Bump to v0.5.0dev0

setup.py

@@ -211,7 +211,7 @@ def run(self):
 
 setup(
     name="diffusers",
-    version="0.4.1",  # 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="0.5.0.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 @@
 )
 
 
-__version__ = "0.4.1"
+__version__ = "0.5.0.dev0"
 
 from .configuration_utils import ConfigMixin
 from .onnx_utils import OnnxRuntimeModel

src/diffusers/schedulers/scheduling_ddim.py

@@ -123,7 +123,7 @@ def __init__(
     ):
         deprecate(
             "tensor_format",
-            "0.5.0",
+            "0.6.0",
             "If you're running your code in PyTorch, you can safely remove this argument.",
             take_from=kwargs,
         )
@@ -192,7 +192,7 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic
                 the number of diffusion steps used when generating samples with a pre-trained model.
         """
         deprecated_offset = deprecate(
-            "offset", "0.5.0", "Please pass `steps_offset` to `__init__` instead.", take_from=kwargs
+            "offset", "0.7.0", "Please pass `steps_offset` to `__init__` instead.", take_from=kwargs
         )
         offset = deprecated_offset or self.config.steps_offset
 

src/diffusers/schedulers/scheduling_ddpm.py

@@ -116,7 +116,7 @@ def __init__(
     ):
         deprecate(
             "tensor_format",
-            "0.5.0",
+            "0.6.0",
             "If you're running your code in PyTorch, you can safely remove this argument.",
             take_from=kwargs,
         )

src/diffusers/schedulers/scheduling_karras_ve.py

@@ -90,7 +90,7 @@ def __init__(
     ):
         deprecate(
             "tensor_format",
-            "0.5.0",
+            "0.6.0",
             "If you're running your code in PyTorch, you can safely remove this argument.",
             take_from=kwargs,
         )

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -78,7 +78,7 @@ def __init__(
     ):
         deprecate(
             "tensor_format",
-            "0.5.0",
+            "0.6.0",
             "If you're running your code in PyTorch, you can safely remove this argument.",
             take_from=kwargs,
         )
@@ -217,7 +217,7 @@ def step(
         ):
             deprecate(
                 "timestep as an index",
-                "0.5.0",
+                "0.7.0",
                 "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.",
@@ -263,7 +263,7 @@ def add_noise(
         if isinstance(timesteps, torch.IntTensor) or isinstance(timesteps, torch.LongTensor):
             deprecate(
                 "timesteps as indices",
-                "0.5.0",
+                "0.7.0",
                 "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

@@ -104,7 +104,7 @@ def __init__(
     ):
         deprecate(
             "tensor_format",
-            "0.5.0",
+            "0.6.0",
             "If you're running your code in PyTorch, you can safely remove this argument.",
             take_from=kwargs,
         )
@@ -159,7 +159,7 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic
                 the number of diffusion steps used when generating samples with a pre-trained model.
         """
         deprecated_offset = deprecate(
-            "offset", "0.5.0", "Please pass `steps_offset` to `__init__` instead.", take_from=kwargs
+            "offset", "0.7.0", "Please pass `steps_offset` to `__init__` instead.", take_from=kwargs
         )
         offset = deprecated_offset or self.config.steps_offset
 

src/diffusers/schedulers/scheduling_sde_ve.py

@@ -79,7 +79,7 @@ def __init__(
     ):
         deprecate(
             "tensor_format",
-            "0.5.0",
+            "0.6.0",
             "If you're running your code in PyTorch, you can safely remove this argument.",
             take_from=kwargs,
         )
@@ -156,18 +156,13 @@ def get_adjacent_sigma(self, timesteps, t):
             self.discrete_sigmas[timesteps - 1].to(timesteps.device),
         )
 
-    def set_seed(self, seed):
-        deprecate("set_seed", "0.5.0", "Please consider passing a generator instead.")
-        torch.manual_seed(seed)
-
     def step_pred(
         self,
         model_output: torch.FloatTensor,
         timestep: int,
         sample: torch.FloatTensor,
         generator: Optional[torch.Generator] = None,
         return_dict: bool = True,
-        **kwargs,
     ) -> Union[SdeVeOutput, Tuple]:
         """
         Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
@@ -186,9 +181,6 @@ def step_pred(
             `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
 
         """
-        if "seed" in kwargs and kwargs["seed"] is not None:
-            self.set_seed(kwargs["seed"])
-
         if self.timesteps is None:
             raise ValueError(
                 "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
@@ -231,7 +223,6 @@ def step_correct(
         sample: torch.FloatTensor,
         generator: Optional[torch.Generator] = None,
         return_dict: bool = True,
-        **kwargs,
     ) -> Union[SchedulerOutput, Tuple]:
         """
         Correct the predicted sample based on the output model_output of the network. This is often run repeatedly
@@ -249,9 +240,6 @@ def step_correct(
             `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
 
         """
-        if "seed" in kwargs and kwargs["seed"] is not None:
-            self.set_seed(kwargs["seed"])
-
         if self.timesteps is None:
             raise ValueError(
                 "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"

src/diffusers/schedulers/scheduling_sde_vp.py

@@ -43,7 +43,7 @@ class ScoreSdeVpScheduler(SchedulerMixin, ConfigMixin):
     def __init__(self, num_train_timesteps=2000, beta_min=0.1, beta_max=20, sampling_eps=1e-3, **kwargs):
         deprecate(
             "tensor_format",
-            "0.5.0",
+            "0.6.0",
             "If you're running your code in PyTorch, you can safely remove this argument.",
             take_from=kwargs,
         )

src/diffusers/schedulers/scheduling_utils.py

@@ -45,7 +45,7 @@ class SchedulerMixin:
     def set_format(self, tensor_format="pt"):
         deprecate(
             "set_format",
-            "0.5.0",
+            "0.6.0",
             "If you're running your code in PyTorch, you can safely remove this function as the schedulers are always"
             " in Pytorch",
         )

src/diffusers/schedulers/scheduling_utils_flax.py

@@ -11,7 +11,6 @@
 # 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 jax.numpy as jnp
@@ -42,12 +41,3 @@ class FlaxSchedulerMixin:
     """
 
     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,
-        )
-        return self