[Bump version] 0.13.0dev0 & Deprecate `predict_epsilon` (#2109)

* [Bump version] 0.13

* Bump model up

* up

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py

@@ -303,7 +303,7 @@ class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
             (nsfw) content, according to the `safety_checker`.
         """
         message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.13.0", message, take_from=kwargs)
+        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
         image = init_image or image
 
         if isinstance(prompt, str):

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -616,7 +616,7 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
             (nsfw) content, according to the `safety_checker`.
         """
         message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.13.0", message, take_from=kwargs)
+        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
         image = init_image or image
 
         # 1. Check inputs. Raise error if not correct

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py

@@ -556,7 +556,7 @@ class StableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
             (nsfw) content, according to the `safety_checker`.
         """
         message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.13.0", message, take_from=kwargs)
+        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
         image = init_image or image
 
         # 1. Check inputs

src/diffusers/schedulers/scheduling_ddim.py

@@ -23,7 +23,7 @@ import numpy as np
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput, deprecate, randn_tensor
+from ..utils import BaseOutput, randn_tensor
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
 
 
@@ -113,7 +113,6 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
     """
 
     _compatibles = [e.name for e in KarrasDiffusionSchedulers]
-    _deprecated_kwargs = ["predict_epsilon"]
     order = 1
 
     @register_to_config
@@ -128,16 +127,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
         set_alpha_to_one: bool = True,
         steps_offset: int = 0,
         prediction_type: str = "epsilon",
-        **kwargs,
     ):
-        message = (
-            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
-            " DDIMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
-        )
-        predict_epsilon = deprecate("predict_epsilon", "0.13.0", message, take_from=kwargs)
-        if predict_epsilon is not None:
-            self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
-
         if trained_betas is not None:
             self.betas = torch.tensor(trained_betas, dtype=torch.float32)
         elif beta_schedule == "linear":

src/diffusers/schedulers/scheduling_ddim_flax.py

@@ -22,7 +22,6 @@ import flax
 import jax.numpy as jnp
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import deprecate
 from .scheduling_utils_flax import (
     CommonSchedulerState,
     FlaxKarrasDiffusionSchedulers,
@@ -103,7 +102,6 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
     """
 
     _compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers]
-    _deprecated_kwargs = ["predict_epsilon"]
 
     dtype: jnp.dtype
 
@@ -123,16 +121,7 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
         steps_offset: int = 0,
         prediction_type: str = "epsilon",
         dtype: jnp.dtype = jnp.float32,
-        **kwargs,
     ):
-        message = (
-            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
-            f" {self.__class__.__name__}.from_pretrained(<model_id>, prediction_type='epsilon')`."
-        )
-        predict_epsilon = deprecate("predict_epsilon", "0.13.0", message, take_from=kwargs)
-        if predict_epsilon is not None:
-            self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
-
         self.dtype = dtype
 
     def create_state(self, common: Optional[CommonSchedulerState] = None) -> DDIMSchedulerState:

src/diffusers/schedulers/scheduling_ddpm.py

@@ -21,8 +21,8 @@ from typing import List, Optional, Tuple, Union
 import numpy as np
 import torch
 
-from ..configuration_utils import ConfigMixin, FrozenDict, register_to_config
-from ..utils import BaseOutput, deprecate, randn_tensor
+from ..configuration_utils import ConfigMixin, register_to_config
+from ..utils import BaseOutput, randn_tensor
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
 
 
@@ -106,7 +106,6 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
     """
 
     _compatibles = [e.name for e in KarrasDiffusionSchedulers]
-    _deprecated_kwargs = ["predict_epsilon"]
     order = 1
 
     @register_to_config
@@ -120,16 +119,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         variance_type: str = "fixed_small",
         clip_sample: bool = True,
         prediction_type: str = "epsilon",
-        **kwargs,
     ):
-        message = (
-            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
-            " DDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
-        )
-        predict_epsilon = deprecate("predict_epsilon", "0.13.0", message, take_from=kwargs)
-        if predict_epsilon is not None:
-            self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
-
         if trained_betas is not None:
             self.betas = torch.tensor(trained_betas, dtype=torch.float32)
         elif beta_schedule == "linear":
@@ -239,7 +229,6 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         sample: torch.FloatTensor,
         generator=None,
         return_dict: bool = True,
-        **kwargs,
     ) -> Union[DDPMSchedulerOutput, Tuple]:
         """
         Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
@@ -259,16 +248,6 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
             returning a tuple, the first element is the sample tensor.
 
         """
-        message = (
-            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
-            " DDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
-        )
-        predict_epsilon = deprecate("predict_epsilon", "0.13.0", message, take_from=kwargs)
-        if predict_epsilon is not None:
-            new_config = dict(self.config)
-            new_config["prediction_type"] = "epsilon" if predict_epsilon else "sample"
-            self._internal_dict = FrozenDict(new_config)
-
         t = timestep
 
         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:

src/diffusers/schedulers/scheduling_ddpm_flax.py

@@ -22,7 +22,6 @@ import jax
 import jax.numpy as jnp
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import deprecate
 from .scheduling_utils_flax import (
     CommonSchedulerState,
     FlaxKarrasDiffusionSchedulers,
@@ -86,7 +85,6 @@ class FlaxDDPMScheduler(FlaxSchedulerMixin, ConfigMixin):
     """
 
     _compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers]
-    _deprecated_kwargs = ["predict_epsilon"]
 
     dtype: jnp.dtype
 
@@ -106,16 +104,7 @@ class FlaxDDPMScheduler(FlaxSchedulerMixin, ConfigMixin):
         clip_sample: bool = True,
         prediction_type: str = "epsilon",
         dtype: jnp.dtype = jnp.float32,
-        **kwargs,
     ):
-        message = (
-            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
-            f" {self.__class__.__name__}.from_pretrained(<model_id>, prediction_type='epsilon')`."
-        )
-        predict_epsilon = deprecate("predict_epsilon", "0.13.0", message, take_from=kwargs)
-        if predict_epsilon is not None:
-            self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
-
         self.dtype = dtype
 
     def create_state(self, common: Optional[CommonSchedulerState] = None) -> DDPMSchedulerState:

src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

@@ -21,7 +21,6 @@ import numpy as np
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import deprecate
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
 
 
@@ -118,7 +117,6 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
     """
 
     _compatibles = [e.name for e in KarrasDiffusionSchedulers]
-    _deprecated_kwargs = ["predict_epsilon"]
     order = 1
 
     @register_to_config
@@ -137,16 +135,7 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         algorithm_type: str = "dpmsolver++",
         solver_type: str = "midpoint",
         lower_order_final: bool = True,
-        **kwargs,
     ):
-        message = (
-            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
-            " DPMSolverMultistepScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
-        )
-        predict_epsilon = deprecate("predict_epsilon", "0.13.0", message, take_from=kwargs)
-        if predict_epsilon is not None:
-            self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
-
         if trained_betas is not None:
             self.betas = torch.tensor(trained_betas, dtype=torch.float32)
         elif beta_schedule == "linear":

src/diffusers/schedulers/scheduling_dpmsolver_multistep_flax.py

@@ -22,7 +22,6 @@ import jax
 import jax.numpy as jnp
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import deprecate
 from .scheduling_utils_flax import (
     CommonSchedulerState,
     FlaxKarrasDiffusionSchedulers,
@@ -141,7 +140,6 @@ class FlaxDPMSolverMultistepScheduler(FlaxSchedulerMixin, ConfigMixin):
     """
 
     _compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers]
-    _deprecated_kwargs = ["predict_epsilon"]
 
     dtype: jnp.dtype
 
@@ -166,16 +164,7 @@ class FlaxDPMSolverMultistepScheduler(FlaxSchedulerMixin, ConfigMixin):
         solver_type: str = "midpoint",
         lower_order_final: bool = True,
         dtype: jnp.dtype = jnp.float32,
-        **kwargs,
     ):
-        message = (
-            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
-            f" {self.__class__.__name__}.from_pretrained(<model_id>, prediction_type='epsilon')`."
-        )
-        predict_epsilon = deprecate("predict_epsilon", "0.13.0", message, take_from=kwargs)
-        if predict_epsilon is not None:
-            self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
-
         self.dtype = dtype
 
     def create_state(self, common: Optional[CommonSchedulerState] = None) -> DPMSolverMultistepSchedulerState:

tests/pipelines/ddpm/test_ddpm.py

@@ -19,7 +19,6 @@ import numpy as np
 import torch
 
 from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
-from diffusers.utils import deprecate
 from diffusers.utils.testing_utils import require_torch_gpu, slow, torch_device
 
 
@@ -67,32 +66,6 @@ class DDPMPipelineFastTests(unittest.TestCase):
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
 
-    def test_inference_deprecated_predict_epsilon(self):
-        deprecate("remove this test", "0.13.0", "remove")
-        unet = self.dummy_uncond_unet
-        scheduler = DDPMScheduler(predict_epsilon=False)
-
-        ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
-        ddpm.to(torch_device)
-        ddpm.set_progress_bar_config(disable=None)
-
-        # Warmup pass when using mps (see #372)
-        if torch_device == "mps":
-            _ = ddpm(num_inference_steps=1)
-
-        generator = torch.manual_seed(0)
-        image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy").images
-
-        generator = torch.manual_seed(0)
-        image_eps = ddpm(generator=generator, num_inference_steps=2, output_type="numpy", predict_epsilon=False)[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_eps_slice = image_eps[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        tolerance = 1e-2 if torch_device != "mps" else 3e-2
-        assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance
-
     def test_inference_predict_sample(self):
         unet = self.dummy_uncond_unet
         scheduler = DDPMScheduler(prediction_type="sample")

tests/test_config.py

@@ -26,7 +26,6 @@ from diffusers import (
     logging,
 )
 from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.utils import deprecate
 from diffusers.utils.testing_utils import CaptureLogger
 
 
@@ -202,20 +201,10 @@ class ConfigTester(unittest.TestCase):
         with CaptureLogger(logger) as cap_logger_2:
             ddpm_2 = DDPMScheduler.from_pretrained("google/ddpm-celebahq-256", beta_start=88)
 
-        with CaptureLogger(logger) as cap_logger:
-            deprecate("remove this case", "0.13.0", "remove")
-            ddpm_3 = DDPMScheduler.from_pretrained(
-                "hf-internal-testing/tiny-stable-diffusion-torch",
-                subfolder="scheduler",
-                predict_epsilon=False,
-                beta_end=8,
-            )
-
         assert ddpm.__class__ == DDPMScheduler
         assert ddpm.config.prediction_type == "sample"
         assert ddpm.config.beta_end == 8
         assert ddpm_2.config.beta_start == 88
-        assert ddpm_3.config.prediction_type == "sample"
 
         # no warning should be thrown
         assert cap_logger.out == ""

tests/test_scheduler.py

@@ -45,7 +45,7 @@ from diffusers import (
 )
 from diffusers.configuration_utils import ConfigMixin, register_to_config
 from diffusers.schedulers.scheduling_utils import SchedulerMixin
-from diffusers.utils import deprecate, torch_device
+from diffusers.utils import torch_device
 from diffusers.utils.testing_utils import CaptureLogger
 
 
@@ -645,35 +645,6 @@ class DDPMSchedulerTest(SchedulerCommonTest):
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(prediction_type=prediction_type)
 
-    def test_deprecated_predict_epsilon(self):
-        deprecate("remove this test", "0.13.0", "remove")
-        for predict_epsilon in [True, False]:
-            self.check_over_configs(predict_epsilon=predict_epsilon)
-
-    def test_deprecated_epsilon(self):
-        deprecate("remove this test", "0.13.0", "remove")
-        scheduler_class = self.scheduler_classes[0]
-        scheduler_config = self.get_scheduler_config()
-
-        sample = self.dummy_sample_deter
-        residual = 0.1 * self.dummy_sample_deter
-        time_step = 4
-
-        scheduler = scheduler_class(**scheduler_config)
-        scheduler_eps = scheduler_class(predict_epsilon=False, **scheduler_config)
-
-        kwargs = {}
-        if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
-            kwargs["generator"] = torch.manual_seed(0)
-        output = scheduler.step(residual, time_step, sample, predict_epsilon=False, **kwargs).prev_sample
-
-        kwargs = {}
-        if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
-            kwargs["generator"] = torch.manual_seed(0)
-        output_eps = scheduler_eps.step(residual, time_step, sample, predict_epsilon=False, **kwargs).prev_sample
-
-        assert (output - output_eps).abs().sum() < 1e-5
-
     def test_time_indices(self):
         for t in [0, 500, 999]:
             self.check_over_forward(time_step=t)

tests/test_scheduler_flax.py

@@ -18,7 +18,7 @@ import unittest
 from typing import Dict, List, Tuple
 
 from diffusers import FlaxDDIMScheduler, FlaxDDPMScheduler, FlaxPNDMScheduler
-from diffusers.utils import deprecate, is_flax_available
+from diffusers.utils import is_flax_available
 from diffusers.utils.testing_utils import require_flax
 
 
@@ -626,22 +626,6 @@ class FlaxDDIMSchedulerTest(FlaxSchedulerCommonTest):
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(prediction_type=prediction_type)
 
-    def test_deprecated_predict_epsilon(self):
-        deprecate("remove this test", "0.13.0", "remove")
-        for predict_epsilon in [True, False]:
-            self.check_over_configs(predict_epsilon=predict_epsilon)
-
-    def test_deprecated_predict_epsilon_to_prediction_type(self):
-        deprecate("remove this test", "0.13.0", "remove")
-        for scheduler_class in self.scheduler_classes:
-            scheduler_config = self.get_scheduler_config(predict_epsilon=True)
-            scheduler = scheduler_class.from_config(scheduler_config)
-            assert scheduler.prediction_type == "epsilon"
-
-            scheduler_config = self.get_scheduler_config(predict_epsilon=False)
-            scheduler = scheduler_class.from_config(scheduler_config)
-            assert scheduler.prediction_type == "sample"
-
 
 @require_flax
 class FlaxPNDMSchedulerTest(FlaxSchedulerCommonTest):