from audio_encoder.AudioMAE import AudioMAEConditionCTPoolRand, extract_kaldi_fbank_feature import torchaudio import torchaudio.transforms as T import torch.nn.functional as F import inspect from typing import Any, Callable, Dict, List, Optional, Union from APadapter.ap_adapter.attention_processor import AttnProcessor2_0, IPAttnProcessor2_0 import random import os import scipy import safetensors import numpy as np import torch from transformers import ( ClapFeatureExtractor, ClapModel, GPT2Model, RobertaTokenizer, RobertaTokenizerFast, SpeechT5HifiGan, T5EncoderModel, T5Tokenizer, T5TokenizerFast, ) from diffusers.loaders import AttnProcsLayers from diffusers import AutoencoderKL from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import ( is_accelerate_available, is_accelerate_version, is_librosa_available, logging, replace_example_docstring, ) from diffusers.utils.torch_utils import randn_tensor from diffusers.pipelines.pipeline_utils import AudioPipelineOutput, DiffusionPipeline from .modeling_audioldm2 import AudioLDM2ProjectionModel, AudioLDM2UNet2DConditionModel from diffusers.loaders import TextualInversionLoaderMixin from tqdm import tqdm # for progress bar from utils.lora_utils_successed_ver1 import train_lora, load_lora, wav_to_mel from utils.model_utils import slerp, do_replace_attn from utils.alpha_scheduler import AlphaScheduler from audioldm.utils import default_audioldm_config from audioldm.audio import TacotronSTFT, read_wav_file from audioldm.audio.tools import get_mel_from_wav, _pad_spec, normalize_wav, pad_wav if is_librosa_available(): import librosa import warnings import matplotlib.pyplot as plt from huggingface_hub import hf_hub_download from .pipeline_audioldm2 import AudioLDM2Pipeline DEVICE = "cuda" if torch.cuda.is_available() else "cpu" pipeline_trained = AudioLDM2Pipeline.from_pretrained("cvssp/audioldm2-large", torch_dtype=torch.float32) pipeline_trained = pipeline_trained.to(DEVICE) layer_num = 0 cross = [None, None, 768, 768, 1024, 1024, None, None] unet = pipeline_trained.unet attn_procs = {} for name in unet.attn_processors.keys(): cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim if name.startswith("mid_block"): hidden_size = unet.config.block_out_channels[-1] elif name.startswith("up_blocks"): block_id = int(name[len("up_blocks.")]) hidden_size = list(reversed(unet.config.block_out_channels))[block_id] elif name.startswith("down_blocks"): block_id = int(name[len("down_blocks.")]) hidden_size = unet.config.block_out_channels[block_id] if cross_attention_dim is None: attn_procs[name] = AttnProcessor2_0() else: cross_attention_dim = cross[layer_num % 8] layer_num += 1 if cross_attention_dim == 768: attn_procs[name] = IPAttnProcessor2_0( hidden_size=hidden_size, name=name, cross_attention_dim=cross_attention_dim, scale=0.5, num_tokens=8, do_copy=False ).to(DEVICE, dtype=torch.float32) else: attn_procs[name] = AttnProcessor2_0() adapter_weight = hf_hub_download( repo_id="DennisHung/Pre-trained_AudioMAE_weights", filename="pytorch_model.bin", ) state_dict = torch.load(adapter_weight, map_location=DEVICE) for name, processor in attn_procs.items(): if hasattr(processor, 'to_v_ip') or hasattr(processor, 'to_k_ip'): weight_name_v = name + ".to_v_ip.weight" weight_name_k = name + ".to_k_ip.weight" processor.to_v_ip.weight = torch.nn.Parameter(state_dict[weight_name_v].half()) processor.to_k_ip.weight = torch.nn.Parameter(state_dict[weight_name_k].half()) unet.set_attn_processor(attn_procs) unet.to(DEVICE, dtype=torch.float32) def visualize_mel_spectrogram(mel_spect_tensor, output_path=None): mel_spect_array = mel_spect_tensor.squeeze().transpose(1, 0).detach().cpu().numpy() plt.figure(figsize=(10, 5)) plt.imshow(mel_spect_array, aspect='auto', origin='lower', cmap='magma') plt.colorbar(label="Log-Mel Energy") plt.title("Mel-Spectrogram") plt.xlabel("Time") plt.ylabel("Mel Frequency Bins") plt.tight_layout() if output_path: plt.savefig(output_path, dpi=300) print(f"Mel-spectrogram saved to {output_path}") else: plt.show() warnings.filterwarnings("ignore", category=FutureWarning) warnings.filterwarnings("ignore", category=UserWarning) logger = logging.get_logger(__name__) # pylint: disable=invalid-name class StoreProcessor(): def __init__(self, original_processor, value_dict, name): self.original_processor = original_processor self.value_dict = value_dict self.name = name self.value_dict[self.name] = dict() self.id = 0 def __call__(self, attn, hidden_states, *args, encoder_hidden_states=None, attention_mask=None, **kwargs): # Is self attention if encoder_hidden_states is None: # 將 hidden_states 存入 value_dict 中，名稱為 self.name # 如果輸入沒有 encoder_hidden_states，表示是自注意力層，則將輸入的 hidden_states 儲存在 value_dict 中。 # print(f'In StoreProcessor: {self.name} {self.id}') self.value_dict[self.name][self.id] = hidden_states.detach() self.id += 1 # 調用原始處理器，執行正常的注意力操作 res = self.original_processor(attn, hidden_states, *args, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, **kwargs) return res class LoadProcessor(): def __init__(self, original_processor, name, aud1_dict, aud2_dict, alpha, beta=0, lamd=0.6): super().__init__() self.original_processor = original_processor self.name = name self.aud1_dict = aud1_dict self.aud2_dict = aud2_dict self.alpha = alpha self.beta = beta self.lamd = lamd self.id = 0 def __call__(self, attn, hidden_states, *args, encoder_hidden_states=None, attention_mask=None, **kwargs): # Is self attention # 判斷是否是自注意力（self-attention） if encoder_hidden_states is None: # 如果當前索引小於 10 倍的 self.lamd，使用自定義的混合邏輯 if self.id < 10 * self.lamd: map0 = self.aud1_dict[self.name][self.id] map1 = self.aud2_dict[self.name][self.id] cross_map = self.beta * hidden_states + \ (1 - self.beta) * ((1 - self.alpha) * map0 + self.alpha * map1) # 調用原始處理器，將 cross_map 作為 encoder_hidden_states 傳入 res = self.original_processor(attn, hidden_states, *args, encoder_hidden_states=cross_map, attention_mask=attention_mask, **kwargs) else: # 否則，使用原始的 encoder_hidden_states（可能為 None） res = self.original_processor(attn, hidden_states, *args, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, **kwargs) self.id += 1 # 如果索引到達 self.aud1_dict[self.name] 的長度，重置索引為 0 if self.id == len(self.aud1_dict[self.name]): self.id = 0 else: # 如果是跨注意力（encoder_hidden_states 不為 None），直接使用原始處理器 res = self.original_processor(attn, hidden_states, *args, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, **kwargs) return res def prepare_inputs_for_generation( inputs_embeds, attention_mask=None, past_key_values=None, **kwargs,): if past_key_values is not None: # only last token for inputs_embeds if past is defined in kwargs inputs_embeds = inputs_embeds[:, -1:] return { "inputs_embeds": inputs_embeds, "attention_mask": attention_mask, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), } class AudioLDM2MorphPipeline(DiffusionPipeline,TextualInversionLoaderMixin): r""" Pipeline for text-to-audio generation using AudioLDM2. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. text_encoder ([`~transformers.ClapModel`]): First frozen text-encoder. AudioLDM2 uses the joint audio-text embedding model [CLAP](https://huggingface.co/docs/transformers/model_doc/clap#transformers.CLAPTextModelWithProjection), specifically the [laion/clap-htsat-unfused](https://huggingface.co/laion/clap-htsat-unfused) variant. The text branch is used to encode the text prompt to a prompt embedding. The full audio-text model is used to rank generated waveforms against the text prompt by computing similarity scores. text_encoder_2 ([`~transformers.T5EncoderModel`]): Second frozen text-encoder. AudioLDM2 uses the encoder of [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) variant. projection_model ([`AudioLDM2ProjectionModel`]): A trained model used to linearly project the hidden-states from the first and second text encoder models and insert learned SOS and EOS token embeddings. The projected hidden-states from the two text encoders are concatenated to give the input to the language model. language_model ([`~transformers.GPT2Model`]): An auto-regressive language model used to generate a sequence of hidden-states conditioned on the projected outputs from the two text encoders. tokenizer ([`~transformers.RobertaTokenizer`]): Tokenizer to tokenize text for the first frozen text-encoder. tokenizer_2 ([`~transformers.T5Tokenizer`]): Tokenizer to tokenize text for the second frozen text-encoder. feature_extractor ([`~transformers.ClapFeatureExtractor`]): Feature extractor to pre-process generated audio waveforms to log-mel spectrograms for automatic scoring. unet ([`UNet2DConditionModel`]): A `UNet2DConditionModel` to denoise the encoded audio latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. vocoder ([`~transformers.SpeechT5HifiGan`]): Vocoder of class `SpeechT5HifiGan` to convert the mel-spectrogram latents to the final audio waveform. """ def __init__( self, vae: AutoencoderKL, text_encoder: ClapModel, text_encoder_2: T5EncoderModel, projection_model: AudioLDM2ProjectionModel, language_model: GPT2Model, tokenizer: Union[RobertaTokenizer, RobertaTokenizerFast], tokenizer_2: Union[T5Tokenizer, T5TokenizerFast], feature_extractor: ClapFeatureExtractor, unet: AudioLDM2UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, vocoder: SpeechT5HifiGan, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, text_encoder_2=text_encoder_2, projection_model=projection_model, language_model=language_model, tokenizer=tokenizer, tokenizer_2=tokenizer_2, feature_extractor=feature_extractor, unet=unet, scheduler=scheduler, vocoder=vocoder, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.aud1_dict = dict() self.aud2_dict = dict() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) model_sequence = [ self.text_encoder.text_model, self.text_encoder.text_projection, self.text_encoder_2, self.projection_model, self.language_model, self.unet, self.vae, self.vocoder, self.text_encoder, ] hook = None for cpu_offloaded_model in model_sequence: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook def generate_language_model( self, inputs_embeds: torch.Tensor = None, max_new_tokens: int = 512, **model_kwargs, ): """ Generates a sequence of hidden-states from the language model, conditioned on the embedding inputs. Parameters: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): The sequence used as a prompt for the generation. max_new_tokens (`int`): Number of new tokens to generate. model_kwargs (`Dict[str, Any]`, *optional*): Ad hoc parametrization of additional model-specific kwargs that will be forwarded to the `forward` function of the model. Return: `inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): The sequence of generated hidden-states. """ max_new_tokens = max_new_tokens if max_new_tokens is not None else self.language_model.config.max_new_tokens model_kwargs = self.language_model._get_initial_cache_position(inputs_embeds, model_kwargs) for _ in range(max_new_tokens): # prepare model inputs model_inputs = prepare_inputs_for_generation(inputs_embeds, **model_kwargs) # forward pass to get next hidden states output = self.language_model(**model_inputs, return_dict=True) next_hidden_states = output.last_hidden_state # Update the model input inputs_embeds = torch.cat([inputs_embeds, next_hidden_states[:, -1:, :]], dim=1) # Update generated hidden states, model inputs, and length for next step model_kwargs = self.language_model._update_model_kwargs_for_generation(output, model_kwargs) return inputs_embeds[:, -max_new_tokens:, :] def encode_prompt( self, prompt, device, num_waveforms_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, generated_prompt_embeds: Optional[torch.FloatTensor] = None, negative_generated_prompt_embeds: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.LongTensor] = None, negative_attention_mask: Optional[torch.LongTensor] = None, max_new_tokens: Optional[int] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device (`torch.device`): torch device num_waveforms_per_prompt (`int`): number of waveforms that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the audio generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-computed text embeddings from the Flan T5 model. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be computed from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-computed negative text embeddings from the Flan T5 model. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be computed from `negative_prompt` input argument. generated_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings from the GPT2 langauge model. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_generated_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings from the GPT2 language model. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be computed from `negative_prompt` input argument. attention_mask (`torch.LongTensor`, *optional*): Pre-computed attention mask to be applied to the `prompt_embeds`. If not provided, attention mask will be computed from `prompt` input argument. negative_attention_mask (`torch.LongTensor`, *optional*): Pre-computed attention mask to be applied to the `negative_prompt_embeds`. If not provided, attention mask will be computed from `negative_prompt` input argument. max_new_tokens (`int`, *optional*, defaults to None): The number of new tokens to generate with the GPT2 language model. Returns: prompt_embeds (`torch.FloatTensor`): Text embeddings from the Flan T5 model. attention_mask (`torch.LongTensor`): Attention mask to be applied to the `prompt_embeds`. generated_prompt_embeds (`torch.FloatTensor`): Text embeddings generated from the GPT2 langauge model. Example: ```python >>> import scipy >>> import torch >>> from diffusers import AudioLDM2Pipeline >>> repo_id = "cvssp/audioldm2" >>> pipe = AudioLDM2Pipeline.from_pretrained(repo_id, torch_dtype=torch.float16) >>> pipe = pipe.to("cuda") >>> # Get text embedding vectors >>> prompt_embeds, attention_mask, generated_prompt_embeds = pipe.encode_prompt( ... prompt="Techno music with a strong, upbeat tempo and high melodic riffs", ... device="cuda", ... do_classifier_free_guidance=True, ... ) >>> # Pass text embeddings to pipeline for text-conditional audio generation >>> audio = pipe( ... prompt_embeds=prompt_embeds, ... attention_mask=attention_mask, ... generated_prompt_embeds=generated_prompt_embeds, ... num_inference_steps=200, ... audio_length_in_s=10.0, ... ).audios[0] >>> # save generated audio sample >>> scipy.io.wavfile.write("techno.wav", rate=16000, data=audio) ```""" # print("prompt",prompt) if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] # Define tokenizers and text encoders tokenizers = [self.tokenizer, self.tokenizer_2] text_encoders = [self.text_encoder, self.text_encoder_2] if prompt_embeds is None: prompt_embeds_list = [] attention_mask_list = [] for tokenizer, text_encoder in zip(tokenizers, text_encoders): text_inputs = tokenizer( prompt, padding="max_length" if isinstance(tokenizer, (RobertaTokenizer, RobertaTokenizerFast)) else True, max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids attention_mask = text_inputs.attention_mask untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1]) logger.warning( f"The following part of your input was truncated because {text_encoder.config.model_type} can " f"only handle sequences up to {tokenizer.model_max_length} tokens: {removed_text}" ) text_input_ids = text_input_ids.to(device) attention_mask = attention_mask.to(device) if text_encoder.config.model_type == "clap": prompt_embeds = text_encoder.get_text_features( text_input_ids, attention_mask=attention_mask, ) # append the seq-len dim: (bs, hidden_size) -> (bs, seq_len, hidden_size) prompt_embeds = prompt_embeds[:, None, :] # make sure that we attend to this single hidden-state attention_mask = attention_mask.new_ones((batch_size, 1)) else: prompt_embeds = text_encoder( text_input_ids, attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds_list.append(prompt_embeds) attention_mask_list.append(attention_mask) projection_output = self.projection_model( hidden_states=prompt_embeds_list[0], hidden_states_1=prompt_embeds_list[1], attention_mask=attention_mask_list[0], attention_mask_1=attention_mask_list[1], ) projected_prompt_embeds = projection_output.hidden_states projected_attention_mask = projection_output.attention_mask generated_prompt_embeds = self.generate_language_model( projected_prompt_embeds, attention_mask=projected_attention_mask, max_new_tokens=max_new_tokens, ) prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) attention_mask = ( attention_mask.to(device=device) if attention_mask is not None else torch.ones(prompt_embeds.shape[:2], dtype=torch.long, device=device) ) generated_prompt_embeds = generated_prompt_embeds.to(dtype=self.language_model.dtype, device=device) bs_embed, seq_len, hidden_size = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_waveforms_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_waveforms_per_prompt, seq_len, hidden_size) # duplicate attention mask for each generation per prompt attention_mask = attention_mask.repeat(1, num_waveforms_per_prompt) attention_mask = attention_mask.view(bs_embed * num_waveforms_per_prompt, seq_len) bs_embed, seq_len, hidden_size = generated_prompt_embeds.shape # duplicate generated embeddings for each generation per prompt, using mps friendly method generated_prompt_embeds = generated_prompt_embeds.repeat(1, num_waveforms_per_prompt, 1) generated_prompt_embeds = generated_prompt_embeds.view( bs_embed * num_waveforms_per_prompt, seq_len, hidden_size ) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt negative_prompt_embeds_list = [] negative_attention_mask_list = [] max_length = prompt_embeds.shape[1] for tokenizer, text_encoder in zip(tokenizers, text_encoders): uncond_input = tokenizer( uncond_tokens, padding="max_length", max_length=tokenizer.model_max_length if isinstance(tokenizer, (RobertaTokenizer, RobertaTokenizerFast)) else max_length, truncation=True, return_tensors="pt", ) uncond_input_ids = uncond_input.input_ids.to(device) negative_attention_mask = uncond_input.attention_mask.to(device) if text_encoder.config.model_type == "clap": negative_prompt_embeds = text_encoder.get_text_features( uncond_input_ids, attention_mask=negative_attention_mask, ) # append the seq-len dim: (bs, hidden_size) -> (bs, seq_len, hidden_size) negative_prompt_embeds = negative_prompt_embeds[:, None, :] # make sure that we attend to this single hidden-state negative_attention_mask = negative_attention_mask.new_ones((batch_size, 1)) else: negative_prompt_embeds = text_encoder( uncond_input_ids, attention_mask=negative_attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] negative_prompt_embeds_list.append(negative_prompt_embeds) negative_attention_mask_list.append(negative_attention_mask) projection_output = self.projection_model( hidden_states=negative_prompt_embeds_list[0], hidden_states_1=negative_prompt_embeds_list[1], attention_mask=negative_attention_mask_list[0], attention_mask_1=negative_attention_mask_list[1], ) negative_projected_prompt_embeds = projection_output.hidden_states negative_projected_attention_mask = projection_output.attention_mask negative_generated_prompt_embeds = self.generate_language_model( negative_projected_prompt_embeds, attention_mask=negative_projected_attention_mask, max_new_tokens=max_new_tokens, ) if do_classifier_free_guidance: seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) negative_attention_mask = ( negative_attention_mask.to(device=device) if negative_attention_mask is not None else torch.ones(negative_prompt_embeds.shape[:2], dtype=torch.long, device=device) ) negative_generated_prompt_embeds = negative_generated_prompt_embeds.to( dtype=self.language_model.dtype, device=device ) # duplicate unconditional embeddings for each generation per prompt, using mps friendly method negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_waveforms_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_waveforms_per_prompt, seq_len, -1) # duplicate unconditional attention mask for each generation per prompt negative_attention_mask = negative_attention_mask.repeat(1, num_waveforms_per_prompt) negative_attention_mask = negative_attention_mask.view(batch_size * num_waveforms_per_prompt, seq_len) # duplicate unconditional generated embeddings for each generation per prompt seq_len = negative_generated_prompt_embeds.shape[1] negative_generated_prompt_embeds = negative_generated_prompt_embeds.repeat(1, num_waveforms_per_prompt, 1) negative_generated_prompt_embeds = negative_generated_prompt_embeds.view( batch_size * num_waveforms_per_prompt, seq_len, -1 ) # For classifier free guidance, we need to do two forward passes. # Here 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]) attention_mask = torch.cat([negative_attention_mask, attention_mask]) generated_prompt_embeds = torch.cat([negative_generated_prompt_embeds, generated_prompt_embeds]) return prompt_embeds, attention_mask, generated_prompt_embeds # Copied from diffusers.pipelines.audioldm.pipeline_audioldm.AudioLDMPipeline.mel_spectrogram_to_waveform def mel_spectrogram_to_waveform(self, mel_spectrogram): if mel_spectrogram.dim() == 4: mel_spectrogram = mel_spectrogram.squeeze(1) waveform = self.vocoder(mel_spectrogram) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 waveform = waveform.cpu().float() return waveform def score_waveforms(self, text, audio, num_waveforms_per_prompt, device, dtype): if not is_librosa_available(): logger.info( "Automatic scoring of the generated audio waveforms against the input prompt text requires the " "`librosa` package to resample the generated waveforms. Returning the audios in the order they were " "generated. To enable automatic scoring, install `librosa` with: `pip install librosa`." ) return audio inputs = self.tokenizer(text, return_tensors="pt", padding=True) resampled_audio = librosa.resample( audio.numpy(), orig_sr=self.vocoder.config.sampling_rate, target_sr=self.feature_extractor.sampling_rate ) inputs["input_features"] = self.feature_extractor( list(resampled_audio), return_tensors="pt", sampling_rate=self.feature_extractor.sampling_rate ).input_features.type(dtype) inputs = inputs.to(device) # compute the audio-text similarity score using the CLAP model logits_per_text = self.text_encoder(**inputs).logits_per_text # sort by the highest matching generations per prompt indices = torch.argsort(logits_per_text, dim=1, descending=True)[:, :num_waveforms_per_prompt] audio = torch.index_select(audio, 0, indices.reshape(-1).cpu()) return audio # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, audio_length_in_s, vocoder_upsample_factor, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, generated_prompt_embeds=None, negative_generated_prompt_embeds=None, attention_mask=None, negative_attention_mask=None,): min_audio_length_in_s = vocoder_upsample_factor * self.vae_scale_factor if audio_length_in_s < min_audio_length_in_s: raise ValueError( f"`audio_length_in_s` has to be a positive value greater than or equal to {min_audio_length_in_s}, but " f"is {audio_length_in_s}." ) if self.vocoder.config.model_in_dim % self.vae_scale_factor != 0: raise ValueError( f"The number of frequency bins in the vocoder's log-mel spectrogram has to be divisible by the " f"VAE scale factor, but got {self.vocoder.config.model_in_dim} bins and a scale factor of " f"{self.vae_scale_factor}." ) if (callback_steps is None) or ( callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and (prompt_embeds is None or generated_prompt_embeds is None): raise ValueError( "Provide either `prompt`, or `prompt_embeds` and `generated_prompt_embeds`. Cannot leave " "`prompt` undefined without specifying both `prompt_embeds` and `generated_prompt_embeds`." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) elif negative_prompt_embeds is not None and negative_generated_prompt_embeds is None: raise ValueError( "Cannot forward `negative_prompt_embeds` without `negative_generated_prompt_embeds`. Ensure that" "both arguments are specified" ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) if attention_mask is not None and attention_mask.shape != prompt_embeds.shape[:2]: raise ValueError( "`attention_mask should have the same batch size and sequence length as `prompt_embeds`, but got:" f"`attention_mask: {attention_mask.shape} != `prompt_embeds` {prompt_embeds.shape}" ) if generated_prompt_embeds is not None and negative_generated_prompt_embeds is not None: if generated_prompt_embeds.shape != negative_generated_prompt_embeds.shape: raise ValueError( "`generated_prompt_embeds` and `negative_generated_prompt_embeds` must have the same shape when " f"passed directly, but got: `generated_prompt_embeds` {generated_prompt_embeds.shape} != " f"`negative_generated_prompt_embeds` {negative_generated_prompt_embeds.shape}." ) if ( negative_attention_mask is not None and negative_attention_mask.shape != negative_prompt_embeds.shape[:2] ): raise ValueError( "`attention_mask should have the same batch size and sequence length as `prompt_embeds`, but got:" f"`attention_mask: {negative_attention_mask.shape} != `prompt_embeds` {negative_prompt_embeds.shape}" ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents with width->self.vocoder.config.model_in_dim def prepare_latents(self, batch_size, num_channels_latents, height, dtype, device, generator, latents=None): shape = ( batch_size, num_channels_latents, height // self.vae_scale_factor, self.vocoder.config.model_in_dim // self.vae_scale_factor, ) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents def pre_check(self, audio_length_in_s, prompt, callback_steps, negative_prompt): """ Step 0: Convert audio input length from seconds to spectrogram height Step 1. Check inputs. Raise error if not correct """ vocoder_upsample_factor = np.prod(self.vocoder.config.upsample_rates) / self.vocoder.config.sampling_rate if audio_length_in_s is None: audio_length_in_s = self.unet.config.sample_size * self.vae_scale_factor * vocoder_upsample_factor height = int(audio_length_in_s / vocoder_upsample_factor) original_waveform_length = int(audio_length_in_s * self.vocoder.config.sampling_rate) if height % self.vae_scale_factor != 0: height = int(np.ceil(height / self.vae_scale_factor)) * self.vae_scale_factor logger.info( f"Audio length in seconds {audio_length_in_s} is increased to {height * vocoder_upsample_factor} " f"so that it can be handled by the model. It will be cut to {audio_length_in_s} after the " f"denoising process." ) # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, audio_length_in_s, vocoder_upsample_factor, callback_steps, negative_prompt, ) return height, original_waveform_length def encode_prompt_for_2_sources(self, prompt_1, prompt_2, negative_prompt_1, negative_prompt_2, max_new_tokens, device, num_waveforms_per_prompt, do_classifier_free_guidance): prompt_embeds_1, attention_mask_1, generated_prompt_embeds_1 = self.encode_prompt( prompt_1, device, num_waveforms_per_prompt, do_classifier_free_guidance, negative_prompt_1, max_new_tokens=max_new_tokens, ) prompt_embeds_2, attention_mask_2, generated_prompt_embeds_2 = self.encode_prompt( prompt_2, device, num_waveforms_per_prompt, do_classifier_free_guidance, negative_prompt_2, max_new_tokens=max_new_tokens, ) return [prompt_embeds_1, attention_mask_1, generated_prompt_embeds_1], [prompt_embeds_2, attention_mask_2, generated_prompt_embeds_2] def process_encoded_prompt(self, encoded_prompt, audio_file, time_pooling, freq_pooling): prompt_embeds, attention_mask, generated_prompt_embeds = encoded_prompt waveform, sr = torchaudio.load(audio_file) fbank = torch.zeros((1024, 128)) ta_kaldi_fbank = extract_kaldi_fbank_feature(waveform, sr, fbank) # print("ta_kaldi_fbank.shape",ta_kaldi_fbank.shape) mel_spect_tensor = ta_kaldi_fbank.unsqueeze(0) model = AudioMAEConditionCTPoolRand().to(next(self.unet.parameters()).device) model.eval() LOA_embed = model(mel_spect_tensor, time_pool=time_pooling, freq_pool=freq_pooling) uncond_LOA_embed = model(torch.zeros_like(mel_spect_tensor), time_pool=time_pooling, freq_pool=freq_pooling) LOA_embeds = LOA_embed[0] uncond_LOA_embeds = uncond_LOA_embed[0] bs_embed, seq_len, _ = LOA_embeds.shape num = prompt_embeds.shape[0] // 2 LOA_embeds = LOA_embeds.view(bs_embed , seq_len, -1) LOA_embeds = LOA_embeds.repeat(num, 1, 1) uncond_LOA_embeds = uncond_LOA_embeds.view(bs_embed , seq_len, -1) uncond_LOA_embeds = uncond_LOA_embeds.repeat(num, 1, 1) negative_g, g = generated_prompt_embeds.chunk(2) uncond = torch.cat([negative_g, uncond_LOA_embeds], dim=1) cond = torch.cat([g, LOA_embeds], dim=1) generated_prompt_embeds = torch.cat([uncond, cond], dim=0) model_dtype = next(self.unet.parameters()).dtype # Convert your tensor to the same dtype as the model generated_prompt_embeds = generated_prompt_embeds.to(model_dtype) return prompt_embeds, attention_mask, generated_prompt_embeds @torch.no_grad() def aud2latent(self, audio_path, audio_length_in_s): DEVICE = torch.device( "cuda") if torch.cuda.is_available() else torch.device("cpu") # waveform, sr = torchaudio.load(audio_path) # fbank = torch.zeros((height, 64)) # ta_kaldi_fbank = extract_kaldi_fbank_feature(waveform, sr, fbank, num_mels=64) # mel_spect_tensor = ta_kaldi_fbank.unsqueeze(0).unsqueeze(0) mel_spect_tensor = wav_to_mel(audio_path, duration=audio_length_in_s).unsqueeze(0) output_path = audio_path.replace('.wav', '_fbank.png') visualize_mel_spectrogram(mel_spect_tensor, output_path) mel_spect_tensor = mel_spect_tensor.to(next(self.vae.parameters()).dtype) # print(f'mel_spect_tensor dtype: {mel_spect_tensor.dtype}') # print(f'self.vae dtype: {next(self.vae.parameters()).dtype}') latents = self.vae.encode(mel_spect_tensor.to(DEVICE))['latent_dist'].mean return latents @torch.no_grad() def ddim_inversion(self, start_latents, prompt_embeds, attention_mask, generated_prompt_embeds, guidance_scale,num_inference_steps): start_step = 0 num_inference_steps = num_inference_steps device = start_latents.device self.scheduler.set_timesteps(num_inference_steps, device=device) start_latents *= self.scheduler.init_noise_sigma latents = start_latents.clone() for i in tqdm(range(start_step, num_inference_steps)): t = self.scheduler.timesteps[i] latent_model_input = torch.cat([latents] * 2) if guidance_scale > 1. else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=generated_prompt_embeds, encoder_hidden_states_1=prompt_embeds, encoder_attention_mask_1=attention_mask).sample if guidance_scale > 1.: noise_pred_uncon, noise_pred_con = noise_pred.chunk(2, dim=0) noise_pred = noise_pred_uncon + guidance_scale * (noise_pred_con - noise_pred_uncon) latents = self.scheduler.step(noise_pred, t, latents).prev_sample return latents def generate_morphing_prompt(self, prompt_1, prompt_2, alpha): closer_prompt = prompt_1 if alpha <= 0.5 else prompt_2 prompt = ( f"A musical performance morphing between '{prompt_1}' and '{prompt_2}'. " f"The sound is closer to '{closer_prompt}' with an interpolation factor of alpha={alpha:.2f}, " f"where alpha=0 represents fully the {prompt_1} and alpha=1 represents fully {prompt_2}." ) return prompt @torch.no_grad() def cal_latent(self,audio_length_in_s,time_pooling, freq_pooling,num_inference_steps, guidance_scale, aud_noise_1, aud_noise_2, prompt_1, prompt_2, prompt_embeds_1, attention_mask_1, generated_prompt_embeds_1, prompt_embeds_2, attention_mask_2, generated_prompt_embeds_2, alpha, original_processor,attn_processor_dict, use_morph_prompt, morphing_with_lora): latents = slerp(aud_noise_1, aud_noise_2, alpha, self.use_adain) if not use_morph_prompt: max_length = max(prompt_embeds_1.shape[1], prompt_embeds_2.shape[1]) if prompt_embeds_1.shape[1] < max_length: pad_size = max_length - prompt_embeds_1.shape[1] padding = torch.zeros( (prompt_embeds_1.shape[0], pad_size, prompt_embeds_1.shape[2]), device=prompt_embeds_1.device, dtype=prompt_embeds_1.dtype ) prompt_embeds_1 = torch.cat([prompt_embeds_1, padding], dim=1) if prompt_embeds_2.shape[1] < max_length: pad_size = max_length - prompt_embeds_2.shape[1] padding = torch.zeros( (prompt_embeds_2.shape[0], pad_size, prompt_embeds_2.shape[2]), device=prompt_embeds_2.device, dtype=prompt_embeds_2.dtype ) prompt_embeds_2 = torch.cat([prompt_embeds_2, padding], dim=1) if attention_mask_1.shape[1] < max_length: pad_size = max_length - attention_mask_1.shape[1] padding = torch.zeros( (attention_mask_1.shape[0], pad_size), device=attention_mask_1.device, dtype=attention_mask_1.dtype ) attention_mask_1 = torch.cat([attention_mask_1, padding], dim=1) if attention_mask_2.shape[1] < max_length: pad_size = max_length - attention_mask_2.shape[1] padding = torch.zeros( (attention_mask_2.shape[0], pad_size), device=attention_mask_2.device, dtype=attention_mask_2.dtype ) attention_mask_2 = torch.cat([attention_mask_2, padding], dim=1) prompt_embeds = (1 - alpha) * prompt_embeds_1 + \ alpha * prompt_embeds_2 generated_prompt_embeds = (1 - alpha) * generated_prompt_embeds_1 + \ alpha * generated_prompt_embeds_2 attention_mask = attention_mask_1 if alpha < 0.5 else attention_mask_2 # attention_mask = attention_mask_1 & attention_mask_2 # attention_mask = attention_mask_1 | attention_mask_2 # attention_mask = (1 - alpha) * attention_mask_1 + alpha * attention_mask_2 # attention_mask = (attention_mask > 0.5).long() if morphing_with_lora: pipeline_trained.unet.set_attn_processor(attn_processor_dict) waveform = pipeline_trained( time_pooling= time_pooling, freq_pooling= freq_pooling, latents = latents, num_inference_steps= num_inference_steps, guidance_scale= guidance_scale, num_waveforms_per_prompt= 1, audio_length_in_s=audio_length_in_s, prompt_embeds = prompt_embeds.chunk(2)[1], negative_prompt_embeds = prompt_embeds.chunk(2)[0], generated_prompt_embeds = generated_prompt_embeds.chunk(2)[1], negative_generated_prompt_embeds = generated_prompt_embeds.chunk(2)[0], attention_mask = attention_mask.chunk(2)[1], negative_attention_mask = attention_mask.chunk(2)[0], ).audios[0] if morphing_with_lora: pipeline_trained.unet.set_attn_processor(original_processor) else: latent_model_input = latents morphing_prompt = self.generate_morphing_prompt(prompt_1, prompt_2, alpha) if morphing_with_lora: pipeline_trained.unet.set_attn_processor(attn_processor_dict) waveform = pipeline_trained( time_pooling= time_pooling, freq_pooling= freq_pooling, latents = latent_model_input, num_inference_steps= num_inference_steps, guidance_scale= guidance_scale, num_waveforms_per_prompt= 1, audio_length_in_s=audio_length_in_s, prompt= morphing_prompt, negative_prompt= 'Low quality', ).audios[0] if morphing_with_lora: pipeline_trained.unet.set_attn_processor(original_processor) return waveform @torch.no_grad() def __call__( self, audio_file = None, audio_file2 = None, save_lora_dir = "./lora", load_lora_path_1 = None, load_lora_path_2 = None, lora_steps = 200, lora_lr = 2e-4, lora_rank = 16, time_pooling = 8, freq_pooling = 8, audio_length_in_s: Optional[float] = None, prompt_1: Union[str, List[str]] = None, prompt_2: Union[str, List[str]] = None, negative_prompt_1: Optional[Union[str, List[str]]] = None, negative_prompt_2: Optional[Union[str, List[str]]] = None, use_lora: bool = True, use_adain: bool = True, use_reschedule: bool = True, output_path: Optional[str] = None, num_inference_steps: int = 200, guidance_scale: float = 7.5, num_waveforms_per_prompt: Optional[int] = 1, attn_beta=0, lamd=0.6, fix_lora=None, save_intermediates=True, num_frames=50, max_new_tokens: Optional[int] = None, callback_steps: Optional[int] = 1, noisy_latent_with_lora=False, morphing_with_lora=False, use_morph_prompt=False, ): # 0. Load the pre-trained AP-adapter model layer_num = 0 cross = [None, None, 768, 768, 1024, 1024, None, None] attn_procs = {} for name in self.unet.attn_processors.keys(): cross_attention_dim = None if name.endswith("attn1.processor") else self.unet.config.cross_attention_dim if name.startswith("mid_block"): hidden_size = self.unet.config.block_out_channels[-1] elif name.startswith("up_blocks"): block_id = int(name[len("up_blocks.")]) hidden_size = list(reversed(self.unet.config.block_out_channels))[block_id] elif name.startswith("down_blocks"): block_id = int(name[len("down_blocks.")]) hidden_size = self.unet.config.block_out_channels[block_id] if cross_attention_dim is None: attn_procs[name] = AttnProcessor2_0() else: cross_attention_dim = cross[layer_num % 8] layer_num += 1 if cross_attention_dim == 768: attn_procs[name] = IPAttnProcessor2_0( hidden_size=hidden_size, name=name, cross_attention_dim=cross_attention_dim, scale=0.5, num_tokens=8, do_copy=False ).to(DEVICE, dtype=torch.float32) else: attn_procs[name] = AttnProcessor2_0() state_dict = torch.load('/Data/home/Dennis/DeepMIR-2024/Final_Project/AP-adapter/pytorch_model.bin', map_location="cuda") for name, processor in attn_procs.items(): if hasattr(processor, 'to_v_ip') or hasattr(processor, 'to_k_ip'): weight_name_v = name + ".to_v_ip.weight" weight_name_k = name + ".to_k_ip.weight" processor.to_v_ip.weight = torch.nn.Parameter(state_dict[weight_name_v].half()) processor.to_k_ip.weight = torch.nn.Parameter(state_dict[weight_name_k].half()) self.unet.set_attn_processor(attn_procs) self.vae= self.vae.to(DEVICE, dtype=torch.float32) self.unet = self.unet.to(DEVICE, dtype=torch.float32) self.language_model = self.language_model.to(DEVICE, dtype=torch.float32) self.projection_model = self.projection_model.to(DEVICE, dtype=torch.float32) self.vocoder = self.vocoder.to(DEVICE, dtype=torch.float32) self.text_encoder = self.text_encoder.to(DEVICE, dtype=torch.float32) self.text_encoder_2 = self.text_encoder_2.to(DEVICE, dtype=torch.float32) # 1. Pre-check height, original_waveform_length = self.pre_check(audio_length_in_s, prompt_1, callback_steps, negative_prompt_1) _, _ = self.pre_check(audio_length_in_s, prompt_2, callback_steps, negative_prompt_2) # print(f"height: {height}, original_waveform_length: {original_waveform_length}") # height: 1000, original_waveform_length: 160000 # # 2. Define call parameters device = "cuda" if torch.cuda.is_available() else "cpu" do_classifier_free_guidance = guidance_scale > 1.0 self.use_lora = use_lora self.use_adain = use_adain self.use_reschedule = use_reschedule self.output_path = output_path if self.use_lora: print("Loading lora...") if not load_lora_path_1: weight_name = f"{output_path.split('/')[-1]}_lora_0.ckpt" load_lora_path_1 = save_lora_dir + "/" + weight_name if not os.path.exists(load_lora_path_1): train_lora(audio_file ,height ,time_pooling ,freq_pooling ,prompt_1, negative_prompt_1, guidance_scale, save_lora_dir, self.tokenizer, self.tokenizer_2, self.text_encoder, self.text_encoder_2, self.language_model, self.projection_model, self.vocoder, self.vae, self.unet, self.scheduler, lora_steps, lora_lr, lora_rank, weight_name=weight_name) print(f"Load from {load_lora_path_1}.") if load_lora_path_1.endswith(".safetensors"): lora_1 = safetensors.torch.load_file( load_lora_path_1, device="cpu") else: lora_1 = torch.load(load_lora_path_1, map_location="cpu") if not load_lora_path_2: weight_name = f"{output_path.split('/')[-1]}_lora_1.ckpt" load_lora_path_2 = save_lora_dir + "/" + weight_name if not os.path.exists(load_lora_path_2): train_lora(audio_file2 ,height,time_pooling ,freq_pooling ,prompt_2, negative_prompt_2, guidance_scale, save_lora_dir, self.tokenizer, self.tokenizer_2, self.text_encoder, self.text_encoder_2, self.language_model, self.projection_model, self.vocoder, self.vae, self.unet, self.scheduler, lora_steps, lora_lr, lora_rank, weight_name=weight_name) print(f"Load from {load_lora_path_2}.") if load_lora_path_2.endswith(".safetensors"): lora_2 = safetensors.torch.load_file( load_lora_path_2, device="cpu") else: lora_2 = torch.load(load_lora_path_2, map_location="cpu") else: lora_1 = lora_2 = None # # 3. Encode input prompt encoded_prompt_1, encoded_prompt_2 = self.encode_prompt_for_2_sources(prompt_1, prompt_2, negative_prompt_1, negative_prompt_2, max_new_tokens, device, num_waveforms_per_prompt, do_classifier_free_guidance) prompt_embeds_1, attention_mask_1, generated_prompt_embeds_1 = self.process_encoded_prompt(encoded_prompt_1, audio_file, time_pooling, freq_pooling) prompt_embeds_2, attention_mask_2, generated_prompt_embeds_2 = self.process_encoded_prompt(encoded_prompt_2, audio_file2, time_pooling, freq_pooling) # 4. Prepare latent variables # For the first audio file original_processor = list(self.unet.attn_processors.values())[0] if noisy_latent_with_lora: self.unet = load_lora(self.unet, lora_1, lora_2, 0) # print(self.unet.attn_processors) # We directly use the latent representation of the audio file for VAE's decoder as the 1st ground truth audio_latent = self.aud2latent(audio_file, audio_length_in_s).to(device) # mel_spectrogram = self.vae.decode(audio_latent).sample # first_audio = self.mel_spectrogram_to_waveform(mel_spectrogram) # first_audio = first_audio[:, :original_waveform_length] # torchaudio.save(f"{self.output_path}/{0:02d}_gt.wav", first_audio, 16000) # aud_noise_1 is the noisy latent representation of the audio file 1 aud_noise_1 = self.ddim_inversion(audio_latent, prompt_embeds_1, attention_mask_1, generated_prompt_embeds_1, guidance_scale, num_inference_steps) # We use the pre-trained model to generate the audio file from the noisy latent representation # waveform = pipeline_trained( # audio_file = audio_file, # time_pooling= 2, # freq_pooling= 2, # prompt= prompt_1, # latents = aud_noise_1, # negative_prompt= negative_prompt_1, # num_inference_steps= 100, # guidance_scale= guidance_scale, # num_waveforms_per_prompt= 1, # audio_length_in_s=10, # ).audios # file_path = os.path.join(self.output_path, f"{0:02d}_gt2.wav") # scipy.io.wavfile.write(file_path, rate=16000, data=waveform[0]) # After reconstructed the audio file 1, we set the original processor back if noisy_latent_with_lora: self.unet.set_attn_processor(original_processor) # print(self.unet.attn_processors) # For the second audio file if noisy_latent_with_lora: self.unet = load_lora(self.unet, lora_1, lora_2, 1) # print(self.unet.attn_processors) # We directly use the latent representation of the audio file for VAE's decoder as the 1st ground truth audio_latent = self.aud2latent(audio_file2, audio_length_in_s) # mel_spectrogram = self.vae.decode(audio_latent).sample # last_audio = self.mel_spectrogram_to_waveform(mel_spectrogram) # last_audio = last_audio[:, :original_waveform_length] # torchaudio.save(f"{self.output_path}/{num_frames-1:02d}_gt.wav", last_audio, 16000) # aud_noise_2 is the noisy latent representation of the audio file 2 aud_noise_2 = self.ddim_inversion(audio_latent, prompt_embeds_2, attention_mask_2, generated_prompt_embeds_2, guidance_scale, num_inference_steps) # waveform = pipeline_trained( # audio_file = audio_file2, # time_pooling= 2, # freq_pooling= 2, # prompt= prompt_2, # latents = aud_noise_2, # negative_prompt= negative_prompt_2, # num_inference_steps= 100, # guidance_scale= guidance_scale, # num_waveforms_per_prompt= 1, # audio_length_in_s=10, # ).audios # file_path = os.path.join(self.output_path, f"{num_frames-1:02d}_gt2.wav") # scipy.io.wavfile.write(file_path, rate=16000, data=waveform[0]) if noisy_latent_with_lora: self.unet.set_attn_processor(original_processor) # print(self.unet.attn_processors) # After reconstructed the audio file 1, we set the original processor back original_processor = list(self.unet.attn_processors.values())[0] def morph(alpha_list, desc): audios = [] # if attn_beta is not None: if self.use_lora: self.unet = load_lora( self.unet, lora_1, lora_2, 0 if fix_lora is None else fix_lora) attn_processor_dict = {} # print(self.unet.attn_processors) for k in self.unet.attn_processors.keys(): # print(k) if do_replace_attn(k): # print(f"Since the key starts with *up*, we replace the processor with StoreProcessor.") if self.use_lora: attn_processor_dict[k] = StoreProcessor(self.unet.attn_processors[k], self.aud1_dict, k) else: attn_processor_dict[k] = StoreProcessor(original_processor, self.aud1_dict, k) else: attn_processor_dict[k] = self.unet.attn_processors[k] # print(attn_processor_dict) # print(attn_processor_dict) # print(self.unet.attn_processors) # self.unet.set_attn_processor(attn_processor_dict) # print(self.unet.attn_processors) first_audio = self.cal_latent( audio_length_in_s, time_pooling, freq_pooling, num_inference_steps, guidance_scale, aud_noise_1, aud_noise_2, prompt_1, prompt_2, prompt_embeds_1, attention_mask_1, generated_prompt_embeds_1, prompt_embeds_2, attention_mask_2, generated_prompt_embeds_2, alpha_list[0], original_processor, attn_processor_dict, use_morph_prompt, morphing_with_lora ) self.unet.set_attn_processor(original_processor) file_path = os.path.join(self.output_path, f"{0:02d}.wav") scipy.io.wavfile.write(file_path, rate=16000, data=first_audio) if self.use_lora: self.unet = load_lora( self.unet, lora_1, lora_2, 1 if fix_lora is None else fix_lora) attn_processor_dict = {} for k in self.unet.attn_processors.keys(): if do_replace_attn(k): # print(f"Since the key starts with *up*, we replace the processor with StoreProcessor.") if self.use_lora: attn_processor_dict[k] = StoreProcessor(self.unet.attn_processors[k], self.aud2_dict, k) else: attn_processor_dict[k] = StoreProcessor(original_processor, self.aud2_dict, k) else: attn_processor_dict[k] = self.unet.attn_processors[k] # self.unet.set_attn_processor(attn_processor_dict) last_audio = self.cal_latent( audio_length_in_s, time_pooling, freq_pooling, num_inference_steps, guidance_scale, aud_noise_1, aud_noise_2, prompt_1, prompt_2, prompt_embeds_1, attention_mask_1, generated_prompt_embeds_1, prompt_embeds_2, attention_mask_2, generated_prompt_embeds_2, alpha_list[-1], original_processor, attn_processor_dict, use_morph_prompt, morphing_with_lora ) file_path = os.path.join(self.output_path, f"{num_frames-1:02d}.wav") scipy.io.wavfile.write(file_path, rate=16000, data=last_audio) self.unet.set_attn_processor(original_processor) for i in tqdm(range(1, num_frames - 1), desc=desc): alpha = alpha_list[i] if self.use_lora: self.unet = load_lora( self.unet, lora_1, lora_2, alpha if fix_lora is None else fix_lora) attn_processor_dict = {} for k in self.unet.attn_processors.keys(): if do_replace_attn(k): if self.use_lora: attn_processor_dict[k] = LoadProcessor( self.unet.attn_processors[k], k, self.aud1_dict, self.aud2_dict, alpha, attn_beta, lamd) else: attn_processor_dict[k] = LoadProcessor( original_processor, k, self.aud1_dict, self.aud2_dict, alpha, attn_beta, lamd) else: attn_processor_dict[k] = self.unet.attn_processors[k] # self.unet.set_attn_processor(attn_processor_dict) audio = self.cal_latent( audio_length_in_s, time_pooling, freq_pooling, num_inference_steps, guidance_scale, aud_noise_1, aud_noise_2, prompt_1, prompt_2, prompt_embeds_1, attention_mask_1, generated_prompt_embeds_1, prompt_embeds_2, attention_mask_2, generated_prompt_embeds_2, alpha_list[i], original_processor, attn_processor_dict, use_morph_prompt, morphing_with_lora ) file_path = os.path.join(self.output_path, f"{i:02d}.wav") scipy.io.wavfile.write(file_path, rate=16000, data=audio) self.unet.set_attn_processor(original_processor) audios.append(audio) audios = [first_audio] + audios + [last_audio] return audios with torch.no_grad(): if self.use_reschedule: alpha_scheduler = AlphaScheduler() alpha_list = list(torch.linspace(0, 1, num_frames)) audios_pt = morph(alpha_list, "Sampling...") audios_pt = [torch.tensor(aud).unsqueeze(0) for aud in audios_pt] alpha_scheduler.from_imgs(audios_pt) alpha_list = alpha_scheduler.get_list() audios = morph(alpha_list, "Reschedule...") else: alpha_list = list(torch.linspace(0, 1, num_frames)) audios = morph(alpha_list, "Sampling...") return audios