import numpy as np from tqdm import tqdm import torch from lvdm.models.utils_diffusion import make_ddim_sampling_parameters, make_ddim_timesteps, rescale_noise_cfg from lvdm.common import noise_like from lvdm.common import extract_into_tensor import copy 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 self.counter = 0 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' to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) if self.model.use_dynamic_rescale: self.ddim_scale_arr = self.model.scale_arr[self.ddim_timesteps] self.ddim_scale_arr_prev = torch.cat([self.ddim_scale_arr[0:1], self.ddim_scale_arr[:-1]]) 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)) 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, 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, schedule_verbose=False, x_T=None, log_every_t=100, unconditional_guidance_scale=1., unconditional_conditioning=None, precision=None, fs=None, timestep_spacing='uniform', #uniform_trailing for starting from last timestep guidance_rescale=0.0, **kwargs ): # check condition bs if conditioning is not None: if isinstance(conditioning, dict): try: cbs = conditioning[list(conditioning.keys())[0]].shape[0] except: cbs = conditioning[list(conditioning.keys())[0]][0].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_discretize=timestep_spacing, ddim_eta=eta, verbose=schedule_verbose) # make shape if len(shape) == 3: C, H, W = shape size = (batch_size, C, H, W) elif len(shape) == 4: C, T, H, W = shape size = (batch_size, C, T, H, W) 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, verbose=verbose, precision=precision, fs=fs, guidance_rescale=guidance_rescale, **kwargs) 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, verbose=True,precision=None,fs=None,guidance_rescale=0.0, **kwargs): device = self.model.betas.device b = shape[0] if x_T is None: img = torch.randn(shape, device=device) else: img = x_T if precision is not None: if precision == 16: img = img.to(dtype=torch.float16) 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 = 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] if verbose: iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) else: iterator = time_range clean_cond = kwargs.pop("clean_cond", False) # cond_copy, unconditional_conditioning_copy = copy.deepcopy(cond), copy.deepcopy(unconditional_conditioning) for i, step in enumerate(iterator): index = total_steps - i - 1 ts = torch.full((b,), step, device=device, dtype=torch.long) ## use mask to blend noised original latent (img_orig) & new sampled latent (img) if mask is not None: assert x0 is not None if clean_cond: img_orig = x0 else: img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? img = img_orig * mask + (1. - mask) * img # keep original & modify use 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, mask=mask,x0=x0,fs=fs,guidance_rescale=guidance_rescale, **kwargs) img, pred_x0 = outs 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_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, uc_type=None, conditional_guidance_scale_temporal=None,mask=None,x0=None,guidance_rescale=0.0,**kwargs): b, *_, device = *x.shape, x.device if x.dim() == 5: is_video = True else: is_video = False if unconditional_conditioning is None or unconditional_guidance_scale == 1.: model_output = self.model.apply_model(x, t, c, **kwargs) # unet denoiser else: ### do_classifier_free_guidance if isinstance(c, torch.Tensor) or isinstance(c, dict): e_t_cond = self.model.apply_model(x, t, c, **kwargs) e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs) else: raise NotImplementedError model_output = e_t_uncond + unconditional_guidance_scale * (e_t_cond - e_t_uncond) if guidance_rescale > 0.0: model_output = rescale_noise_cfg(model_output, e_t_cond, guidance_rescale=guidance_rescale) if self.model.parameterization == "v": e_t = self.model.predict_eps_from_z_and_v(x, t, model_output) else: e_t = model_output if score_corrector is not None: assert self.model.parameterization == "eps", 'not implemented' 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 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 sigmas = self.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas # select parameters corresponding to the currently considered timestep if is_video: size = (b, 1, 1, 1, 1) else: size = (b, 1, 1, 1) a_t = torch.full(size, alphas[index], device=device) a_prev = torch.full(size, alphas_prev[index], device=device) sigma_t = torch.full(size, sigmas[index], device=device) sqrt_one_minus_at = torch.full(size, sqrt_one_minus_alphas[index],device=device) # current prediction for x_0 if self.model.parameterization != "v": pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() else: pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output) if self.model.use_dynamic_rescale: scale_t = torch.full(size, self.ddim_scale_arr[index], device=device) prev_scale_t = torch.full(size, self.ddim_scale_arr_prev[index], device=device) rescale = (prev_scale_t / scale_t) pred_x0 *= rescale if quantize_denoised: pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) # 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 decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, use_original_steps=False, callback=None): 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) if callback: callback(i) return x_dec @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)