Bump to 0.6.0.dev0 (#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 <patrick.v.platen@gmail.com>

* Update src/diffusers/schedulers/scheduling_ddim.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/schedulers/scheduling_karras_ve.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/schedulers/scheduling_lms_discrete.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/schedulers/scheduling_pndm.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/schedulers/scheduling_sde_ve.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/schedulers/scheduling_sde_vp.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

setup.py

@@ -211,7 +211,7 @@ install_requires = [
 
 setup(
     name="diffusers",
-    version="0.5.1",  # 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="0.6.0.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__ = "0.5.1"
+__version__ = "0.6.0.dev0"
 
 from .configuration_utils import ConfigMixin
 from .onnx_utils import OnnxRuntimeModel

src/diffusers/schedulers/scheduling_ddim.py

@@ -119,15 +119,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
         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":

src/diffusers/schedulers/scheduling_ddpm.py

@@ -22,7 +22,7 @@ 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
 
 
@@ -112,15 +112,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         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":

src/diffusers/schedulers/scheduling_karras_ve.py

@@ -20,7 +20,7 @@ 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
 
 
@@ -86,15 +86,7 @@ class KarrasVeScheduler(SchedulerMixin, ConfigMixin):
         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
 

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -74,15 +74,7 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
         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":

src/diffusers/schedulers/scheduling_pndm.py

@@ -100,15 +100,7 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
         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":

src/diffusers/schedulers/scheduling_sde_ve.py

@@ -21,7 +21,7 @@ from typing import Optional, Tuple, Union
 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 @@ class ScoreSdeVeScheduler(SchedulerMixin, ConfigMixin):
         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
 

src/diffusers/schedulers/scheduling_sde_vp.py

@@ -20,7 +20,6 @@ from typing import Union
 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

src/diffusers/schedulers/scheduling_utils.py

@@ -15,7 +15,7 @@ from dataclasses import dataclass
 
 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

src/diffusers/utils/outputs.py

@@ -21,7 +21,6 @@ from typing import Any, Tuple
 
 import numpy as np
 
-from .deprecation_utils import deprecate
 from .import_utils import is_torch_available
 
 
@@ -86,9 +85,6 @@ class BaseOutput(OrderedDict):
     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]

tests/test_pipelines.py

@@ -318,14 +318,14 @@ class PipelineFastTests(unittest.TestCase):
         # Warmup pass when using mps (see #372)
         if torch_device == "mps":
             generator = torch.manual_seed(0)
-            _ = ldm([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=1, output_type="numpy")[
-                "sample"
-            ]
+            _ = ldm(
+                [prompt], generator=generator, guidance_scale=6.0, num_inference_steps=1, output_type="numpy"
+            ).images
 
         generator = torch.manual_seed(0)
-        image = ldm([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="numpy")[
-            "sample"
-        ]
+        image = ldm(
+            [prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="numpy"
+        ).images
 
         generator = torch.manual_seed(0)
         image_from_tuple = ldm(
@@ -1535,9 +1535,9 @@ class PipelineTesterMixin(unittest.TestCase):
 
         prompt = "A painting of a squirrel eating a burger"
         generator = torch.manual_seed(0)
-        image = ldm([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="numpy")[
-            "sample"
-        ]
+        image = ldm(
+            [prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="numpy"
+        ).images
 
         image_slice = image[0, -3:, -3:, -1]