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renzhc
diffusers_dcu
Commits
a14d774b
Commit
a14d774b
authored
Jun 10, 2022
by
Patrick von Platen
Browse files
fix readme again
parent
d90a7367
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README.md
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a14d774b
...
@@ -23,7 +23,7 @@
...
@@ -23,7 +23,7 @@
```
```
git clone https://github.com/huggingface/diffusers.git
git clone https://github.com/huggingface/diffusers.git
cd diffusers && pip install -e .
cd diffusers && pip install -e .
``
``
`
### 1. `diffusers` as a central modular diffusion and sampler library
### 1. `diffusers` as a central modular diffusion and sampler library
...
@@ -55,7 +55,7 @@ num_prediction_steps = len(noise_scheduler)
...
@@ -55,7 +55,7 @@ num_prediction_steps = len(noise_scheduler)
for
t
in
tqdm
.
tqdm
(
reversed
(
range
(
num_prediction_steps
)),
total
=
num_prediction_steps
):
for
t
in
tqdm
.
tqdm
(
reversed
(
range
(
num_prediction_steps
)),
total
=
num_prediction_steps
):
# predict noise residual
# predict noise residual
with
torch
.
no_grad
():
with
torch
.
no_grad
():
residual =
self.
unet(image, t)
residual
=
unet
(
image
,
t
)
# predict previous mean of image x_t-1
# predict previous mean of image x_t-1
pred_prev_image
=
noise_scheduler
.
compute_prev_image_step
(
residual
,
image
,
t
)
pred_prev_image
=
noise_scheduler
.
compute_prev_image_step
(
residual
,
image
,
t
)
...
@@ -105,7 +105,7 @@ eta = 0.0 # <- deterministic sampling
...
@@ -105,7 +105,7 @@ eta = 0.0 # <- deterministic sampling
for
t
in
tqdm
.
tqdm
(
reversed
(
range
(
num_inference_steps
)),
total
=
num_inference_steps
):
for
t
in
tqdm
.
tqdm
(
reversed
(
range
(
num_inference_steps
)),
total
=
num_inference_steps
):
# 1. predict noise residual
# 1. predict noise residual
with
torch
.
no_grad
():
with
torch
.
no_grad
():
residual =
self.
unet(image, inference_step_times[t])
residual
=
unet
(
image
,
inference_step_times
[
t
])
# 2. predict previous mean of image x_t-1
# 2. predict previous mean of image x_t-1
pred_prev_image
=
noise_scheduler
.
compute_prev_image_step
(
residual
,
image
,
t
,
num_inference_steps
,
eta
)
pred_prev_image
=
noise_scheduler
.
compute_prev_image_step
(
residual
,
image
,
t
,
num_inference_steps
,
eta
)
...
...
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