import torch import numpy as np from fcbh.ldm.modules.diffusionmodules.util import make_beta_schedule class EPS: def calculate_input(self, sigma, noise): sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 def calculate_denoised(self, sigma, model_output, model_input): sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) return model_input - model_output * sigma class V_PREDICTION(EPS): def calculate_denoised(self, sigma, model_output, model_input): sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 class ModelSamplingDiscrete(torch.nn.Module): def __init__(self, model_config=None): super().__init__() beta_schedule = "linear" if model_config is not None: beta_schedule = model_config.sampling_settings.get("beta_schedule", beta_schedule) self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3) self.sigma_data = 1.0 def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if given_betas is not None: betas = given_betas else: betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) alphas = 1. - betas alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32) # alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32)) # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32)) # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32)) sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5 self.set_sigmas(sigmas) def set_sigmas(self, sigmas): self.register_buffer('sigmas', sigmas) self.register_buffer('log_sigmas', sigmas.log()) @property def sigma_min(self): return self.sigmas[0] @property def sigma_max(self): return self.sigmas[-1] def timestep(self, sigma): log_sigma = sigma.log() dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] return dists.abs().argmin(dim=0).view(sigma.shape) def sigma(self, timestep): t = torch.clamp(timestep.float(), min=0, max=(len(self.sigmas) - 1)) low_idx = t.floor().long() high_idx = t.ceil().long() w = t.frac() log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] return log_sigma.exp() def percent_to_sigma(self, percent): if percent <= 0.0: return 999999999.9 if percent >= 1.0: return 0.0 percent = 1.0 - percent return self.sigma(torch.tensor(percent * 999.0)).item()