Remove deprecated torch_device kwarg (#623)

* Remove deprecated `torch_device` kwarg.

* Remove unused imports.

src/diffusers/pipelines/ddim/pipeline_ddim.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 
-import warnings
 from typing import Optional, Tuple, Union
 
 import torch
@@ -74,20 +73,6 @@ class DDIMPipeline(DiffusionPipeline):
             generated images.
         """
 
-        if "torch_device" in kwargs:
-            device = kwargs.pop("torch_device")
-            warnings.warn(
-                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
-                " Consider using `pipe.to(torch_device)` instead."
-            )
-
-            # Set device as before (to be removed in 0.3.0)
-            if device is None:
-                device = "cuda" if torch.cuda.is_available() else "cpu"
-            self.to(device)
-
-        # eta corresponds to η in paper and should be between [0, 1]
-
         # Sample gaussian noise to begin loop
         image = torch.randn(
             (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
@@ -103,6 +88,7 @@ class DDIMPipeline(DiffusionPipeline):
             model_output = self.unet(image, t).sample
 
             # 2. predict previous mean of image x_t-1 and add variance depending on eta
+            # eta corresponds to η in paper and should be between [0, 1]
             # do x_t -> x_t-1
             image = self.scheduler.step(model_output, t, image, eta).prev_sample
 

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 
-import warnings
 from typing import Optional, Tuple, Union
 
 import torch
@@ -66,17 +65,6 @@ class DDPMPipeline(DiffusionPipeline):
             `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
             generated images.
         """
-        if "torch_device" in kwargs:
-            device = kwargs.pop("torch_device")
-            warnings.warn(
-                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
-                " Consider using `pipe.to(torch_device)` instead."
-            )
-
-            # Set device as before (to be removed in 0.3.0)
-            if device is None:
-                device = "cuda" if torch.cuda.is_available() else "cpu"
-            self.to(device)
 
         # Sample gaussian noise to begin loop
         image = torch.randn(

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py

@@ -1,5 +1,4 @@
 import inspect
-import warnings
 from typing import List, Optional, Tuple, Union
 
 import torch
@@ -94,17 +93,6 @@ class LDMTextToImagePipeline(DiffusionPipeline):
             `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
             generated images.
         """
-        if "torch_device" in kwargs:
-            device = kwargs.pop("torch_device")
-            warnings.warn(
-                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
-                " Consider using `pipe.to(torch_device)` instead."
-            )
-
-            # Set device as before (to be removed in 0.3.0)
-            if device is None:
-                device = "cuda" if torch.cuda.is_available() else "cpu"
-            self.to(device)
 
         if isinstance(prompt, str):
             batch_size = 1

src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py

@@ -1,5 +1,4 @@
 import inspect
-import warnings
 from typing import Optional, Tuple, Union
 
 import torch
@@ -60,18 +59,6 @@ class LDMPipeline(DiffusionPipeline):
             generated images.
         """
 
-        if "torch_device" in kwargs:
-            device = kwargs.pop("torch_device")
-            warnings.warn(
-                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
-                " Consider using `pipe.to(torch_device)` instead."
-            )
-
-            # Set device as before (to be removed in 0.3.0)
-            if device is None:
-                device = "cuda" if torch.cuda.is_available() else "cpu"
-            self.to(device)
-
         latents = torch.randn(
             (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
             generator=generator,

src/diffusers/pipelines/pndm/pipeline_pndm.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 
-import warnings
 from typing import Optional, Tuple, Union
 
 import torch
@@ -75,18 +74,6 @@ class PNDMPipeline(DiffusionPipeline):
         # For more information on the sampling method you can take a look at Algorithm 2 of
         # the official paper: https://arxiv.org/pdf/2202.09778.pdf
 
-        if "torch_device" in kwargs:
-            device = kwargs.pop("torch_device")
-            warnings.warn(
-                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
-                " Consider using `pipe.to(torch_device)` instead."
-            )
-
-            # Set device as before (to be removed in 0.3.0)
-            if device is None:
-                device = "cuda" if torch.cuda.is_available() else "cpu"
-            self.to(device)
-
         # Sample gaussian noise to begin loop
         image = torch.randn(
             (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),

src/diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-import warnings
 from typing import Optional, Tuple, Union
 
 import torch
@@ -53,18 +52,6 @@ class ScoreSdeVePipeline(DiffusionPipeline):
             generated images.
         """
 
-        if "torch_device" in kwargs:
-            device = kwargs.pop("torch_device")
-            warnings.warn(
-                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
-                " Consider using `pipe.to(torch_device)` instead."
-            )
-
-            # Set device as before (to be removed in 0.3.0)
-            if device is None:
-                device = "cuda" if torch.cuda.is_available() else "cpu"
-            self.to(device)
-
         img_size = self.unet.config.sample_size
         shape = (batch_size, 3, img_size, img_size)
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -169,18 +169,6 @@ class StableDiffusionPipeline(DiffusionPipeline):
             (nsfw) content, according to the `safety_checker`.
         """
 
-        if "torch_device" in kwargs:
-            device = kwargs.pop("torch_device")
-            warnings.warn(
-                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
-                " Consider using `pipe.to(torch_device)` instead."
-            )
-
-            # Set device as before (to be removed in 0.3.0)
-            if device is None:
-                device = "cuda" if torch.cuda.is_available() else "cpu"
-            self.to(device)
-
         if isinstance(prompt, str):
             batch_size = 1
         elif isinstance(prompt, list):

src/diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-import warnings
 from typing import Optional, Tuple, Union
 
 import torch
@@ -64,17 +63,6 @@ class KarrasVePipeline(DiffusionPipeline):
             `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
             generated images.
         """
-        if "torch_device" in kwargs:
-            device = kwargs.pop("torch_device")
-            warnings.warn(
-                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
-                " Consider using `pipe.to(torch_device)` instead."
-            )
-
-            # Set device as before (to be removed in 0.3.0)
-            if device is None:
-                device = "cuda" if torch.cuda.is_available() else "cpu"
-            self.to(device)
 
         img_size = self.unet.config.sample_size
         shape = (batch_size, 3, img_size, img_size)