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[Schedulers] Add beta sigmas / beta noise schedule (#9509)

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Add beta sigmas / beta noise schedule
This commit is contained in:
hlky
2024-09-26 00:30:32 +01:00
committed by GitHub
parent aa3c46d99a
commit 1c6ede9371
1 changed files with 52 additions and 3 deletions
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55
src/diffusers/schedulers/scheduling_euler_discrete.py
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@@ -20,11 +20,14 @@ import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging
from ..utils import BaseOutput, is_scipy_available, logging
from ..utils.torch_utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
if is_scipy_available():
import scipy.stats
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@@ -160,6 +163,9 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
the sigmas are determined according to a sequence of noise levels {σi}.
use_exponential_sigmas (`bool`, *optional*, defaults to `False`):
Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process.
use_beta_sigmas (`bool`, *optional*, defaults to `False`):
Whether to use beta sigmas for step sizes in the noise schedule during the sampling process. Refer to [Beta
Sampling is All You Need](https://huggingface.co/papers/2407.12173) for more information.
timestep_spacing (`str`, defaults to `"linspace"`):
The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
@@ -189,6 +195,7 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
interpolation_type: str = "linear",
use_karras_sigmas: Optional[bool] = False,
use_exponential_sigmas: Optional[bool] = False,
use_beta_sigmas: Optional[bool] = False,
sigma_min: Optional[float] = None,
sigma_max: Optional[float] = None,
timestep_spacing: str = "linspace",
@@ -197,8 +204,12 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
rescale_betas_zero_snr: bool = False,
final_sigmas_type: str = "zero", # can be "zero" or "sigma_min"
):
if sum([self.config.use_exponential_sigmas, self.config.use_karras_sigmas]) > 1:
raise ValueError("Only one of `config.use_exponential_sigmas`, `config.use_karras_sigmas` can be used.")
if self.config.use_beta_sigmas and not is_scipy_available():
raise ImportError("Make sure to install scipy if you want to use beta sigmas.")
if sum([self.config.use_beta_sigmas, self.config.use_exponential_sigmas, self.config.use_karras_sigmas]) > 1:
raise ValueError(
"Only one of `config.use_beta_sigmas`, `config.use_exponential_sigmas`, `config.use_karras_sigmas` can be used."
)
if trained_betas is not None:
self.betas = torch.tensor(trained_betas, dtype=torch.float32)
elif beta_schedule == "linear":
@@ -241,6 +252,7 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
self.is_scale_input_called = False
self.use_karras_sigmas = use_karras_sigmas
self.use_exponential_sigmas = use_exponential_sigmas
self.use_beta_sigmas = use_beta_sigmas
self._step_index = None
self._begin_index = None
@@ -340,6 +352,8 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
raise ValueError("Cannot set `timesteps` with `config.use_karras_sigmas = True`.")
if timesteps is not None and self.config.use_exponential_sigmas:
raise ValueError("Cannot set `timesteps` with `config.use_exponential_sigmas = True`.")
if timesteps is not None and self.config.use_beta_sigmas:
raise ValueError("Cannot set `timesteps` with `config.use_beta_sigmas = True`.")
if (
timesteps is not None
and self.config.timestep_type == "continuous"
@@ -408,6 +422,10 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
sigmas = self._convert_to_exponential(in_sigmas=sigmas, num_inference_steps=self.num_inference_steps)
timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
elif self.config.use_beta_sigmas:
sigmas = self._convert_to_beta(in_sigmas=sigmas, num_inference_steps=self.num_inference_steps)
timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
if self.config.final_sigmas_type == "sigma_min":
sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5
elif self.config.final_sigmas_type == "zero":
@@ -502,6 +520,37 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), num_inference_steps).exp()
return sigmas
def _convert_to_beta(
self, in_sigmas: torch.Tensor, num_inference_steps: int, alpha: float = 0.6, beta: float = 0.6
) -> torch.Tensor:
"""From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024)"""
# Hack to make sure that other schedulers which copy this function don't break
# TODO: Add this logic to the other schedulers
if hasattr(self.config, "sigma_min"):
sigma_min = self.config.sigma_min
else:
sigma_min = None
if hasattr(self.config, "sigma_max"):
sigma_max = self.config.sigma_max
else:
sigma_max = None
sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()
sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()
sigmas = torch.Tensor(
[
sigma_min + (ppf * (sigma_max - sigma_min))
for ppf in [
scipy.stats.beta.ppf(timestep, alpha, beta)
for timestep in 1 - np.linspace(0, 1, num_inference_steps)
]
]
)
return sigmas
def index_for_timestep(self, timestep, schedule_timesteps=None):
if schedule_timesteps is None:
schedule_timesteps = self.timesteps