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ldm/models/diffusion/ddpm.py
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Robin Rombach
Update ddpm.py
17 янв 2022, 23:24
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17 янв 2022, 23:24
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""" wild mixture of https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py https://github.com/CompVis/taming-transformers -- merci """ import torch import torch.nn as nn import numpy as np import pytorch_lightning as pl from torch.optim.lr_scheduler import LambdaLR from einops import rearrange, repeat from contextlib import contextmanager from functools import partial from tqdm import tqdm from torchvision.utils import make_grid from pytorch_lightning.utilities.distributed import rank_zero_only from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config from ldm.modules.ema import LitEma from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like from ldm.models.diffusion.ddim import DDIMSampler __conditioning_keys__ = {'concat': 'c_concat', 'crossattn': 'c_crossattn', 'adm': 'y'} def disabled_train(self, mode=True): """Overwrite model.train with this function to make sure train/eval mode does not change anymore.""" return self def uniform_on_device(r1, r2, shape, device): return (r1 - r2) * torch.rand(*shape, device=device) + r2 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="val/loss", 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., ): super().__init__() assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"' self.parameterization = parameterization print(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.image_size = image_size # try conv? self.channels = channels 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 if self.use_ema: self.model_ema = LitEma(self.model) print(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) 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))) 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))) # 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)) 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() @contextmanager 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: print(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: print(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): print("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) print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") if len(missing) > 0: print(f"Missing Keys: {missing}") if len(unexpected) > 0: print(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 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 @torch.no_grad() 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 @torch.no_grad() 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 @torch.no_grad() 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_loss(self, pred, target, mean=True): if self.loss_type == 'l1': loss = (target - pred).abs() if mean: loss = loss.mean() elif self.loss_type == 'l2': if mean: loss = torch.nn.functional.mse_loss(target, pred) else: loss = torch.nn.functional.mse_loss(target, pred, reduction='none') else: raise NotImplementedError("unknown loss type '{loss_type}'") return loss def p_losses(self, x_start, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) model_out = self.model(x_noisy, t) loss_dict = {} if self.parameterization == "eps": target = noise elif self.parameterization == "x0": target = x_start else: raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported") loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) log_prefix = 'train' if self.training else 'val' loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()}) loss_simple = loss.mean() * self.l_simple_weight loss_vlb = (self.lvlb_weights[t] * loss).mean() loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb}) loss = loss_simple + self.original_elbo_weight * loss_vlb loss_dict.update({f'{log_prefix}/loss': loss}) return loss, loss_dict def forward(self, x, *args, **kwargs): # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size # assert h == img_size and w == img_size, f'height and width of image must be {img_size}' t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() return self.p_losses(x, t, *args, **kwargs) def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = rearrange(x, 'b h w c -> b c h w') x = x.to(memory_format=torch.contiguous_format).float() return x def shared_step(self, batch): x = self.get_input(batch, self.first_stage_key) loss, loss_dict = self(x) return loss, loss_dict def training_step(self, batch, batch_idx): loss, loss_dict = self.shared_step(batch) self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True) self.log("global_step", self.global_step, prog_bar=True, logger=True, on_step=True, on_epoch=False) if self.use_scheduler: lr = self.optimizers().param_groups[0]['lr'] self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) return loss @torch.no_grad() def validation_step(self, batch, batch_idx): _, loss_dict_no_ema = self.shared_step(batch) with self.ema_scope(): _, loss_dict_ema = self.shared_step(batch) loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema} self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) def on_train_batch_end(self, *args, **kwargs): if self.use_ema: self.model_ema(self.model) 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 @torch.no_grad() 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 def configure_optimizers(self): lr = self.learning_rate params = list(self.model.parameters()) if self.learn_logvar: params = params + [self.logvar] opt = torch.optim.AdamW(params, lr=lr) return opt class LatentDiffusion(DDPM): """main class""" def __init__(self, first_stage_config, cond_stage_config, num_timesteps_cond=None, cond_stage_key="image", cond_stage_trainable=False, concat_mode=True, cond_stage_forward=None, conditioning_key=None, scale_factor=1.0, scale_by_std=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 if conditioning_key is None: conditioning_key = 'concat' if concat_mode else 'crossattn' if cond_stage_config == '__is_unconditional__': conditioning_key = None ckpt_path = kwargs.pop("ckpt_path", None) ignore_keys = kwargs.pop("ignore_keys", []) super().__init__(conditioning_key=conditioning_key, *args, **kwargs) self.concat_mode = concat_mode self.cond_stage_trainable = cond_stage_trainable self.cond_stage_key = cond_stage_key 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)) self.instantiate_first_stage(first_stage_config) self.instantiate_cond_stage(cond_stage_config) self.cond_stage_forward = cond_stage_forward self.clip_denoised = False self.bbox_tokenizer = None self.restarted_from_ckpt = False if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys) 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 @rank_zero_only @torch.no_grad() def on_train_batch_start(self, batch, batch_idx, dataloader_idx): # only for very first batch if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' # set rescale weight to 1./std of encodings print("### USING STD-RESCALING ###") x = super().get_input(batch, self.first_stage_key) x = x.to(self.device) encoder_posterior = self.encode_first_stage(x) z = self.get_first_stage_encoding(encoder_posterior).detach() del self.scale_factor self.register_buffer('scale_factor', 1. / z.flatten().std()) print(f"setting self.scale_factor to {self.scale_factor}") print("### USING STD-RESCALING ###") def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) self.shorten_cond_schedule = self.num_timesteps_cond > 1 if self.shorten_cond_schedule: self.make_cond_schedule() 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: if config == "__is_first_stage__": print("Using first stage also as cond stage.") self.cond_stage_model = self.first_stage_model elif config == "__is_unconditional__": print(f"Training {self.__class__.__name__} as an unconditional model.") self.cond_stage_model = None # self.be_unconditional = True else: 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: assert config != '__is_first_stage__' assert config != '__is_unconditional__' model = instantiate_from_config(config) self.cond_stage_model = model def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): denoise_row = [] for zd in tqdm(samples, desc=desc): denoise_row.append(self.decode_first_stage(zd.to(self.device), force_not_quantize=force_no_decoder_quantization)) n_imgs_per_row = len(denoise_row) denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W 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_imgs_per_row) return denoise_grid def get_first_stage_encoding(self, encoder_posterior): if isinstance(encoder_posterior, DiagonalGaussianDistribution): z = encoder_posterior.sample() 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 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 meshgrid(self, h, w): y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) arr = torch.cat([y, x], dim=-1) return arr def delta_border(self, h, w): """ :param h: height :param w: width :return: normalized distance to image border, wtith min distance = 0 at border and max dist = 0.5 at image center """ lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) arr = self.meshgrid(h, w) / lower_right_corner dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] return edge_dist def get_weighting(self, h, w, Ly, Lx, device): weighting = self.delta_border(h, w) weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], self.split_input_params["clip_max_weight"], ) weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) if self.split_input_params["tie_braker"]: L_weighting = self.delta_border(Ly, Lx) L_weighting = torch.clip(L_weighting, self.split_input_params["clip_min_tie_weight"], self.split_input_params["clip_max_tie_weight"]) L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) weighting = weighting * L_weighting return weighting def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code """ :param x: img of size (bs, c, h, w) :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) """ bs, nc, h, w = x.shape # number of crops in image Ly = (h - kernel_size[0]) // stride[0] + 1 Lx = (w - kernel_size[1]) // stride[1] + 1 if uf == 1 and df == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) elif uf > 1 and df == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), dilation=1, padding=0, stride=(stride[0] * uf, stride[1] * uf)) fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) elif df > 1 and uf == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), dilation=1, padding=0, stride=(stride[0] // df, stride[1] // df)) fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) else: raise NotImplementedError return fold, unfold, normalization, weighting @torch.no_grad() def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, cond_key=None, return_original_cond=False, bs=None): x = super().get_input(batch, k) if bs is not None: x = x[:bs] x = x.to(self.device) encoder_posterior = self.encode_first_stage(x) z = self.get_first_stage_encoding(encoder_posterior).detach() if self.model.conditioning_key is not None: if cond_key is None: cond_key = self.cond_stage_key if cond_key != self.first_stage_key: if cond_key in ['caption', 'coordinates_bbox']: xc = batch[cond_key] elif cond_key == 'class_label': xc = batch else: xc = super().get_input(batch, cond_key).to(self.device) else: xc = x if not self.cond_stage_trainable or force_c_encode: if isinstance(xc, dict) or isinstance(xc, list): # import pudb; pudb.set_trace() c = self.get_learned_conditioning(xc) else: c = self.get_learned_conditioning(xc.to(self.device)) else: c = xc if bs is not None: c = c[:bs] if self.use_positional_encodings: pos_x, pos_y = self.compute_latent_shifts(batch) ckey = __conditioning_keys__[self.model.conditioning_key] c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y} else: c = None xc = None if self.use_positional_encodings: pos_x, pos_y = self.compute_latent_shifts(batch) c = {'pos_x': pos_x, 'pos_y': pos_y} out = [z, c] if return_first_stage_outputs: xrec = self.decode_first_stage(z) out.extend([x, xrec]) if return_original_cond: out.append(xc) return out @torch.no_grad() def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): if predict_cids: if z.dim() == 4: z = torch.argmax(z.exp(), dim=1).long() z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) z = rearrange(z, 'b h w c -> b c h w').contiguous() z = 1. / self.scale_factor * z if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) uf = self.split_input_params["vqf"] bs, nc, h, w = z.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) z = unfold(z) # (bn, nc * prod(**ks), L) # 1. Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) # 2. apply model loop over last dim if isinstance(self.first_stage_model, VQModelInterface): output_list = [self.first_stage_model.decode(z[:, :, :, :, i], force_not_quantize=predict_cids or force_not_quantize) for i in range(z.shape[-1])] else: output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) o = o * weighting # Reverse 1. reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization # norm is shape (1, 1, h, w) return decoded else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) # same as above but without decorator def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): if predict_cids: if z.dim() == 4: z = torch.argmax(z.exp(), dim=1).long() z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) z = rearrange(z, 'b h w c -> b c h w').contiguous() z = 1. / self.scale_factor * z if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) uf = self.split_input_params["vqf"] bs, nc, h, w = z.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) z = unfold(z) # (bn, nc * prod(**ks), L) # 1. Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) # 2. apply model loop over last dim if isinstance(self.first_stage_model, VQModelInterface): output_list = [self.first_stage_model.decode(z[:, :, :, :, i], force_not_quantize=predict_cids or force_not_quantize) for i in range(z.shape[-1])] else: output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) o = o * weighting # Reverse 1. reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization # norm is shape (1, 1, h, w) return decoded else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) @torch.no_grad() def encode_first_stage(self, x): if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) df = self.split_input_params["vqf"] self.split_input_params['original_image_size'] = x.shape[-2:] bs, nc, h, w = x.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df) z = unfold(x) # (bn, nc * prod(**ks), L) # Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) output_list = [self.first_stage_model.encode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) o = o * weighting # Reverse reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization return decoded else: return self.first_stage_model.encode(x) else: return self.first_stage_model.encode(x) def shared_step(self, batch, **kwargs): x, c = self.get_input(batch, self.first_stage_key) loss = self(x, c) return loss def forward(self, x, c, *args, **kwargs): t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() if self.model.conditioning_key is not None: assert c is not None if self.cond_stage_trainable: c = self.get_learned_conditioning(c) if self.shorten_cond_schedule: # TODO: drop this option tc = self.cond_ids[t].to(self.device) c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) return self.p_losses(x, c, t, *args, **kwargs) def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset def rescale_bbox(bbox): x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2]) y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3]) w = min(bbox[2] / crop_coordinates[2], 1 - x0) h = min(bbox[3] / crop_coordinates[3], 1 - y0) return x0, y0, w, h return [rescale_bbox(b) for b in bboxes] def apply_model(self, x_noisy, t, cond, return_ids=False): 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} if hasattr(self, "split_input_params"): assert len(cond) == 1 # todo can only deal with one conditioning atm assert not return_ids ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) h, w = x_noisy.shape[-2:] fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride) z = unfold(x_noisy) # (bn, nc * prod(**ks), L) # Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])] if self.cond_stage_key in ["image", "LR_image", "segmentation", 'bbox_img'] and self.model.conditioning_key: # todo check for completeness c_key = next(iter(cond.keys())) # get key c = next(iter(cond.values())) # get value assert (len(c) == 1) # todo extend to list with more than one elem c = c[0] # get element c = unfold(c) c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L ) cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])] elif self.cond_stage_key == 'coordinates_bbox': assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size' # assuming padding of unfold is always 0 and its dilation is always 1 n_patches_per_row = int((w - ks[0]) / stride[0] + 1) full_img_h, full_img_w = self.split_input_params['original_image_size'] # as we are operating on latents, we need the factor from the original image size to the # spatial latent size to properly rescale the crops for regenerating the bbox annotations num_downs = self.first_stage_model.encoder.num_resolutions - 1 rescale_latent = 2 ** (num_downs) # get top left postions of patches as conforming for the bbbox tokenizer, therefore we # need to rescale the tl patch coordinates to be in between (0,1) tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w, rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h) for patch_nr in range(z.shape[-1])] # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w) patch_limits = [(x_tl, y_tl, rescale_latent * ks[0] / full_img_w, rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates] # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates] # tokenize crop coordinates for the bounding boxes of the respective patches patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device) for bbox in patch_limits] # list of length l with tensors of shape (1, 2) print(patch_limits_tknzd[0].shape) # cut tknzd crop position from conditioning assert isinstance(cond, dict), 'cond must be dict to be fed into model' cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device) print(cut_cond.shape) adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]) adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n') print(adapted_cond.shape) adapted_cond = self.get_learned_conditioning(adapted_cond) print(adapted_cond.shape) adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]) print(adapted_cond.shape) cond_list = [{'c_crossattn': [e]} for e in adapted_cond] else: cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient # apply model by loop over crops output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])] assert not isinstance(output_list[0], tuple) # todo cant deal with multiple model outputs check this never happens o = torch.stack(output_list, axis=-1) o = o * weighting # Reverse reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together x_recon = fold(o) / normalization else: x_recon = self.model(x_noisy, t, **cond) if isinstance(x_recon, tuple) and not return_ids: return x_recon[0] else: return x_recon def _predict_eps_from_xstart(self, x_t, t, pred_xstart): return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) def _prior_bpd(self, x_start): """ Get the prior KL term for the variational lower-bound, measured in bits-per-dim. This term can't be optimized, as it only depends on the encoder. :param x_start: the [N x C x ...] tensor of inputs. :return: a batch of [N] KL values (in bits), one per batch element. """ batch_size = x_start.shape[0] t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) return mean_flat(kl_prior) / np.log(2.0) def p_losses(self, x_start, cond, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) model_output = self.apply_model(x_noisy, t, cond) loss_dict = {} prefix = 'train' if self.training else 'val' if self.parameterization == "x0": target = x_start elif self.parameterization == "eps": target = noise else: raise NotImplementedError() loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) logvar_t = self.logvar[t].to(self.device) loss = loss_simple / torch.exp(logvar_t) + logvar_t # loss = loss_simple / torch.exp(self.logvar) + self.logvar if self.learn_logvar: loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) loss_dict.update({'logvar': self.logvar.data.mean()}) loss = self.l_simple_weight * loss.mean() loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) loss += (self.original_elbo_weight * loss_vlb) loss_dict.update({f'{prefix}/loss': loss}) return loss, loss_dict def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, return_x0=False, score_corrector=None, corrector_kwargs=None): t_in = t model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) 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 return_codebook_ids: model_out, logits = model_out 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.) if quantize_denoised: x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) if return_codebook_ids: return model_mean, posterior_variance, posterior_log_variance, logits elif return_x0: return model_mean, posterior_variance, posterior_log_variance, x_recon else: return model_mean, posterior_variance, posterior_log_variance @torch.no_grad() def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, return_codebook_ids=False, quantize_denoised=False, return_x0=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): b, *_, device = *x.shape, x.device outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, return_codebook_ids=return_codebook_ids, quantize_denoised=quantize_denoised, return_x0=return_x0, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) if return_codebook_ids: raise DeprecationWarning("Support dropped.") model_mean, _, model_log_variance, logits = outputs elif 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_codebook_ids: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=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 @torch.no_grad() def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, log_every_t=None): if not log_every_t: log_every_t = self.log_every_t timesteps = self.num_timesteps if batch_size is not None: b = batch_size if batch_size is not None else shape[0] shape = [batch_size] + list(shape) else: b = batch_size = shape[0] if x_T is None: img = torch.randn(shape, device=self.device) else: img = x_T intermediates = [] if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else list(map(lambda x: x[:batch_size], cond[key])) for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] if start_T is not None: timesteps = min(timesteps, start_T) iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', total=timesteps) if verbose else reversed( range(0, timesteps)) if type(temperature) == float: temperature = [temperature] * timesteps for i in iterator: ts = torch.full((b,), i, device=self.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, x0_partial = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, quantize_denoised=quantize_denoised, return_x0=True, temperature=temperature[i], noise_dropout=noise_dropout, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) if mask is not None: assert x0 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(x0_partial) if callback: callback(i) if img_callback: img_callback(img, i) return img, intermediates @torch.no_grad() def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, start_T=None, log_every_t=None): if not log_every_t: log_every_t = self.log_every_t device = self.betas.device b = shape[0] 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, quantize_denoised=quantize_denoised) 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 @torch.no_grad() def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, verbose=True, timesteps=None, quantize_denoised=False, mask=None, x0=None, shape=None,**kwargs): if shape is None: shape = (batch_size, self.channels, self.image_size, self.image_size) if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else list(map(lambda x: x[:batch_size], cond[key])) for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] return self.p_sample_loop(cond, shape, return_intermediates=return_intermediates, x_T=x_T, verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, mask=mask, x0=x0) @torch.no_grad() def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs): if ddim: ddim_sampler = DDIMSampler(self) shape = (self.channels, self.image_size, self.image_size) samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size, shape,cond,verbose=False,**kwargs) else: samples, intermediates = self.sample(cond=cond, batch_size=batch_size, return_intermediates=True,**kwargs) return samples, intermediates @torch.no_grad() def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, **kwargs): use_ddim = ddim_steps is not None log = dict() z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, return_first_stage_outputs=True, force_c_encode=True, return_original_cond=True, bs=N) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) log["inputs"] = x log["reconstruction"] = xrec if self.model.conditioning_key is not None: if hasattr(self.cond_stage_model, "decode"): xc = self.cond_stage_model.decode(c) log["conditioning"] = xc elif self.cond_stage_key in ["caption"]: xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"]) log["conditioning"] = xc elif self.cond_stage_key == 'class_label': xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) log['conditioning'] = xc elif isimage(xc): log["conditioning"] = xc if ismap(xc): log["original_conditioning"] = self.to_rgb(xc) if plot_diffusion_rows: # get diffusion row diffusion_row = list() z_start = z[: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(z_start) z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) diffusion_row.append(self.decode_first_stage(z_noisy)) diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) log["diffusion_row"] = diffusion_grid if sample: # get denoise row with self.ema_scope("Plotting"): samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, ddim_steps=ddim_steps,eta=ddim_eta) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) x_samples = self.decode_first_stage(samples) log["samples"] = x_samples if plot_denoise_rows: denoise_grid = self._get_denoise_row_from_list(z_denoise_row) log["denoise_row"] = denoise_grid if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( self.first_stage_model, IdentityFirstStage): # also display when quantizing x0 while sampling with self.ema_scope("Plotting Quantized Denoised"): samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, ddim_steps=ddim_steps,eta=ddim_eta, quantize_denoised=True) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, # quantize_denoised=True) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_x0_quantized"] = x_samples if inpaint: # make a simple center square b, h, w = z.shape[0], z.shape[2], z.shape[3] mask = torch.ones(N, h, w).to(self.device) # zeros will be filled in mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. mask = mask[:, None, ...] with self.ema_scope("Plotting Inpaint"): samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta, ddim_steps=ddim_steps, x0=z[:N], mask=mask) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_inpainting"] = x_samples log["mask"] = mask # outpaint with self.ema_scope("Plotting Outpaint"): samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta, ddim_steps=ddim_steps, x0=z[:N], mask=mask) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_outpainting"] = x_samples if plot_progressive_rows: with self.ema_scope("Plotting Progressives"): img, progressives = self.progressive_denoising(c, shape=(self.channels, self.image_size, self.image_size), batch_size=N) prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") log["progressive_row"] = prog_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 def configure_optimizers(self): lr = self.learning_rate params = list(self.model.parameters()) if self.cond_stage_trainable: print(f"{self.__class__.__name__}: Also optimizing conditioner params!") params = params + list(self.cond_stage_model.parameters()) if self.learn_logvar: print('Diffusion model optimizing logvar') params.append(self.logvar) opt = torch.optim.AdamW(params, lr=lr) if self.use_scheduler: assert 'target' in self.scheduler_config scheduler = instantiate_from_config(self.scheduler_config) print("Setting up LambdaLR scheduler...") scheduler = [ { 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }] return [opt], scheduler return opt @torch.no_grad() def to_rgb(self, x): x = x.float() if not hasattr(self, "colorize"): self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) x = nn.functional.conv2d(x, weight=self.colorize) x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. return x 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 assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm'] def forward(self, x, t, c_concat: list = None, c_crossattn: list = None): 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) elif self.conditioning_key == 'crossattn': cc = torch.cat(c_crossattn, 1) out = self.diffusion_model(x, t, context=cc) elif self.conditioning_key == 'hybrid': xc = torch.cat([x] + c_concat, dim=1) cc = torch.cat(c_crossattn, 1) out = self.diffusion_model(xc, t, context=cc) elif self.conditioning_key == 'adm': cc = c_crossattn[0] out = self.diffusion_model(x, t, y=cc) else: raise NotImplementedError() return out class Layout2ImgDiffusion(LatentDiffusion): # TODO: move all layout-specific hacks to this class def __init__(self, cond_stage_key, *args, **kwargs): assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"' super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs) def log_images(self, batch, N=8, *args, **kwargs): logs = super().log_images(batch=batch, N=N, *args, **kwargs) key = 'train' if self.training else 'validation' dset = self.trainer.datamodule.datasets[key] mapper = dset.conditional_builders[self.cond_stage_key] bbox_imgs = [] map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno)) for tknzd_bbox in batch[self.cond_stage_key][:N]: bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256)) bbox_imgs.append(bboximg) cond_img = torch.stack(bbox_imgs, dim=0) logs['bbox_image'] = cond_img return logs