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"""
The following code is copied from https://github.com/modelscope/DiffSynth-Studio/blob/main/diffsynth/schedulers/flow_match.py
"""
import torch
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):
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.set_timesteps(num_inference_steps)
def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, training=False):
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])
self.sigmas = self.shift * self.sigmas / \
(1 + (self.shift - 1) * self.sigmas)
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
def step(self, model_output, timestep, sample, to_final=False):
self.sigmas = self.sigmas.to(model_output.device)
self.timesteps = self.timesteps.to(model_output.device)
timestep_id = torch.argmin(
(self.timesteps - timestep).abs(), dim=0)
sigma = self.sigmas[timestep_id].reshape(-1, 1, 1, 1)
if to_final or (timestep_id + 1 >= len(self.timesteps)).any():
sigma_ = 1 if (
self.inverse_timesteps or self.reverse_sigmas) else 0
else:
sigma_ = self.sigmas[timestep_id + 1].reshape(-1, 1, 1, 1)
prev_sample = sample + model_output * (sigma_ - sigma)
return prev_sample
def add_noise(self, original_samples, noise, timestep):
"""
Diffusion forward corruption process.
Input:
- clean_latent: the clean latent with shape [B, C, H, W]
- noise: the noise with shape [B, C, H, W]
- timestep: the timestep with shape [B]
Output: the corrupted latent with shape [B, C, H, W]
"""
self.sigmas = self.sigmas.to(noise.device)
self.timesteps = self.timesteps.to(noise.device)
timestep_id = torch.argmin(
(self.timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
sigma = self.sigmas[timestep_id].reshape(-1, 1, 1, 1)
sample = (1 - sigma) * original_samples + sigma * noise
return sample.type_as(noise)
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 |