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Running
on
Zero
File size: 4,847 Bytes
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import torch, math
class FlowMatchScheduler():
def __init__(
self,
num_inference_steps=100,
num_train_timesteps=1000,
shift=3.0,
sigma_max=1.0,
sigma_min=0.003/1.002,
inverse_timesteps=False,
extra_one_step=False,
reverse_sigmas=False,
exponential_shift=False,
exponential_shift_mu=None,
shift_terminal=None,
):
self.num_train_timesteps = num_train_timesteps
self.shift = shift
self.sigma_max = sigma_max
self.sigma_min = sigma_min
self.inverse_timesteps = inverse_timesteps
self.extra_one_step = extra_one_step
self.reverse_sigmas = reverse_sigmas
self.exponential_shift = exponential_shift
self.exponential_shift_mu = exponential_shift_mu
self.shift_terminal = shift_terminal
self.set_timesteps(num_inference_steps)
def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, training=False, shift=None, dynamic_shift_len=None):
if shift is not None:
self.shift = shift
sigma_start = self.sigma_min + (self.sigma_max - self.sigma_min) * denoising_strength
if self.extra_one_step:
self.sigmas = torch.linspace(sigma_start, self.sigma_min, num_inference_steps + 1)[:-1]
else:
self.sigmas = torch.linspace(sigma_start, self.sigma_min, num_inference_steps)
if self.inverse_timesteps:
self.sigmas = torch.flip(self.sigmas, dims=[0])
if self.exponential_shift:
mu = self.calculate_shift(dynamic_shift_len) if dynamic_shift_len is not None else self.exponential_shift_mu
self.sigmas = math.exp(mu) / (math.exp(mu) + (1 / self.sigmas - 1))
else:
self.sigmas = self.shift * self.sigmas / (1 + (self.shift - 1) * self.sigmas)
if self.shift_terminal is not None:
one_minus_z = 1 - self.sigmas
scale_factor = one_minus_z[-1] / (1 - self.shift_terminal)
self.sigmas = 1 - (one_minus_z / scale_factor)
if self.reverse_sigmas:
self.sigmas = 1 - self.sigmas
self.timesteps = self.sigmas * self.num_train_timesteps
if training:
x = self.timesteps
y = torch.exp(-2 * ((x - num_inference_steps / 2) / num_inference_steps) ** 2)
y_shifted = y - y.min()
bsmntw_weighing = y_shifted * (num_inference_steps / y_shifted.sum())
self.linear_timesteps_weights = bsmntw_weighing
self.training = True
else:
self.training = False
def step(self, model_output, timestep, sample, to_final=False, **kwargs):
if isinstance(timestep, torch.Tensor):
timestep = timestep.cpu()
timestep_id = torch.argmin((self.timesteps - timestep).abs())
sigma = self.sigmas[timestep_id] #* 当前的sigma值, 也就是加了多少比例的噪声
if to_final or timestep_id + 1 >= len(self.timesteps): #* 下一步的噪声比例,
sigma_ = 1 if (self.inverse_timesteps or self.reverse_sigmas) else 0
else:
sigma_ = self.sigmas[timestep_id + 1]
prev_sample = sample + model_output * (sigma_ - sigma)
return prev_sample
def return_to_timestep(self, timestep, sample, sample_stablized):
if isinstance(timestep, torch.Tensor):
timestep = timestep.cpu()
timestep_id = torch.argmin((self.timesteps - timestep).abs())
sigma = self.sigmas[timestep_id]
model_output = (sample - sample_stablized) / sigma
return model_output
def add_noise(self, original_samples, noise, timestep):
if isinstance(timestep, torch.Tensor):
timestep = timestep.cpu()
timestep_id = torch.argmin((self.timesteps - timestep).abs())
sigma = self.sigmas[timestep_id]
sample = (1 - sigma) * original_samples + sigma * noise
return sample
def training_target(self, sample, noise, timestep):
#* so: noise - target = sample
#* sample + target = noise
target = noise - sample
#* noise: is rgb images
return target
def training_weight(self, timestep):
timestep_id = torch.argmin((self.timesteps - timestep.to(self.timesteps.device)).abs())
weights = self.linear_timesteps_weights[timestep_id]
return weights
def calculate_shift(
self,
image_seq_len,
base_seq_len: int = 256,
max_seq_len: int = 8192,
base_shift: float = 0.5,
max_shift: float = 0.9,
):
m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
b = base_shift - m * base_seq_len
mu = image_seq_len * m + b
return mu
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