"""SAMPLING ONLY.""" from functools import partial import numpy as np import torch from einops import rearrange from tqdm import tqdm from .sampling_util import (norm_thresholding, renorm_thresholding, spatial_norm_thresholding) from .util_diffusion import (extract_into_tensor, make_ddim_sampling_parameters, make_ddim_timesteps, noise_like) class DDIMSampler(object): def __init__(self, model, schedule='linear', **kwargs): super().__init__() self.model = model self.ddpm_num_timesteps = model.num_timesteps self.schedule = schedule def to(self, device): """Same as to in torch module Don't really underestand why this isn't a module in the first place""" for k, v in self.__dict__.items(): if isinstance(v, torch.Tensor): new_v = getattr(self, k).to(device) setattr(self, k, new_v) 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): 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)) alpha_1 = (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) alpha_2 = (1 - self.alphas_cumprod / self.alphas_cumprod_prev) sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( alpha_1 * alpha_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, 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 DDIM sampling is {size}, eta {eta}') samples, intermediates = self.ddim_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 ddim_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, t_start=-1): 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] timesteps = timesteps[:t_start] intermediates = {'x_inter': [img], 'pred_x0': [img]} time_range = 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 DDIM Sampling with {total_steps} timesteps') iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) for i, step in enumerate(iterator): index = total_steps - i - 1 ts = torch.full((b, ), step, 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_ddim( 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, dynamic_threshold=dynamic_threshold) img, pred_x0 = outs if callback: img = callback(i, img, pred_x0) 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_ddim(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, dynamic_threshold=None): b, *_, device = *x.shape, x.device 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) 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 # 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 @torch.no_grad() def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None, unconditional_guidance_scale=1.0, unconditional_conditioning=None): num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[ 0] assert t_enc <= num_reference_steps num_steps = t_enc if use_original_steps: alphas_next = self.alphas_cumprod[:num_steps] alphas = self.alphas_cumprod_prev[:num_steps] else: alphas_next = self.ddim_alphas[:num_steps] alphas = torch.tensor(self.ddim_alphas_prev[:num_steps]) x_next = x0 intermediates = [] inter_steps = [] for i in tqdm(range(num_steps), desc='Encoding Image'): t = torch.full((x0.shape[0], ), i, device=self.model.device, dtype=torch.long) if unconditional_guidance_scale == 1.: noise_pred = self.model.apply_model(x_next, t, c) else: assert unconditional_conditioning is not None e_t_uncond, noise_pred = torch.chunk( self.model.apply_model( torch.cat((x_next, x_next)), torch.cat((t, t)), torch.cat((unconditional_conditioning, c))), 2) noise_pred = e_t_uncond + unconditional_guidance_scale * ( noise_pred - e_t_uncond) xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next alp_1 = (1 / alphas_next[i] - 1).sqrt() alp_2 = (1 / alphas[i] - 1).sqrt() weighted_noise_pred = alphas_next[i].sqrt() * ( alp_1 - alp_2) * noise_pred x_next = xt_weighted + weighted_noise_pred if return_intermediates and i % (num_steps // return_intermediates ) == 0 and i < num_steps - 1: intermediates.append(x_next) inter_steps.append(i) elif return_intermediates and i >= num_steps - 2: intermediates.append(x_next) inter_steps.append(i) out = {'x_encoded': x_next, 'intermediate_steps': inter_steps} if return_intermediates: out.update({'intermediates': intermediates}) return x_next, out @torch.no_grad() def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): # fast, but does not allow for exact reconstruction # t serves as an index to gather the correct alphas if use_original_steps: sqrt_alphas_cumprod = self.sqrt_alphas_cumprod sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod else: 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(x0) return ( extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise) @torch.no_grad() def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, use_original_steps=False): timesteps = np.arange(self.ddpm_num_timesteps ) if use_original_steps else self.ddim_timesteps timesteps = timesteps[:t_start] time_range = np.flip(timesteps) total_steps = timesteps.shape[0] print(f'Running DDIM Sampling with {total_steps} timesteps') iterator = tqdm(time_range, desc='Decoding image', total=total_steps) x_dec = x_latent 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) x_dec, _ = self.p_sample_ddim( x_dec, cond, ts, index=index, use_original_steps=use_original_steps, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning) return x_dec