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| 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) | |
| 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 | |
| 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? <ddim inversion> | |
| 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 | |
| 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 | |
| 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) | |
| 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): | |
| 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 |