Bump the version to 0.7.0.dev0 (#912)

* Bump the version to 0.7.0.dev0

* deprecate offsets

* deprecate LMS timesteps

* LMS 0.7.0->0.8.0

setup.py

@@ -211,7 +211,7 @@ install_requires = [
 
 setup(
     name="diffusers",
-    version="0.6.0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
+    version="0.7.0.dev0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
     description="Diffusers",
     long_description=open("README.md", "r", encoding="utf-8").read(),
     long_description_content_type="text/markdown",

src/diffusers/__init__.py

@@ -9,7 +9,7 @@ from .utils import (
 )
 
 
-__version__ = "0.6.0"
+__version__ = "0.7.0.dev0"
 
 from .configuration_utils import ConfigMixin
 from .onnx_utils import OnnxRuntimeModel

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
+from ..utils import BaseOutput
 from .scheduling_utils import SchedulerMixin
 
 
@@ -175,7 +175,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
 
         return variance
 
-    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None, **kwargs):
+    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
         """
         Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
 
@@ -183,18 +183,13 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
             num_inference_steps (`int`):
                 the number of diffusion steps used when generating samples with a pre-trained model.
         """
-        deprecated_offset = deprecate(
-            "offset", "0.7.0", "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
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)
         self.timesteps = torch.from_numpy(timesteps).to(device)
-        self.timesteps += offset
+        self.timesteps += self.config.steps_offset
 
     def step(
         self,

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -209,7 +209,7 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
         ):
             deprecate(
                 "timestep as an index",
-                "0.7.0",
+                "0.8.0",
                 "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                 " `LMSDiscreteScheduler.step()` will not be supported in future versions. Make sure to pass"
                 " one of the `scheduler.timesteps` as a timestep.",
@@ -259,7 +259,7 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
         if isinstance(timesteps, torch.IntTensor) or isinstance(timesteps, torch.LongTensor):
             deprecate(
                 "timesteps as indices",
-                "0.7.0",
+                "0.8.0",
                 "Passing integer indices  (e.g. from `enumerate(timesteps)`) as timesteps to"
                 " `LMSDiscreteScheduler.add_noise()` will not be supported in future versions. Make sure to"
                 " pass values from `scheduler.timesteps` as timesteps.",

src/diffusers/schedulers/scheduling_pndm.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 SchedulerMixin, SchedulerOutput
 
 
@@ -142,7 +141,7 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
         self.plms_timesteps = None
         self.timesteps = None
 
-    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None, **kwargs):
+    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
         """
         Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
 
@@ -150,17 +149,13 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
             num_inference_steps (`int`):
                 the number of diffusion steps used when generating samples with a pre-trained model.
         """
-        deprecated_offset = deprecate(
-            "offset", "0.7.0", "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
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         self._timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()
-        self._timesteps += offset
+        self._timesteps += self.config.steps_offset
 
         if self.config.skip_prk_steps:
             # for some models like stable diffusion the prk steps can/should be skipped to