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fix loose comments

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This commit is contained in:
Nathan Lambert
2022-11-17 14:58:52 -08:00
parent e701a97838
commit 172b242c2a
2 changed files with 6 additions and 10 deletions
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10
src/diffusers/schedulers/scheduling_ddim.py
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@@ -30,7 +30,7 @@ from .scheduling_utils import SchedulerMixin
def expand_to_shape(input, timesteps, shape, device):
"""
Helper indexes a 1D tensor `input` using a 1D index tensor `timesteps`, then reshapes the result to broadcast
nicely with `shape`. Useful for parellizing operations over `shape[0]` number of diffusion steps at once.
nicely with `shape`. Useful for parallelizing operations over `shape[0]` number of diffusion steps at once.
"""
out = torch.gather(input.to(device), 0, timesteps.to(device))
reshape = [shape[0]] + [1] * (len(shape) - 1)
@@ -131,7 +131,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
stable diffusion.
prediction_type (`Literal["epsilon", "sample", "velocity"]`, optional):
prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
process), `sample` (directly predicting the noisy sample`) or `v` (see section 2.4
process), `sample` (directly predicting the noisy sample`) or `velocity` (see section 2.4
https://imagen.research.google/video/paper.pdf)
"""
@@ -265,10 +265,6 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor`):
current instance of sample being created by diffusion process.
prediction_type (`str`):
prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
process), `sample` (directly predicting the noisy sample), or `v` (see section 2.4
https://imagen.research.google/video/paper.pdf)
eta (`float`): weight of noise for added noise in diffusion step.
use_clipped_model_output (`bool`): if `True`, compute "corrected" `model_output` from the clipped
predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] when
@@ -326,7 +322,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
eps = model_output * self.alphas[timestep] + sample * self.sigmas[timestep]
else:
raise ValueError(
f"prediction_type given as {self.prediction_type} must be one of `epsilon`, `sample`, or `v`"
f"prediction_type given as {self.prediction_type} must be one of `epsilon`, `sample`, or `velocity`"
)
# 4. Clip "predicted x_0"

6
src/diffusers/schedulers/scheduling_ddpm.py
View File

@@ -29,7 +29,7 @@ from .scheduling_utils import SchedulerMixin
def expand_to_shape(input, timesteps, shape, device):
"""
Helper indexes a 1D tensor `input` using a 1D index tensor `timesteps`, then reshapes the result to broadcast
nicely with `shape`. Useful for parellizing operations over `shape[0]` number of diffusion steps at once.
nicely with `shape`. Useful for parallelizing operations over `shape[0]` number of diffusion steps at once.
"""
out = torch.gather(input.to(device), 0, timesteps.to(device))
reshape = [shape[0]] + [1] * (len(shape) - 1)
@@ -112,7 +112,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
option to clip predicted sample between -1 and 1 for numerical stability.
prediction_type (`Literal["epsilon", "sample", "velocity"]`, optional):
prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
process), `sample` (directly predicting the noisy sample`) or `v` (see section 2.4
process), `sample` (directly predicting the noisy sample`) or `velocity` (see section 2.4
https://imagen.research.google/video/paper.pdf)
predict_epsilon (`bool`, default `True`):
depreciated flag (removing v0.10.0) for epsilon vs. direct sample prediction.
@@ -300,7 +300,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
else:
raise ValueError(
f"prediction_type given as {self.prediction_type} must be one of `epsilon`, `sample`, or `v`"
f"prediction_type given as {self.prediction_type} must be one of `epsilon`, `sample`, or `velocity`"
)
# 3. Clip "predicted x_0"