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OpenDAS
diffusers
Commits
24575991
Commit
24575991
authored
Oct 02, 2023
by
Patrick von Platen
Browse files
make fix copies
parent
bdd16116
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src/diffusers/schedulers/scheduling_unipc_multistep.py
src/diffusers/schedulers/scheduling_unipc_multistep.py
+3
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src/diffusers/schedulers/scheduling_unipc_multistep.py
View file @
24575991
...
@@ -282,13 +282,13 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
...
@@ -282,13 +282,13 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
https://arxiv.org/abs/2205.11487
https://arxiv.org/abs/2205.11487
"""
"""
dtype
=
sample
.
dtype
dtype
=
sample
.
dtype
batch_size
,
channels
,
*
remaining_dims
=
sample
.
shape
batch_size
,
channels
,
height
,
width
=
sample
.
shape
if
dtype
not
in
(
torch
.
float32
,
torch
.
float64
):
if
dtype
not
in
(
torch
.
float32
,
torch
.
float64
):
sample
=
sample
.
float
()
# upcast for quantile calculation, and clamp not implemented for cpu half
sample
=
sample
.
float
()
# upcast for quantile calculation, and clamp not implemented for cpu half
# Flatten sample for doing quantile calculation along each image
# Flatten sample for doing quantile calculation along each image
sample
=
sample
.
reshape
(
batch_size
,
channels
*
np
.
prod
(
remaining_dims
)
)
sample
=
sample
.
reshape
(
batch_size
,
channels
*
height
*
width
)
abs_sample
=
sample
.
abs
()
# "a certain percentile absolute pixel value"
abs_sample
=
sample
.
abs
()
# "a certain percentile absolute pixel value"
...
@@ -300,7 +300,7 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
...
@@ -300,7 +300,7 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
s
=
s
.
unsqueeze
(
1
)
# (batch_size, 1) because clamp will broadcast along dim=0
s
=
s
.
unsqueeze
(
1
)
# (batch_size, 1) because clamp will broadcast along dim=0
sample
=
torch
.
clamp
(
sample
,
-
s
,
s
)
/
s
# "we threshold xt0 to the range [-s, s] and then divide by s"
sample
=
torch
.
clamp
(
sample
,
-
s
,
s
)
/
s
# "we threshold xt0 to the range [-s, s] and then divide by s"
sample
=
sample
.
reshape
(
batch_size
,
channels
,
*
remaining_dims
)
sample
=
sample
.
reshape
(
batch_size
,
channels
,
height
,
width
)
sample
=
sample
.
to
(
dtype
)
sample
=
sample
.
to
(
dtype
)
return
sample
return
sample
...
...
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