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Running
on
Zero
File size: 1,211 Bytes
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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)
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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