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| # 1st edit by https://github.com/comfyanonymous/ComfyUI | |
| # 2nd edit by Forge Official | |
| import torch | |
| import numpy as np | |
| from ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule | |
| import math | |
| 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__() | |
| if model_config is not None: | |
| sampling_settings = model_config.sampling_settings | |
| else: | |
| sampling_settings = {} | |
| beta_schedule = sampling_settings.get("beta_schedule", "linear") | |
| linear_start = sampling_settings.get("linear_start", 0.00085) | |
| linear_end = sampling_settings.get("linear_end", 0.012) | |
| self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=linear_start, linear_end=linear_end, 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()) | |
| def sigma_min(self): | |
| return self.sigmas[0] | |
| 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).to(sigma.device) | |
| def sigma(self, timestep): | |
| t = torch.clamp(timestep.float().to(self.log_sigmas.device), 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().to(timestep.device) | |
| 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() | |
| class ModelSamplingContinuousEDM(torch.nn.Module): | |
| def __init__(self, model_config=None): | |
| super().__init__() | |
| self.sigma_data = 1.0 | |
| if model_config is not None: | |
| sampling_settings = model_config.sampling_settings | |
| else: | |
| sampling_settings = {} | |
| sigma_min = sampling_settings.get("sigma_min", 0.002) | |
| sigma_max = sampling_settings.get("sigma_max", 120.0) | |
| self.set_sigma_range(sigma_min, sigma_max) | |
| def set_sigma_range(self, sigma_min, sigma_max): | |
| sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp() | |
| self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers | |
| self.register_buffer('log_sigmas', sigmas.log()) | |
| def sigma_min(self): | |
| return self.sigmas[0] | |
| def sigma_max(self): | |
| return self.sigmas[-1] | |
| def timestep(self, sigma): | |
| return 0.25 * sigma.log() | |
| def sigma(self, timestep): | |
| return (timestep / 0.25).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 | |
| log_sigma_min = math.log(self.sigma_min) | |
| return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min) | |