| """ |
| The following code is copied from https://github.com/modelscope/DiffSynth-Studio/blob/main/diffsynth/schedulers/flow_match.py |
| """ |
| import torch |
|
|
|
|
| class FlowMatchScheduler(): |
| is_stateful = False |
|
|
| 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 |
