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Improve docstrings and type hints in scheduling_ddim_cogvideox.py (#12992)

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docs: improve docstring scheduling_ddim_cogvideox.py
This commit is contained in:
David El Malih
2026-01-20 21:33:50 +01:00
committed by GitHub
parent 4b843c8430
commit ec37629371
26 changed files with 162 additions and 103 deletions
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6
src/diffusers/schedulers/scheduling_consistency_decoder.py
View File

@@ -14,7 +14,7 @@ from .scheduling_utils import SchedulerMixin
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -28,8 +28,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -51,7 +51,7 @@ class DDIMSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -65,8 +65,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

53
src/diffusers/schedulers/scheduling_ddim_cogvideox.py
View File

@@ -51,7 +51,7 @@ class DDIMSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -65,8 +65,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:
@@ -100,14 +100,13 @@ def betas_for_alpha_bar(
return torch.tensor(betas, dtype=torch.float32)
def rescale_zero_terminal_snr(alphas_cumprod):
def rescale_zero_terminal_snr(alphas_cumprod: torch.Tensor) -> torch.Tensor:
"""
Rescales betas to have zero terminal SNR Based on https://huggingface.co/papers/2305.08891 (Algorithm 1)
Rescales betas to have zero terminal SNR Based on (Algorithm 1)[https://huggingface.co/papers/2305.08891]
Args:
betas (`torch.Tensor`):
the betas that the scheduler is being initialized with.
alphas_cumprod (`torch.Tensor`):
The alphas cumulative products that the scheduler is being initialized with.
Returns:
`torch.Tensor`: rescaled betas with zero terminal SNR
@@ -142,11 +141,11 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
Args:
num_train_timesteps (`int`, defaults to 1000):
The number of diffusion steps to train the model.
beta_start (`float`, defaults to 0.0001):
beta_start (`float`, defaults to 0.00085):
The starting `beta` value of inference.
beta_end (`float`, defaults to 0.02):
beta_end (`float`, defaults to 0.0120):
The final `beta` value.
beta_schedule (`str`, defaults to `"linear"`):
beta_schedule (`str`, defaults to `"scaled_linear"`):
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
`linear`, `scaled_linear`, or `squaredcos_cap_v2`.
trained_betas (`np.ndarray`, *optional*):
@@ -179,6 +178,8 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
dark samples instead of limiting it to samples with medium brightness. Loosely related to
[`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
snr_shift_scale (`float`, defaults to 3.0):
Shift scale for SNR.
"""
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
@@ -190,15 +191,15 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
num_train_timesteps: int = 1000,
beta_start: float = 0.00085,
beta_end: float = 0.0120,
beta_schedule: str = "scaled_linear",
beta_schedule: Literal["linear", "scaled_linear", "squaredcos_cap_v2"] = "scaled_linear",
trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
clip_sample: bool = True,
set_alpha_to_one: bool = True,
steps_offset: int = 0,
prediction_type: str = "epsilon",
prediction_type: Literal["epsilon", "sample", "v_prediction"] = "epsilon",
clip_sample_range: float = 1.0,
sample_max_value: float = 1.0,
timestep_spacing: str = "leading",
timestep_spacing: Literal["linspace", "leading", "trailing"] = "leading",
rescale_betas_zero_snr: bool = False,
snr_shift_scale: float = 3.0,
):
@@ -208,7 +209,15 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float64) ** 2
self.betas = (
torch.linspace(
beta_start**0.5,
beta_end**0.5,
num_train_timesteps,
dtype=torch.float64,
)
** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
self.betas = betas_for_alpha_bar(num_train_timesteps)
@@ -238,7 +247,7 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
self.num_inference_steps = None
self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))
def _get_variance(self, timestep, prev_timestep):
def _get_variance(self, timestep: int, prev_timestep: int) -> torch.Tensor:
alpha_prod_t = self.alphas_cumprod[timestep]
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
@@ -265,7 +274,11 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
"""
return sample
def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
def set_timesteps(
self,
num_inference_steps: int,
device: Optional[Union[str, torch.device]] = None,
) -> None:
"""
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
@@ -317,7 +330,7 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
sample: torch.Tensor,
eta: float = 0.0,
use_clipped_model_output: bool = False,
generator=None,
generator: Optional[torch.Generator] = None,
variance_noise: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[DDIMSchedulerOutput, Tuple]:
@@ -328,7 +341,7 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
Args:
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
timestep (`float`):
timestep (`int`):
The current discrete timestep in the diffusion chain.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
@@ -487,5 +500,5 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
def __len__(self):
def __len__(self) -> int:
return self.config.num_train_timesteps

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

@@ -49,7 +49,7 @@ class DDIMSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -63,8 +63,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -51,7 +51,7 @@ class DDIMParallelSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -65,8 +65,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

42
src/diffusers/schedulers/scheduling_ddpm.py
View File

@@ -48,7 +48,7 @@ class DDPMSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -62,8 +62,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:
@@ -192,7 +192,12 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
beta_schedule: Literal["linear", "scaled_linear", "squaredcos_cap_v2", "sigmoid"] = "linear",
trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
variance_type: Literal[
"fixed_small", "fixed_small_log", "fixed_large", "fixed_large_log", "learned", "learned_range"
"fixed_small",
"fixed_small_log",
"fixed_large",
"fixed_large_log",
"learned",
"learned_range",
] = "fixed_small",
clip_sample: bool = True,
prediction_type: Literal["epsilon", "sample", "v_prediction"] = "epsilon",
@@ -210,7 +215,15 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
self.betas = (
torch.linspace(
beta_start**0.5,
beta_end**0.5,
num_train_timesteps,
dtype=torch.float32,
)
** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
self.betas = betas_for_alpha_bar(num_train_timesteps)
@@ -337,7 +350,14 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
t: int,
predicted_variance: Optional[torch.Tensor] = None,
variance_type: Optional[
Literal["fixed_small", "fixed_small_log", "fixed_large", "fixed_large_log", "learned", "learned_range"]
Literal[
"fixed_small",
"fixed_small_log",
"fixed_large",
"fixed_large_log",
"learned",
"learned_range",
]
] = None,
) -> torch.Tensor:
"""
@@ -472,7 +492,10 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
prev_t = self.previous_timestep(t)
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in [
"learned",
"learned_range",
]:
model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)
else:
predicted_variance = None
@@ -521,7 +544,10 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
if t > 0:
device = model_output.device
variance_noise = randn_tensor(
model_output.shape, generator=generator, device=device, dtype=model_output.dtype
model_output.shape,
generator=generator,
device=device,
dtype=model_output.dtype,
)
if self.variance_type == "fixed_small_log":
variance = self._get_variance(t, predicted_variance=predicted_variance) * variance_noise

32
src/diffusers/schedulers/scheduling_ddpm_parallel.py
View File

@@ -50,7 +50,7 @@ class DDPMParallelSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -64,8 +64,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:
@@ -202,7 +202,12 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
beta_schedule: Literal["linear", "scaled_linear", "squaredcos_cap_v2", "sigmoid"] = "linear",
trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
variance_type: Literal[
"fixed_small", "fixed_small_log", "fixed_large", "fixed_large_log", "learned", "learned_range"
"fixed_small",
"fixed_small_log",
"fixed_large",
"fixed_large_log",
"learned",
"learned_range",
] = "fixed_small",
clip_sample: bool = True,
prediction_type: Literal["epsilon", "sample", "v_prediction"] = "epsilon",
@@ -220,7 +225,15 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
self.betas = (
torch.linspace(
beta_start**0.5,
beta_end**0.5,
num_train_timesteps,
dtype=torch.float32,
)
** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
self.betas = betas_for_alpha_bar(num_train_timesteps)
@@ -350,7 +363,14 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
t: int,
predicted_variance: Optional[torch.Tensor] = None,
variance_type: Optional[
Literal["fixed_small", "fixed_small_log", "fixed_large", "fixed_large_log", "learned", "learned_range"]
Literal[
"fixed_small",
"fixed_small_log",
"fixed_large",
"fixed_large_log",
"learned",
"learned_range",
]
] = None,
) -> torch.Tensor:
"""

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

@@ -34,7 +34,7 @@ if is_scipy_available():
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -48,8 +48,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -52,7 +52,7 @@ class DDIMSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -66,8 +66,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -34,7 +34,7 @@ if is_scipy_available():
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -48,8 +48,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -34,7 +34,7 @@ if is_scipy_available():
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -48,8 +48,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -117,7 +117,7 @@ class BrownianTreeNoiseSampler:
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -131,8 +131,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -36,7 +36,7 @@ logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -50,8 +50,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -51,7 +51,7 @@ class EulerAncestralDiscreteSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -65,8 +65,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -54,7 +54,7 @@ class EulerDiscreteSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -68,8 +68,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -51,7 +51,7 @@ class HeunDiscreteSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -65,8 +65,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

6
src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py
View File

@@ -52,7 +52,7 @@ class KDPM2AncestralDiscreteSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -66,8 +66,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

6
src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py
View File

@@ -51,7 +51,7 @@ class KDPM2DiscreteSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -65,8 +65,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -53,7 +53,7 @@ class LCMSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -67,8 +67,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -49,7 +49,7 @@ class LMSDiscreteSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -63,8 +63,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -28,7 +28,7 @@ from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, Schedul
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -42,8 +42,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -47,7 +47,7 @@ class RePaintSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -61,8 +61,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -35,7 +35,7 @@ if is_scipy_available():
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -49,8 +49,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -52,7 +52,7 @@ class TCDSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -66,8 +66,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -48,7 +48,7 @@ class UnCLIPSchedulerOutput(BaseOutput):
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -62,8 +62,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:

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

@@ -34,7 +34,7 @@ if is_scipy_available():
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "cosine",
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -48,8 +48,8 @@ def betas_for_alpha_bar(
The number of betas to produce.
max_beta (`float`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`"cosine"` or `"exp"`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine` or `exp`.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`: