"""E3Diff Gaussian Diffusion - exact copy from original with fixed imports.""" import math import torch from torch import nn import torch.nn.functional as F from inspect import isfunction from functools import partial import numpy as np def _warmup_beta(linear_start, linear_end, n_timestep, warmup_frac): betas = linear_end * np.ones(n_timestep, dtype=np.float64) warmup_time = int(n_timestep * warmup_frac) betas[:warmup_time] = np.linspace( linear_start, linear_end, warmup_time, dtype=np.float64) return betas def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if schedule == 'quad': betas = np.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=np.float64) ** 2 elif schedule == 'linear': betas = np.linspace(linear_start, linear_end, n_timestep, dtype=np.float64) elif schedule == 'warmup10': betas = _warmup_beta(linear_start, linear_end, n_timestep, 0.1) elif schedule == 'warmup50': betas = _warmup_beta(linear_start, linear_end, n_timestep, 0.5) elif schedule == 'const': betas = linear_end * np.ones(n_timestep, dtype=np.float64) elif schedule == 'jsd': betas = 1. / np.linspace(n_timestep, 1, n_timestep, dtype=np.float64) elif schedule == "cosine": timesteps = ( torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s ) alphas = timesteps / (1 + cosine_s) * math.pi / 2 alphas = torch.cos(alphas).pow(2) alphas = alphas / alphas[0] betas = 1 - alphas[1:] / alphas[:-1] betas = betas.clamp(max=0.999) else: raise NotImplementedError(schedule) return betas def exists(x): return x is not None def default(val, d): if exists(val): return val return d() if isfunction(d) else d class GaussianDiffusion(nn.Module): def __init__( self, denoise_fn, image_size, channels=3, loss_type='l1', conditional=True, schedule_opt=None, xT_noise_r=0.1, seed=1, opt=None ): super().__init__() self.lq_noiselevel_val = schedule_opt["lq_noiselevel"] self.opt = opt self.channels = channels self.image_size = image_size self.denoise_fn = denoise_fn self.loss_type = loss_type self.conditional = conditional self.ddim = schedule_opt['ddim'] self.xT_noise_r = xT_noise_r self.seed = seed def set_loss(self, device): if self.loss_type == 'l1': self.loss_func = nn.L1Loss(reduction='sum').to(device) elif self.loss_type == 'l2': self.loss_func = nn.MSELoss(reduction='sum').to(device) else: raise NotImplementedError() def set_new_noise_schedule(self, schedule_opt, device, num_train_timesteps=1000): self.ddim = schedule_opt['ddim'] self.num_train_timesteps = num_train_timesteps to_torch = partial(torch.tensor, dtype=torch.float32, device=device) betas = make_beta_schedule( schedule=schedule_opt['schedule'], n_timestep=num_train_timesteps, linear_start=schedule_opt['linear_start'], linear_end=schedule_opt['linear_end']) betas = betas.detach().cpu().numpy() if isinstance( betas, torch.Tensor) else betas alphas = 1. - betas alphas_cumprod = np.cumprod(alphas, axis=0) alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) self.sqrt_alphas_cumprod_prev = np.sqrt( np.append(1., alphas_cumprod)) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.register_buffer('betas', to_torch(betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) # calculations for posterior q(x_{t-1} | x_t, x_0) posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod) self.register_buffer('posterior_variance', to_torch(posterior_variance)) self.register_buffer('posterior_log_variance_clipped', to_torch( np.log(np.maximum(posterior_variance, 1e-20)))) self.register_buffer('posterior_mean_coef1', to_torch( betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) self.register_buffer('posterior_mean_coef2', to_torch( (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) self.schedule_type = schedule_opt['schedule'] if self.ddim > 0: self.ddim_num_steps = schedule_opt['n_timestep'] def predict_start_from_noise(self, x_t, t, noise): return self.sqrt_recip_alphas_cumprod[t] * x_t - \ self.sqrt_recipm1_alphas_cumprod[t] * noise def q_posterior(self, x_start, x_t, t): posterior_mean = self.posterior_mean_coef1[t] * \ x_start + self.posterior_mean_coef2[t] * x_t posterior_log_variance_clipped = self.posterior_log_variance_clipped[t] return posterior_mean, posterior_log_variance_clipped def p_mean_variance(self, x, t, clip_denoised: bool, condition_x=None): batch_size = x.shape[0] noise_level = torch.FloatTensor( [self.sqrt_alphas_cumprod_prev[t+1]]).repeat(batch_size, 1).to(x.device) if condition_x is not None: x_recon = self.predict_start_from_noise( x, t=t, noise=self.denoise_fn(torch.cat([condition_x, x], dim=1), noise_level, t)) else: x_recon = self.predict_start_from_noise( x, t=t, noise=self.denoise_fn(x, noise_level)) if clip_denoised: x_recon.clamp_(-1., 1.) model_mean, posterior_log_variance = self.q_posterior( x_start=x_recon, x_t=x, t=t) return model_mean, posterior_log_variance, x_recon def ddim_sample(self, condition_x, img_or_shape, device, seed=1, img_s1=None): if self.schedule_type == 'linear': self.ddim_sampling_eta = 0.8 simple_var = False threshold_x = False elif self.schedule_type == 'cosine': self.ddim_sampling_eta = 0.8 simple_var = False threshold_x = False batch, total_timesteps, sampling_timesteps, eta = \ img_or_shape[0], self.num_train_timesteps, \ self.ddim_num_steps, self.ddim_sampling_eta noisy_img_s1 = None if simple_var: eta = 1 ts = torch.linspace(total_timesteps, 0, (sampling_timesteps + 1)).to(device).to(torch.long) x = torch.randn(img_or_shape).to(device) batch_size = x.shape[0] imgs = [x] img_onestep = [condition_x[:, :self.channels, ...]] tbar = range(1, sampling_timesteps + 1) for i in tbar: cur_t = ts[i - 1] - 1 prev_t = ts[i] - 1 noise_level = torch.FloatTensor( [self.sqrt_alphas_cumprod_prev[cur_t]]).repeat(batch_size, 1).to(x.device) alpha_prod_t = self.alphas_cumprod[cur_t] alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else 1 beta_prod_t = 1 - alpha_prod_t # pred noise model_output = self.denoise_fn(torch.cat([condition_x, x], dim=1), noise_level) sigma_2 = eta * (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev) noise = torch.randn_like(x) pred_original_sample = (x - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) if threshold_x: pred_original_sample = self._threshold_sample(pred_original_sample) else: pred_original_sample = pred_original_sample.clamp(-1, 1) pred_sample_direction = (1 - alpha_prod_t_prev - sigma_2) ** (0.5) * model_output if simple_var: third_term = (1 - alpha_prod_t / alpha_prod_t_prev) ** 0.5 * noise else: third_term = sigma_2 ** 0.5 * noise x = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction + third_term imgs.append(x) img_onestep.append(pred_original_sample) imgs = torch.concat(imgs, dim=0) img_onestep = torch.concat(img_onestep, dim=0) return imgs, img_onestep @torch.no_grad() def p_sample(self, x, t, clip_denoised=True, condition_x=None): model_mean, model_log_variance, x_recon = self.p_mean_variance( x=x, t=t, clip_denoised=clip_denoised, condition_x=condition_x) noise = torch.randn_like(x) if t > 0 else torch.zeros_like(x) return model_mean + noise * (0.5 * model_log_variance).exp(), x_recon @torch.no_grad() def p_sample_loop(self, x_in, continous=False, seed=1, img_s1=None): device = self.betas.device sample_inter = 1 if not self.conditional: shape = x_in img = torch.randn(shape, device=device) ret_img = img if not self.ddim: for i in reversed(range(0, self.num_timesteps)): img, x_recon = self.p_sample(img, i) if i % sample_inter == 0: ret_img = torch.cat([ret_img, img], dim=0) else: for i in range(0, len(self.ddim_timesteps)): ddim_t = self.ddim_timesteps[i] img = self.ddim_sample(img, ddim_t) if i % sample_inter == 0: ret_img = torch.cat([ret_img, img], dim=0) else: x = x_in shape = (x.shape[0], self.channels, x.shape[-2], x.shape[-1]) if self.xT_noise_r > 0: img0 = torch.randn(shape, device=device) x_start = x_in[:, 0:1, ...] continuous_sqrt_alpha_cumprod = torch.FloatTensor( np.random.uniform( self.sqrt_alphas_cumprod_prev[self.num_timesteps-1], self.sqrt_alphas_cumprod_prev[self.num_timesteps], size=x_start.shape[0] )).to(x_start.device) continuous_sqrt_alpha_cumprod = continuous_sqrt_alpha_cumprod.view(x_start.shape[0], -1) noise = default(x_start, lambda: torch.randn_like(x_start)) img = self.q_sample( x_start=x_start, continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod.view(-1, 1, 1, 1), noise=noise) img = self.xT_noise_r * img + (1 - self.xT_noise_r) * img0 else: img = torch.randn(shape, device=device) ret_img = x img_onestep = x if self.opt['stage'] != 2: if not self.ddim: for i in reversed(range(0, self.num_timesteps)): img, x_recon = self.p_sample(img, i, condition_x=x) if i % sample_inter == 0: ret_img = torch.cat([ret_img[:, :self.channels, ...], img], dim=0) if i % sample_inter == 0 or i == self.num_timesteps - 1: img_onestep = torch.cat([img_onestep[:, :self.channels, ...], x_recon], dim=0) else: ret_img, img_onestep = self.ddim_sample(condition_x=x, img_or_shape=shape, device=device, seed=seed, img_s1=img_s1) if continous: return ret_img, img_onestep else: return ret_img[-x_in.shape[0]:], img_onestep else: self.ddim_num_steps = self.opt['ddim_steps'] ret_img, img_onestep = self.ddim_sample(condition_x=x, img_or_shape=shape, device=device, seed=seed, img_s1=img_s1) if continous: return ret_img, img_onestep else: return ret_img[-x_in.shape[0]:], img_onestep @torch.no_grad() def sample(self, batch_size=1, continous=False): image_size = self.image_size channels = self.channels return self.p_sample_loop((batch_size, channels, image_size, image_size), continous) @torch.no_grad() def super_resolution(self, x_in, continous=False, seed=1, img_s1=None): return self.p_sample_loop(x_in, continous, seed=seed, img_s1=img_s1) def q_sample(self, x_start, continuous_sqrt_alpha_cumprod, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) return ( continuous_sqrt_alpha_cumprod * x_start + (1 - continuous_sqrt_alpha_cumprod ** 2).sqrt() * noise )