Bump to 0.6.0.dev0 (huggingface#831)

* Bump to 0.6.0.dev0

* Deprecate tensor_format and .samples

* style

* upd

* upd

* style

* sample -> images

* Update src/diffusers/schedulers/scheduling_ddpm.py

Co-authored-by: Patrick von Platen <[email protected]>

* Update src/diffusers/schedulers/scheduling_ddim.py

Co-authored-by: Patrick von Platen <[email protected]>

* Update src/diffusers/schedulers/scheduling_karras_ve.py

Co-authored-by: Patrick von Platen <[email protected]>

* Update src/diffusers/schedulers/scheduling_lms_discrete.py

Co-authored-by: Patrick von Platen <[email protected]>

* Update src/diffusers/schedulers/scheduling_pndm.py

Co-authored-by: Patrick von Platen <[email protected]>

* Update src/diffusers/schedulers/scheduling_sde_ve.py

Co-authored-by: Patrick von Platen <[email protected]>

* Update src/diffusers/schedulers/scheduling_sde_vp.py

Co-authored-by: Patrick von Platen <[email protected]>

Co-authored-by: Patrick von Platen <[email protected]>

Author: anton-l

__init__.py

@@ -9,7 +9,7 @@
 )
 
 
-__version__ = "0.5.1"
+__version__ = "0.6.0.dev0"
 
 from .configuration_utils import ConfigMixin
 from .onnx_utils import OnnxRuntimeModel

schedulers/scheduling_ddim.py

@@ -119,15 +119,7 @@ def __init__(
         clip_sample: bool = True,
         set_alpha_to_one: bool = True,
         steps_offset: int = 0,
-        **kwargs,
     ):
-        deprecate(
-            "tensor_format",
-            "0.6.0",
-            "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)
         elif beta_schedule == "linear":

schedulers/scheduling_ddpm.py

@@ -22,7 +22,7 @@
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput, deprecate
+from ..utils import BaseOutput
 from .scheduling_utils import SchedulerMixin
 
 
@@ -112,15 +112,7 @@ def __init__(
         trained_betas: Optional[np.ndarray] = None,
         variance_type: str = "fixed_small",
         clip_sample: bool = True,
-        **kwargs,
     ):
-        deprecate(
-            "tensor_format",
-            "0.6.0",
-            "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)
         elif beta_schedule == "linear":

schedulers/scheduling_karras_ve.py

@@ -20,7 +20,7 @@
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput, deprecate
+from ..utils import BaseOutput
 from .scheduling_utils import SchedulerMixin
 
 
@@ -86,15 +86,7 @@ def __init__(
         s_churn: float = 80,
         s_min: float = 0.05,
         s_max: float = 50,
-        **kwargs,
     ):
-        deprecate(
-            "tensor_format",
-            "0.6.0",
-            "If you're running your code in PyTorch, you can safely remove this argument.",
-            take_from=kwargs,
-        )
-
         # standard deviation of the initial noise distribution
         self.init_noise_sigma = sigma_max
 

schedulers/scheduling_lms_discrete.py

@@ -74,15 +74,7 @@ def __init__(
         beta_end: float = 0.02,
         beta_schedule: str = "linear",
         trained_betas: Optional[np.ndarray] = None,
-        **kwargs,
     ):
-        deprecate(
-            "tensor_format",
-            "0.6.0",
-            "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)
         elif beta_schedule == "linear":

schedulers/scheduling_pndm.py

@@ -100,15 +100,7 @@ def __init__(
         skip_prk_steps: bool = False,
         set_alpha_to_one: bool = False,
         steps_offset: int = 0,
-        **kwargs,
     ):
-        deprecate(
-            "tensor_format",
-            "0.6.0",
-            "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)
         elif beta_schedule == "linear":

schedulers/scheduling_sde_ve.py

@@ -21,7 +21,7 @@
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput, deprecate
+from ..utils import BaseOutput
 from .scheduling_utils import SchedulerMixin, SchedulerOutput
 
 
@@ -75,15 +75,7 @@ def __init__(
         sigma_max: float = 1348.0,
         sampling_eps: float = 1e-5,
         correct_steps: int = 1,
-        **kwargs,
     ):
-        deprecate(
-            "tensor_format",
-            "0.6.0",
-            "If you're running your code in PyTorch, you can safely remove this argument.",
-            take_from=kwargs,
-        )
-
         # standard deviation of the initial noise distribution
         self.init_noise_sigma = sigma_max
 

schedulers/scheduling_sde_vp.py

@@ -20,7 +20,6 @@
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import deprecate
 from .scheduling_utils import SchedulerMixin
 
 
@@ -40,13 +39,7 @@ 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):
-        deprecate(
-            "tensor_format",
-            "0.6.0",
-            "If you're running your code in PyTorch, you can safely remove this argument.",
-            take_from=kwargs,
-        )
+    def __init__(self, num_train_timesteps=2000, beta_min=0.1, beta_max=20, sampling_eps=1e-3):
         self.sigmas = None
         self.discrete_sigmas = None
         self.timesteps = None

schedulers/scheduling_utils.py

@@ -15,7 +15,7 @@
 
 import torch
 
-from ..utils import BaseOutput, deprecate
+from ..utils import BaseOutput
 
 
 SCHEDULER_CONFIG_NAME = "scheduler_config.json"
@@ -41,12 +41,3 @@ class SchedulerMixin:
     """
 
     config_name = SCHEDULER_CONFIG_NAME
-
-    def set_format(self, tensor_format="pt"):
-        deprecate(
-            "set_format",
-            "0.6.0",
-            "If you're running your code in PyTorch, you can safely remove this function as the schedulers are always"
-            " in Pytorch",
-        )
-        return self

utils/outputs.py

@@ -21,7 +21,6 @@
 
 import numpy as np
 
-from .deprecation_utils import deprecate
 from .import_utils import is_torch_available
 
 
@@ -86,9 +85,6 @@ def update(self, *args, **kwargs):
     def __getitem__(self, k):
         if isinstance(k, str):
             inner_dict = {k: v for (k, v) in self.items()}
-            if self.__class__.__name__ in ["StableDiffusionPipelineOutput", "ImagePipelineOutput"] and k == "sample":
-                deprecate("samples", "0.6.0", "Please use `.images` or `'images'` instead.")
-                return inner_dict["images"]
             return inner_dict[k]
         else:
             return self.to_tuple()[k]