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Zero
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import torch
import numpy as np
class BaseSchedule():
def __init__(self, *args, force_limits=True, discrete_steps=None, shift=1, **kwargs):
self.setup(*args, **kwargs)
self.limits = None
self.discrete_steps = discrete_steps
self.shift = shift
if force_limits:
self.reset_limits()
def reset_limits(self, shift=1, disable=False):
try:
self.limits = None if disable else self(torch.tensor([1.0, 0.0]), shift=shift).tolist() # min, max
return self.limits
except Exception:
print("WARNING: this schedule doesn't support t and will be unbounded")
return None
def setup(self, *args, **kwargs):
raise NotImplementedError("this method needs to be overriden")
def schedule(self, *args, **kwargs):
raise NotImplementedError("this method needs to be overriden")
def __call__(self, t, *args, shift=1, **kwargs):
if isinstance(t, torch.Tensor):
batch_size = None
if self.discrete_steps is not None:
if t.dtype != torch.long:
t = (t * (self.discrete_steps-1)).round().long()
t = t / (self.discrete_steps-1)
t = t.clamp(0, 1)
else:
batch_size = t
t = None
logSNR = self.schedule(t, batch_size, *args, **kwargs)
if shift*self.shift != 1:
logSNR += 2 * np.log(1/(shift*self.shift))
if self.limits is not None:
logSNR = logSNR.clamp(*self.limits)
return logSNR
class CosineSchedule(BaseSchedule):
def setup(self, s=0.008, clamp_range=[0.0001, 0.9999], norm_instead=False):
self.s = torch.tensor([s])
self.clamp_range = clamp_range
self.norm_instead = norm_instead
self.min_var = torch.cos(self.s / (1 + self.s) * torch.pi * 0.5) ** 2
def schedule(self, t, batch_size):
if t is None:
t = (1-torch.rand(batch_size)).add(0.001).clamp(0.001, 1.0)
s, min_var = self.s.to(t.device), self.min_var.to(t.device)
var = torch.cos((s + t)/(1+s) * torch.pi * 0.5).clamp(0, 1) ** 2 / min_var
if self.norm_instead:
var = var * (self.clamp_range[1]-self.clamp_range[0]) + self.clamp_range[0]
else:
var = var.clamp(*self.clamp_range)
logSNR = (var/(1-var)).log()
return logSNR
class CosineSchedule2(BaseSchedule):
def setup(self, logsnr_range=[-15, 15]):
self.t_min = np.arctan(np.exp(-0.5 * logsnr_range[1]))
self.t_max = np.arctan(np.exp(-0.5 * logsnr_range[0]))
def schedule(self, t, batch_size):
if t is None:
t = 1-torch.rand(batch_size)
return -2 * (self.t_min + t*(self.t_max-self.t_min)).tan().log()
class SqrtSchedule(BaseSchedule):
def setup(self, s=1e-4, clamp_range=[0.0001, 0.9999], norm_instead=False):
self.s = s
self.clamp_range = clamp_range
self.norm_instead = norm_instead
def schedule(self, t, batch_size):
if t is None:
t = 1-torch.rand(batch_size)
var = 1 - (t + self.s)**0.5
if self.norm_instead:
var = var * (self.clamp_range[1]-self.clamp_range[0]) + self.clamp_range[0]
else:
var = var.clamp(*self.clamp_range)
logSNR = (var/(1-var)).log()
return logSNR
class RectifiedFlowsSchedule(BaseSchedule):
def setup(self, logsnr_range=[-15, 15]):
self.logsnr_range = logsnr_range
def schedule(self, t, batch_size):
if t is None:
t = 1-torch.rand(batch_size)
logSNR = (((1-t)**2)/(t**2)).log()
logSNR = logSNR.clamp(*self.logsnr_range)
return logSNR
class EDMSampleSchedule(BaseSchedule):
def setup(self, sigma_range=[0.002, 80], p=7):
self.sigma_range = sigma_range
self.p = p
def schedule(self, t, batch_size):
if t is None:
t = 1-torch.rand(batch_size)
smin, smax, p = *self.sigma_range, self.p
sigma = (smax ** (1/p) + (1-t) * (smin ** (1/p) - smax ** (1/p))) ** p
logSNR = (1/sigma**2).log()
return logSNR
class EDMTrainSchedule(BaseSchedule):
def setup(self, mu=-1.2, std=1.2):
self.mu = mu
self.std = std
def schedule(self, t, batch_size):
if t is not None:
raise Exception("EDMTrainSchedule doesn't support passing timesteps: t")
logSNR = -2*(torch.randn(batch_size) * self.std - self.mu)
return logSNR
class LinearSchedule(BaseSchedule):
def setup(self, logsnr_range=[-10, 10]):
self.logsnr_range = logsnr_range
def schedule(self, t, batch_size):
if t is None:
t = 1-torch.rand(batch_size)
logSNR = t * (self.logsnr_range[0]-self.logsnr_range[1]) + self.logsnr_range[1]
return logSNR
# Any schedule that cannot be described easily as a continuous function of t
# It needs to define self.x and self.y in the setup() method
class PiecewiseLinearSchedule(BaseSchedule):
def setup(self):
self.x = None
self.y = None
def piecewise_linear(self, x, xs, ys):
indices = torch.searchsorted(xs[:-1], x) - 1
x_min, x_max = xs[indices], xs[indices+1]
y_min, y_max = ys[indices], ys[indices+1]
var = y_min + (y_max - y_min) * (x - x_min) / (x_max - x_min)
return var
def schedule(self, t, batch_size):
if t is None:
t = 1-torch.rand(batch_size)
var = self.piecewise_linear(t, self.x.to(t.device), self.y.to(t.device))
logSNR = (var/(1-var)).log()
return logSNR
class StableDiffusionSchedule(PiecewiseLinearSchedule):
def setup(self, linear_range=[0.00085, 0.012], total_steps=1000):
linear_range_sqrt = [r**0.5 for r in linear_range]
self.x = torch.linspace(0, 1, total_steps+1)
alphas = 1-(linear_range_sqrt[0]*(1-self.x) + linear_range_sqrt[1]*self.x)**2
self.y = alphas.cumprod(dim=-1)
class AdaptiveTrainSchedule(BaseSchedule):
def setup(self, logsnr_range=[-10, 10], buckets=100, min_probs=0.0):
th = torch.linspace(logsnr_range[0], logsnr_range[1], buckets+1)
self.bucket_ranges = torch.tensor([(th[i], th[i+1]) for i in range(buckets)])
self.bucket_probs = torch.ones(buckets)
self.min_probs = min_probs
def schedule(self, t, batch_size):
if t is not None:
raise Exception("AdaptiveTrainSchedule doesn't support passing timesteps: t")
norm_probs = ((self.bucket_probs+self.min_probs) / (self.bucket_probs+self.min_probs).sum())
buckets = torch.multinomial(norm_probs, batch_size, replacement=True)
ranges = self.bucket_ranges[buckets]
logSNR = torch.rand(batch_size) * (ranges[:, 1]-ranges[:, 0]) + ranges[:, 0]
return logSNR
def update_buckets(self, logSNR, loss, beta=0.99):
range_mtx = self.bucket_ranges.unsqueeze(0).expand(logSNR.size(0), -1, -1).to(logSNR.device)
range_mask = (range_mtx[:, :, 0] <= logSNR[:, None]) * (range_mtx[:, :, 1] > logSNR[:, None]).float()
range_idx = range_mask.argmax(-1).cpu()
self.bucket_probs[range_idx] = self.bucket_probs[range_idx] * beta + loss.detach().cpu() * (1-beta)
class InterpolatedSchedule(BaseSchedule):
def setup(self, scheduler1, scheduler2, shifts=[1.0, 1.0]):
self.scheduler1 = scheduler1
self.scheduler2 = scheduler2
self.shifts = shifts
def schedule(self, t, batch_size):
if t is None:
t = 1-torch.rand(batch_size)
t = t.clamp(1e-7, 1-1e-7) # avoid infinities multiplied by 0 which cause nan
low_logSNR = self.scheduler1(t, shift=self.shifts[0])
high_logSNR = self.scheduler2(t, shift=self.shifts[1])
return low_logSNR * t + high_logSNR * (1-t)
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