from typing import Sequence import random from typing import Any from tqdm import tqdm import torch import torch.nn as nn import torch.nn.functional as F import diffusers.schedulers as noise_schedulers from diffusers.schedulers.scheduling_utils import SchedulerMixin from diffusers.utils.torch_utils import randn_tensor from models.autoencoder.autoencoder_base import AutoEncoderBase from models.content_encoder.content_encoder import ContentEncoder from models.content_adapter import ContentAdapterBase, ContentEncoderAdapterMixin from models.common import ( LoadPretrainedBase, CountParamsBase, SaveTrainableParamsBase, DurationAdapterMixin ) from utils.torch_utilities import ( create_alignment_path, create_mask_from_length, loss_with_mask, trim_or_pad_length ) class DiffusionMixin: def __init__( self, noise_scheduler_name: str = "stabilityai/stable-diffusion-2-1", snr_gamma: float = None, cfg_drop_ratio: float = 0.2 ) -> None: self.noise_scheduler_name = noise_scheduler_name self.snr_gamma = snr_gamma self.classifier_free_guidance = cfg_drop_ratio > 0.0 self.cfg_drop_ratio = cfg_drop_ratio self.noise_scheduler = noise_schedulers.DDPMScheduler.from_pretrained( self.noise_scheduler_name, subfolder="scheduler" ) def compute_snr(self, timesteps) -> torch.Tensor: """ Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849 """ alphas_cumprod = self.noise_scheduler.alphas_cumprod sqrt_alphas_cumprod = alphas_cumprod**0.5 sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod)**0.5 # Expand the tensors. # Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026 sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device )[timesteps].float() while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape): sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None] alpha = sqrt_alphas_cumprod.expand(timesteps.shape) sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to( device=timesteps.device )[timesteps].float() while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape): sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None] sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape) # Compute SNR. snr = (alpha / sigma)**2 return snr def get_timesteps( self, batch_size: int, device: torch.device, training: bool = True ) -> torch.Tensor: if training: timesteps = torch.randint( 0, self.noise_scheduler.config.num_train_timesteps, (batch_size, ), device=device ) else: # validation on half of the total timesteps timesteps = (self.noise_scheduler.config.num_train_timesteps // 2) * torch.ones((batch_size, ), dtype=torch.int64, device=device) timesteps = timesteps.long() return timesteps def get_input_target_and_timesteps( self, latent: torch.Tensor, training: bool, ): batch_size = latent.shape[0] device = latent.device num_train_timesteps = self.noise_scheduler.config.num_train_timesteps self.noise_scheduler.set_timesteps(num_train_timesteps, device=device) timesteps = self.get_timesteps(batch_size, device, training=training) noise = torch.randn_like(latent) noisy_latent = self.noise_scheduler.add_noise(latent, noise, timesteps) target = self.get_target(latent, noise, timesteps) return noisy_latent, target, timesteps def get_target( self, latent: torch.Tensor, noise: torch.Tensor, timesteps: torch.Tensor ) -> torch.Tensor: """ Get the target for loss depending on the prediction type """ if self.noise_scheduler.config.prediction_type == "epsilon": target = noise elif self.noise_scheduler.config.prediction_type == "v_prediction": target = self.noise_scheduler.get_velocity( latent, noise, timesteps ) else: raise ValueError( f"Unknown prediction type {self.noise_scheduler.config.prediction_type}" ) return target def loss_with_snr( self, pred: torch.Tensor, target: torch.Tensor, timesteps: torch.Tensor, mask: torch.Tensor, reduce: bool = True ) -> torch.Tensor: if self.snr_gamma is None: loss = F.mse_loss(pred.float(), target.float(), reduction="none") loss = loss_with_mask(loss, mask, reduce=reduce) else: # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556. # Adapted from https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py#L1006 snr = self.compute_snr(timesteps) mse_loss_weights = torch.stack( [ snr, self.snr_gamma * torch.ones_like(timesteps), ], dim=1, ).min(dim=1)[0] # division by (snr + 1) does not work well, not clear about the reason mse_loss_weights = mse_loss_weights / snr loss = F.mse_loss(pred.float(), target.float(), reduction="none") loss = loss_with_mask(loss, mask, reduce=False) * mse_loss_weights if reduce: loss = loss.mean() return loss def rescale_cfg( self, pred_cond: torch.Tensor, pred_cfg: torch.Tensor, guidance_rescale: float ): """ Rescale `pred_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 """ std_cond = pred_cond.std( dim=list(range(1, pred_cond.ndim)), keepdim=True ) std_cfg = pred_cfg.std(dim=list(range(1, pred_cfg.ndim)), keepdim=True) pred_rescaled = pred_cfg * (std_cond / std_cfg) pred_cfg = guidance_rescale * pred_rescaled + ( 1 - guidance_rescale ) * pred_cfg return pred_cfg class SingleTaskCrossAttentionAudioDiffusion( LoadPretrainedBase, CountParamsBase, SaveTrainableParamsBase, DiffusionMixin, ContentEncoderAdapterMixin ): def __init__( self, autoencoder: AutoEncoderBase, content_encoder: ContentEncoder, backbone: nn.Module, noise_scheduler_name: str = "stabilityai/stable-diffusion-2-1", snr_gamma: float = None, cfg_drop_ratio: float = 0.2, ): nn.Module.__init__(self) DiffusionMixin.__init__( self, noise_scheduler_name, snr_gamma, cfg_drop_ratio ) ContentEncoderAdapterMixin.__init__( self, content_encoder=content_encoder ) self.autoencoder = autoencoder for param in self.autoencoder.parameters(): param.requires_grad = False if hasattr(self.content_encoder, "audio_encoder"): self.content_encoder.audio_encoder.model = self.autoencoder self.backbone = backbone self.dummy_param = nn.Parameter(torch.empty(0)) def forward( self, content: list[Any], condition: list[Any], task: list[str], waveform: torch.Tensor, waveform_lengths: torch.Tensor, **kwargs ): device = self.dummy_param.device self.autoencoder.eval() with torch.no_grad(): latent, latent_mask = self.autoencoder.encode( waveform.unsqueeze(1), waveform_lengths ) content_dict = self.encode_content(content, task, device) content, content_mask = content_dict["content"], content_dict[ "content_mask"] if self.training and self.classifier_free_guidance: mask_indices = [ k for k in range(len(waveform)) if random.random() < self.cfg_drop_ratio ] if len(mask_indices) > 0: content[mask_indices] = 0 noisy_latent, target, timesteps = self.get_input_target_and_timesteps( latent, self.training ) pred: torch.Tensor = self.backbone( x=noisy_latent, timesteps=timesteps, context=content, x_mask=latent_mask, context_mask=content_mask ) pred = pred.transpose(1, self.autoencoder.time_dim) target = target.transpose(1, self.autoencoder.time_dim) loss = self.loss_with_snr(pred, target, timesteps, latent_mask) return loss def prepare_latent( self, batch_size: int, scheduler: SchedulerMixin, latent_shape: Sequence[int], dtype: torch.dtype, device: str ): shape = (batch_size, *latent_shape) latent = randn_tensor( shape, generator=None, device=device, dtype=dtype ) # scale the initial noise by the standard deviation required by the scheduler latent = latent * scheduler.init_noise_sigma return latent def iterative_denoise( self, latent: torch.Tensor, scheduler: SchedulerMixin, verbose: bool, cfg: bool, cfg_scale: float, cfg_rescale: float, backbone_input: dict, ): timesteps = scheduler.timesteps num_steps = len(timesteps) num_warmup_steps = len(timesteps) - num_steps * scheduler.order progress_bar = tqdm(range(num_steps), disable=not verbose) for i, timestep in enumerate(timesteps): # expand the latent if we are doing classifier free guidance if cfg: latent_input = torch.cat([latent, latent]) else: latent_input = latent latent_input = scheduler.scale_model_input(latent_input, timestep) noise_pred = self.backbone( x=latent_input, timesteps=timestep, **backbone_input ) # perform guidance if cfg: noise_pred_uncond, noise_pred_content = noise_pred.chunk(2) noise_pred = noise_pred_uncond + cfg_scale * ( noise_pred_content - noise_pred_uncond ) if cfg_rescale != 0.0: noise_pred = self.rescale_cfg( noise_pred_content, noise_pred, cfg_rescale ) # compute the previous noisy sample x_t -> x_t-1 latent = scheduler.step(noise_pred, timestep, latent).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0): progress_bar.update(1) progress_bar.close() return latent @torch.no_grad() def inference( self, content: list[Any], condition: list[Any], task: list[str], latent_shape: Sequence[int], scheduler: SchedulerMixin, num_steps: int = 50, guidance_scale: float = 3.0, guidance_rescale: float = 0.0, disable_progress: bool = True, **kwargs ): device = self.dummy_param.device classifier_free_guidance = guidance_scale > 1.0 batch_size = len(content) content_output: dict[str, torch.Tensor] = self.encode_content( content, task, device ) content, content_mask = content_output["content"], content_output[ "content_mask"] if classifier_free_guidance: uncond_content = torch.zeros_like(content) uncond_content_mask = content_mask.detach().clone() content = torch.cat([uncond_content, content]) content_mask = torch.cat([uncond_content_mask, content_mask]) scheduler.set_timesteps(num_steps, device=device) latent = self.prepare_latent( batch_size, scheduler, latent_shape, content.dtype, device ) latent = self.iterative_denoise( latent=latent, scheduler=scheduler, verbose=not disable_progress, cfg=classifier_free_guidance, cfg_scale=guidance_scale, cfg_rescale=guidance_rescale, backbone_input={ "context": content, "context_mask": content_mask }, ) waveform = self.autoencoder.decode(latent) return waveform class CrossAttentionAudioDiffusion( SingleTaskCrossAttentionAudioDiffusion, DurationAdapterMixin ): def __init__( self, autoencoder: AutoEncoderBase, content_encoder: ContentEncoder, content_adapter: ContentAdapterBase, backbone: nn.Module, content_dim: int = None, frame_resolution: float = None, duration_offset: float = 1.0, noise_scheduler_name: str = "stabilityai/stable-diffusion-2-1", snr_gamma: float = None, cfg_drop_ratio: float = 0.2, ): super().__init__( autoencoder=autoencoder, content_encoder=content_encoder, backbone=backbone, noise_scheduler_name=noise_scheduler_name, snr_gamma=snr_gamma, cfg_drop_ratio=cfg_drop_ratio ) ContentEncoderAdapterMixin.__init__( self, content_encoder=content_encoder, content_adapter=content_adapter, ) DurationAdapterMixin.__init__( self, latent_token_rate=autoencoder.latent_token_rate, offset=duration_offset, ) def encode_content_with_instruction( self, content: list[Any], task: list[str], device: str | torch.device, instruction: torch.Tensor, instruction_lengths: torch.Tensor, ): content_dict = self.encode_content( content, task, device, instruction, instruction_lengths ) return ( content_dict["content"], content_dict["content_mask"], content_dict["global_duration_pred"], content_dict["local_duration_pred"], content_dict["length_aligned_content"], ) def forward( self, content: list[Any], task: list[str], waveform: torch.Tensor, waveform_lengths: torch.Tensor, instruction: torch.Tensor, instruction_lengths: Sequence[int], loss_reduce: bool = True, **kwargs ): device = self.dummy_param.device loss_reduce = self.training or (loss_reduce and not self.training) self.autoencoder.eval() with torch.no_grad(): latent, latent_mask = self.autoencoder.encode( waveform.unsqueeze(1), waveform_lengths ) content, content_mask, global_duration_pred, _, _ = \ self.encode_content_with_instruction( content, task, device, instruction, instruction_lengths ) global_duration_loss = self.get_global_duration_loss( global_duration_pred, latent_mask, reduce=loss_reduce ) if self.training and self.classifier_free_guidance: mask_indices = [ k for k in range(len(waveform)) if random.random() < self.cfg_drop_ratio ] if len(mask_indices) > 0: content[mask_indices] = 0 noisy_latent, target, timesteps = self.get_input_target_and_timesteps( latent, training=self.training ) pred: torch.Tensor = self.backbone( x=noisy_latent, timesteps=timesteps, context=content, x_mask=latent_mask, context_mask=content_mask ) pred = pred.transpose(1, self.autoencoder.time_dim) target = target.transpose(1, self.autoencoder.time_dim) diff_loss = self.loss_with_snr(pred, target, timesteps, latent_mask) return { "diff_loss": diff_loss, "global_duration_loss": global_duration_loss, } @torch.no_grad() def inference( self, content: list[Any], condition: list[Any], task: list[str], is_time_aligned: Sequence[bool], instruction: torch.Tensor, instruction_lengths: Sequence[int], scheduler: SchedulerMixin, num_steps: int = 50, guidance_scale: float = 3.0, guidance_rescale: float = 0.0, disable_progress: bool = True, use_gt_duration: bool = False, **kwargs ): device = self.dummy_param.device classifier_free_guidance = guidance_scale > 1.0 ( content, content_mask, global_duration_pred, local_duration_pred, _, ) = self.encode_content_with_instruction( content, task, device, instruction, instruction_lengths ) if use_gt_duration: raise NotImplementedError( "Using ground truth global duration only is not implemented yet" ) # prepare global duration global_duration = self.prepare_global_duration( global_duration_pred, local_duration_pred, is_time_aligned, use_local=False ) latent_length = torch.round(global_duration * self.latent_token_rate) latent_mask = create_mask_from_length(latent_length).to(device) max_latent_length = latent_mask.sum(1).max().item() # prepare latent and noise if classifier_free_guidance: uncond_content = torch.zeros_like(content) uncond_content_mask = content_mask.detach().clone() context = torch.cat([uncond_content, content]) context_mask = torch.cat([uncond_content_mask, content_mask]) else: context = content context_mask = content_mask batch_size = content.size(0) latent_shape = tuple( max_latent_length if dim is None else dim for dim in self.autoencoder.latent_shape ) latent = self.prepare_latent( batch_size, scheduler, latent_shape, content.dtype, device ) scheduler.set_timesteps(num_steps, device=device) latent = self.iterative_denoise( latent=latent, scheduler=scheduler, verbose=not disable_progress, cfg=classifier_free_guidance, cfg_scale=guidance_scale, cfg_rescale=guidance_rescale, backbone_input={ "x_mask": latent_mask, "context": context, "context_mask": context_mask, } ) waveform = self.autoencoder.decode(latent) return waveform class DummyContentAudioDiffusion(CrossAttentionAudioDiffusion): def __init__( self, autoencoder: AutoEncoderBase, content_encoder: ContentEncoder, content_adapter: ContentAdapterBase, backbone: nn.Module, content_dim: int, frame_resolution: float, duration_offset: float = 1.0, noise_scheduler_name: str = "stabilityai/stable-diffusion-2-1", snr_gamma: float = None, cfg_drop_ratio: float = 0.2, ): """ Args: autoencoder: Pretrained audio autoencoder that encodes raw waveforms into latent space and decodes latents back to waveforms. content_encoder: Module that produces content embeddings (e.g., from text, MIDI, or other modalities) used to guide the diffusion. content_adapter (ContentAdapterBase): Adapter module that fuses task instruction embeddings and content embeddings, and performs duration prediction for time-aligned tasks. backbone: U‑Net or Transformer backbone that performs the core denoising operations in latent space. content_dim: Dimension of the content embeddings produced by the `content_encoder` and `content_adapter`. frame_resolution: Time resolution, in seconds, of each content frame when predicting duration alignment. Used when calculating duration loss. duration_offset: A small positive offset (frame number) added to predicted durations to ensure numerical stability of log-scaled duration prediction. noise_scheduler_name: Identifier of the pretrained noise scheduler to use. snr_gamma: Clipping value in min-SNR diffusion loss weighting strategy. cfg_drop_ratio: Probability of dropping the content conditioning during training to support CFG. """ super().__init__( autoencoder=autoencoder, content_encoder=content_encoder, content_adapter=content_adapter, backbone=backbone, duration_offset=duration_offset, noise_scheduler_name=noise_scheduler_name, snr_gamma=snr_gamma, cfg_drop_ratio=cfg_drop_ratio, ) self.frame_resolution = frame_resolution self.dummy_nta_embed = nn.Parameter(torch.zeros(content_dim)) self.dummy_ta_embed = nn.Parameter(torch.zeros(content_dim)) def get_backbone_input( self, target_length: int, content: torch.Tensor, content_mask: torch.Tensor, time_aligned_content: torch.Tensor, length_aligned_content: torch.Tensor, is_time_aligned: torch.Tensor, ): # TODO compatility for 2D spectrogram VAE time_aligned_content = trim_or_pad_length( time_aligned_content, target_length, 1 ) length_aligned_content = trim_or_pad_length( length_aligned_content, target_length, 1 ) # time_aligned_content: from monotonic aligned input, without frame expansion (phoneme) # length_aligned_content: from aligned input (f0/energy) time_aligned_content = time_aligned_content + length_aligned_content time_aligned_content[~is_time_aligned] = self.dummy_ta_embed.to( time_aligned_content.dtype ) context = content context[is_time_aligned] = self.dummy_nta_embed.to(context.dtype) # only use the first dummy non time aligned embedding context_mask = content_mask.detach().clone() context_mask[is_time_aligned, 1:] = False # truncate dummy non time aligned context if is_time_aligned.sum().item() < content.size(0): trunc_nta_length = content_mask[~is_time_aligned].sum(1).max() else: trunc_nta_length = content.size(1) context = context[:, :trunc_nta_length] context_mask = context_mask[:, :trunc_nta_length] return context, context_mask, time_aligned_content def forward( self, content: list[Any], task: list[str], is_time_aligned: Sequence[bool], duration: Sequence[float], waveform: torch.Tensor, waveform_lengths: torch.Tensor, instruction: torch.Tensor, instruction_lengths: Sequence[int], loss_reduce: bool = True, **kwargs ): device = self.dummy_param.device loss_reduce = self.training or (loss_reduce and not self.training) self.autoencoder.eval() with torch.no_grad(): latent, latent_mask = self.autoencoder.encode( waveform.unsqueeze(1), waveform_lengths ) ( content, content_mask, global_duration_pred, local_duration_pred, length_aligned_content ) = self.encode_content_with_instruction( content, task, device, instruction, instruction_lengths ) # truncate unused non time aligned duration prediction if is_time_aligned.sum() > 0: trunc_ta_length = content_mask[is_time_aligned].sum(1).max() else: trunc_ta_length = content.size(1) # duration loss local_duration_pred = local_duration_pred[:, :trunc_ta_length] ta_content_mask = content_mask[:, :trunc_ta_length] local_duration_loss = self.get_local_duration_loss( duration, local_duration_pred, ta_content_mask, is_time_aligned, reduce=loss_reduce ) global_duration_loss = self.get_global_duration_loss( global_duration_pred, latent_mask, reduce=loss_reduce ) # -------------------------------------------------------------------- # prepare latent and diffusion-related noise # -------------------------------------------------------------------- noisy_latent, target, timesteps = self.get_input_target_and_timesteps( latent, training=self.training ) # -------------------------------------------------------------------- # duration adapter # -------------------------------------------------------------------- if is_time_aligned.sum() == 0 and \ duration.size(1) < content_mask.size(1): # for non time-aligned tasks like TTA, `duration` is dummy one duration = F.pad( duration, (0, content_mask.size(1) - duration.size(1)) ) time_aligned_content, _ = self.expand_by_duration( x=content[:, :trunc_ta_length], content_mask=ta_content_mask, local_duration=duration, ) # -------------------------------------------------------------------- # prepare input to the backbone # -------------------------------------------------------------------- # TODO compatility for 2D spectrogram VAE latent_length = noisy_latent.size(self.autoencoder.time_dim) context, context_mask, time_aligned_content = self.get_backbone_input( latent_length, content, content_mask, time_aligned_content, length_aligned_content, is_time_aligned ) # -------------------------------------------------------------------- # classifier free guidance # -------------------------------------------------------------------- if self.training and self.classifier_free_guidance: mask_indices = [ k for k in range(len(waveform)) if random.random() < self.cfg_drop_ratio ] if len(mask_indices) > 0: context[mask_indices] = 0 time_aligned_content[mask_indices] = 0 pred: torch.Tensor = self.backbone( x=noisy_latent, x_mask=latent_mask, timesteps=timesteps, context=context, context_mask=context_mask, time_aligned_context=time_aligned_content, ) pred = pred.transpose(1, self.autoencoder.time_dim) target = target.transpose(1, self.autoencoder.time_dim) diff_loss = self.loss_with_snr( pred, target, timesteps, latent_mask, reduce=loss_reduce ) return { "diff_loss": diff_loss, "local_duration_loss": local_duration_loss, "global_duration_loss": global_duration_loss } @torch.no_grad() def inference( self, content: list[Any], condition: list[Any], task: list[str], is_time_aligned: list[bool], instruction: torch.Tensor, instruction_lengths: Sequence[int], scheduler: SchedulerMixin, num_steps: int = 20, guidance_scale: float = 3.0, guidance_rescale: float = 0.0, disable_progress: bool = True, use_gt_duration: bool = False, **kwargs ): device = self.dummy_param.device classifier_free_guidance = guidance_scale > 1.0 ( content, content_mask, global_duration_pred, local_duration_pred, length_aligned_content ) = self.encode_content_with_instruction( content, task, device, instruction, instruction_lengths ) batch_size = content.size(0) # truncate dummy time aligned duration prediction is_time_aligned = torch.as_tensor(is_time_aligned) if is_time_aligned.sum() > 0: trunc_ta_length = content_mask[is_time_aligned].sum(1).max() else: trunc_ta_length = content.size(1) # prepare local duration local_duration = self.prepare_local_duration( local_duration_pred, content_mask ) local_duration = local_duration[:, :trunc_ta_length] # use ground truth duration if use_gt_duration and "duration" in kwargs: local_duration = torch.as_tensor(kwargs["duration"]).to(device) # prepare global duration global_duration = self.prepare_global_duration( global_duration_pred, local_duration, is_time_aligned ) # -------------------------------------------------------------------- # duration adapter # -------------------------------------------------------------------- time_aligned_content, latent_mask = self.expand_by_duration( x=content[:, :trunc_ta_length], content_mask=content_mask[:, :trunc_ta_length], local_duration=local_duration, global_duration=global_duration, ) context, context_mask, time_aligned_content = self.get_backbone_input( target_length=time_aligned_content.size(1), content=content, content_mask=content_mask, time_aligned_content=time_aligned_content, length_aligned_content=length_aligned_content, is_time_aligned=is_time_aligned ) # -------------------------------------------------------------------- # prepare unconditional input # -------------------------------------------------------------------- if classifier_free_guidance: uncond_time_aligned_content = torch.zeros_like( time_aligned_content ) uncond_context = torch.zeros_like(context) uncond_context_mask = context_mask.detach().clone() time_aligned_content = torch.cat([ uncond_time_aligned_content, time_aligned_content ]) context = torch.cat([uncond_context, context]) context_mask = torch.cat([uncond_context_mask, context_mask]) latent_mask = torch.cat([ latent_mask, latent_mask.detach().clone() ]) # -------------------------------------------------------------------- # prepare input to the backbone # -------------------------------------------------------------------- latent_length = latent_mask.sum(1).max().item() latent_shape = tuple( latent_length if dim is None else dim for dim in self.autoencoder.latent_shape ) latent = self.prepare_latent( batch_size, scheduler, latent_shape, content.dtype, device ) scheduler.set_timesteps(num_steps, device=device) latent = self.iterative_denoise( latent=latent, scheduler=scheduler, verbose=not disable_progress, cfg=classifier_free_guidance, cfg_scale=guidance_scale, cfg_rescale=guidance_rescale, backbone_input={ "x_mask": latent_mask, "context": context, "context_mask": context_mask, "time_aligned_context": time_aligned_content, } ) # TODO variable length decoding, using `latent_mask` waveform = self.autoencoder.decode(latent) return waveform class DoubleContentAudioDiffusion(DummyContentAudioDiffusion): def get_backbone_input( self, target_length: int, content: torch.Tensor, content_mask: torch.Tensor, time_aligned_content: torch.Tensor, length_aligned_content: torch.Tensor, is_time_aligned: torch.Tensor, ): time_aligned_content = trim_or_pad_length( time_aligned_content, target_length, 1 ) context_length = min(content.size(1), time_aligned_content.size(1)) time_aligned_content[~is_time_aligned, :context_length] = content[ ~is_time_aligned, :context_length] length_aligned_content = trim_or_pad_length( length_aligned_content, target_length, 1 ) time_aligned_content = time_aligned_content + length_aligned_content context = content context_mask = content_mask.detach().clone() return context, context_mask, time_aligned_content class HybridContentAudioDiffusion(DummyContentAudioDiffusion): def get_backbone_input( self, target_length: int, content: torch.Tensor, content_mask: torch.Tensor, time_aligned_content: torch.Tensor, length_aligned_content: torch.Tensor, is_time_aligned: torch.Tensor, ): # TODO compatility for 2D spectrogram VAE time_aligned_content = trim_or_pad_length( time_aligned_content, target_length, 1 ) length_aligned_content = trim_or_pad_length( length_aligned_content, target_length, 1 ) # time_aligned_content: from monotonic aligned input, without frame expansion (phoneme) # length_aligned_content: from aligned input (f0/energy) time_aligned_content = time_aligned_content + length_aligned_content time_aligned_content[~is_time_aligned] = self.dummy_ta_embed.to( time_aligned_content.dtype ) context = content context_mask = content_mask.detach().clone() return context, context_mask, time_aligned_content