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import math
from collections.abc import Callable
import torch
from torch import Tensor
def get_noise(num_samples: int, height: int, width: int, device: torch.device, dtype: torch.dtype, seed: int):
return torch.randn(
num_samples,
16,
# allow for packing
2 * math.ceil(height / 16),
2 * math.ceil(width / 16),
device=device,
dtype=dtype,
generator=torch.Generator(device=device).manual_seed(seed),
)
def time_shift(mu: float, sigma: float, t: Tensor):
return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
def get_lin_function(x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15) -> Callable[[float], float]:
m = (y2 - y1) / (x2 - x1)
b = y1 - m * x1
return lambda x: m * x + b
def get_schedule(
num_steps: int,
image_seq_len: int,
base_shift: float = 0.5,
max_shift: float = 1.15,
shift: bool = True,
) -> list[float]:
# extra step for zero
timesteps = torch.linspace(1, 0, num_steps + 1)
# shifting the schedule to favor high timesteps for higher signal images
if shift:
# estimate mu based on linear estimation between two points
mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
timesteps = time_shift(mu, 1.0, timesteps)
return timesteps.tolist()
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