import numpy as np from tqdm import tqdm import torch from lvdm.models.utils_diffusion import make_ddim_sampling_parameters, make_ddim_timesteps from lvdm.common import 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 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) 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)) self.use_scale = self.model.use_scale if self.use_scale: self.register_buffer('scale_arr', to_torch(self.model.scale_arr)) ddim_scale_arr = self.scale_arr.cpu()[self.ddim_timesteps] self.register_buffer('ddim_scale_arr', ddim_scale_arr) ddim_scale_arr = np.asarray([self.scale_arr.cpu()[0]] + self.scale_arr.cpu()[self.ddim_timesteps[:-1]].tolist()) self.register_buffer('ddim_scale_arr_prev', ddim_scale_arr) # 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, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... **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_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) # 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, verbose=verbose, **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, cond_tau=1., target_size=None, start_timesteps=None, **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 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 init_x0 = False clean_cond = kwargs.pop("clean_cond", False) for i, step in enumerate(iterator): index = total_steps - i - 1 ts = torch.full((b,), step, device=device, dtype=torch.long) if start_timesteps is not None: assert x0 is not None if step > start_timesteps*time_range[0]: continue elif not init_x0: img = self.model.q_sample(x0, ts) init_x0 = True # 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 index_clip = int((1 - cond_tau) * total_steps) if index <= index_clip and target_size is not None: target_size_ = [target_size[0], target_size[1]//8, target_size[2]//8] img = torch.nn.functional.interpolate( img, size=target_size_, mode="nearest", ) 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, x0=x0, **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, **kwargs): b, *_, device = *x.shape, x.device if x.dim() == 5: is_video = True else: is_video = False uncond_kwargs = kwargs.copy() uncond_kwargs['append_to_context'] = None if unconditional_conditioning is None or unconditional_guidance_scale == 1.: e_t = self.model.apply_model(x, t, c, **kwargs) # unet denoiser else: # with unconditional condition if isinstance(c, torch.Tensor): e_t = self.model.apply_model(x, t, c, **kwargs) e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **uncond_kwargs) elif isinstance(c, dict): e_t = self.model.apply_model(x, t, c, **kwargs) e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **uncond_kwargs) else: raise NotImplementedError # text cfg if uc_type is None: e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) else: if uc_type == 'cfg_original': e_t = e_t + unconditional_guidance_scale * (e_t - e_t_uncond) elif uc_type == 'cfg_ours': e_t = e_t + unconditional_guidance_scale * (e_t_uncond - e_t) else: raise NotImplementedError # temporal guidance if conditional_guidance_scale_temporal is not None: e_t_temporal = self.model.apply_model(x, t, c, **kwargs) e_t_image = self.model.apply_model(x, t, c, no_temporal_attn=True, **kwargs) e_t = e_t + conditional_guidance_scale_temporal * (e_t_temporal - e_t_image) 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 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 # 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 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. - 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) alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas if self.use_scale: scale_arr = self.model.scale_arr if use_original_steps else self.ddim_scale_arr scale_t = torch.full(size, scale_arr[index], device=device) scale_arr_prev = self.model.scale_arr_prev if use_original_steps else self.ddim_scale_arr_prev scale_t_prev = torch.full(size, scale_arr_prev[index], device=device) pred_x0 /= scale_t x_prev = a_prev.sqrt() * scale_t_prev * pred_x0 + dir_xt + noise else: x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise return x_prev, pred_x0 @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) def extract_into_tensor(a, t, x_shape): b, *_ = t.shape out = a.gather(-1, t) return out.reshape(b, *((1,) * (len(x_shape) - 1))) 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 class DDIMStyleSampler(DDIMSampler): @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_guidance_scale_style=None, unconditional_conditioning=None, uc_type=None, conditional_guidance_scale_temporal=None, **kwargs): b, *_, device = *x.shape, x.device if x.dim() == 5: is_video = True else: is_video = False uncond_kwargs = kwargs.copy() uncond_kwargs['append_to_context'] = None if unconditional_conditioning is None or unconditional_guidance_scale == 1.: e_t = self.model.apply_model(x, t, c, **kwargs) # unet denoiser else: # with unconditional condition if isinstance(c, torch.Tensor): e_t = self.model.apply_model(x, t, c, **kwargs) e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **uncond_kwargs) if unconditional_guidance_scale_style is not None: e_t_uncond_style = self.model.apply_model(x, t, c, **uncond_kwargs) elif isinstance(c, dict): e_t = self.model.apply_model(x, t, c, **kwargs) e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **uncond_kwargs) if unconditional_guidance_scale_style is not None: e_t_uncond_style = self.model.apply_model(x, t, c, **uncond_kwargs) else: raise NotImplementedError if unconditional_guidance_scale_style is None: e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) else: e_t = e_t + unconditional_guidance_scale_style * (e_t - e_t_uncond_style) + \ unconditional_guidance_scale * (e_t_uncond_style - e_t_uncond) # temporal guidance if conditional_guidance_scale_temporal is not None: e_t_temporal = self.model.apply_model(x, t, c, **kwargs) e_t_image = self.model.apply_model(x, t, c, no_temporal_attn=True, **kwargs) e_t = e_t + conditional_guidance_scale_temporal * (e_t_temporal - e_t_image) 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 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 # 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 pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() # print(f't={t}, pred_x0, min={torch.min(pred_x0)}, max={torch.max(pred_x0)}',file=f) 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 # # norm pred_x0 # p=2 # s=() # pred_x0 = pred_x0 - torch.max(torch.abs(pred_x0)) 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