Spaces:
Running
on
Zero
Running
on
Zero
| """ | |
| wild mixture of | |
| https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py | |
| https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py | |
| https://github.com/CompVis/taming-transformers | |
| -- merci | |
| """ | |
| from functools import partial | |
| from contextlib import contextmanager | |
| import numpy as np | |
| from tqdm import tqdm | |
| from einops import rearrange, repeat | |
| import logging | |
| mainlogger = logging.getLogger('mainlogger') | |
| import torch | |
| import torch.nn as nn | |
| from torchvision.utils import make_grid | |
| import pytorch_lightning as pl | |
| from utils.utils import instantiate_from_config | |
| from lvdm.ema import LitEma | |
| from lvdm.distributions import DiagonalGaussianDistribution | |
| from lvdm.models.utils_diffusion import make_beta_schedule, rescale_zero_terminal_snr | |
| from lvdm.basics import disabled_train | |
| from lvdm.common import ( | |
| extract_into_tensor, | |
| noise_like, | |
| exists, | |
| default | |
| ) | |
| __conditioning_keys__ = {'concat': 'c_concat', | |
| 'crossattn': 'c_crossattn', | |
| 'adm': 'y'} | |
| class DDPM(pl.LightningModule): | |
| # classic DDPM with Gaussian diffusion, in image space | |
| def __init__(self, | |
| unet_config, | |
| timesteps=1000, | |
| beta_schedule="linear", | |
| loss_type="l2", | |
| ckpt_path=None, | |
| ignore_keys=[], | |
| load_only_unet=False, | |
| monitor=None, | |
| use_ema=True, | |
| first_stage_key="image", | |
| image_size=256, | |
| channels=3, | |
| log_every_t=100, | |
| clip_denoised=True, | |
| linear_start=1e-4, | |
| linear_end=2e-2, | |
| cosine_s=8e-3, | |
| given_betas=None, | |
| original_elbo_weight=0., | |
| v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta | |
| l_simple_weight=1., | |
| conditioning_key=None, | |
| parameterization="eps", # all assuming fixed variance schedules | |
| scheduler_config=None, | |
| use_positional_encodings=False, | |
| learn_logvar=False, | |
| logvar_init=0., | |
| rescale_betas_zero_snr=False, | |
| ): | |
| super().__init__() | |
| assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"' | |
| self.parameterization = parameterization | |
| mainlogger.info(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") | |
| self.cond_stage_model = None | |
| self.clip_denoised = clip_denoised | |
| self.log_every_t = log_every_t | |
| self.first_stage_key = first_stage_key | |
| self.channels = channels | |
| self.temporal_length = unet_config.params.temporal_length | |
| self.image_size = image_size # try conv? | |
| if isinstance(self.image_size, int): | |
| self.image_size = [self.image_size, self.image_size] | |
| self.use_positional_encodings = use_positional_encodings | |
| self.model = DiffusionWrapper(unet_config, conditioning_key) | |
| #count_params(self.model, verbose=True) | |
| self.use_ema = use_ema | |
| self.rescale_betas_zero_snr = rescale_betas_zero_snr | |
| if self.use_ema: | |
| self.model_ema = LitEma(self.model) | |
| mainlogger.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") | |
| self.use_scheduler = scheduler_config is not None | |
| if self.use_scheduler: | |
| self.scheduler_config = scheduler_config | |
| self.v_posterior = v_posterior | |
| self.original_elbo_weight = original_elbo_weight | |
| self.l_simple_weight = l_simple_weight | |
| if monitor is not None: | |
| self.monitor = monitor | |
| if ckpt_path is not None: | |
| self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet) | |
| self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps, | |
| linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) | |
| self.loss_type = loss_type | |
| self.learn_logvar = learn_logvar | |
| self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,)) | |
| if self.learn_logvar: | |
| self.logvar = nn.Parameter(self.logvar, requires_grad=True) | |
| def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, | |
| linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): | |
| if exists(given_betas): | |
| betas = given_betas | |
| else: | |
| betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, | |
| cosine_s=cosine_s) | |
| if self.rescale_betas_zero_snr: | |
| betas = rescale_zero_terminal_snr(betas) | |
| alphas = 1. - betas | |
| alphas_cumprod = np.cumprod(alphas, axis=0) | |
| alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) | |
| timesteps, = betas.shape | |
| self.num_timesteps = int(timesteps) | |
| self.linear_start = linear_start | |
| self.linear_end = linear_end | |
| assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' | |
| to_torch = partial(torch.tensor, dtype=torch.float32) | |
| self.register_buffer('betas', to_torch(betas)) | |
| self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) | |
| self.register_buffer('alphas_cumprod_prev', to_torch(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))) | |
| self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) | |
| self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) | |
| if self.parameterization != 'v': | |
| self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) | |
| self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) | |
| else: | |
| self.register_buffer('sqrt_recip_alphas_cumprod', torch.zeros_like(to_torch(alphas_cumprod))) | |
| self.register_buffer('sqrt_recipm1_alphas_cumprod', torch.zeros_like(to_torch(alphas_cumprod))) | |
| # calculations for posterior q(x_{t-1} | x_t, x_0) | |
| posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( | |
| 1. - alphas_cumprod) + self.v_posterior * betas | |
| # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) | |
| self.register_buffer('posterior_variance', to_torch(posterior_variance)) | |
| # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain | |
| self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) | |
| self.register_buffer('posterior_mean_coef1', to_torch( | |
| betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) | |
| self.register_buffer('posterior_mean_coef2', to_torch( | |
| (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) | |
| if self.parameterization == "eps": | |
| lvlb_weights = self.betas ** 2 / ( | |
| 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) | |
| elif self.parameterization == "x0": | |
| lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod)) | |
| elif self.parameterization == "v": | |
| lvlb_weights = torch.ones_like(self.betas ** 2 / ( | |
| 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))) | |
| else: | |
| raise NotImplementedError("mu not supported") | |
| # TODO how to choose this term | |
| lvlb_weights[0] = lvlb_weights[1] | |
| self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) | |
| assert not torch.isnan(self.lvlb_weights).all() | |
| def ema_scope(self, context=None): | |
| if self.use_ema: | |
| self.model_ema.store(self.model.parameters()) | |
| self.model_ema.copy_to(self.model) | |
| if context is not None: | |
| mainlogger.info(f"{context}: Switched to EMA weights") | |
| try: | |
| yield None | |
| finally: | |
| if self.use_ema: | |
| self.model_ema.restore(self.model.parameters()) | |
| if context is not None: | |
| mainlogger.info(f"{context}: Restored training weights") | |
| def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): | |
| sd = torch.load(path, map_location="cpu") | |
| if "state_dict" in list(sd.keys()): | |
| sd = sd["state_dict"] | |
| keys = list(sd.keys()) | |
| for k in keys: | |
| for ik in ignore_keys: | |
| if k.startswith(ik): | |
| mainlogger.info("Deleting key {} from state_dict.".format(k)) | |
| del sd[k] | |
| missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( | |
| sd, strict=False) | |
| mainlogger.info(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") | |
| if len(missing) > 0: | |
| mainlogger.info(f"Missing Keys: {missing}") | |
| if len(unexpected) > 0: | |
| mainlogger.info(f"Unexpected Keys: {unexpected}") | |
| def q_mean_variance(self, x_start, t): | |
| """ | |
| Get the distribution q(x_t | x_0). | |
| :param x_start: the [N x C x ...] tensor of noiseless inputs. | |
| :param t: the number of diffusion steps (minus 1). Here, 0 means one step. | |
| :return: A tuple (mean, variance, log_variance), all of x_start's shape. | |
| """ | |
| mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) | |
| variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) | |
| log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) | |
| return mean, variance, log_variance | |
| def predict_start_from_noise(self, x_t, t, noise): | |
| return ( | |
| extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - | |
| extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise | |
| ) | |
| def predict_start_from_z_and_v(self, x_t, t, v): | |
| # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) | |
| # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) | |
| return ( | |
| extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t - | |
| extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v | |
| ) | |
| def predict_eps_from_z_and_v(self, x_t, t, v): | |
| return ( | |
| extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v + | |
| extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t | |
| ) | |
| def q_posterior(self, x_start, x_t, t): | |
| posterior_mean = ( | |
| extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + | |
| extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t | |
| ) | |
| posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) | |
| posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) | |
| return posterior_mean, posterior_variance, posterior_log_variance_clipped | |
| def p_mean_variance(self, x, t, clip_denoised: bool): | |
| model_out = self.model(x, t) | |
| if self.parameterization == "eps": | |
| x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) | |
| elif self.parameterization == "x0": | |
| x_recon = model_out | |
| if clip_denoised: | |
| x_recon.clamp_(-1., 1.) | |
| model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) | |
| return model_mean, posterior_variance, posterior_log_variance | |
| def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): | |
| b, *_, device = *x.shape, x.device | |
| model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) | |
| noise = noise_like(x.shape, device, repeat_noise) | |
| # no noise when t == 0 | |
| nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) | |
| return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise | |
| def p_sample_loop(self, shape, return_intermediates=False): | |
| device = self.betas.device | |
| b = shape[0] | |
| img = torch.randn(shape, device=device) | |
| intermediates = [img] | |
| for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps): | |
| img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long), | |
| clip_denoised=self.clip_denoised) | |
| if i % self.log_every_t == 0 or i == self.num_timesteps - 1: | |
| intermediates.append(img) | |
| if return_intermediates: | |
| return img, intermediates | |
| return img | |
| def sample(self, batch_size=16, return_intermediates=False): | |
| image_size = self.image_size | |
| channels = self.channels | |
| return self.p_sample_loop((batch_size, channels, image_size, image_size), | |
| return_intermediates=return_intermediates) | |
| def q_sample(self, x_start, t, noise=None): | |
| noise = default(noise, lambda: torch.randn_like(x_start)) | |
| return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + | |
| extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) | |
| def get_v(self, x, noise, t): | |
| return ( | |
| extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise - | |
| extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x | |
| ) | |
| def get_input(self, batch, k): | |
| x = batch[k] | |
| x = x.to(memory_format=torch.contiguous_format).float() | |
| return x | |
| def _get_rows_from_list(self, samples): | |
| n_imgs_per_row = len(samples) | |
| denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') | |
| denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') | |
| denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) | |
| return denoise_grid | |
| def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): | |
| log = dict() | |
| x = self.get_input(batch, self.first_stage_key) | |
| N = min(x.shape[0], N) | |
| n_row = min(x.shape[0], n_row) | |
| x = x.to(self.device)[:N] | |
| log["inputs"] = x | |
| # get diffusion row | |
| diffusion_row = list() | |
| x_start = x[:n_row] | |
| for t in range(self.num_timesteps): | |
| if t % self.log_every_t == 0 or t == self.num_timesteps - 1: | |
| t = repeat(torch.tensor([t]), '1 -> b', b=n_row) | |
| t = t.to(self.device).long() | |
| noise = torch.randn_like(x_start) | |
| x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) | |
| diffusion_row.append(x_noisy) | |
| log["diffusion_row"] = self._get_rows_from_list(diffusion_row) | |
| if sample: | |
| # get denoise row | |
| with self.ema_scope("Plotting"): | |
| samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) | |
| log["samples"] = samples | |
| log["denoise_row"] = self._get_rows_from_list(denoise_row) | |
| if return_keys: | |
| if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: | |
| return log | |
| else: | |
| return {key: log[key] for key in return_keys} | |
| return log | |
| class LatentDiffusion(DDPM): | |
| """main class""" | |
| def __init__(self, | |
| first_stage_config, | |
| cond_stage_config, | |
| num_timesteps_cond=None, | |
| cond_stage_key="caption", | |
| cond_stage_trainable=False, | |
| cond_stage_forward=None, | |
| conditioning_key=None, | |
| uncond_prob=0.2, | |
| uncond_type="empty_seq", | |
| scale_factor=1.0, | |
| scale_by_std=False, | |
| encoder_type="2d", | |
| only_model=False, | |
| noise_strength=0, | |
| use_dynamic_rescale=False, | |
| base_scale=0.7, | |
| turning_step=400, | |
| loop_video=False, | |
| fps_condition_type='fs', | |
| perframe_ae=False, | |
| *args, **kwargs): | |
| self.num_timesteps_cond = default(num_timesteps_cond, 1) | |
| self.scale_by_std = scale_by_std | |
| assert self.num_timesteps_cond <= kwargs['timesteps'] | |
| # for backwards compatibility after implementation of DiffusionWrapper | |
| ckpt_path = kwargs.pop("ckpt_path", None) | |
| ignore_keys = kwargs.pop("ignore_keys", []) | |
| conditioning_key = default(conditioning_key, 'crossattn') | |
| super().__init__(conditioning_key=conditioning_key, *args, **kwargs) | |
| self.cond_stage_trainable = cond_stage_trainable | |
| self.cond_stage_key = cond_stage_key | |
| self.noise_strength = noise_strength | |
| self.use_dynamic_rescale = use_dynamic_rescale | |
| self.loop_video = loop_video | |
| self.fps_condition_type = fps_condition_type | |
| self.perframe_ae = perframe_ae | |
| try: | |
| self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 | |
| except: | |
| self.num_downs = 0 | |
| if not scale_by_std: | |
| self.scale_factor = scale_factor | |
| else: | |
| self.register_buffer('scale_factor', torch.tensor(scale_factor)) | |
| if use_dynamic_rescale: | |
| scale_arr1 = np.linspace(1.0, base_scale, turning_step) | |
| scale_arr2 = np.full(self.num_timesteps, base_scale) | |
| scale_arr = np.concatenate((scale_arr1, scale_arr2)) | |
| to_torch = partial(torch.tensor, dtype=torch.float32) | |
| self.register_buffer('scale_arr', to_torch(scale_arr)) | |
| self.instantiate_first_stage(first_stage_config) | |
| self.instantiate_cond_stage(cond_stage_config) | |
| self.first_stage_config = first_stage_config | |
| self.cond_stage_config = cond_stage_config | |
| self.clip_denoised = False | |
| self.cond_stage_forward = cond_stage_forward | |
| self.encoder_type = encoder_type | |
| assert(encoder_type in ["2d", "3d"]) | |
| self.uncond_prob = uncond_prob | |
| self.classifier_free_guidance = True if uncond_prob > 0 else False | |
| assert(uncond_type in ["zero_embed", "empty_seq"]) | |
| self.uncond_type = uncond_type | |
| self.restarted_from_ckpt = False | |
| if ckpt_path is not None: | |
| self.init_from_ckpt(ckpt_path, ignore_keys, only_model=only_model) | |
| self.restarted_from_ckpt = True | |
| def make_cond_schedule(self, ): | |
| self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) | |
| ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() | |
| self.cond_ids[:self.num_timesteps_cond] = ids | |
| def instantiate_first_stage(self, config): | |
| model = instantiate_from_config(config) | |
| self.first_stage_model = model.eval() | |
| self.first_stage_model.train = disabled_train | |
| for param in self.first_stage_model.parameters(): | |
| param.requires_grad = False | |
| def instantiate_cond_stage(self, config): | |
| if not self.cond_stage_trainable: | |
| model = instantiate_from_config(config) | |
| self.cond_stage_model = model.eval() | |
| self.cond_stage_model.train = disabled_train | |
| for param in self.cond_stage_model.parameters(): | |
| param.requires_grad = False | |
| else: | |
| model = instantiate_from_config(config) | |
| self.cond_stage_model = model | |
| def get_learned_conditioning(self, c): | |
| if self.cond_stage_forward is None: | |
| if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): | |
| c = self.cond_stage_model.encode(c) | |
| if isinstance(c, DiagonalGaussianDistribution): | |
| c = c.mode() | |
| else: | |
| c = self.cond_stage_model(c) | |
| else: | |
| assert hasattr(self.cond_stage_model, self.cond_stage_forward) | |
| c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) | |
| return c | |
| def get_first_stage_encoding(self, encoder_posterior, noise=None): | |
| if isinstance(encoder_posterior, DiagonalGaussianDistribution): | |
| z = encoder_posterior.sample(noise=noise) | |
| elif isinstance(encoder_posterior, torch.Tensor): | |
| z = encoder_posterior | |
| else: | |
| raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") | |
| return self.scale_factor * z | |
| def encode_first_stage(self, x): | |
| if self.encoder_type == "2d" and x.dim() == 5: | |
| b, _, t, _, _ = x.shape | |
| x = rearrange(x, 'b c t h w -> (b t) c h w') | |
| reshape_back = True | |
| else: | |
| reshape_back = False | |
| ## consume more GPU memory but faster | |
| if not self.perframe_ae: | |
| encoder_posterior = self.first_stage_model.encode(x) | |
| results = self.get_first_stage_encoding(encoder_posterior).detach() | |
| else: ## consume less GPU memory but slower | |
| results = [] | |
| for index in range(x.shape[0]): | |
| frame_batch = self.first_stage_model.encode(x[index:index+1,:,:,:]) | |
| frame_result = self.get_first_stage_encoding(frame_batch).detach() | |
| results.append(frame_result) | |
| results = torch.cat(results, dim=0) | |
| if reshape_back: | |
| results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t) | |
| return results | |
| def decode_core(self, z, **kwargs): | |
| if self.encoder_type == "2d" and z.dim() == 5: | |
| b, _, t, _, _ = z.shape | |
| z = rearrange(z, 'b c t h w -> (b t) c h w') | |
| reshape_back = True | |
| else: | |
| reshape_back = False | |
| if not self.perframe_ae: | |
| z = 1. / self.scale_factor * z | |
| results = self.first_stage_model.decode(z, **kwargs) | |
| else: | |
| results = [] | |
| for index in range(z.shape[0]): | |
| frame_z = 1. / self.scale_factor * z[index:index+1,:,:,:] | |
| frame_result = self.first_stage_model.decode(frame_z, **kwargs) | |
| results.append(frame_result) | |
| results = torch.cat(results, dim=0) | |
| if reshape_back: | |
| results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t) | |
| return results | |
| def decode_first_stage(self, z, **kwargs): | |
| return self.decode_core(z, **kwargs) | |
| # same as above but without decorator | |
| def differentiable_decode_first_stage(self, z, **kwargs): | |
| return self.decode_core(z, **kwargs) | |
| def forward(self, x, c, **kwargs): | |
| t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() | |
| if self.use_dynamic_rescale: | |
| x = x * extract_into_tensor(self.scale_arr, t, x.shape) | |
| return self.p_losses(x, c, t, **kwargs) | |
| def apply_model(self, x_noisy, t, cond, **kwargs): | |
| if isinstance(cond, dict): | |
| # hybrid case, cond is exptected to be a dict | |
| pass | |
| else: | |
| if not isinstance(cond, list): | |
| cond = [cond] | |
| key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' | |
| cond = {key: cond} | |
| x_recon = self.model(x_noisy, t, **cond, **kwargs) | |
| if isinstance(x_recon, tuple): | |
| return x_recon[0] | |
| else: | |
| return x_recon | |
| def _get_denoise_row_from_list(self, samples, desc=''): | |
| denoise_row = [] | |
| for zd in tqdm(samples, desc=desc): | |
| denoise_row.append(self.decode_first_stage(zd.to(self.device))) | |
| n_log_timesteps = len(denoise_row) | |
| denoise_row = torch.stack(denoise_row) # n_log_timesteps, b, C, H, W | |
| if denoise_row.dim() == 5: | |
| denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') | |
| denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') | |
| denoise_grid = make_grid(denoise_grid, nrow=n_log_timesteps) | |
| elif denoise_row.dim() == 6: | |
| # video, grid_size=[n_log_timesteps*bs, t] | |
| video_length = denoise_row.shape[3] | |
| denoise_grid = rearrange(denoise_row, 'n b c t h w -> b n c t h w') | |
| denoise_grid = rearrange(denoise_grid, 'b n c t h w -> (b n) c t h w') | |
| denoise_grid = rearrange(denoise_grid, 'n c t h w -> (n t) c h w') | |
| denoise_grid = make_grid(denoise_grid, nrow=video_length) | |
| else: | |
| raise ValueError | |
| return denoise_grid | |
| def p_mean_variance(self, x, c, t, clip_denoised: bool, return_x0=False, score_corrector=None, corrector_kwargs=None, **kwargs): | |
| t_in = t | |
| model_out = self.apply_model(x, t_in, c, **kwargs) | |
| if score_corrector is not None: | |
| assert self.parameterization == "eps" | |
| model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) | |
| if self.parameterization == "eps": | |
| x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) | |
| elif self.parameterization == "x0": | |
| x_recon = model_out | |
| else: | |
| raise NotImplementedError() | |
| if clip_denoised: | |
| x_recon.clamp_(-1., 1.) | |
| model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) | |
| if return_x0: | |
| return model_mean, posterior_variance, posterior_log_variance, x_recon | |
| else: | |
| return model_mean, posterior_variance, posterior_log_variance | |
| def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, return_x0=False, \ | |
| temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, **kwargs): | |
| b, *_, device = *x.shape, x.device | |
| outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, return_x0=return_x0, \ | |
| score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, **kwargs) | |
| if return_x0: | |
| model_mean, _, model_log_variance, x0 = outputs | |
| else: | |
| model_mean, _, model_log_variance = outputs | |
| noise = noise_like(x.shape, device, repeat_noise) * temperature | |
| if noise_dropout > 0.: | |
| noise = torch.nn.functional.dropout(noise, p=noise_dropout) | |
| # no noise when t == 0 | |
| nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) | |
| if return_x0: | |
| return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 | |
| else: | |
| return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise | |
| def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, \ | |
| timesteps=None, mask=None, x0=None, img_callback=None, start_T=None, log_every_t=None, **kwargs): | |
| if not log_every_t: | |
| log_every_t = self.log_every_t | |
| device = self.betas.device | |
| b = shape[0] | |
| # sample an initial noise | |
| if x_T is None: | |
| img = torch.randn(shape, device=device) | |
| else: | |
| img = x_T | |
| intermediates = [img] | |
| if timesteps is None: | |
| timesteps = self.num_timesteps | |
| if start_T is not None: | |
| timesteps = min(timesteps, start_T) | |
| iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(range(0, timesteps)) | |
| if mask is not None: | |
| assert x0 is not None | |
| assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match | |
| for i in iterator: | |
| ts = torch.full((b,), i, device=device, dtype=torch.long) | |
| if self.shorten_cond_schedule: | |
| assert self.model.conditioning_key != 'hybrid' | |
| tc = self.cond_ids[ts].to(cond.device) | |
| cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) | |
| img = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, **kwargs) | |
| if mask is not None: | |
| img_orig = self.q_sample(x0, ts) | |
| img = img_orig * mask + (1. - mask) * img | |
| if i % log_every_t == 0 or i == timesteps - 1: | |
| intermediates.append(img) | |
| if callback: callback(i) | |
| if img_callback: img_callback(img, i) | |
| if return_intermediates: | |
| return img, intermediates | |
| return img | |
| class LatentVisualDiffusion(LatentDiffusion): | |
| def __init__(self, img_cond_stage_config, image_proj_stage_config, freeze_embedder=True, *args, **kwargs): | |
| super().__init__(*args, **kwargs) | |
| self._init_embedder(img_cond_stage_config, freeze_embedder) | |
| self.image_proj_model = instantiate_from_config(image_proj_stage_config) | |
| def _init_embedder(self, config, freeze=True): | |
| embedder = instantiate_from_config(config) | |
| if freeze: | |
| self.embedder = embedder.eval() | |
| self.embedder.train = disabled_train | |
| for param in self.embedder.parameters(): | |
| param.requires_grad = False | |
| class DiffusionWrapper(pl.LightningModule): | |
| def __init__(self, diff_model_config, conditioning_key): | |
| super().__init__() | |
| self.diffusion_model = instantiate_from_config(diff_model_config) | |
| self.conditioning_key = conditioning_key | |
| def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, | |
| c_adm=None, s=None, mask=None, **kwargs): | |
| # temporal_context = fps is foNone | |
| if self.conditioning_key is None: | |
| out = self.diffusion_model(x, t) | |
| elif self.conditioning_key == 'concat': | |
| xc = torch.cat([x] + c_concat, dim=1) | |
| out = self.diffusion_model(xc, t, **kwargs) | |
| elif self.conditioning_key == 'crossattn': | |
| cc = torch.cat(c_crossattn, 1) | |
| out = self.diffusion_model(x, t, context=cc, **kwargs) | |
| elif self.conditioning_key == 'hybrid': | |
| ## it is just right [b,c,t,h,w]: concatenate in channel dim | |
| xc = torch.cat([x] + c_concat, dim=1) | |
| cc = torch.cat(c_crossattn, 1) | |
| out = self.diffusion_model(xc, t, context=cc, **kwargs) | |
| elif self.conditioning_key == 'resblockcond': | |
| cc = c_crossattn[0] | |
| out = self.diffusion_model(x, t, context=cc) | |
| elif self.conditioning_key == 'adm': | |
| cc = c_crossattn[0] | |
| out = self.diffusion_model(x, t, y=cc) | |
| elif self.conditioning_key == 'hybrid-adm': | |
| assert c_adm is not None | |
| xc = torch.cat([x] + c_concat, dim=1) | |
| cc = torch.cat(c_crossattn, 1) | |
| out = self.diffusion_model(xc, t, context=cc, y=c_adm, **kwargs) | |
| elif self.conditioning_key == 'hybrid-time': | |
| assert s is not None | |
| xc = torch.cat([x] + c_concat, dim=1) | |
| cc = torch.cat(c_crossattn, 1) | |
| out = self.diffusion_model(xc, t, context=cc, s=s) | |
| elif self.conditioning_key == 'concat-time-mask': | |
| # assert s is not None | |
| xc = torch.cat([x] + c_concat, dim=1) | |
| out = self.diffusion_model(xc, t, context=None, s=s, mask=mask) | |
| elif self.conditioning_key == 'concat-adm-mask': | |
| # assert s is not None | |
| if c_concat is not None: | |
| xc = torch.cat([x] + c_concat, dim=1) | |
| else: | |
| xc = x | |
| out = self.diffusion_model(xc, t, context=None, y=s, mask=mask) | |
| elif self.conditioning_key == 'hybrid-adm-mask': | |
| cc = torch.cat(c_crossattn, 1) | |
| if c_concat is not None: | |
| xc = torch.cat([x] + c_concat, dim=1) | |
| else: | |
| xc = x | |
| out = self.diffusion_model(xc, t, context=cc, y=s, mask=mask) | |
| elif self.conditioning_key == 'hybrid-time-adm': # adm means y, e.g., class index | |
| # assert s is not None | |
| assert c_adm is not None | |
| xc = torch.cat([x] + c_concat, dim=1) | |
| cc = torch.cat(c_crossattn, 1) | |
| out = self.diffusion_model(xc, t, context=cc, s=s, y=c_adm) | |
| elif self.conditioning_key == 'crossattn-adm': | |
| assert c_adm is not None | |
| cc = torch.cat(c_crossattn, 1) | |
| out = self.diffusion_model(x, t, context=cc, y=c_adm) | |
| else: | |
| raise NotImplementedError() | |
| return out |