import yaml import random import inspect import numpy as np from tqdm import tqdm import torch import torch.nn as nn import torch.nn.functional as F from einops import repeat from tools.torch_tools import wav_to_fbank from audioldm.audio.stft import TacotronSTFT from audioldm.variational_autoencoder import AutoencoderKL from audioldm.utils import default_audioldm_config, get_metadata from transformers import CLIPTokenizer, AutoTokenizer, AutoProcessor from transformers import CLIPTextModel, T5EncoderModel, AutoModel, ClapAudioModel, ClapTextModel import sys sys.path.insert(0, "diffusers/src") import diffusers from diffusers.utils import randn_tensor from diffusers import DDPMScheduler, UNet2DConditionModel, UNet2DConditionModelMusic from diffusers import AutoencoderKL as DiffuserAutoencoderKL from layers.layers import chord_tokenizer, beat_tokenizer, Chord_Embedding, Beat_Embedding, Music_PositionalEncoding, Fundamental_Music_Embedding def build_pretrained_models(name): checkpoint = torch.load(get_metadata()[name]["path"], map_location="cpu") scale_factor = checkpoint["state_dict"]["scale_factor"].item() vae_state_dict = {k[18:]: v for k, v in checkpoint["state_dict"].items() if "first_stage_model." in k} config = default_audioldm_config(name) vae_config = config["model"]["params"]["first_stage_config"]["params"] vae_config["scale_factor"] = scale_factor vae = AutoencoderKL(**vae_config) vae.load_state_dict(vae_state_dict) fn_STFT = TacotronSTFT( config["preprocessing"]["stft"]["filter_length"], config["preprocessing"]["stft"]["hop_length"], config["preprocessing"]["stft"]["win_length"], config["preprocessing"]["mel"]["n_mel_channels"], config["preprocessing"]["audio"]["sampling_rate"], config["preprocessing"]["mel"]["mel_fmin"], config["preprocessing"]["mel"]["mel_fmax"], ) vae.eval() fn_STFT.eval() return vae, fn_STFT class AudioDiffusion(nn.Module): def __init__( self, text_encoder_name, scheduler_name, unet_model_name=None, unet_model_config_path=None, snr_gamma=None, freeze_text_encoder=True, uncondition=False, ): super().__init__() assert unet_model_name is not None or unet_model_config_path is not None, "Either UNet pretrain model name or a config file path is required" self.text_encoder_name = text_encoder_name self.scheduler_name = scheduler_name self.unet_model_name = unet_model_name self.unet_model_config_path = unet_model_config_path self.snr_gamma = snr_gamma self.freeze_text_encoder = freeze_text_encoder self.uncondition = uncondition # https://huggingface.co/docs/diffusers/v0.14.0/en/api/schedulers/overview self.noise_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler") self.inference_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler") if unet_model_config_path: unet_config = UNet2DConditionModel.load_config(unet_model_config_path) self.unet = UNet2DConditionModel.from_config(unet_config, subfolder="unet") self.set_from = "random" print("UNet initialized randomly.") else: self.unet = UNet2DConditionModel.from_pretrained(unet_model_name, subfolder="unet") self.set_from = "pre-trained" self.group_in = nn.Sequential(nn.Linear(8, 512), nn.Linear(512, 4)) self.group_out = nn.Sequential(nn.Linear(4, 512), nn.Linear(512, 8)) print("UNet initialized from stable diffusion checkpoint.") if "stable-diffusion" in self.text_encoder_name: self.tokenizer = CLIPTokenizer.from_pretrained(self.text_encoder_name, subfolder="tokenizer") self.text_encoder = CLIPTextModel.from_pretrained(self.text_encoder_name, subfolder="text_encoder") elif "t5" in self.text_encoder_name: self.tokenizer = AutoTokenizer.from_pretrained(self.text_encoder_name) self.text_encoder = T5EncoderModel.from_pretrained(self.text_encoder_name) else: self.tokenizer = AutoTokenizer.from_pretrained(self.text_encoder_name) self.text_encoder = AutoModel.from_pretrained(self.text_encoder_name) def compute_snr(self, timesteps): """ 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 encode_text(self, prompt): device = self.text_encoder.device batch = self.tokenizer( prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt" ) input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device) if self.freeze_text_encoder: with torch.no_grad(): encoder_hidden_states = self.text_encoder( input_ids=input_ids, attention_mask=attention_mask )[0] else: encoder_hidden_states = self.text_encoder( input_ids=input_ids, attention_mask=attention_mask )[0] boolean_encoder_mask = (attention_mask == 1).to(device) return encoder_hidden_states, boolean_encoder_mask def forward(self, latents, prompt, validation_mode=False): device = self.text_encoder.device num_train_timesteps = self.noise_scheduler.num_train_timesteps self.noise_scheduler.set_timesteps(num_train_timesteps, device=device) encoder_hidden_states, boolean_encoder_mask = self.encode_text(prompt) if self.uncondition: mask_indices = [k for k in range(len(prompt)) if random.random() < 0.1] if len(mask_indices) > 0: encoder_hidden_states[mask_indices] = 0 bsz = latents.shape[0] if validation_mode: timesteps = (self.noise_scheduler.num_train_timesteps//2) * torch.ones((bsz,), dtype=torch.int64, device=device) else: # Sample a random timestep for each instance timesteps = torch.randint(0, self.noise_scheduler.num_train_timesteps, (bsz,), device=device) # print('in if ', timesteps) timesteps = timesteps.long() # print('outside if ' , timesteps) noise = torch.randn_like(latents) noisy_latents = self.noise_scheduler.add_noise(latents, noise, timesteps) # 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(latents, noise, timesteps) else: raise ValueError(f"Unknown prediction type {self.noise_scheduler.config.prediction_type}") if self.set_from == "random": model_pred = self.unet( noisy_latents, timesteps, encoder_hidden_states, encoder_attention_mask=boolean_encoder_mask ).sample elif self.set_from == "pre-trained": compressed_latents = self.group_in(noisy_latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous() model_pred = self.unet( compressed_latents, timesteps, encoder_hidden_states, encoder_attention_mask=boolean_encoder_mask ).sample model_pred = self.group_out(model_pred.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous() if self.snr_gamma is None: loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") else: # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556. # Adaptef from huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py snr = self.compute_snr(timesteps) mse_loss_weights = ( torch.stack([snr, self.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr ) loss = F.mse_loss(model_pred.float(), target.float(), reduction="none") loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights loss = loss.mean() return loss @torch.no_grad() def inference(self, prompt, inference_scheduler, num_steps=20, guidance_scale=3, num_samples_per_prompt=1, disable_progress=True): device = self.text_encoder.device classifier_free_guidance = guidance_scale > 1.0 batch_size = len(prompt) * num_samples_per_prompt if classifier_free_guidance: prompt_embeds, boolean_prompt_mask = self.encode_text_classifier_free(prompt, num_samples_per_prompt) else: prompt_embeds, boolean_prompt_mask = self.encode_text(prompt) prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0) boolean_prompt_mask = boolean_prompt_mask.repeat_interleave(num_samples_per_prompt, 0) inference_scheduler.set_timesteps(num_steps, device=device) timesteps = inference_scheduler.timesteps num_channels_latents = self.unet.in_channels latents = self.prepare_latents(batch_size, inference_scheduler, num_channels_latents, prompt_embeds.dtype, device) num_warmup_steps = len(timesteps) - num_steps * inference_scheduler.order progress_bar = tqdm(range(num_steps), disable=disable_progress) for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if classifier_free_guidance else latents latent_model_input = inference_scheduler.scale_model_input(latent_model_input, t) noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, encoder_attention_mask=boolean_prompt_mask ).sample # perform guidance if classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = inference_scheduler.step(noise_pred, t, latents).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % inference_scheduler.order == 0): progress_bar.update(1) if self.set_from == "pre-trained": latents = self.group_out(latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous() return latents def prepare_latents(self, batch_size, inference_scheduler, num_channels_latents, dtype, device): shape = (batch_size, num_channels_latents, 256, 16) latents = randn_tensor(shape, generator=None, device=device, dtype=dtype) # scale the initial noise by the standard deviation required by the scheduler latents = latents * inference_scheduler.init_noise_sigma return latents def encode_text_classifier_free(self, prompt, num_samples_per_prompt): device = self.text_encoder.device batch = self.tokenizer( prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt" ) input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device) with torch.no_grad(): prompt_embeds = self.text_encoder( input_ids=input_ids, attention_mask=attention_mask )[0] prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0) attention_mask = attention_mask.repeat_interleave(num_samples_per_prompt, 0) # get unconditional embeddings for classifier free guidance uncond_tokens = [""] * len(prompt) max_length = prompt_embeds.shape[1] uncond_batch = self.tokenizer( uncond_tokens, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt", ) uncond_input_ids = uncond_batch.input_ids.to(device) uncond_attention_mask = uncond_batch.attention_mask.to(device) with torch.no_grad(): negative_prompt_embeds = self.text_encoder( input_ids=uncond_input_ids, attention_mask=uncond_attention_mask )[0] negative_prompt_embeds = negative_prompt_embeds.repeat_interleave(num_samples_per_prompt, 0) uncond_attention_mask = uncond_attention_mask.repeat_interleave(num_samples_per_prompt, 0) # For classifier free guidance, we need to do two forward passes. # We concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) prompt_mask = torch.cat([uncond_attention_mask, attention_mask]) boolean_prompt_mask = (prompt_mask == 1).to(device) return prompt_embeds, boolean_prompt_mask class MusicAudioDiffusion(nn.Module): def __init__( self, text_encoder_name, scheduler_name, unet_model_name=None, unet_model_config_path=None, snr_gamma=None, freeze_text_encoder=True, uncondition=False, d_fme = 1024, #FME fme_type = "se", base = 1, if_trainable = True, translation_bias_type = "nd", emb_nn = True, d_pe = 1024, #PE if_index = True, if_global_timing = True, if_modulo_timing = False, d_beat = 1024, #Beat d_oh_beat_type = 7, beat_len = 50, d_chord = 1024, #Chord d_oh_chord_type = 12, d_oh_inv_type = 4, chord_len = 20, ): super().__init__() assert unet_model_name is not None or unet_model_config_path is not None, "Either UNet pretrain model name or a config file path is required" self.text_encoder_name = text_encoder_name self.scheduler_name = scheduler_name self.unet_model_name = unet_model_name self.unet_model_config_path = unet_model_config_path self.snr_gamma = snr_gamma self.freeze_text_encoder = freeze_text_encoder self.uncondition = uncondition # https://huggingface.co/docs/diffusers/v0.14.0/en/api/schedulers/overview self.noise_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler") self.inference_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler") if unet_model_config_path: unet_config = UNet2DConditionModelMusic.load_config(unet_model_config_path) self.unet = UNet2DConditionModelMusic.from_config(unet_config, subfolder="unet") self.set_from = "random" print("UNet initialized randomly.") else: self.unet = UNet2DConditionModel.from_pretrained(unet_model_name, subfolder="unet") self.set_from = "pre-trained" self.group_in = nn.Sequential(nn.Linear(8, 512), nn.Linear(512, 4)) self.group_out = nn.Sequential(nn.Linear(4, 512), nn.Linear(512, 8)) print("UNet initialized from stable diffusion checkpoint.") if "stable-diffusion" in self.text_encoder_name: self.tokenizer = CLIPTokenizer.from_pretrained(self.text_encoder_name, subfolder="tokenizer") self.text_encoder = CLIPTextModel.from_pretrained(self.text_encoder_name, subfolder="text_encoder") elif "t5" in self.text_encoder_name: self.tokenizer = AutoTokenizer.from_pretrained(self.text_encoder_name) self.text_encoder = T5EncoderModel.from_pretrained(self.text_encoder_name) else: self.tokenizer = AutoTokenizer.from_pretrained(self.text_encoder_name) self.text_encoder = AutoModel.from_pretrained(self.text_encoder_name) self.device = self.text_encoder.device #Music Feature Encoder self.FME = Fundamental_Music_Embedding(d_model = d_fme, base= base, if_trainable = False, type = fme_type,emb_nn=emb_nn,translation_bias_type = translation_bias_type) self.PE = Music_PositionalEncoding(d_model = d_pe, if_index = if_index, if_global_timing = if_global_timing, if_modulo_timing = if_modulo_timing, device = self.device) # self.PE2 = Music_PositionalEncoding(d_model = d_pe, if_index = if_index, if_global_timing = if_global_timing, if_modulo_timing = if_modulo_timing, device = self.device) self.beat_tokenizer = beat_tokenizer(seq_len_beat=beat_len, if_pad = True) self.beat_embedding_layer = Beat_Embedding(self.PE, d_model = d_beat, d_oh_beat_type = d_oh_beat_type) self.chord_embedding_layer = Chord_Embedding(self.FME, self.PE, d_model = d_chord, d_oh_type = d_oh_chord_type, d_oh_inv = d_oh_inv_type) self.chord_tokenizer = chord_tokenizer(seq_len_chord=chord_len, if_pad = True) def compute_snr(self, timesteps): """ 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 encode_text(self, prompt): device = self.text_encoder.device batch = self.tokenizer( prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt" ) input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device) #cuda if self.freeze_text_encoder: with torch.no_grad(): encoder_hidden_states = self.text_encoder( input_ids=input_ids, attention_mask=attention_mask )[0] #batch, len_text, dim else: encoder_hidden_states = self.text_encoder( input_ids=input_ids, attention_mask=attention_mask )[0] boolean_encoder_mask = (attention_mask == 1).to(device) ##batch, len_text return encoder_hidden_states, boolean_encoder_mask def encode_beats(self, beats): device = self.device out_beat = [] out_beat_timing = [] out_mask = [] for beat in beats: tokenized_beats,tokenized_beats_timing, tokenized_beat_mask = self.beat_tokenizer(beat) out_beat.append(tokenized_beats) out_beat_timing.append(tokenized_beats_timing) out_mask.append(tokenized_beat_mask) out_beat, out_beat_timing, out_mask = torch.tensor(out_beat).to(device), torch.tensor(out_beat_timing).to(device), torch.tensor(out_mask).to(device) #batch, len_beat embedded_beat = self.beat_embedding_layer(out_beat, out_beat_timing, device) return embedded_beat, out_mask def encode_chords(self, chords,chords_time): device = self.device out_chord_root = [] out_chord_type = [] out_chord_inv = [] out_chord_timing = [] out_mask = [] for chord, chord_time in zip(chords,chords_time): #batch loop tokenized_chord_root, tokenized_chord_type, tokenized_chord_inv, tokenized_chord_time, tokenized_chord_mask = self.chord_tokenizer(chord, chord_time) out_chord_root.append(tokenized_chord_root) out_chord_type.append(tokenized_chord_type) out_chord_inv.append(tokenized_chord_inv) out_chord_timing.append(tokenized_chord_time) out_mask.append(tokenized_chord_mask) #chords: (B, LEN, 4) out_chord_root, out_chord_type, out_chord_inv, out_chord_timing, out_mask = torch.tensor(out_chord_root).to(device), torch.tensor(out_chord_type).to(device), torch.tensor(out_chord_inv).to(device), torch.tensor(out_chord_timing).to(device), torch.tensor(out_mask).to(device) embedded_chord = self.chord_embedding_layer(out_chord_root, out_chord_type, out_chord_inv, out_chord_timing, device) return embedded_chord, out_mask # return out_chord_root, out_mask def forward(self, latents, prompt, beats, chords,chords_time, validation_mode=False): device = self.text_encoder.device num_train_timesteps = self.noise_scheduler.num_train_timesteps self.noise_scheduler.set_timesteps(num_train_timesteps, device=device) encoder_hidden_states, boolean_encoder_mask = self.encode_text(prompt) # with torch.no_grad(): encoded_beats, beat_mask = self.encode_beats(beats) #batch, len_beats, dim; batch, len_beats encoded_chords, chord_mask = self.encode_chords(chords,chords_time) if self.uncondition: mask_indices = [k for k in range(len(prompt)) if random.random() < 0.1] if len(mask_indices) > 0: encoder_hidden_states[mask_indices] = 0 encoded_chords[mask_indices] = 0 encoded_beats[mask_indices] = 0 bsz = latents.shape[0] if validation_mode: timesteps = (self.noise_scheduler.num_train_timesteps//2) * torch.ones((bsz,), dtype=torch.int64, device=device) else: timesteps = torch.randint(0, self.noise_scheduler.num_train_timesteps, (bsz,), device=device) timesteps = timesteps.long() noise = torch.randn_like(latents) noisy_latents = self.noise_scheduler.add_noise(latents, noise, timesteps) # 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(latents, noise, timesteps) else: raise ValueError(f"Unknown prediction type {self.noise_scheduler.config.prediction_type}") if self.set_from == "random": # model_pred = torch.zeros((bsz,8,256,16)).to(device) model_pred = self.unet( noisy_latents, timesteps, encoder_hidden_states, encoded_beats, encoded_chords, encoder_attention_mask=boolean_encoder_mask, beat_attention_mask = beat_mask, chord_attention_mask = chord_mask ).sample elif self.set_from == "pre-trained": compressed_latents = self.group_in(noisy_latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous() model_pred = self.unet( compressed_latents, timesteps, encoder_hidden_states, encoder_attention_mask=boolean_encoder_mask ).sample model_pred = self.group_out(model_pred.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous() if self.snr_gamma is None: loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") else: # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556. # Adaptef from huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py snr = self.compute_snr(timesteps) mse_loss_weights = ( torch.stack([snr, self.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr ) loss = F.mse_loss(model_pred.float(), target.float(), reduction="none") loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights loss = loss.mean() return loss @torch.no_grad() def inference(self, prompt, beats, chords,chords_time, inference_scheduler, num_steps=20, guidance_scale=3, num_samples_per_prompt=1, disable_progress=True): device = self.text_encoder.device classifier_free_guidance = guidance_scale > 1.0 batch_size = len(prompt) * num_samples_per_prompt if classifier_free_guidance: prompt_embeds, boolean_prompt_mask = self.encode_text_classifier_free(prompt, num_samples_per_prompt) encoded_beats, beat_mask = self.encode_beats_classifier_free(beats, num_samples_per_prompt) #batch, len_beats, dim; batch, len_beats encoded_chords, chord_mask = self.encode_chords_classifier_free(chords, chords_time, num_samples_per_prompt) else: prompt_embeds, boolean_prompt_mask = self.encode_text(prompt) prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0) boolean_prompt_mask = boolean_prompt_mask.repeat_interleave(num_samples_per_prompt, 0) encoded_beats, beat_mask = self.encode_beats(beats) #batch, len_beats, dim; batch, len_beats encoded_beats = encoded_beats.repeat_interleave(num_samples_per_prompt, 0) beat_mask = beat_mask.repeat_interleave(num_samples_per_prompt, 0) encoded_chords, chord_mask = self.encode_chords(chords,chords_time) encoded_chords = encoded_chords.repeat_interleave(num_samples_per_prompt, 0) chord_mask = chord_mask.repeat_interleave(num_samples_per_prompt, 0) # print(f"encoded_chords:{encoded_chords.shape}, chord_mask:{chord_mask.shape}, prompt_embeds:{prompt_embeds.shape},boolean_prompt_mask:{boolean_prompt_mask.shape} ") inference_scheduler.set_timesteps(num_steps, device=device) timesteps = inference_scheduler.timesteps num_channels_latents = self.unet.in_channels latents = self.prepare_latents(batch_size, inference_scheduler, num_channels_latents, prompt_embeds.dtype, device) num_warmup_steps = len(timesteps) - num_steps * inference_scheduler.order progress_bar = tqdm(range(num_steps), disable=disable_progress) for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if classifier_free_guidance else latents latent_model_input = inference_scheduler.scale_model_input(latent_model_input, t) noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, encoder_attention_mask=boolean_prompt_mask, beat_features = encoded_beats, beat_attention_mask = beat_mask, chord_features = encoded_chords,chord_attention_mask = chord_mask ).sample # perform guidance if classifier_free_guidance: #should work for beats and chords too noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = inference_scheduler.step(noise_pred, t, latents).prev_sample # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % inference_scheduler.order == 0): progress_bar.update(1) if self.set_from == "pre-trained": latents = self.group_out(latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous() return latents def prepare_latents(self, batch_size, inference_scheduler, num_channels_latents, dtype, device): shape = (batch_size, num_channels_latents, 256, 16) latents = randn_tensor(shape, generator=None, device=device, dtype=dtype) # scale the initial noise by the standard deviation required by the scheduler latents = latents * inference_scheduler.init_noise_sigma return latents def encode_text_classifier_free(self, prompt, num_samples_per_prompt): device = self.text_encoder.device batch = self.tokenizer( prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt" ) input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device) with torch.no_grad(): prompt_embeds = self.text_encoder( input_ids=input_ids, attention_mask=attention_mask )[0] prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0) attention_mask = attention_mask.repeat_interleave(num_samples_per_prompt, 0) # get unconditional embeddings for classifier free guidance # print(len(prompt), 'this is prompt len') uncond_tokens = [""] * len(prompt) max_length = prompt_embeds.shape[1] uncond_batch = self.tokenizer( uncond_tokens, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt", ) uncond_input_ids = uncond_batch.input_ids.to(device) uncond_attention_mask = uncond_batch.attention_mask.to(device) with torch.no_grad(): negative_prompt_embeds = self.text_encoder( input_ids=uncond_input_ids, attention_mask=uncond_attention_mask )[0] negative_prompt_embeds = negative_prompt_embeds.repeat_interleave(num_samples_per_prompt, 0) uncond_attention_mask = uncond_attention_mask.repeat_interleave(num_samples_per_prompt, 0) # For classifier free guidance, we need to do two forward passes. # We concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) prompt_mask = torch.cat([uncond_attention_mask, attention_mask]) boolean_prompt_mask = (prompt_mask == 1).to(device) return prompt_embeds, boolean_prompt_mask def encode_beats_classifier_free(self, beats, num_samples_per_prompt): device = self.device with torch.no_grad(): out_beat = [] out_beat_timing = [] out_mask = [] for beat in beats: tokenized_beats,tokenized_beats_timing, tokenized_beat_mask = self.beat_tokenizer(beat) out_beat.append(tokenized_beats) out_beat_timing.append(tokenized_beats_timing) out_mask.append(tokenized_beat_mask) out_beat, out_beat_timing, out_mask = torch.tensor(out_beat).to(device), torch.tensor(out_beat_timing).to(device), torch.tensor(out_mask).to(device) #batch, len_beat embedded_beat = self.beat_embedding_layer(out_beat, out_beat_timing, device) embedded_beat = embedded_beat.repeat_interleave(num_samples_per_prompt, 0) out_mask = out_mask.repeat_interleave(num_samples_per_prompt, 0) uncond_beats = [[[],[]]] * len(beats) max_length = embedded_beat.shape[1] with torch.no_grad(): out_beat_unc = [] out_beat_timing_unc = [] out_mask_unc = [] for beat in uncond_beats: tokenized_beats, tokenized_beats_timing, tokenized_beat_mask = self.beat_tokenizer(beat) out_beat_unc.append(tokenized_beats) out_beat_timing_unc.append(tokenized_beats_timing) out_mask_unc.append(tokenized_beat_mask) out_beat_unc, out_beat_timing_unc, out_mask_unc = torch.tensor(out_beat_unc).to(device), torch.tensor(out_beat_timing_unc).to(device), torch.tensor(out_mask_unc).to(device) #batch, len_beat embedded_beat_unc = self.beat_embedding_layer(out_beat_unc, out_beat_timing_unc, device) embedded_beat_unc = embedded_beat_unc.repeat_interleave(num_samples_per_prompt, 0) out_mask_unc = out_mask_unc.repeat_interleave(num_samples_per_prompt, 0) embedded_beat = torch.cat([embedded_beat_unc, embedded_beat]) out_mask = torch.cat([out_mask_unc, out_mask]) return embedded_beat, out_mask def encode_chords_classifier_free(self, chords, chords_time, num_samples_per_prompt): device = self.device with torch.no_grad(): out_chord_root = [] out_chord_type = [] out_chord_inv = [] out_chord_timing = [] out_mask = [] for chord, chord_time in zip(chords,chords_time): #batch loop tokenized_chord_root, tokenized_chord_type, tokenized_chord_inv, tokenized_chord_time, tokenized_chord_mask = self.chord_tokenizer(chord, chord_time) out_chord_root.append(tokenized_chord_root) out_chord_type.append(tokenized_chord_type) out_chord_inv.append(tokenized_chord_inv) out_chord_timing.append(tokenized_chord_time) out_mask.append(tokenized_chord_mask) out_chord_root, out_chord_type, out_chord_inv, out_chord_timing, out_mask = torch.tensor(out_chord_root).to(device), torch.tensor(out_chord_type).to(device), torch.tensor(out_chord_inv).to(device), torch.tensor(out_chord_timing).to(device), torch.tensor(out_mask).to(device) embedded_chord = self.chord_embedding_layer(out_chord_root, out_chord_type, out_chord_inv, out_chord_timing, device) embedded_chord = embedded_chord.repeat_interleave(num_samples_per_prompt, 0) out_mask = out_mask.repeat_interleave(num_samples_per_prompt, 0) chords_unc=[[]] * len(chords) chords_time_unc=[[]] * len(chords_time) max_length = embedded_chord.shape[1] with torch.no_grad(): out_chord_root_unc = [] out_chord_type_unc = [] out_chord_inv_unc = [] out_chord_timing_unc = [] out_mask_unc = [] for chord, chord_time in zip(chords_unc,chords_time_unc): #batch loop tokenized_chord_root, tokenized_chord_type, tokenized_chord_inv, tokenized_chord_time, tokenized_chord_mask = self.chord_tokenizer(chord, chord_time) out_chord_root_unc.append(tokenized_chord_root) out_chord_type_unc.append(tokenized_chord_type) out_chord_inv_unc.append(tokenized_chord_inv) out_chord_timing_unc.append(tokenized_chord_time) out_mask_unc.append(tokenized_chord_mask) out_chord_root_unc, out_chord_type_unc, out_chord_inv_unc, out_chord_timing_unc, out_mask_unc = torch.tensor(out_chord_root_unc).to(device), torch.tensor(out_chord_type_unc).to(device), torch.tensor(out_chord_inv_unc).to(device), torch.tensor(out_chord_timing_unc).to(device), torch.tensor(out_mask_unc).to(device) embedded_chord_unc = self.chord_embedding_layer(out_chord_root_unc, out_chord_type_unc, out_chord_inv_unc, out_chord_timing_unc, device) embedded_chord_unc = embedded_chord_unc.repeat_interleave(num_samples_per_prompt, 0) out_mask_unc = out_mask_unc.repeat_interleave(num_samples_per_prompt, 0) embedded_chord = torch.cat([embedded_chord_unc, embedded_chord]) out_mask = torch.cat([out_mask_unc, out_mask]) return embedded_chord, out_mask