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from typing_extensions import Literal
class FlowMatchScheduler():
def __init__(self, template: Literal["FLUX.1", "Wan", "Qwen-Image", "FLUX.2", "Z-Image", "LTX-2", "Qwen-Image-Lightning"] = "FLUX.1"):
self.set_timesteps_fn = {
"FLUX.1": FlowMatchScheduler.set_timesteps_flux,
"Wan": FlowMatchScheduler.set_timesteps_wan,
"Qwen-Image": FlowMatchScheduler.set_timesteps_qwen_image,
"FLUX.2": FlowMatchScheduler.set_timesteps_flux2,
"Z-Image": FlowMatchScheduler.set_timesteps_z_image,
"LTX-2": FlowMatchScheduler.set_timesteps_ltx2,
"Qwen-Image-Lightning": FlowMatchScheduler.set_timesteps_qwen_image_lightning,
}.get(template, FlowMatchScheduler.set_timesteps_flux)
self.num_train_timesteps = 1000
@staticmethod
def set_timesteps_flux(num_inference_steps=100, denoising_strength=1.0, shift=None):
sigma_min = 0.003/1.002
sigma_max = 1.0
shift = 3 if shift is None else shift
num_train_timesteps = 1000
sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps)
sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
timesteps = sigmas * num_train_timesteps
return sigmas, timesteps
@staticmethod
def set_timesteps_wan(num_inference_steps=100, denoising_strength=1.0, shift=None):
sigma_min = 0.0
sigma_max = 1.0
shift = 5 if shift is None else shift
num_train_timesteps = 1000
sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
timesteps = sigmas * num_train_timesteps
return sigmas, timesteps
@staticmethod
def _calculate_shift_qwen_image(image_seq_len, base_seq_len=256, max_seq_len=8192, base_shift=0.5, max_shift=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
@staticmethod
def set_timesteps_qwen_image(num_inference_steps=100, denoising_strength=1.0, exponential_shift_mu=None, dynamic_shift_len=None):
sigma_min = 0.0
sigma_max = 1.0
num_train_timesteps = 1000
shift_terminal = 0.02
# Sigmas
sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
# Mu
if exponential_shift_mu is not None:
mu = exponential_shift_mu
elif dynamic_shift_len is not None:
mu = FlowMatchScheduler._calculate_shift_qwen_image(dynamic_shift_len)
else:
mu = 0.8
sigmas = math.exp(mu) / (math.exp(mu) + (1 / sigmas - 1))
# Shift terminal
one_minus_z = 1 - sigmas
scale_factor = one_minus_z[-1] / (1 - shift_terminal)
sigmas = 1 - (one_minus_z / scale_factor)
# Timesteps
timesteps = sigmas * num_train_timesteps
return sigmas, timesteps
@staticmethod
def set_timesteps_qwen_image_lightning(num_inference_steps=100, denoising_strength=1.0, exponential_shift_mu=None, dynamic_shift_len=None):
sigma_min = 0.0
sigma_max = 1.0
num_train_timesteps = 1000
base_shift = math.log(3)
max_shift = math.log(3)
# Sigmas
sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
# Mu
if exponential_shift_mu is not None:
mu = exponential_shift_mu
elif dynamic_shift_len is not None:
mu = FlowMatchScheduler._calculate_shift_qwen_image(dynamic_shift_len, base_shift=base_shift, max_shift=max_shift)
else:
mu = 0.8
sigmas = math.exp(mu) / (math.exp(mu) + (1 / sigmas - 1))
# Timesteps
timesteps = sigmas * num_train_timesteps
return sigmas, timesteps
@staticmethod
def compute_empirical_mu(image_seq_len, num_steps):
a1, b1 = 8.73809524e-05, 1.89833333
a2, b2 = 0.00016927, 0.45666666
if image_seq_len > 4300:
mu = a2 * image_seq_len + b2
return float(mu)
m_200 = a2 * image_seq_len + b2
m_10 = a1 * image_seq_len + b1
a = (m_200 - m_10) / 190.0
b = m_200 - 200.0 * a
mu = a * num_steps + b
return float(mu)
@staticmethod
def set_timesteps_flux2(num_inference_steps=100, denoising_strength=1.0, dynamic_shift_len=None):
sigma_min = 1 / num_inference_steps
sigma_max = 1.0
num_train_timesteps = 1000
sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps)
if dynamic_shift_len is None:
# If you ask me why I set mu=0.8,
# I can only say that it yields better training results.
mu = 0.8
else:
mu = FlowMatchScheduler.compute_empirical_mu(dynamic_shift_len, num_inference_steps)
sigmas = math.exp(mu) / (math.exp(mu) + (1 / sigmas - 1))
timesteps = sigmas * num_train_timesteps
return sigmas, timesteps
@staticmethod
def set_timesteps_z_image(num_inference_steps=100, denoising_strength=1.0, shift=None, target_timesteps=None):
sigma_min = 0.0
sigma_max = 1.0
shift = 3 if shift is None else shift
num_train_timesteps = 1000
sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
timesteps = sigmas * num_train_timesteps
if target_timesteps is not None:
target_timesteps = target_timesteps.to(dtype=timesteps.dtype, device=timesteps.device)
for timestep in target_timesteps:
timestep_id = torch.argmin((timesteps - timestep).abs())
timesteps[timestep_id] = timestep
return sigmas, timesteps
@staticmethod
def set_timesteps_ltx2(num_inference_steps=100, denoising_strength=1.0, dynamic_shift_len=None, terminal=0.1, special_case=None):
num_train_timesteps = 1000
if special_case == "stage2":
sigmas = torch.Tensor([0.909375, 0.725, 0.421875])
elif special_case == "ditilled_stage1":
sigmas = torch.Tensor([1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875])
else:
dynamic_shift_len = dynamic_shift_len or 4096
sigma_shift = FlowMatchScheduler._calculate_shift_qwen_image(
image_seq_len=dynamic_shift_len,
base_seq_len=1024,
max_seq_len=4096,
base_shift=0.95,
max_shift=2.05,
)
sigma_min = 0.0
sigma_max = 1.0
sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
sigmas = math.exp(sigma_shift) / (math.exp(sigma_shift) + (1 / sigmas - 1))
# Shift terminal
one_minus_z = 1.0 - sigmas
scale_factor = one_minus_z[-1] / (1 - terminal)
sigmas = 1.0 - (one_minus_z / scale_factor)
timesteps = sigmas * num_train_timesteps
return sigmas, timesteps
def set_training_weight(self):
steps = 1000
x = self.timesteps
y = torch.exp(-2 * ((x - steps / 2) / steps) ** 2)
y_shifted = y - y.min()
bsmntw_weighing = y_shifted * (steps / y_shifted.sum())
if len(self.timesteps) != 1000:
# This is an empirical formula.
bsmntw_weighing = bsmntw_weighing * (len(self.timesteps) / steps)
bsmntw_weighing = bsmntw_weighing + bsmntw_weighing[1]
self.linear_timesteps_weights = bsmntw_weighing
def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, training=False, **kwargs):
self.sigmas, self.timesteps = self.set_timesteps_fn(
num_inference_steps=num_inference_steps,
denoising_strength=denoising_strength,
**kwargs,
)
if training:
self.set_training_weight()
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]
if to_final or timestep_id + 1 >= len(self.timesteps):
sigma_ = 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):
target = noise - sample
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
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