8000 [Type hint] PNDM schedulers by daspartho · Pull Request #335 · huggingface/diffusers · GitHub
[go: up one dir, main page]
Skip to content

[Type hint] PNDM schedulers #335

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 6 commits into from
Sep 4, 2022
Merged

[Type hint] PNDM schedulers #335

Changes from all commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
7d1f48f
[Type hint] PNDM Schedulers
daspartho Sep 2, 2022
58f6b95
ran make style
daspartho Sep 2, 2022
71a373c
updated timesteps type hint
daspartho Sep 3, 2022
de51dcb
apply suggestions from code review
daspartho Sep 4, 2022
e9c24d5
ran make style
daspartho Sep 4, 2022
50a6d74
removed unused import
daspartho Sep 4, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to 8000
Jump to file
Failed to load files.
Loading
Diff view
Diff view
22 changes: 14 additions & 8 deletions src/diffusers/schedulers/scheduling_pndm.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -51,12 +51,12 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
@register_to_config
def __init__(
self,
num_train_timesteps=1000,
beta_start=0.0001,
beta_end=0.02,
beta_schedule="linear",
tensor_format="pt",
skip_prk_steps=False,
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
beta_schedule: str = "linear",
tensor_format: str = "pt",
skip_prk_steps: bool = False,
):

if beta_schedule == "linear":
Expand Down Expand Up @@ -97,7 +97,7 @@ def __init__(
self.tensor_format = tensor_format
self.set_format(tensor_format=tensor_format)

def set_timesteps(self, num_inference_steps, offset=0):
def set_timesteps(self, num_inference_steps: int, offset: int = 0) -> torch.FloatTensor:
self.num_inference_steps = num_inference_steps
self._timesteps = list(
range(0, self.config.num_train_timesteps, self.config.num_train_timesteps // num_inference_steps)
Expand Down Expand Up @@ -264,7 +264,13 @@ def _get_prev_sample(self, sample, timestep, timestep_prev, model_output):

return prev_sample

def add_noise(self, original_samples, noise, timesteps):
def add_noise(
self,
original_samples: Union[torch.FloatTensor, np.ndarray],
noise: Union[torch.FloatTensor, np.ndarray],
timesteps: Union[torch.IntTensor, np.ndarray],
) -> torch.Tensor:

sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
sqrt_alpha_prod = self.match_shape(sqrt_alpha_prod, original_samples)
sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
Expand Down
0