-                    "The keyword 'samples' is deprecated and will be removed in version 0.4.0. Please use `.images` or"
-                    " `'images'` instead.",
-                    DeprecationWarning,
-                )
+                deprecate("samples", "0.6.0", "Please use `.images` or `'images'` instead.")
                 return inner_dict["images"]
             return inner_dict[k]
         else:

tests/conftest.py

@@ -31,13 +31,13 @@ warnings.simplefilter(action="ignore", category=FutureWarning)
 
 
 def pytest_addoption(parser):
-    from diffusers.testing_utils import pytest_addoption_shared
+    from diffusers.utils.testing_utils import pytest_addoption_shared
 
     pytest_addoption_shared(parser)
 
 
 def pytest_terminal_summary(terminalreporter):
-    from diffusers.testing_utils import pytest_terminal_summary_main
+    from diffusers.utils.testing_utils import pytest_terminal_summary_main
 
     make_reports = terminalreporter.config.getoption("--make-reports")
     if make_reports:

tests/test_layers_utils.py

@@ -22,7 +22,7 @@ import torch
 from diffusers.models.attention import AttentionBlock, SpatialTransformer
 from diffusers.models.embeddings import get_timestep_embedding
 from diffusers.models.resnet import Downsample2D, Upsample2D
-from diffusers.testing_utils import torch_device
+from diffusers.utils import torch_device
 
 
 torch.backends.cuda.matmul.allow_tf32 = False

tests/test_modeling_common.py

@@ -22,8 +22,8 @@ import numpy as np
 import torch
 
 from diffusers.modeling_utils import ModelMixin
-from diffusers.testing_utils import torch_device
 from diffusers.training_utils import EMAModel
+from diffusers.utils import torch_device
 
 
 class ModelTesterMixin: