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| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| from tqdm import tqdm | |
| from ldm.util import default | |
| from ldm.modules.diffusionmodules.util import extract_into_tensor | |
| from .ddpm import DDPM | |
| class LatentDiffusion(DDPM): | |
| def __init__(self, *args, **kwargs): | |
| super().__init__(*args, **kwargs) | |
| # hardcoded | |
| self.clip_denoised = False | |
| def q_sample(self, x_start, t, noise=None): | |
| noise = default(noise, lambda: torch.randn_like(x_start)) | |
| return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + | |
| extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) | |
| "Does not support DDPM sampling anymore. Only do DDIM or PLMS" | |
| # = = = = = = = = = = = = Below is for sampling = = = = = = = = = = = = # | |
| # def predict_start_from_noise(self, x_t, t, noise): | |
| # return ( extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - | |
| # extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise ) | |
| # def q_posterior(self, x_start, x_t, t): | |
| # posterior_mean = ( | |
| # extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + | |
| # extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t | |
| # ) | |
| # posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) | |
| # posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) | |
| # return posterior_mean, posterior_variance, posterior_log_variance_clipped | |
| # def p_mean_variance(self, model, x, c, t): | |
| # model_out = model(x, t, c) | |
| # x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) | |
| # if self.clip_denoised: | |
| # x_recon.clamp_(-1., 1.) | |
| # model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) | |
| # return model_mean, posterior_variance, posterior_log_variance, x_recon | |
| # @torch.no_grad() | |
| # def p_sample(self, model, x, c, t): | |
| # b, *_, device = *x.shape, x.device | |
| # model_mean, _, model_log_variance, x0 = self.p_mean_variance(model, x=x, c=c, t=t, ) | |
| # noise = torch.randn_like(x) | |
| # # no noise when t == 0 | |
| # nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) | |
| # return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 | |
| # @torch.no_grad() | |
| # def p_sample_loop(self, model, shape, c): | |
| # device = self.betas.device | |
| # b = shape[0] | |
| # img = torch.randn(shape, device=device) | |
| # iterator = tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps) | |
| # for i in iterator: | |
| # ts = torch.full((b,), i, device=device, dtype=torch.long) | |
| # img, x0 = self.p_sample(model, img, c, ts) | |
| # return img | |
| # @torch.no_grad() | |
| # def sample(self, model, shape, c, uc=None, guidance_scale=None): | |
| # return self.p_sample_loop(model, shape, c) | |