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Unverified Commit b671cb09 authored by Pedro Cuenca's avatar Pedro Cuenca Committed by GitHub
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Remove deprecated `torch_device` kwarg (#623) 

* Remove deprecated `torch_device` kwarg.

* Remove unused imports.
parent bb0c5d15
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Showing with 1 addition and 102 deletions
src/diffusers/pipelines/ddim/pipeline_ddim.py
View file @ b671cb09
......@@ -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
View file @ b671cb09
......@@ -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
View file @ b671cb09
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
View file @ b671cb09
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
View file @ b671cb09
......@@ -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
View file @ b671cb09
#!/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
View file @ b671cb09
......@@ -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
View file @ b671cb09
#!/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)
......
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