249 lines
10 KiB
Python
249 lines
10 KiB
Python
# Copyright 2022 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.
|
|
|
|
|
|
from math import acos, sin
|
|
from typing import List, Tuple, Union
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
from PIL import Image
|
|
|
|
from ...models import AutoencoderKL, UNet2DConditionModel
|
|
from ...pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
|
|
from ...schedulers import DDIMScheduler, DDPMScheduler
|
|
from .mel import Mel
|
|
|
|
|
|
class AudioDiffusionPipeline(DiffusionPipeline):
|
|
"""
|
|
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
|
|
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
|
|
|
|
Parameters:
|
|
vqae ([`AutoencoderKL`]): Variational AutoEncoder for Latent Audio Diffusion or None
|
|
unet ([`UNet2DConditionModel`]): UNET model
|
|
mel ([`Mel`]): transform audio <-> spectrogram
|
|
scheduler ([`DDIMScheduler` or `DDPMScheduler`]): de-noising scheduler
|
|
"""
|
|
|
|
_optional_components = ["vqvae"]
|
|
|
|
def __init__(
|
|
self,
|
|
vqvae: AutoencoderKL,
|
|
unet: UNet2DConditionModel,
|
|
mel: Mel,
|
|
scheduler: Union[DDIMScheduler, DDPMScheduler],
|
|
):
|
|
super().__init__()
|
|
self.register_modules(unet=unet, scheduler=scheduler, mel=mel, vqvae=vqvae)
|
|
|
|
def get_input_dims(self) -> Tuple:
|
|
"""Returns dimension of input image
|
|
|
|
Returns:
|
|
`Tuple`: (height, width)
|
|
"""
|
|
input_module = self.vqvae if self.vqvae is not None else self.unet
|
|
# For backwards compatibility
|
|
sample_size = (
|
|
(input_module.sample_size, input_module.sample_size)
|
|
if type(input_module.sample_size) == int
|
|
else input_module.sample_size
|
|
)
|
|
return sample_size
|
|
|
|
def get_default_steps(self) -> int:
|
|
"""Returns default number of steps recommended for inference
|
|
|
|
Returns:
|
|
`int`: number of steps
|
|
"""
|
|
return 50 if isinstance(self.scheduler, DDIMScheduler) else 1000
|
|
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self,
|
|
batch_size: int = 1,
|
|
audio_file: str = None,
|
|
raw_audio: np.ndarray = None,
|
|
slice: int = 0,
|
|
start_step: int = 0,
|
|
steps: int = None,
|
|
generator: torch.Generator = None,
|
|
mask_start_secs: float = 0,
|
|
mask_end_secs: float = 0,
|
|
step_generator: torch.Generator = None,
|
|
eta: float = 0,
|
|
noise: torch.Tensor = None,
|
|
return_dict=True,
|
|
) -> Union[
|
|
Union[AudioPipelineOutput, ImagePipelineOutput], Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]]
|
|
]:
|
|
"""Generate random mel spectrogram from audio input and convert to audio.
|
|
|
|
Args:
|
|
batch_size (`int`): number of samples to generate
|
|
audio_file (`str`): must be a file on disk due to Librosa limitation or
|
|
raw_audio (`np.ndarray`): audio as numpy array
|
|
slice (`int`): slice number of audio to convert
|
|
start_step (int): step to start from
|
|
steps (`int`): number of de-noising steps (defaults to 50 for DDIM, 1000 for DDPM)
|
|
generator (`torch.Generator`): random number generator or None
|
|
mask_start_secs (`float`): number of seconds of audio to mask (not generate) at start
|
|
mask_end_secs (`float`): number of seconds of audio to mask (not generate) at end
|
|
step_generator (`torch.Generator`): random number generator used to de-noise or None
|
|
eta (`float`): parameter between 0 and 1 used with DDIM scheduler
|
|
noise (`torch.Tensor`): noise tensor of shape (batch_size, 1, height, width) or None
|
|
return_dict (`bool`): if True return AudioPipelineOutput, ImagePipelineOutput else Tuple
|
|
|
|
Returns:
|
|
`List[PIL Image]`: mel spectrograms (`float`, `List[np.ndarray]`): sample rate and raw audios
|
|
"""
|
|
|
|
steps = steps or self.get_default_steps()
|
|
self.scheduler.set_timesteps(steps)
|
|
step_generator = step_generator or generator
|
|
# For backwards compatibility
|
|
if type(self.unet.sample_size) == int:
|
|
self.unet.sample_size = (self.unet.sample_size, self.unet.sample_size)
|
|
input_dims = self.get_input_dims()
|
|
self.mel.set_resolution(x_res=input_dims[1], y_res=input_dims[0])
|
|
if noise is None:
|
|
noise = torch.randn(
|
|
(batch_size, self.unet.in_channels, self.unet.sample_size[0], self.unet.sample_size[1]),
|
|
generator=generator,
|
|
device=self.device,
|
|
)
|
|
images = noise
|
|
mask = None
|
|
|
|
if audio_file is not None or raw_audio is not None:
|
|
self.mel.load_audio(audio_file, raw_audio)
|
|
input_image = self.mel.audio_slice_to_image(slice)
|
|
input_image = np.frombuffer(input_image.tobytes(), dtype="uint8").reshape(
|
|
(input_image.height, input_image.width)
|
|
)
|
|
input_image = (input_image / 255) * 2 - 1
|
|
input_images = torch.tensor(input_image[np.newaxis, :, :], dtype=torch.float).to(self.device)
|
|
|
|
if self.vqvae is not None:
|
|
input_images = self.vqvae.encode(torch.unsqueeze(input_images, 0)).latent_dist.sample(
|
|
generator=generator
|
|
)[0]
|
|
input_images = 0.18215 * input_images
|
|
|
|
if start_step > 0:
|
|
images[0, 0] = self.scheduler.add_noise(input_images, noise, self.scheduler.timesteps[start_step - 1])
|
|
|
|
pixels_per_second = (
|
|
self.unet.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
|
|
)
|
|
mask_start = int(mask_start_secs * pixels_per_second)
|
|
mask_end = int(mask_end_secs * pixels_per_second)
|
|
mask = self.scheduler.add_noise(input_images, noise, torch.tensor(self.scheduler.timesteps[start_step:]))
|
|
|
|
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
|
|
model_output = self.unet(images, t)["sample"]
|
|
|
|
if isinstance(self.scheduler, DDIMScheduler):
|
|
images = self.scheduler.step(
|
|
model_output=model_output, timestep=t, sample=images, eta=eta, generator=step_generator
|
|
)["prev_sample"]
|
|
else:
|
|
images = self.scheduler.step(
|
|
model_output=model_output, timestep=t, sample=images, generator=step_generator
|
|
)["prev_sample"]
|
|
|
|
if mask is not None:
|
|
if mask_start > 0:
|
|
images[:, :, :, :mask_start] = mask[:, step, :, :mask_start]
|
|
if mask_end > 0:
|
|
images[:, :, :, -mask_end:] = mask[:, step, :, -mask_end:]
|
|
|
|
if self.vqvae is not None:
|
|
# 0.18215 was scaling factor used in training to ensure unit variance
|
|
images = 1 / 0.18215 * images
|
|
images = self.vqvae.decode(images)["sample"]
|
|
|
|
images = (images / 2 + 0.5).clamp(0, 1)
|
|
images = images.cpu().permute(0, 2, 3, 1).numpy()
|
|
images = (images * 255).round().astype("uint8")
|
|
images = list(
|
|
map(lambda _: Image.fromarray(_[:, :, 0]), images)
|
|
if images.shape[3] == 1
|
|
else map(lambda _: Image.fromarray(_, mode="RGB").convert("L"), images)
|
|
)
|
|
|
|
audios = list(map(lambda _: self.mel.image_to_audio(_), images))
|
|
if not return_dict:
|
|
return images, (self.mel.get_sample_rate(), audios)
|
|
|
|
return BaseOutput(**AudioPipelineOutput(np.array(audios)[:, np.newaxis, :]), **ImagePipelineOutput(images))
|
|
|
|
@torch.no_grad()
|
|
def encode(self, images: List[Image.Image], steps: int = 50) -> np.ndarray:
|
|
"""Reverse step process: recover noisy image from generated image.
|
|
|
|
Args:
|
|
images (`List[PIL Image]`): list of images to encode
|
|
steps (`int`): number of encoding steps to perform (defaults to 50)
|
|
|
|
Returns:
|
|
`np.ndarray`: noise tensor of shape (batch_size, 1, height, width)
|
|
"""
|
|
|
|
# Only works with DDIM as this method is deterministic
|
|
assert isinstance(self.scheduler, DDIMScheduler)
|
|
self.scheduler.set_timesteps(steps)
|
|
sample = np.array(
|
|
[np.frombuffer(image.tobytes(), dtype="uint8").reshape((1, image.height, image.width)) for image in images]
|
|
)
|
|
sample = (sample / 255) * 2 - 1
|
|
sample = torch.Tensor(sample).to(self.device)
|
|
|
|
for t in self.progress_bar(torch.flip(self.scheduler.timesteps, (0,))):
|
|
prev_timestep = t - self.scheduler.num_train_timesteps // self.scheduler.num_inference_steps
|
|
alpha_prod_t = self.scheduler.alphas_cumprod[t]
|
|
alpha_prod_t_prev = (
|
|
self.scheduler.alphas_cumprod[prev_timestep]
|
|
if prev_timestep >= 0
|
|
else self.scheduler.final_alpha_cumprod
|
|
)
|
|
beta_prod_t = 1 - alpha_prod_t
|
|
model_output = self.unet(sample, t)["sample"]
|
|
pred_sample_direction = (1 - alpha_prod_t_prev) ** (0.5) * model_output
|
|
sample = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
|
|
sample = sample * alpha_prod_t ** (0.5) + beta_prod_t ** (0.5) * model_output
|
|
|
|
return sample
|
|
|
|
@staticmethod
|
|
def slerp(x0: torch.Tensor, x1: torch.Tensor, alpha: float) -> torch.Tensor:
|
|
"""Spherical Linear intERPolation
|
|
|
|
Args:
|
|
x0 (`torch.Tensor`): first tensor to interpolate between
|
|
x1 (`torch.Tensor`): seconds tensor to interpolate between
|
|
alpha (`float`): interpolation between 0 and 1
|
|
|
|
Returns:
|
|
`torch.Tensor`: interpolated tensor
|
|
"""
|
|
|
|
theta = acos(torch.dot(torch.flatten(x0), torch.flatten(x1)) / torch.norm(x0) / torch.norm(x1))
|
|
return sin((1 - alpha) * theta) * x0 / sin(theta) + sin(alpha * theta) * x1 / sin(theta)
|