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01fdb75 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | import math
import torch
from src.diffusion.base.scheduling import *
class LinearScheduler(BaseScheduler):
def alpha(self, t) -> Tensor:
return (t).view(-1, 1, 1, 1)
def sigma(self, t) -> Tensor:
return (1-t).view(-1, 1, 1, 1)
def dalpha(self, t) -> Tensor:
return torch.full_like(t, 1.0).view(-1, 1, 1, 1)
def dsigma(self, t) -> Tensor:
return torch.full_like(t, -1.0).view(-1, 1, 1, 1)
# SoTA for ImageNet!
class GVPScheduler(BaseScheduler):
def alpha(self, t) -> Tensor:
return torch.cos(t * (math.pi / 2)).view(-1, 1, 1, 1)
def sigma(self, t) -> Tensor:
return torch.sin(t * (math.pi / 2)).view(-1, 1, 1, 1)
def dalpha(self, t) -> Tensor:
return -torch.sin(t * (math.pi / 2)).view(-1, 1, 1, 1)
def dsigma(self, t) -> Tensor:
return torch.cos(t * (math.pi / 2)).view(-1, 1, 1, 1)
def w(self, t):
return torch.sin(t)**2
class ConstScheduler(BaseScheduler):
def w(self, t):
return torch.ones(1, 1, 1, 1).to(t.device, t.dtype)
class GammaScheduler(BaseScheduler):
def __init__(self, gamma=0.3):
self.gamma = gamma
def w(self, t):
return torch.full((1, 1, 1, 1), self.gamma).to(t.device, t.dtype)
from src.diffusion.ddpm.scheduling import VPScheduler
class VPBetaScheduler(VPScheduler):
def w(self, t):
return self.beta(t).view(-1, 1, 1, 1)
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