558 lines
26 KiB
Python
558 lines
26 KiB
Python
# Copyright 2025 The HuggingFace Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import inspect
|
|
from typing import Any, Callable, Dict, List, Optional, Union
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from transformers import ClapTextModelWithProjection, RobertaTokenizer, RobertaTokenizerFast, SpeechT5HifiGan
|
|
|
|
from ...models import AutoencoderKL, UNet2DConditionModel
|
|
from ...schedulers import KarrasDiffusionSchedulers
|
|
from ...utils import is_torch_xla_available, logging, replace_example_docstring
|
|
from ...utils.torch_utils import randn_tensor
|
|
from ..pipeline_utils import AudioPipelineOutput, DeprecatedPipelineMixin, DiffusionPipeline, StableDiffusionMixin
|
|
|
|
|
|
if is_torch_xla_available():
|
|
import torch_xla.core.xla_model as xm
|
|
|
|
XLA_AVAILABLE = True
|
|
else:
|
|
XLA_AVAILABLE = False
|
|
|
|
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
|
|
|
|
|
EXAMPLE_DOC_STRING = """
|
|
Examples:
|
|
```py
|
|
>>> from diffusers import AudioLDMPipeline
|
|
>>> import torch
|
|
>>> import scipy
|
|
|
|
>>> repo_id = "cvssp/audioldm-s-full-v2"
|
|
>>> pipe = AudioLDMPipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
|
|
>>> pipe = pipe.to("cuda")
|
|
|
|
>>> prompt = "Techno music with a strong, upbeat tempo and high melodic riffs"
|
|
>>> audio = pipe(prompt, num_inference_steps=10, audio_length_in_s=5.0).audios[0]
|
|
|
|
>>> # save the audio sample as a .wav file
|
|
>>> scipy.io.wavfile.write("techno.wav", rate=16000, data=audio)
|
|
```
|
|
"""
|
|
|
|
|
|
class AudioLDMPipeline(DeprecatedPipelineMixin, DiffusionPipeline, StableDiffusionMixin):
|
|
r"""
|
|
Pipeline for text-to-audio generation using AudioLDM.
|
|
|
|
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.ClapTextModelWithProjection`]):
|
|
Frozen text-encoder (`ClapTextModelWithProjection`, specifically the
|
|
[laion/clap-htsat-unfused](https://huggingface.co/laion/clap-htsat-unfused) variant.
|
|
tokenizer ([`PreTrainedTokenizer`]):
|
|
A [`~transformers.RobertaTokenizer`] to tokenize text.
|
|
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`.
|
|
"""
|
|
|
|
_last_supported_version = "0.33.1"
|
|
model_cpu_offload_seq = "text_encoder->unet->vae"
|
|
|
|
def __init__(
|
|
self,
|
|
vae: AutoencoderKL,
|
|
text_encoder: ClapTextModelWithProjection,
|
|
tokenizer: Union[RobertaTokenizer, RobertaTokenizerFast],
|
|
unet: UNet2DConditionModel,
|
|
scheduler: KarrasDiffusionSchedulers,
|
|
vocoder: SpeechT5HifiGan,
|
|
):
|
|
super().__init__()
|
|
|
|
self.register_modules(
|
|
vae=vae,
|
|
text_encoder=text_encoder,
|
|
tokenizer=tokenizer,
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
vocoder=vocoder,
|
|
)
|
|
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
|
|
|
|
def _encode_prompt(
|
|
self,
|
|
prompt,
|
|
device,
|
|
num_waveforms_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt=None,
|
|
prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_prompt_embeds: Optional[torch.Tensor] = 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.Tensor`, *optional*):
|
|
Pre-generated text embeddings. 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_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
|
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
|
argument.
|
|
"""
|
|
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]
|
|
|
|
if prompt_embeds is None:
|
|
text_inputs = self.tokenizer(
|
|
prompt,
|
|
padding="max_length",
|
|
max_length=self.tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
text_input_ids = text_inputs.input_ids
|
|
attention_mask = text_inputs.attention_mask
|
|
untruncated_ids = self.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 = self.tokenizer.batch_decode(
|
|
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
|
|
)
|
|
logger.warning(
|
|
"The following part of your input was truncated because CLAP can only handle sequences up to"
|
|
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
|
)
|
|
|
|
prompt_embeds = self.text_encoder(
|
|
text_input_ids.to(device),
|
|
attention_mask=attention_mask.to(device),
|
|
)
|
|
prompt_embeds = prompt_embeds.text_embeds
|
|
# additional L_2 normalization over each hidden-state
|
|
prompt_embeds = F.normalize(prompt_embeds, dim=-1)
|
|
|
|
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
|
|
|
|
(
|
|
bs_embed,
|
|
seq_len,
|
|
) = 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)
|
|
prompt_embeds = prompt_embeds.view(bs_embed * num_waveforms_per_prompt, seq_len)
|
|
|
|
# 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
|
|
|
|
max_length = prompt_embeds.shape[1]
|
|
uncond_input = self.tokenizer(
|
|
uncond_tokens,
|
|
padding="max_length",
|
|
max_length=max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
|
|
uncond_input_ids = uncond_input.input_ids.to(device)
|
|
attention_mask = uncond_input.attention_mask.to(device)
|
|
|
|
negative_prompt_embeds = self.text_encoder(
|
|
uncond_input_ids,
|
|
attention_mask=attention_mask,
|
|
)
|
|
negative_prompt_embeds = negative_prompt_embeds.text_embeds
|
|
# additional L_2 normalization over each hidden-state
|
|
negative_prompt_embeds = F.normalize(negative_prompt_embeds, dim=-1)
|
|
|
|
if do_classifier_free_guidance:
|
|
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
|
seq_len = negative_prompt_embeds.shape[1]
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_waveforms_per_prompt)
|
|
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_waveforms_per_prompt, seq_len)
|
|
|
|
# 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])
|
|
|
|
return prompt_embeds
|
|
|
|
def decode_latents(self, latents):
|
|
latents = 1 / self.vae.config.scaling_factor * latents
|
|
mel_spectrogram = self.vae.decode(latents).sample
|
|
return mel_spectrogram
|
|
|
|
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
|
|
|
|
# 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://huggingface.co/papers/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,
|
|
):
|
|
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:
|
|
raise ValueError(
|
|
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
|
)
|
|
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."
|
|
)
|
|
|
|
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}."
|
|
)
|
|
|
|
# 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,
|
|
int(height) // self.vae_scale_factor,
|
|
int(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
|
|
|
|
@torch.no_grad()
|
|
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
|
def __call__(
|
|
self,
|
|
prompt: Union[str, List[str]] = None,
|
|
audio_length_in_s: Optional[float] = None,
|
|
num_inference_steps: int = 10,
|
|
guidance_scale: float = 2.5,
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
num_waveforms_per_prompt: Optional[int] = 1,
|
|
eta: float = 0.0,
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
latents: Optional[torch.Tensor] = None,
|
|
prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
|
return_dict: bool = True,
|
|
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
|
|
callback_steps: Optional[int] = 1,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
output_type: Optional[str] = "np",
|
|
):
|
|
r"""
|
|
The call function to the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide audio generation. If not defined, you need to pass `prompt_embeds`.
|
|
audio_length_in_s (`int`, *optional*, defaults to 5.12):
|
|
The length of the generated audio sample in seconds.
|
|
num_inference_steps (`int`, *optional*, defaults to 10):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality audio at the
|
|
expense of slower inference.
|
|
guidance_scale (`float`, *optional*, defaults to 2.5):
|
|
A higher guidance scale value encourages the model to generate audio that is closely linked to the text
|
|
`prompt` at the expense of lower sound quality. Guidance scale is enabled when `guidance_scale > 1`.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide what to not include in audio generation. If not defined, you need to
|
|
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
|
|
num_waveforms_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of waveforms to generate per prompt.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) from the [DDIM](https://huggingface.co/papers/2010.02502) paper. Only
|
|
applies to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
|
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
|
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
|
generation deterministic.
|
|
latents (`torch.Tensor`, *optional*):
|
|
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
|
|
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
|
tensor is generated by sampling using the supplied random `generator`.
|
|
prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
|
|
provided, text embeddings are generated from the `prompt` input argument.
|
|
negative_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
|
|
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple.
|
|
callback (`Callable`, *optional*):
|
|
A function that calls every `callback_steps` steps during inference. The function is called with the
|
|
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
|
|
callback_steps (`int`, *optional*, defaults to 1):
|
|
The frequency at which the `callback` function is called. If not specified, the callback is called at
|
|
every step.
|
|
cross_attention_kwargs (`dict`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
|
|
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
|
output_type (`str`, *optional*, defaults to `"np"`):
|
|
The output format of the generated image. Choose between `"np"` to return a NumPy `np.ndarray` or
|
|
`"pt"` to return a PyTorch `torch.Tensor` object.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.AudioPipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`~pipelines.AudioPipelineOutput`] is returned, otherwise a `tuple` is
|
|
returned where the first element is a list with the generated audio.
|
|
"""
|
|
# 0. Convert audio input length from seconds to spectrogram height
|
|
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,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
)
|
|
|
|
# 2. Define call parameters
|
|
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]
|
|
|
|
device = self._execution_device
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
do_classifier_free_guidance = guidance_scale > 1.0
|
|
|
|
# 3. Encode input prompt
|
|
prompt_embeds = self._encode_prompt(
|
|
prompt,
|
|
device,
|
|
num_waveforms_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
)
|
|
|
|
# 4. Prepare timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
|
timesteps = self.scheduler.timesteps
|
|
|
|
# 5. Prepare latent variables
|
|
num_channels_latents = self.unet.config.in_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_waveforms_per_prompt,
|
|
num_channels_latents,
|
|
height,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
# 6. Prepare extra step kwargs
|
|
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
|
|
|
# 7. Denoising loop
|
|
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
|
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
|
|
|
# predict the noise residual
|
|
noise_pred = self.unet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=None,
|
|
class_labels=prompt_embeds,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
).sample
|
|
|
|
# perform guidance
|
|
if do_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 = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
|
|
|
|
# call the callback, if provided
|
|
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
|
progress_bar.update()
|
|
if callback is not None and i % callback_steps == 0:
|
|
step_idx = i // getattr(self.scheduler, "order", 1)
|
|
callback(step_idx, t, latents)
|
|
|
|
if XLA_AVAILABLE:
|
|
xm.mark_step()
|
|
|
|
# 8. Post-processing
|
|
mel_spectrogram = self.decode_latents(latents)
|
|
|
|
audio = self.mel_spectrogram_to_waveform(mel_spectrogram)
|
|
|
|
audio = audio[:, :original_waveform_length]
|
|
|
|
if output_type == "np":
|
|
audio = audio.numpy()
|
|
|
|
if not return_dict:
|
|
return (audio,)
|
|
|
|
return AudioPipelineOutput(audios=audio)
|