"""SAMPLING ONLY.""" import logging import numpy as np import torch from tqdm import tqdm from imaginairy.img_log import log_latent from imaginairy.modules.diffusion.util import ( extract_into_tensor, make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, ) from imaginairy.utils import get_device logger = logging.getLogger(__name__) class PLMSSampler: """probabilistic least-mean-squares""" def __init__(self, model, **kwargs): self.model = model self.ddpm_num_timesteps = model.num_timesteps self.device_available = get_device() def register_buffer(self, name, attr): if type(attr) == torch.Tensor: if attr.device != torch.device(self.device_available): attr = attr.to(torch.float32).to(torch.device(self.device_available)) setattr(self, name, attr) def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0): 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, ) alphas_cumprod = self.model.alphas_cumprod assert ( alphas_cumprod.shape[0] == self.ddpm_num_timesteps ), "alphas have to be defined for each timestep" to_torch = lambda x: 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(1.0 - alphas_cumprod.cpu())), ) self.register_buffer( "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu())) ) self.register_buffer( "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu())) ) self.register_buffer( "sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / 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, ) 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(1.0 - ddim_alphas)) sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (1 - self.alphas_cumprod / self.alphas_cumprod_prev) ) self.register_buffer( "ddim_sigmas_for_original_num_steps", sigmas_for_original_sampling_steps ) @torch.no_grad() def sample( self, num_steps, batch_size, shape, conditioning=None, callback=None, normals_sequence=None, img_callback=None, quantize_x0=False, eta=0.0, mask=None, x0=None, temperature=1.0, noise_dropout=0.0, score_corrector=None, corrector_kwargs=None, x_T=None, unconditional_guidance_scale=1.0, unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... **kwargs, ): if conditioning is not None: if isinstance(conditioning, dict): cbs = conditioning[list(conditioning.keys())[0]].shape[0] if cbs != batch_size: logger.warning( f"Warning: Got {cbs} conditionings but batch-size is {batch_size}" ) else: if conditioning.shape[0] != batch_size: logger.warning( f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}" ) self.make_schedule(ddim_num_steps=num_steps, ddim_eta=eta) samples = self.plms_sampling( conditioning, (batch_size, *shape), 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, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, ) return samples @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, temperature=1.0, noise_dropout=0.0, score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1.0, unconditional_conditioning=None, ): device = self.model.betas.device b = shape[0] if x_T is None: img = torch.randn(shape, device="cpu").to(device) else: img = x_T log_latent(img, "initial img") 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] 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] logger.debug(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 + (1.0 - mask) * img img, pred_x0, e_t = 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, ) old_eps.append(e_t) if len(old_eps) >= 4: old_eps.pop(0) if callback: callback(i) if img_callback: img_callback(img, "img") img_callback(pred_x0, "pred_x0") return img @torch.no_grad() def p_sample_plms( self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, temperature=1.0, noise_dropout=0.0, score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1.0, unconditional_conditioning=None, old_eps=None, t_next=None, ): b, *_, device = *x.shape, x.device def get_model_output(x, t): if ( unconditional_conditioning is None or unconditional_guidance_scale == 1.0 ): e_t = self.model.apply_model(x, t, c) else: x_in = torch.cat([x] * 2) t_in = torch.cat([t] * 2) c_in = torch.cat([unconditional_conditioning, c]) e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) log_latent(e_t_uncond, "noise pred uncond") log_latent(e_t, "noise pred cond") e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) log_latent(e_t, "noise pred combined") 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 alphas_prev = ( self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev ) sqrt_one_minus_alphas = ( self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else 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) # direction pointing to x_t dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature if noise_dropout > 0.0: 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: # 3nd 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) log_latent(x_prev, "x_prev") log_latent(pred_x0, "pred_x0") return x_prev, pred_x0, e_t @torch.no_grad() def stochastic_encode(self, init_latent, t, noise=None): # fast, but does not allow for exact reconstruction # t serves as an index to gather the correct alphas sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas if noise is None: noise = torch.randn_like(init_latent, device="cpu").to(get_device()) return ( extract_into_tensor(sqrt_alphas_cumprod, t, init_latent.shape) * init_latent + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, init_latent.shape) * noise ) @torch.no_grad() def decode( self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, img_callback=None, score_corrector=None, temperature=1.0, mask=None, orig_latent=None, ): timesteps = self.ddim_timesteps[:t_start] time_range = np.flip(timesteps) total_steps = timesteps.shape[0] iterator = tqdm(time_range, desc="PLMS altering image", total=total_steps) x_dec = x_latent old_eps = [] for i, step in enumerate(iterator): index = total_steps - i - 1 ts = torch.full( (x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long ) ts_next = torch.full( (x_latent.shape[0],), time_range[min(i + 1, len(time_range) - 1)], device=x_latent.device, dtype=torch.long, ) if mask is not None: assert orig_latent is not None xdec_orig = self.model.q_sample(orig_latent, ts) log_latent(xdec_orig, "xdec_orig") log_latent(xdec_orig * mask, "masked_xdec_orig") x_dec = xdec_orig * mask + (1.0 - mask) * x_dec log_latent(x_dec, "x_dec") x_dec, pred_x0, e_t = self.p_sample_plms( x_dec, cond, ts, index=index, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, temperature=temperature, old_eps=old_eps, t_next=ts_next, ) # original_loss = ((x_dec - x_latent).abs().mean()*70) # sigma_t = torch.full((1, 1, 1, 1), self.ddim_sigmas[index], device=get_device()) # x_dec = x_dec.detach() + (original_loss * 0.1) ** 2 # cond_grad = -torch.autograd.grad(original_loss, x_dec)[0] # x_dec = x_dec.detach() + cond_grad * sigma_t ** 2 ## x_dec_alt = x_dec + (original_loss * 0.1) ** 2 old_eps.append(e_t) if len(old_eps) >= 4: old_eps.pop(0) if img_callback: img_callback(x_dec, "x_dec") img_callback(pred_x0, "pred_x0") log_latent(x_dec, f"x_dec {i}") log_latent(pred_x0, f"pred_x0 {i}") return x_dec