""" 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 """ import random import torch import torch.nn as nn import copy import numpy as np import pytorch_lightning as pl import torch.nn.functional as F import logging mainlogger = logging.getLogger('mainlogger') from functools import partial from contextlib import contextmanager from tqdm import tqdm from einops import rearrange, repeat, reduce from torch.optim.lr_scheduler import LambdaLR, CosineAnnealingLR from torchvision.utils import make_grid from pytorch_lightning.utilities import rank_zero_only from omegaconf import OmegaConf from typing import Optional, Sequence, Any, Tuple, Union, List, Dict from collections.abc import Mapping, Iterable, Callable from torch import Tensor from unifolm_wma.utils.utils import instantiate_from_config from unifolm_wma.utils.ema import LitEma from unifolm_wma.utils.distributions import DiagonalGaussianDistribution from unifolm_wma.utils.diffusion import make_beta_schedule, rescale_zero_terminal_snr from unifolm_wma.utils.basics import disabled_train from unifolm_wma.utils.common import (extract_into_tensor, noise_like, exists, default) from unifolm_wma.models.samplers.ddim import DDIMSampler from unifolm_wma.models.diffusion_head.common.lr_scheduler import get_scheduler, SelectiveLRScheduler from unifolm_wma.models.diffusion_head.ema_model import EMAModel from unifolm_wma.models.diffusion_head.positional_embedding import SinusoidalPosEmb from unifolm_wma.modules.encoders.condition import MLPProjector from unifolm_wma.data.normolize import Normalize, Unnormalize __conditioning_keys__ = { 'concat': 'c_concat', 'crossattn': 'c_crossattn', 'adm': 'y' } class DDPM(pl.LightningModule): """ Denoising Diffusion Probabilistic Model (DDPM) LightningModule. """ def __init__( self, wma_config: OmegaConf, timesteps: int = 1000, beta_schedule: str = "linear", loss_type: str = "l2", ckpt_path: Optional[str] = None, ignore_keys: Optional[Sequence[str]] = [], load_only_unet: bool = False, monitor: str = None, use_ema: bool = True, first_stage_key: str = "image", image_size: int = 256, channels: int = 3, log_every_t: int = 100, clip_denoised: bool = True, linear_start: float = 1e-4, linear_end: float = 2e-2, cosine_s: float = 8e-3, given_betas: Optional[np.ndarray] = None, original_elbo_weight: float = 0.0, v_posterior: float = 0.0, l_simple_weight: float = 1.0, conditioning_key: Optional[str] = None, parameterization: str = "eps", scheduler_config: Optional[Mapping[str, Any]] = None, use_positional_encodings: bool = False, learn_logvar: bool = False, logvar_init: float = 0.0, rescale_betas_zero_snr: bool = False, ): """ wma_config: Config object used to build the underlying model. timesteps: Number of diffusion steps. beta_schedule: Schedule type for betas (e.g., 'linear', 'cosine'). loss_type: Loss type. ckpt_path: Optional checkpoint path to restore weights. ignore_keys: Keys to ignore when loading the checkpoint. load_only_unet: If True, load the backbone into self.model only. monitor: Metric key for monitoring. use_ema: If True, maintain EMA weights. first_stage_key: Key in batch dict for inputs. image_size: Image size. channels: Number of channels. log_every_t: Interval of timesteps to log intermediates during sampling. clip_denoised: Clamp x_0 predictions or not. linear_start: Linear schedule start. linear_end: Linear schedule end. cosine_s: Cosine schedule s parameter. given_betas: Externally provided betas; overrides schedule if set. original_elbo_weight: Weight for VLB term. v_posterior: Interpolation weight for posterior variance. l_simple_weight: Weight for simple loss term. conditioning_key: Conditioning mechanism key (if used by wrapper). parameterization: One of {'eps','x0','v'}. scheduler_config: Optional LR scheduler config. use_positional_encodings: Whether to inject positional encodings. learn_logvar: If True, learn per-timestep log-variance. logvar_init: Initial value for log-variance. rescale_betas_zero_snr: If True, apply zero-SNR rescaling to betas. """ 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 = wma_config.params.temporal_length self.image_size = image_size 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(wma_config, conditioning_key) 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.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) # For reschedule self.given_betas = given_betas self.beta_schedule = beta_schedule self.timesteps = timesteps self.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: Optional[np.ndarray] = None, beta_schedule: str = "linear", timesteps: int = 1000, linear_start: float = 1e-4, linear_end: float = 2e-2, cosine_s: float = 8e-3) -> None: """ Create and register diffusion buffers (betas, alphas, posteriors, weights). Args: given_betas: If provided, use these instead of building a schedule. beta_schedule: Name of schedule to create if betas not given. timesteps: Number of diffusion steps. linear_start: Linear schedule start. linear_end: Linear schedule end. cosine_s: Cosine schedule parameter """ 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)) 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))) 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") 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: Optional[str] = None) -> Iterable[None]: """ Context manager that temporarily swaps to EMA weights (if enabled). Args: context: Optional label for logging. """ 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: str, ignore_keys: Sequence[str] = tuple(), only_model: bool = False) -> None: """ Load a checkpoint, optionally filtering keys or loading only the inner model. Args: path: Path to checkpoint. ignore_keys: State-dict keys (prefix match) to drop. only_model: If True, load only into self.model. """ 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: Tensor, t: Tensor) -> Tuple[Tensor, Tensor, Tensor]: """ Compute q(x_t | x_0): mean, variance, and log-variance. Args: x_start: the [N x C x ...] tensor of noiseless inputs.. t: the number of diffusion steps (minus 1). Here, 0 means one step.. Returns: (mean, variance, log_variance), each shaped like x_start. """ 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: Tensor, t: Tensor, noise: Tensor) -> Tensor: """ Predict x_0 from x_t and 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: Tensor, t: Tensor, v: Tensor) -> Tensor: """ Predict x_0 from z and v (v-parameterization). """ 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: Tensor, t: Tensor, v: Tensor) -> Tensor: """ Predict epsilon from z and v (v-parameterization). """ 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: Tensor, x_t: Tensor, t: Tensor) -> Tuple[Tensor, Tensor, Tensor]: """ Compute posterior q(x_{t-1} | x_t, x_0): mean and (log-)variance. """ 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: Tensor, t: Tensor, clip_denoised: bool) -> Tuple[Tensor, Tensor, Tensor]: """ Predict mean and variance of p(x_{t-1} | x_t) using the model. """ 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: Tensor, t: Tensor, clip_denoised: bool = True, repeat_noise: bool = False) -> Tensor: """ Draw a single reverse-diffusion sample step: x_{t-1} from x_t. Args: x: Current noisy sample (B, C, ...). t: Current timestep indices (B,). clip_denoised: Clamp x_0 predictions or not. repeat_noise: Reuse the same noise across the batch. Returns: Next sample x_{t-1}. """ 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: Sequence[int], return_intermediates: bool = False ) -> Union[Tensor, Tuple[Tensor, List[Tensor]]]: """ Run the full reverse process starting from Gaussian noise. Args: shape: Output tensor shape (B, C, ...). return_intermediates: If True, also return intermediate frames. Returns: Final sample, and optionally the intermediate list. """ 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: int = 16, return_intermediates: bool = False ) -> Union[Tensor, Tuple[Tensor, List[Tensor]]]: """ Convenience wrapper to sample square images of configured size. Args: batch_size: Number of samples. return_intermediates: If True, also return intermediate frames. Returns: Final sample (and optionally intermediates). """ 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: Tensor, t: Tensor, noise: Optional[Tensor] = None) -> Tensor: """ Forward noising step: sample x_t ~ q(x_t | x_0). """ 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: Tensor, noise: Tensor, t: Tensor) -> Tensor: """ Compute v-target given x and epsilon. """ 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_loss(self, pred: Tensor, target: Tensor, mean: bool = True) -> Tensor: """ Compute training loss between prediction and target. Args: pred: Model output. target: Target tensor. mean: If True, reduce to mean. Returns: Loss tensor (scalar if reduced). """ 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: Tensor, t: Tensor, noise: Optional[Tensor] = None ) -> Tuple[Tensor, Dict[str, Tensor]]: """ Compute the per-step training loss for a batch. Args: x_start: Clean inputs (B, C, ...). t: Timesteps (B,). noise: Optional pre-sampled epsilon. Returns: (loss, log_dict) """ 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 elif self.parameterization == "v": target = self.get_v(x_start, noise, t) 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: Tensor, *args: Any, **kwargs: Any) -> Tuple[Tensor, Dict[str, Tensor]]: """ Lightning forward: sample random timesteps and compute losses. Args: x: Clean batch (B, C, ...). Returns: (loss, log_dict) """ 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: Mapping[str, Tensor], k: str) -> Tensor: """ Fetch and format the network input from batch. Args: batch: Batch mapping. k: Key for the tensor to use. Returns: (B, C, ...) float32 contiguous tensor. """ 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: Mapping[str, Tensor]) -> Tuple[Tensor, Dict[str, Tensor]]: """ Common train/val step computing loss and logs. """ x = self.get_input(batch, self.first_stage_key) loss, loss_dict = self(x) return loss, loss_dict def training_step(self, batch: Mapping[str, Tensor], batch_idx: int) -> Tensor: """ PyTorch Lightning training step. """ 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: Mapping[str, Tensor], batch_idx: int) -> None: """ PyTorch Lightning validation step with and without EMA. """ _, 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: Any, **kwargs: Any) -> None: """ Update EMA after each train batch (if enabled). """ if self.use_ema: self.model_ema(self.model) def _get_rows_from_list(self, samples: List[Tensor]) -> Tensor: """ Arrange a list of (B, C, ...) tensors into a grid for logging. Args: samples: List of tensors at different timesteps. Returns: Grid image tensor suitable for visualization. """ 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: Mapping[str, Tensor], N: int = 8, n_row: int = 2, sample: bool = True, return_keys: Optional[Sequence[str]] = None, **kwargs: Any, ) -> Dict[str, Tensor]: """ Create tensors for image logging: inputs, diffusion row, (optional) samples. Args: batch: Batch mapping. N: Number of examples to visualize. n_row: Number of examples for diffusion-row visualization. sample: If True, also run reverse diffusion to produce samples. return_keys: If provided, filter the returned dict to these keys. Returns: Dict of image tensors. """ 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) -> torch.optim.Optimizer: """ Build the optimizer (AdamW) over model parameters (+ logvar if learned). """ 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: Latent-diffusion model on top of DDPM (first/cond stages + guidance). """ def __init__(self, first_stage_config: OmegaConf, cond_stage_config: OmegaConf, num_timesteps_cond: int | None = None, cond_stage_key: str = "instruction", cond_stage_trainable: bool = False, cond_stage_forward: str | None = None, conditioning_key: str | None = None, uncond_prob: float = 0.2, uncond_type: str = "empty_seq", scale_factor: str = 1.0, scale_by_std: bool = False, encoder_type: str = "2d", only_model: bool = False, noise_strength: float = 0.0, use_dynamic_rescale: bool = False, base_scale: float = 0.7, turning_step: int = 400, interp_mode: bool = False, fps_condition_type: str = 'fs', perframe_ae: bool = False, logdir: str | None = None, rand_cond_frame: bool = False, en_and_decode_n_samples_a_time: int | None = None, *args, **kwargs): """ Args: first_stage_config: OmegaConf config for the first-stage autoencoder. cond_stage_config: OmegaConf config for the conditioning encoder/module. num_timesteps_cond: Number of condition timesteps used for schedule shortening. cond_stage_key: Batch key for conditioning input (e.g., "instruction"). cond_stage_trainable: Whether the conditioning module is trainable. cond_stage_forward: Optional method name to call on cond model instead of default. conditioning_key: Conditioning mode (e.g., "crossattn", "concat"). uncond_prob: Probability to drop/zero the condition for classifier-free guidance. uncond_type: Strategy for unconditional condition ("zero_embed" or "empty_seq"). scale_factor: Fixed latent scale multiplier if not using std-scaling. scale_by_std: If True, compute scale as 1/std of latents at first batch. encoder_type: "2d" (per-frame) or "3d" (volumetric) first-stage behavior. only_model: If True, load only inner model weights when restoring from ckpt. noise_strength: Extra offset noise strength for inputs (when > 0). use_dynamic_rescale: If True, apply time-dependent rescaling array. base_scale: Target base scale used by dynamic rescaling after turning_step. turning_step: Steps to transition from 1.0 to base_scale in dynamic rescaling. interp_mode: Flag for interpolation-specific behaviors (reserved). fps_condition_type: Frame-per-second conditioning mode label. perframe_ae: If True, encode/decode one frame at a time. logdir: Optional directory for logs. rand_cond_frame: If True, randomly select conditioning frames. en_and_decode_n_samples_a_time: Optional per-step batch size for (en|de)code loops. """ 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.interp_mode = interp_mode self.fps_condition_type = fps_condition_type self.perframe_ae = perframe_ae self.logdir = logdir self.rand_cond_frame = rand_cond_frame self.en_and_decode_n_samples_a_time = en_and_decode_n_samples_a_time 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) -> None: """ Build the condition timestep schedule. """ 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: Mapping[str, Any], batch_idx: int, dataloader_idx: int | None = None) -> None: """ Args: batch: Current batch mapping. batch_idx: Index of the batch within the epoch. dataloader_idx: Optional dataloader index in multi-loader setups. """ # Only for very first batch, reset the self.scale_factor 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 mainlogger.info("### 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()) mainlogger.info( f"setting self.scale_factor to {self.scale_factor}") mainlogger.info("### USING STD-RESCALING ###") mainlogger.info(f"std={z.flatten().std()}") def register_schedule(self, given_betas: np.ndarray | None = None, beta_schedule: str = "linear", timesteps: int = 1000, linear_start: float = 1e-4, linear_end: float = 2e-2, cosine_s: float = 8e-3) -> None: """ Extend base schedule registration and optionally shorten conditioning schedule. Args: given_betas: Optional precomputed beta schedule. beta_schedule: Name of schedule function ("linear", "cosine", etc.). timesteps: Number of diffusion steps. linear_start: Linear schedule start beta (if used). linear_end: Linear schedule end beta (if used). cosine_s: Cosine schedule parameter (if used). """ 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: OmegaConf) -> None: """ Build and freeze the first-stage (autoencoder) model. Args: config: OmegaConf config describing the first-stage model to instantiate. """ 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: OmegaConf) -> None: """ Build the conditioning stage model. Args: config: OmegaConf config describing the conditioning model to instantiate. """ 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: Any) -> Tensor: """ Encode conditioning input into an embedding tensor. Args: c: Raw conditioning input (tensor, list/dict of strings, tokens, etc.). Returns: Conditioning embedding as a tensor (shape depends on cond model). """ 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: DiagonalGaussianDistribution | Tensor, noise: Tensor | None = None) -> Tensor: """ Convert encoder posterior to latent z and apply scaling. Args: encoder_posterior: First-stage output; either a Gaussian posterior or a latent tensor. noise: Optional noise for sampling if posterior is Gaussian. Returns: Scaled latent tensor z. """ 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 @torch.no_grad() def encode_first_stage(self, x: Tensor) -> Tensor: """ Encode input frames/images into latent space. Args: x: Input tensor, either (B, C, ...). Returns: Latent tensor with shape matched to input. """ 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: Tensor, **kwargs: Any) -> Tensor: """ Decode latent z back to pixel space (2D or per-frame). Args: z: Latent tensor (B, C, ...). Returns: Decoded tensor in pixel space with shape matching the input layout. """ 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 @torch.no_grad() def decode_first_stage(self, z: Tensor, **kwargs: Any) -> Tensor: """ Decode latent with no gradient. Args: z: Latent tensor to decode. **kwargs: Extra args for the decoder. Returns: Decoded tensor in pixel space. """ return self.decode_core(z, **kwargs) # Same as above but without decorator def differentiable_decode_first_stage(self, z: Tensor, **kwargs: Any) -> Tensor: """ Decode latent with gradients enabled. Args: z: Latent tensor to decode. Returns: ecoded tensor in pixel space. """ return self.decode_core(z, **kwargs) @torch.no_grad() def get_batch_input(self, batch: Mapping[str, Any], random_uncond: bool, return_first_stage_outputs: bool = False, return_original_cond: bool = False) -> list[Any]: """ Prepare batch: encode inputs to latents and produce conditioning embeddings. Args: batch: Batch mapping containing first-stage inputs and conditioning. random_uncond: If True and `uncond_type` allows, randomly drop conditions. return_first_stage_outputs: If True, also decode z to xrec for logging. return_original_cond: If True, also return the raw condition object. """ x = super().get_input(batch, self.first_stage_key) # Encode video frames x to z via a 2D encoder z = self.encode_first_stage(x) # Get instruction condition cond = batch[self.cond_stage_key] if random_uncond and self.uncond_type == 'empty_seq': for i, ci in enumerate(cond): if random.random() < self.uncond_prob: cond[i] = "" if isinstance(cond, dict) or isinstance(cond, list): cond_emb = self.get_learned_conditioning(cond) else: cond_emb = self.get_learned_conditioning(cond.to(self.device)) if random_uncond and self.uncond_type == 'zero_embed': for i, ci in enumerate(cond): if random.random() < self.uncond_prob: cond_emb[i] = torch.zeros_like(cond_emb[i]) out = [z, cond_emb] if return_first_stage_outputs: xrec = self.decode_first_stage(z) out.extend([xrec]) if return_original_cond: out.append(cond) return out def forward( self, x: Tensor, x_action: Tensor, x_state: Tensor, c: Any, **kwargs: Any, ) -> tuple[Tensor, dict[str, Tensor]]: """ Args: x: Input latent (or pixel) tensor for the primary stream. x_action: Action tensor associated with the batch. x_state: State tensor associated with the batch. c: Conditioning object (tensor/list/dict) consumed by `apply_model`. Returns: (loss, loss_dict) tuple. """ 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, x_action, x_state, c, t, **kwargs) def shared_step(self, batch: Mapping[str, Any], random_uncond: bool, **kwargs: Any) -> tuple[Tensor, dict[str, Tensor]]: """ Common train/val step: build inputs, run forward, return loss/logs. Args: batch: Input batch mapping. random_uncond: Whether to apply classifier-free guidance dropout to cond. **kwargs: Extra args forwarded to `forward`. Returns: (loss, loss_dict) tuple. """ x, c = self.get_batch_input(batch, random_uncond=random_uncond) loss, loss_dict = self(x, c, **kwargs) return loss, loss_dict def apply_model(self, x_noisy: Tensor, x_action_noisy: Tensor, x_state_noisy: Tensor, t: Tensor, cond: Any, **kwargs: Any) -> Tensor | tuple[Tensor, Tensor, Tensor]: """ Apply inner diffusion model with standardized conditioning dict. Args: x_noisy: Noisy latent input for the primary stream. x_action_noisy: Noisy action tensor aligned with t. x_state_noisy: Noisy state tensor aligned with t. t: Timestep indices (B,). cond: Raw conditioning; will be wrapped into the proper key if not a dict. **kwargs: Extra args forwarded to the inner model call. Returns: Either a single tensor or a tuple of tensors (x, action, state) depending on model. """ if isinstance(cond, 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, x_action_recon, x_state_recon = self.model( x_noisy, x_action_noisy, x_state_noisy, t, **cond, **kwargs) if isinstance(x_recon, tuple): return x_recon[0] else: return x_recon, x_action_recon, x_state_recon def p_losses( self, x_start: Tensor, x_action_start: Tensor, x_state_start: Tensor, cond: Any, t: Tensor, noise: Tensor | None = None, action_noise: Tensor | None = None, **kwargs: Any, ) -> tuple[Tensor, dict[str, Tensor]]: """ Compute the per-step training losses for latent diffusion. Args: x_start: Clean primary latent (or pixel) tensor. x_action_start: Clean action tensor aligned with x_start. x_state_start: Clean state tensor aligned with x_start. cond: Conditioning object; may include masks for action/state losses. t: Timestep indices (B,). noise: Optional epsilon noise for the primary stream (else sampled). action_noise: Optional epsilon noise for the action stream (else sampled). **kwargs: Extra args forwarded into `apply_model` (and logged if needed). Returns: (loss, loss_dict) """ if self.noise_strength > 0: b, c, f, _, _ = x_start.shape offset_noise = torch.randn(b, c, f, 1, 1, device=x_start.device) noise = default( noise, lambda: torch.randn_like(x_start) + self.noise_strength * offset_noise) else: noise = default(noise, lambda: torch.randn_like(x_start)) action_noise = torch.randn(x_action_start.shape, device=x_action_start.device) action_noise_new = action_noise + self.input_pertub * torch.randn( x_action_start.shape, device=x_action_start.device) state_noise = torch.randn(x_state_start.shape, device=x_state_start.device) state_noise_new = state_noise + self.input_pertub * torch.randn( x_state_start.shape, device=x_state_start.device) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) x_action_noisy = self.dp_noise_scheduler_action.add_noise( x_action_start, action_noise_new, t[:x_action_start.shape[0]]) x_state_noisy = self.dp_noise_scheduler_state.add_noise( x_state_start, state_noise_new, t[:x_state_start.shape[0]]) kwargs['x_start'] = x_start model_output, model_action_output, model_state_output = self.apply_model( x_noisy, x_action_noisy, x_state_noisy, t, cond, **kwargs) loss_dict = {} prefix = 'train' if self.training else 'val' if self.parameterization == "x0": target = x_start elif self.parameterization == "eps": target = noise elif self.parameterization == "v": target = self.get_v(x_start, noise, t) else: raise NotImplementedError() target_action = action_noise target_state = state_noise loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3, 4]) loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) loss_action_simple = F.mse_loss(model_action_output, target_action, reduction='none') action_mask = cond['c_crossattn_action'][-1] loss_action_simple *= action_mask loss_action_simple = loss_action_simple.type(loss_action_simple.dtype) loss_action_simple = reduce(loss_action_simple, 'b ... -> b (...)', 'mean') loss_action_simple = loss_action_simple.sum() / action_mask.sum() loss_dict.update({f'{prefix}/loss_action_simple': loss_action_simple}) loss_state_simple = F.mse_loss(model_state_output, target_state, reduction='none') state_mask = cond['c_crossattn_action'][-2] loss_state_simple *= state_mask loss_state_simple = loss_state_simple.type(loss_state_simple.dtype) loss_state_simple = reduce(loss_state_simple, 'b ... -> b (...)', 'mean') loss_state_simple = loss_state_simple.sum() / state_mask.sum() loss_dict.update({f'{prefix}/loss_state_simple': loss_state_simple}) if self.logvar.device is not self.device: self.logvar = self.logvar.to(self.device) logvar_t = self.logvar[t] loss = loss_simple / torch.exp(logvar_t) + logvar_t loss_action = loss_action_simple loss_state = loss_state_simple 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, 4)) loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) loss_dict.update({f'{prefix}/loss_action_vlb': loss_action}) loss_dict.update({f'{prefix}/loss_state_vlb': loss_state}) loss += (self.original_elbo_weight * loss_vlb) loss_dict.update({f'{prefix}/loss': loss}) loss_dict.update({f'{prefix}/loss_action': loss_action}) loss_dict.update({f'{prefix}/loss_state': loss_state}) if cond['c_crossattn_action'][2]: return loss + loss_state + loss_action * 0.0, loss_dict else: return loss + loss_action + loss_state * 0.0, loss_dict def training_step(self, batch: Mapping[str, Any], batch_idx: int) -> Tensor: """ Lightning training step: compute loss and log metrics. Args: batch: Training batch mapping. batch_idx: Batch index within current epoch. Returns: Scalar loss tensor for optimization. """ loss, loss_dict = self.shared_step( batch, random_uncond=self.classifier_free_guidance) loss_dict.update( {'lr': self.trainer.optimizers[0].param_groups[0]['lr']}) loss_dict.update({ 'lr_action_unet': self.trainer.optimizers[0].param_groups[1]['lr'] }) # Sync_dist | rank_zero_only self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=False) if (batch_idx + 1) % self.log_every_t == 0: mainlogger.info( f"batch:{batch_idx}|epoch:{self.current_epoch} [globalstep:{self.global_step}]: loss={loss}" ) return loss @torch.no_grad() def validation_step(self, batch: Mapping[str, Any], batch_idx: int) -> None: """ Lightning validation step: compute loss and log metrics. Args: batch: Validation batch mapping. batch_idx: Batch index in validation loop. """ _, loss_dict_no_ema = self.shared_step(batch, random_uncond=False) self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) def _get_denoise_row_from_list(self, samples: Sequence[Tensor], desc: str = '') -> Tensor: """ Decode a list of latents and pack into a grid for visualization. Args: samples: Sequence of latent tensors to decode and tile. desc: Optional tqdm description string. Returns: Grid image tensor suitable for logging. """ 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) 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_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 @torch.no_grad() def log_images(self, batch: Mapping[str, Any], sample: bool = True, ddim_steps: int = 200, ddim_eta: float = 1.0, plot_denoise_rows: bool = False, unconditional_guidance_scale: float = 1.0, **kwargs: Any) -> dict[str, Tensor]: """ Log images for LatentDiffusion """ # Control sampled imgae for logging, larger value may cause OOM sampled_img_num = 2 for key in batch.keys(): batch[key] = batch[key][:sampled_img_num] # TBD: currently, classifier_free_guidance sampling is only supported by DDIM use_ddim = ddim_steps is not None log = dict() z, c, xrec, xc = self.get_batch_input(batch, random_uncond=False, return_first_stage_outputs=True, return_original_cond=True, logging=True) N = xrec.shape[0] log["reconst"] = xrec log["condition"] = xc if sample: uc = None 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, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=uc, x0=z, **kwargs) 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 return log def p_mean_variance( self, x: Tensor, c: Any, t: Tensor, clip_denoised: bool, return_x0: bool = False, score_corrector: Any = None, corrector_kwargs: Mapping[str, Any] | None = None, **kwargs: Any ) -> tuple[Tensor, Tensor, Tensor] | tuple[Tensor, Tensor, Tensor, Tensor]: """ Predict posterior parameters (and optionally x0) at timestep t. Args: x: Current latent at timestep t. c: Conditioning object passed to the inner model/score corrector. t: Timestep indices (B,). clip_denoised: If True, clamp predicted x0 to [-1, 1]. return_x0: If True, also return predicted x0. score_corrector: Optional score-corrector object with `modify_score`. corrector_kwargs: Extra kwargs for the score corrector. **kwargs: Forwarded to `apply_model`. Returns: (mean, var, log_var) or (mean, var, log_var, x0) tensors. """ 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 @torch.no_grad() def p_sample(self, x: Tensor, c: Any, t: Tensor, clip_denoised: bool = False, repeat_noise: bool = False, return_x0: bool = False, temperature: float = 1.0, noise_dropout: float = 0.0, score_corrector: Any = None, corrector_kwargs: Mapping[str, Any] | None = None, **kwargs: Any) -> Tensor | tuple[Tensor, Tensor]: """ Draw a single reverse-diffusion step (optionally return x0). Args: x: Current latent at timestep t. c: Conditioning object for the model. t: Timestep indices (B,). clip_denoised: Clamp predicted x0 to [-1, 1] when forming the mean. repeat_noise: If True, reuse the same noise across batch. return_x0: If True, also return the predicted x0. temperature: Temperature for sampling noise scale. noise_dropout: Dropout probability applied to the sampled noise. score_corrector: Optional score-corrector to adjust model outputs. corrector_kwargs: Extra kwargs for the corrector. **kwargs: Forwarded to `p_mean_variance`. Returns: Next latent (and optionally x0). """ 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 @torch.no_grad() def p_sample_loop(self, cond: Any, shape: Sequence[int], return_intermediates: bool = False, x_T: Tensor | None = None, verbose: bool = True, callback: Callable[[int], Any] | None = None, timesteps: int | None = None, mask: Tensor | None = None, x0: Tensor | None = None, img_callback: Callable[[Tensor, int], Any] | None = None, start_T: int | None = None, log_every_t: int | None = None, **kwargs: Any) -> Tensor | tuple[Tensor, list[Tensor]]: """ Run the full reverse process from noise to sample(s). Args: cond: Conditioning object (tensor/dict/list), optionally noised when cond schedule is shortened. shape: Output latent shape (B, C, ...). return_intermediates: If True, also return intermediate latents. x_T: Optional starting noise latent (else sampled from N(0, I)). verbose: If True, show tqdm progress. callback: Optional function called with the current timestep i. timesteps: Number of reverse steps to perform (default: self.num_timesteps). mask: Optional inpainting mask; ones keep original x0 regions. x0: Optional original latent for masked regions (when using `mask`). img_callback: Optional function called with (img, i) every step. start_T: Optional cap to limit starting step (min(timesteps, start_T)). log_every_t: Logging frequency for collecting intermediates (defaults to self.log_every_t). Returns: Final latent sample (and optionally the list of intermediates). """ 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] 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 @torch.no_grad() def sample(self, cond, batch_size: int = 16, return_intermediates: bool = False, x_T: Tensor | None = None, verbose: bool = True, timesteps: int | None = None, mask: Tensor | None = None, x0: Tensor | None = None, shape: Sequence[int] | None = None, **kwargs: Any) -> Tensor | tuple[Tensor, list[Tensor]]: """ Convenience wrapper to run `p_sample_loop` with a full batch. Args: cond: Conditioning object; dict/list items are truncated to batch_size. batch_size: Number of samples to generate. return_intermediates: If True, return intermediates as well. x_T: Optional starting noise latent (else sampled). verbose: Whether to print sampling progress. timesteps: Number of reverse steps (default: self.num_timesteps). mask: Optional mask for partial generation/inpainting. x0: Optional original latent used with `mask` during sampling. shape: Optional output shape; if None, uses (B, C, T, H, W) from model config. Returns: Final latent (and optionally intermediates). """ if shape is None: shape = (batch_size, self.channels, self.temporal_length, *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, mask=mask, x0=x0, **kwargs) @torch.no_grad() def sample_log(self, cond: Any, batch_size: int, ddim: bool, ddim_steps: int, **kwargs: Any) -> tuple[Any, Any, Any, Any]: """ Produce samples (and intermediates), optionally via DDIM sampler. Args: cond: Conditioning object passed to the sampler. batch_size: Number of samples to generate. ddim: If True, use DDIM sampler; otherwise use ancestral sampling. ddim_steps: Number of DDIM steps when `ddim` is True. """ if ddim: ddim_sampler = DDIMSampler(self) shape = (self.channels, self.temporal_length, *self.image_size) samples, actions, states, 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, actions, states, intermediates def configure_schedulers( self, optimizer: torch.optim.Optimizer) -> dict[str, Any]: """ Build LR scheduler dict compatible with PyTorch Lightning. Args: optimizer: Optimizer instance for which to build the scheduler dict. Returns: Dict with keys {'scheduler', 'interval', 'frequency'} per Lightning API. """ assert 'target' in self.scheduler_config scheduler_name = self.scheduler_config.target.split('.')[-1] interval = self.scheduler_config.interval frequency = self.scheduler_config.frequency if scheduler_name == "LambdaLRScheduler": scheduler = instantiate_from_config(self.scheduler_config) scheduler.start_step = self.global_step lr_scheduler = { 'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule), 'interval': interval, 'frequency': frequency } elif scheduler_name == "CosineAnnealingLRScheduler": scheduler = instantiate_from_config(self.scheduler_config) decay_steps = scheduler.decay_steps last_step = -1 if self.global_step == 0 else scheduler.start_step lr_scheduler = { 'scheduler': CosineAnnealingLR(optimizer, T_max=decay_steps, last_epoch=last_step), 'interval': interval, 'frequency': frequency } else: raise NotImplementedError return lr_scheduler class LatentVisualDiffusion(LatentDiffusion): """ Visual-conditioned latent diffusion with action/state heads and schedulers. """ def __init__(self, img_cond_stage_config: OmegaConf, image_proj_stage_config: OmegaConf, noise_scheduler_config: OmegaConf, dp_optimizer_config: OmegaConf, dp_ema_config: OmegaConf, freeze_embedder: bool = True, image_proj_model_trainable: bool = True, n_obs_steps_imagen: int = 2, n_obs_steps_acting: int = 2, agent_state_dim: int = 14, agent_action_dim: int = 14, global_emb_dim: int = 1024, input_pertub: float = 0.1, lr_scheduler: str = 'cosine', lr_warmup_steps: int = 500, num_epochs: int = 15000, gradient_accumulate_every: int = 1, use_scheduler: bool = False, dp_use_ema: bool = False, pretrained_checkpoint: str | None = None, decision_making_only: bool = True, projector_bf16: bool = True, *args, **kwargs): """ Args: img_cond_stage_config: OmegaConf for the *image* conditioning encoder. image_proj_stage_config: OmegaConf for the image feature projector. noise_scheduler_config: OmegaConf for DP noise schedulers (state/action). dp_optimizer_config: OmegaConf for optimizer params of the UNet heads. dp_ema_config: Optional EMA config for the action UNet. freeze_embedder: If True, freeze the image embedder params. image_proj_model_trainable: If True, train the image projector. n_obs_steps_imagen: Number of observed steps for image conditions. n_obs_steps_acting: Number of observed steps for acting head. agent_state_dim: Dimension of agent state vector. agent_action_dim: Dimension of agent action vector. global_emb_dim: Embedding size for state/action/text/image fusion. input_pertub: Perturbation scale added to action/state noises. lr_scheduler: Name of LR scheduler (for SelectiveLRScheduler wrapper). lr_warmup_steps: Warmup steps for scheduler creation. num_epochs: Total training epochs. gradient_accumulate_every: Gradient accumulation steps. use_scheduler: If True, enable LR scheduling. dp_use_ema: If True, maintain EMA for action UNet head. pretrained_checkpoint: Optional path to a pretrained checkpoint. decision_making_only: If True, use decision-only augmentation path. projector_bf16: If True, run image/state/action projectors under BF16 autocast. """ super().__init__(*args, **kwargs) self.image_proj_model_trainable = image_proj_model_trainable self.agent_state_dim = agent_state_dim self.agent_action_dim = agent_action_dim self.global_emb_dim = global_emb_dim self.n_obs_steps_imagen = n_obs_steps_imagen self.n_obs_steps_acting = n_obs_steps_acting self.decision_making_only = decision_making_only self.projector_bf16 = projector_bf16 self._init_embedder(img_cond_stage_config, freeze_embedder) self._init_img_ctx_projector(image_proj_stage_config, image_proj_model_trainable) self._init_dp_noise_scheduler(noise_scheduler_config) self._init_projectors() if dp_use_ema: self._init_dp_ema(dp_ema_config) # Create a pos_embedder for state and action info, our state and action have an unified vector space self.pos_embedder = SinusoidalPosEmb(self.global_emb_dim) self.register_buffer('cond_pos_emb', self.pos_embedder(torch.arange( 0, 16))) #NOTE HAND-CODE 16 self.input_pertub = input_pertub self.dp_optimizer_config = dp_optimizer_config self.lr_scheduler = lr_scheduler self.lr_warmup_steps = lr_warmup_steps self.num_epochs = num_epochs self.gradient_accumulate_every = gradient_accumulate_every self.use_scheduler = use_scheduler self.dp_use_ema = dp_use_ema self.pretrained_checkpoint = pretrained_checkpoint def _init_img_ctx_projector(self, config: OmegaConf, trainable: bool) -> None: """ Instantiate image context projector; optionally freeze. Args: config: OmegaConf for the projector module to instantiate. trainable: If False, freeze the projector. """ self.image_proj_model = instantiate_from_config(config) if not trainable: self.image_proj_model.eval() self.image_proj_model.train = disabled_train for param in self.image_proj_model.parameters(): param.requires_grad = False def _init_embedder(self, config: OmegaConf, freeze: bool = True) -> None: """ Instantiate the image embedder; optionally freeze. Args: config: OmegaConf for the embedder to instantiate. freeze: If True, set to eval/disable grads. """ self.embedder = instantiate_from_config(config) if freeze: self.embedder.eval() self.embedder.train = disabled_train for param in self.embedder.parameters(): param.requires_grad = False def init_normalizers(self, normalize_config: OmegaConf, dataset_stats: Mapping[str, Any]) -> None: """ Create normalization and unnormalization utilities. Args: normalize_config: Config with shapes and normalization modes. dataset_stats: Statistics dict used to compute normalization. """ self.normalize_inputs = Normalize( normalize_config.input_shapes, normalize_config.input_normalization_modes, dataset_stats) self.unnormalize_outputs = Unnormalize( normalize_config.output_shapes, normalize_config.output_normalization_modes, dataset_stats) def _init_dp_noise_scheduler(self, config: OmegaConf) -> None: """ Instantiate separate DP noise schedulers for action and state. Args: config: OmegaConf used to create scheduler instances. """ self.dp_noise_scheduler_action = instantiate_from_config(config) self.dp_noise_scheduler_state = instantiate_from_config(config) def _init_dp_ema(self, config: OmegaConf | None) -> None: """ Initialize EMA for UNet head. Args: config: EMA config, must contain 'params' sub-dict. """ self.dp_ema_model = copy.deepcopy( self.model.diffusion_model.action_unet) self.dp_ema_model_on_device = False self.dp_ema = EMAModel(**config['params'], model=self.dp_ema_model) def _init_projectors(self): """ Build small MLP projectors and positional embeddings for state/action. """ self.state_projector = MLPProjector(self.agent_state_dim, 1024) # NOTE HAND CODE self.action_projector = MLPProjector(self.agent_action_dim, 1024) # NOTE HAND CODE self.agent_action_pos_emb = nn.Parameter( torch.randn(1, 16, self.global_emb_dim)) self.agent_state_pos_emb = nn.Parameter( torch.randn(1, self.n_obs_steps_imagen, self.global_emb_dim)) def _projector_forward(self, projector: nn.Module, x: Tensor, target_dtype: torch.dtype | None) -> Tensor: use_bf16 = (self.projector_bf16 and x.device.type == "cuda" and torch.cuda.is_bf16_supported()) if not use_bf16: weight_dtype = None for param in projector.parameters(): weight_dtype = param.dtype break if weight_dtype is not None and x.dtype != weight_dtype: x = x.to(dtype=weight_dtype) if use_bf16: with torch.autocast(device_type="cuda", dtype=torch.bfloat16): out = projector(x) else: out = projector(x) if not hasattr(self, "_printed_projector_bf16"): print( ">>> projector bf16 autocast: " f"enabled={self.projector_bf16} " f"use_bf16={use_bf16} " f"input={x.dtype} " f"output={out.dtype} " f"target={target_dtype}") self._printed_projector_bf16 = True if target_dtype is not None and out.dtype != target_dtype: out = out.to(dtype=target_dtype) return out def _get_augmented_batch( self, z: Tensor, state: Tensor, obs_state: Tensor, action: Tensor, ins: Tensor, null_ins: Tensor, img: Tensor, sim_mode: bool = False, pre_action: Tensor | None = None, logging: bool = False) -> tuple[Tensor, Tensor, list[Tensor]]: """ Construct augmented conditioning batch for decision/simulation modes. Args: z: Latent video tensor (B, C, ...). state: Full state tensor (B, T, D_s). obs_state: Observed state embeddings (B, T, E). action: Action embeddings (B, T, E). ins: Instruction/text embeddings (B, L, E) after projector. null_ins: Null/empty instruction embedding for CFG. img: Image conditioning embedding (B, E_img) or batched equivalent. sim_mode: If True, build simulated-mode batch; else decision-making. pre_action: Optional previous action(s). (unused here; reserved) logging: If True, may include extra returns for logs. (unused) Returns: Tuple of (z, state, [mode_batch]) where mode_batch is a single tensor combining the selected conditioning streams. """ b, _, t, _, _ = z.shape if self.decision_making_only: mode_batch = torch.cat([obs_state, ins, img], dim=1) return z, state, [mode_batch] if not sim_mode: zero_action = torch.zeros_like(action) mode_batch = torch.cat([obs_state, zero_action, ins, img], dim=1) else: null_ins_batch = null_ins.repeat_interleave(repeats=ins.shape[0], dim=0) mode_batch = torch.cat([obs_state, action, null_ins_batch, img], dim=1) return z, state, [mode_batch] def on_train_batch_end(self, outputs: Any, batch: Mapping[str, Any], batch_idx: int) -> None: """ Update EMA for action UNet after each train batch (if enabled). Args: batch: Current training batch mapping. batch_idx: Batch index within the epoch. """ if self.dp_use_ema: if self.dp_ema_model is not None and not self.dp_ema_model_on_device: device = self.model.device self.dp_ema_model.to(device) self.dp_ema_model_on_device = True self.dp_ema.step(self.model.diffusion_model.action_unet) def shared_step(self, batch: Mapping[str, Any], random_uncond: bool, **kwargs: Any) -> tuple[Tensor, dict[str, Tensor]]: """ Common train/val step for visual diffusion. Args: batch: Input batch mapping. random_uncond: Whether to apply classifier-free guidance dropout. Returns: (loss, loss_dict) tuple. """ x, x_action, x_state, c, fs = self.get_batch_input( batch, random_uncond=random_uncond, return_fs=True) kwargs.update({"fs": fs.long()}) loss, loss_dict = self(x, x_action, x_state, c, **kwargs) return loss, loss_dict def get_batch_input(self, batch: Mapping[str, Any], random_uncond: bool, return_first_stage_outputs: bool = False, return_original_cond: bool = False, return_fs: bool = False, return_cond_frame: bool = False, return_original_input: bool = False, logging: bool = False, **kwargs: Any) -> list[Any]: """ Prepare model inputs & conditioning from a raw training batch. Args: batch: Batch mapping with keys like image/state/action/obs/etc. random_uncond: Apply stochastic condition dropout for CFG. return_first_stage_outputs: If True, also return xrec (decoded z). return_original_cond: If True, also return raw instruction text. return_fs: If True, return fps or frame_stride per config. return_cond_frame: If True, return conditioning frames (obs images). return_original_input: If True, return original x (pre-encoding). logging: If True, append sim_mode flag at the end. Returns: A list of inputs """ # x: b c t h w x = super().get_input(batch, self.first_stage_key) b, _, t, _, _ = x.shape # Get actions: b t d action = super().get_input(batch, 'action') # Get states: b t d state = super().get_input(batch, 'next.state') # Get observable states: b t d obs_state = super().get_input(batch, 'observation.state') # Get observable images: b c t h w obs = super().get_input(batch, 'observation.image') # Encode video frames x to z via a 2D encoder z = self.encode_first_stage(x) cond = {} # Get instruction condition cond_ins_input = batch[self.cond_stage_key] if isinstance(cond_ins_input, dict) or isinstance( cond_ins_input, list): cond_ins_emb = self.get_learned_conditioning(cond_ins_input) else: cond_ins_emb = self.get_learned_conditioning( cond_ins_input.to(self.device)) # To support classifier-free guidance, randomly drop out only text conditioning # 5%, only image conditioning 5%, and both 5%. if random_uncond: random_num = torch.rand(b, device=x.device) else: random_num = torch.ones(b, device=x.device) prompt_mask = rearrange(random_num < 2 * self.uncond_prob, "n -> n 1 1") null_prompt = self.get_learned_conditioning([""]) cond_ins_emb = torch.where(prompt_mask, null_prompt, cond_ins_emb.detach()) target_dtype = cond_ins_emb.dtype # Get conditioning frames cond_frame_index = 0 img = obs[:, :, -1, ...] input_mask = 1 - rearrange( (random_num >= self.uncond_prob).float() * (random_num < 3 * self.uncond_prob).float(), "n -> n 1 1 1") cond_img = input_mask * img cond_img_emb = self.embedder(cond_img) cond_img_emb = self._projector_forward(self.image_proj_model, cond_img_emb, target_dtype) if self.model.conditioning_key == 'hybrid': if self.interp_mode: img_cat_cond = torch.zeros_like(z) img_cat_cond[:, :, 0, :, :] = z[:, :, 0, :, :] img_cat_cond[:, :, -1, :, :] = z[:, :, -1, :, :] else: img_cat_cond = z[:, :, cond_frame_index, :, :] img_cat_cond = img_cat_cond.unsqueeze(2) img_cat_cond = repeat(img_cat_cond, 'b c t h w -> b c (repeat t) h w', repeat=z.shape[2]) cond["c_concat"] = [img_cat_cond] cond_action = self._projector_forward(self.action_projector, action, target_dtype) cond_action_emb = self.agent_action_pos_emb.to( dtype=target_dtype) + cond_action # Get conditioning states cond_state = self._projector_forward(self.state_projector, obs_state, target_dtype) cond_state_emb = self.agent_state_pos_emb.to( dtype=target_dtype) + cond_state if self.decision_making_only: is_sim_mode = False else: is_sim_mode = torch.rand(1) < 0.5 z, state, cond["c_crossattn"] = self._get_augmented_batch( z, state, cond_state_emb, cond_action_emb, cond_ins_emb, null_prompt, cond_img_emb, sim_mode=is_sim_mode, logging=logging) cond["c_crossattn_action"] = [ obs[:, :, -self.n_obs_steps_acting:], state[:, -self.n_obs_steps_acting:], is_sim_mode, batch['state_mask'], batch['action_mask'] ] out = [z, action, state, cond] if return_first_stage_outputs: xrec = self.decode_first_stage(z) out.extend([xrec]) if return_original_cond: out.append(cond_ins_input) if return_fs: if self.fps_condition_type == 'fs': fs = super().get_input(batch, 'frame_stride') elif self.fps_condition_type == 'fps': fs = super().get_input(batch, 'fps') out.append(fs) if return_cond_frame: out.append(obs) if return_original_input: out.append(x) if logging: out.append(is_sim_mode) return out @torch.no_grad() def log_images(self, batch: Mapping[str, Any], sample: bool = True, ddim_steps: int = 50, ddim_eta: float = 1.0, plot_denoise_rows: bool = False, unconditional_guidance_scale: float = 1.0, mask: Tensor | None = None, **kwargs) -> dict[str, Tensor]: """ Log images for LatentVisualDiffusion Args: batch: Batch mapping used to form inputs/conditions. sample: If True, also run sampling for visualization. ddim_steps: Number of DDIM steps when using DDIM. ddim_eta: DDIM eta parameter (stochasticity). plot_denoise_rows: If True, include denoise progression grid. unconditional_guidance_scale: Guidance scale for CFG sampling. mask: Optional mask for sampling-time inpainting. Returns: Dict of visualization tensors (images/actions/states/progress). """ ##### sampled_img_num: control sampled imgae for logging, larger value may cause OOM sampled_img_num = 1 for key in batch.keys(): batch[key] = batch[key][:sampled_img_num] ## TBD: currently, classifier_free_guidance sampling is only supported by DDIM use_ddim = ddim_steps is not None log = dict() z, act, state, c, xrec, xc, fs, cond_x, is_sim_mode = self.get_batch_input( batch, random_uncond=False, return_first_stage_outputs=True, return_original_cond=True, return_fs=True, return_cond_frame=True, logging=True) kwargs['x_start'] = z N = xrec.shape[0] log["image_condition"] = cond_x log["reconst"] = xrec if is_sim_mode: xc = ["NULL"] xc_with_fs = [] for idx, content in enumerate(xc): xc_with_fs.append(content + '_fs=' + str(fs[idx].item())) log['instruction'] = xc log["condition"] = xc_with_fs kwargs.update({"fs": fs.long()}) if sample: uc = None with self.ema_scope("Plotting"): samples, action_samples, state_samples, z_denoise_row = self.sample_log( cond=c, batch_size=N, ddim=use_ddim, ddim_steps=ddim_steps, eta=ddim_eta, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=uc, x0=z, **kwargs) x_samples = self.decode_first_stage(samples) log["samples"] = x_samples # Log actions mb, mt, _ = batch['action_mask'].shape act_mask = batch['action_mask'] == 1.0 action_target = act[act_mask].reshape(mb, mt, -1) action_samples = action_samples[act_mask].reshape(mb, mt, -1) log["action"] = torch.cat((action_target, action_samples), dim=0) # Log states mb, mt, _ = batch['state_mask'].shape state_mask = batch['state_mask'] == 1.0 state_target = state[state_mask].reshape(mb, mt, -1) state_samples = state_samples[state_mask].reshape(mb, mt, -1) log["state"] = torch.cat((state_target, state_samples), dim=0) if plot_denoise_rows: denoise_grid = self._get_denoise_row_from_list(z_denoise_row) log["denoise_row"] = denoise_grid log["video_idx"] = batch["path"][0].split('/')[-1][:-4] return log def configure_optimizers(self): """ configure_optimizers for LatentDiffusion """ lr = self.learning_rate params = [ param for name, param in self.model.named_parameters() if not name.startswith("diffusion_model.action_unet") and not name.startswith("diffusion_model.state_unet") ] params_unet_head = list( self.model.diffusion_model.action_unet.parameters()) + list( self.model.diffusion_model.state_unet.parameters()) mainlogger.info(f"@Training [{len(params)}] Full Paramters.") if self.cond_stage_trainable: params_cond_stage = [ p for p in self.cond_stage_model.parameters() if p.requires_grad == True ] mainlogger.info( f"@Training [{len(params_cond_stage)}] Paramters for Cond_stage_model." ) params.extend(params_cond_stage) if self.image_proj_model_trainable: mainlogger.info( f"@Training [{len(list(self.image_proj_model.parameters()))}] Paramters for Image_proj_model." ) params.extend(list(self.image_proj_model.parameters())) if self.learn_logvar: mainlogger.info('Diffusion model optimizing logvar') if isinstance(params[0], dict): params.append({"params": [self.logvar]}) else: params.append(self.logvar) params_group = [{ 'params': params, 'lr': lr }, { 'params': params_unet_head, 'lr': self.dp_optimizer_config['params']['lr'], 'betas': self.dp_optimizer_config['params']['betas'], 'eps': self.dp_optimizer_config['params']['eps'], 'weight_decay': self.dp_optimizer_config['params']['weight_decay'] }] optimizer = torch.optim.AdamW(params_group, lr=lr) if self.use_scheduler: # mainlogger.info("Setting up scheduler...") lr_scheduler = get_scheduler( self.lr_scheduler, optimizer=optimizer, num_warmup_steps=self.lr_warmup_steps, num_training_steps=(self.datasets_len * self.num_epochs) // self.gradient_accumulate_every, # 50 is handcode last_epoch=-1) scheduler = SelectiveLRScheduler( optimizer=optimizer, base_scheduler=lr_scheduler, group_indices=[1], default_lr=[lr, self.dp_optimizer_config['params']['lr']]) return [optimizer], [{'scheduler': scheduler, 'interval': 'step'}] return [optimizer] class DiffusionWrapper(pl.LightningModule): """Thin wrapper that routes inputs/conditions to the underlying diffusion model.""" def __init__(self, diff_model_config: OmegaConf, conditioning_key: str | None) -> None: """ Args: diff_model_config: OmegaConf describing the inner diffusion model to instantiate. conditioning_key: How conditioning is applied. """ super().__init__() self.diffusion_model = instantiate_from_config(diff_model_config) self.conditioning_key = conditioning_key def forward( self, x: Tensor, x_action: Tensor | None, x_state: Tensor | None, t: Tensor, c_concat: Sequence[Tensor] | None = None, c_crossattn: Sequence[Tensor] | None = None, c_crossattn_action: list[Any] | None = None, c_adm: Tensor | None = None, s: Tensor | None = None, mask: Tensor | None = None, **kwargs: Any, ) -> Any: """ Route input(s) and condition(s) into the inner diffusion model based on `conditioning_key`. Args: x: Primary input tensor (e.g., latent/image) at timestep `t`. x_action: Action stream tensor (used by 'hybrid' variants). x_state: State stream tensor (used by 'hybrid' variants). t: Timestep indices (B,). c_concat: List of tensors to be concatenated channel-wise with `x` (for 'concat' / 'hybrid' modes). c_crossattn: List of context tensors concatenated along sequence/channel dim for cross-attention. c_crossattn_action: Mixed list used by action/state heads. c_adm: Class/ADM conditioning (e.g., labels) when required by '*adm*' modes. s: Optional additional time-like / scalar conditioning (e.g., fps/frame-stride) for '*time*' modes. mask: Optional spatial/temporal mask (e.g., inpainting) for '*mask*' modes. **kwargs: Extra keyword arguments forwarded to the inner diffusion model. Returns: Output from the inner diffusion model (tensor or tuple, depending on the model). """ 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': xc = torch.cat([x] + c_concat, dim=1) cc = torch.cat(c_crossattn, 1) cc_action = c_crossattn_action out = self.diffusion_model(xc, x_action, x_state, t, context=cc, context_action=cc_action, **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': 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': 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': 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