"""SAMPLING ONLY.""" from functools import partial import numpy as np import torch from tqdm import tqdm from .sampling_util import norm_thresholding from .util_diffusion import (make_ddim_sampling_parameters, make_ddim_timesteps, noise_like) class PLMSSampler(object): def __init__(self, model, schedule='linear', **kwargs): super().__init__() self.model = model self.ddpm_num_timesteps = model.num_timesteps self.schedule = schedule def register_buffer(self, name, attr): if type(attr) == torch.Tensor: if attr.device != torch.device('cuda'): attr = attr.to(torch.device('cuda')) setattr(self, name, attr) def make_schedule(self, ddim_num_steps, ddim_discretize='uniform', ddim_eta=0., verbose=True): if ddim_eta != 0: raise ValueError('ddim_eta must be 0 for PLMS') self.ddim_timesteps = make_ddim_timesteps( ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, num_ddpm_timesteps=self.ddpm_num_timesteps, verbose=verbose) alphas_cumprod = self.model.alphas_cumprod assert alphas_cumprod.shape[ 0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' def to_torch(x): return x.clone().detach().to(torch.float32).to(self.model.device) self.register_buffer('betas', to_torch(self.model.betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(self.model.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.cpu()))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) # ddim sampling parameters ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters( alphacums=alphas_cumprod.cpu(), ddim_timesteps=self.ddim_timesteps, eta=ddim_eta, verbose=verbose) self.register_buffer('ddim_sigmas', ddim_sigmas) self.register_buffer('ddim_alphas', ddim_alphas) self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) alp_1 = (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) alp_2 = (1 - self.alphas_cumprod / self.alphas_cumprod_prev) sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( alp_1 * alp_2) self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) @torch.no_grad() def sample( self, S, batch_size, shape, conditioning=None, callback=None, normals_sequence=None, img_callback=None, quantize_x0=False, eta=0., mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, verbose=True, x_T=None, log_every_t=100, unconditional_guidance_scale=1., unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... dynamic_threshold=None, **kwargs): if conditioning is not None: if isinstance(conditioning, dict): ctmp = conditioning[list(conditioning.keys())[0]] while isinstance(ctmp, list): ctmp = ctmp[0] cbs = ctmp.shape[0] if cbs != batch_size: print( f'Warning: Got {cbs} conditionings but batch-size is {batch_size}' ) else: if conditioning.shape[0] != batch_size: print( f'Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}' ) self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) # sampling C, H, W = shape size = (batch_size, C, H, W) print(f'Data shape for PLMS sampling is {size}') samples, intermediates = self.plms_sampling( conditioning, size, callback=callback, img_callback=img_callback, quantize_denoised=quantize_x0, mask=mask, x0=x0, ddim_use_original_steps=False, noise_dropout=noise_dropout, temperature=temperature, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, x_T=x_T, log_every_t=log_every_t, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, dynamic_threshold=dynamic_threshold, ) return samples, intermediates @torch.no_grad() def plms_sampling(self, cond, shape, x_T=None, ddim_use_original_steps=False, callback=None, timesteps=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, log_every_t=100, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None): device = self.model.betas.device b = shape[0] if x_T is None: img = torch.randn(shape, device=device) else: img = x_T if timesteps is None: timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps elif timesteps is not None and not ddim_use_original_steps: subset_end = int( min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 timesteps = self.ddim_timesteps[:subset_end] intermediates = {'x_inter': [img], 'pred_x0': [img]} time_range = list(reversed(range( 0, timesteps))) if ddim_use_original_steps else np.flip(timesteps) total_steps = timesteps if ddim_use_original_steps else timesteps.shape[ 0] print(f'Running PLMS Sampling with {total_steps} timesteps') iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps) old_eps = [] for i, step in enumerate(iterator): index = total_steps - i - 1 ts = torch.full((b, ), step, device=device, dtype=torch.long) ts_next = torch.full((b, ), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long) if mask is not None: assert x0 is not None img_orig = self.model.q_sample( x0, ts) # TODO: deterministic forward pass? img = img_orig * mask + (1. - mask) * img outs = self.p_sample_plms( img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, quantize_denoised=quantize_denoised, temperature=temperature, noise_dropout=noise_dropout, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, old_eps=old_eps, t_next=ts_next, dynamic_threshold=dynamic_threshold) img, pred_x0, e_t = outs old_eps.append(e_t) if len(old_eps) >= 4: old_eps.pop(0) if callback: callback(i) if img_callback: img_callback(pred_x0, i) if index % log_every_t == 0 or index == total_steps - 1: intermediates['x_inter'].append(img) intermediates['pred_x0'].append(pred_x0) return img, intermediates @torch.no_grad() def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, dynamic_threshold=None): b, *_, device = *x.shape, x.device def get_model_output(x, t): if unconditional_conditioning is None or unconditional_guidance_scale == 1.: e_t = self.model.apply_model(x, t, c) else: x_in = torch.cat([x] * 2) t_in = torch.cat([t] * 2) if isinstance(c, dict): assert isinstance(unconditional_conditioning, dict) c_in = dict() for k in c: if isinstance(c[k], list): c_in[k] = [ torch.cat([ unconditional_conditioning[k][i], c[k][i] ]) for i in range(len(c[k])) ] else: c_in[k] = torch.cat( [unconditional_conditioning[k], c[k]]) else: c_in = torch.cat([unconditional_conditioning, c]) e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) e_t = e_t_uncond + unconditional_guidance_scale * ( e_t - e_t_uncond) if score_corrector is not None: assert self.model.parameterization == 'eps' e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) return e_t alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas if use_original_steps: alphas_prev = self.model.alphas_cumprod_prev sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod else: alphas_prev = self.ddim_alphas_prev sqrt_one_minus_alphas = self.ddim_sqrt_one_minus_alphas sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas def get_x_prev_and_pred_x0(e_t, index): # select parameters corresponding to the currently considered timestep a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device) # current prediction for x_0 pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() if quantize_denoised: pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) if dynamic_threshold is not None: pred_x0 = norm_thresholding(pred_x0, dynamic_threshold) # direction pointing to x_t dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature if noise_dropout > 0.: noise = torch.nn.functional.dropout(noise, p=noise_dropout) x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise return x_prev, pred_x0 e_t = get_model_output(x, t) if len(old_eps) == 0: # Pseudo Improved Euler (2nd order) x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) e_t_next = get_model_output(x_prev, t_next) e_t_prime = (e_t + e_t_next) / 2 elif len(old_eps) == 1: # 2nd order Pseudo Linear Multistep (Adams-Bashforth) e_t_prime = (3 * e_t - old_eps[-1]) / 2 elif len(old_eps) == 2: # 3rd order Pseudo Linear Multistep (Adams-Bashforth) e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 elif len(old_eps) >= 3: # 4nd order Pseudo Linear Multistep (Adams-Bashforth) e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) return x_prev, pred_x0, e_t