330 lines
13 KiB
Python
330 lines
13 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from math import acos, sin
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from typing import List, Tuple, Union
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import numpy as np
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import torch
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from PIL import Image
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from ...models import AutoencoderKL, UNet2DConditionModel
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from ...schedulers import DDIMScheduler, DDPMScheduler
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from ...utils import randn_tensor
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from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
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from .mel import Mel
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class AudioDiffusionPipeline(DiffusionPipeline):
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"""
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Pipeline for audio diffusion.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
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implemented for all pipelines (downloading, saving, running on a particular device, etc.).
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Parameters:
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vqae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
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unet ([`UNet2DConditionModel`]):
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A `UNet2DConditionModel` to denoise the encoded image latents.
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mel ([`Mel`]):
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Transform audio into a spectrogram.
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scheduler ([`DDIMScheduler`] or [`DDPMScheduler`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
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[`DDIMScheduler`] or [`DDPMScheduler`].
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"""
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_optional_components = ["vqvae"]
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def __init__(
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self,
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vqvae: AutoencoderKL,
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unet: UNet2DConditionModel,
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mel: Mel,
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scheduler: Union[DDIMScheduler, DDPMScheduler],
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):
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super().__init__()
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self.register_modules(unet=unet, scheduler=scheduler, mel=mel, vqvae=vqvae)
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def get_default_steps(self) -> int:
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"""Returns default number of steps recommended for inference.
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Returns:
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`int`:
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The number of steps.
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"""
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return 50 if isinstance(self.scheduler, DDIMScheduler) else 1000
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@torch.no_grad()
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def __call__(
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self,
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batch_size: int = 1,
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audio_file: str = None,
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raw_audio: np.ndarray = None,
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slice: int = 0,
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start_step: int = 0,
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steps: int = None,
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generator: torch.Generator = None,
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mask_start_secs: float = 0,
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mask_end_secs: float = 0,
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step_generator: torch.Generator = None,
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eta: float = 0,
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noise: torch.Tensor = None,
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encoding: torch.Tensor = None,
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return_dict=True,
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) -> Union[
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Union[AudioPipelineOutput, ImagePipelineOutput],
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Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
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]:
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"""
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The call function to the pipeline for generation.
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Args:
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batch_size (`int`):
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Number of samples to generate.
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audio_file (`str`):
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An audio file that must be on disk due to [Librosa](https://librosa.org/) limitation.
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raw_audio (`np.ndarray`):
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The raw audio file as a NumPy array.
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slice (`int`):
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Slice number of audio to convert.
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start_step (int):
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Step to start diffusion from.
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steps (`int`):
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Number of denoising steps (defaults to `50` for DDIM and `1000` for DDPM).
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generator (`torch.Generator`):
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A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
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generation deterministic.
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mask_start_secs (`float`):
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Number of seconds of audio to mask (not generate) at start.
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mask_end_secs (`float`):
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Number of seconds of audio to mask (not generate) at end.
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step_generator (`torch.Generator`):
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A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) used to denoise.
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None
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eta (`float`):
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Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
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to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
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noise (`torch.Tensor`):
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A noise tensor of shape `(batch_size, 1, height, width)` or `None`.
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encoding (`torch.Tensor`):
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A tensor for [`UNet2DConditionModel`] of shape `(batch_size, seq_length, cross_attention_dim)`.
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return_dict (`bool`):
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Whether or not to return a [`AudioPipelineOutput`], [`ImagePipelineOutput`] or a plain tuple.
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Examples:
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For audio diffusion:
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```py
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import torch
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from IPython.display import Audio
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from diffusers import DiffusionPipeline
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-256").to(device)
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output = pipe()
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display(output.images[0])
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display(Audio(output.audios[0], rate=mel.get_sample_rate()))
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```
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For latent audio diffusion:
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```py
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import torch
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from IPython.display import Audio
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from diffusers import DiffusionPipeline
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipe = DiffusionPipeline.from_pretrained("teticio/latent-audio-diffusion-256").to(device)
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output = pipe()
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display(output.images[0])
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display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
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```
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For other tasks like variation, inpainting, outpainting, etc:
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```py
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output = pipe(
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raw_audio=output.audios[0, 0],
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start_step=int(pipe.get_default_steps() / 2),
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mask_start_secs=1,
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mask_end_secs=1,
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)
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display(output.images[0])
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display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
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```
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Returns:
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`List[PIL Image]`:
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A list of Mel spectrograms (`float`, `List[np.ndarray]`) with the sample rate and raw audio.
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"""
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steps = steps or self.get_default_steps()
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self.scheduler.set_timesteps(steps)
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step_generator = step_generator or generator
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# For backwards compatibility
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if type(self.unet.config.sample_size) == int:
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self.unet.config.sample_size = (self.unet.config.sample_size, self.unet.config.sample_size)
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if noise is None:
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noise = randn_tensor(
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(
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batch_size,
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self.unet.config.in_channels,
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self.unet.config.sample_size[0],
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self.unet.config.sample_size[1],
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),
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generator=generator,
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device=self.device,
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)
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images = noise
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mask = None
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if audio_file is not None or raw_audio is not None:
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self.mel.load_audio(audio_file, raw_audio)
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input_image = self.mel.audio_slice_to_image(slice)
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input_image = np.frombuffer(input_image.tobytes(), dtype="uint8").reshape(
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(input_image.height, input_image.width)
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)
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input_image = (input_image / 255) * 2 - 1
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input_images = torch.tensor(input_image[np.newaxis, :, :], dtype=torch.float).to(self.device)
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if self.vqvae is not None:
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input_images = self.vqvae.encode(torch.unsqueeze(input_images, 0)).latent_dist.sample(
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generator=generator
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)[0]
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input_images = self.vqvae.config.scaling_factor * input_images
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if start_step > 0:
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images[0, 0] = self.scheduler.add_noise(input_images, noise, self.scheduler.timesteps[start_step - 1])
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pixels_per_second = (
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self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
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)
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mask_start = int(mask_start_secs * pixels_per_second)
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mask_end = int(mask_end_secs * pixels_per_second)
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mask = self.scheduler.add_noise(input_images, noise, torch.tensor(self.scheduler.timesteps[start_step:]))
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for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
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if isinstance(self.unet, UNet2DConditionModel):
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model_output = self.unet(images, t, encoding)["sample"]
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else:
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model_output = self.unet(images, t)["sample"]
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if isinstance(self.scheduler, DDIMScheduler):
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images = self.scheduler.step(
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model_output=model_output,
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timestep=t,
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sample=images,
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eta=eta,
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generator=step_generator,
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)["prev_sample"]
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else:
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images = self.scheduler.step(
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model_output=model_output,
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timestep=t,
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sample=images,
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generator=step_generator,
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)["prev_sample"]
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if mask is not None:
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if mask_start > 0:
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images[:, :, :, :mask_start] = mask[:, step, :, :mask_start]
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if mask_end > 0:
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images[:, :, :, -mask_end:] = mask[:, step, :, -mask_end:]
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if self.vqvae is not None:
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# 0.18215 was scaling factor used in training to ensure unit variance
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images = 1 / self.vqvae.config.scaling_factor * images
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images = self.vqvae.decode(images)["sample"]
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images = (images / 2 + 0.5).clamp(0, 1)
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images = images.cpu().permute(0, 2, 3, 1).numpy()
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images = (images * 255).round().astype("uint8")
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images = list(
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(Image.fromarray(_[:, :, 0]) for _ in images)
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if images.shape[3] == 1
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else (Image.fromarray(_, mode="RGB").convert("L") for _ in images)
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)
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audios = [self.mel.image_to_audio(_) for _ in images]
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if not return_dict:
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return images, (self.mel.get_sample_rate(), audios)
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return BaseOutput(**AudioPipelineOutput(np.array(audios)[:, np.newaxis, :]), **ImagePipelineOutput(images))
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@torch.no_grad()
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def encode(self, images: List[Image.Image], steps: int = 50) -> np.ndarray:
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"""
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Reverse the denoising step process to recover a noisy image from the generated image.
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Args:
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images (`List[PIL Image]`):
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List of images to encode.
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steps (`int`):
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Number of encoding steps to perform (defaults to `50`).
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Returns:
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`np.ndarray`:
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A noise tensor of shape `(batch_size, 1, height, width)`.
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"""
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# Only works with DDIM as this method is deterministic
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assert isinstance(self.scheduler, DDIMScheduler)
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self.scheduler.set_timesteps(steps)
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sample = np.array(
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[np.frombuffer(image.tobytes(), dtype="uint8").reshape((1, image.height, image.width)) for image in images]
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)
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sample = (sample / 255) * 2 - 1
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sample = torch.Tensor(sample).to(self.device)
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for t in self.progress_bar(torch.flip(self.scheduler.timesteps, (0,))):
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prev_timestep = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
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alpha_prod_t = self.scheduler.alphas_cumprod[t]
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alpha_prod_t_prev = (
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self.scheduler.alphas_cumprod[prev_timestep]
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if prev_timestep >= 0
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else self.scheduler.final_alpha_cumprod
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)
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beta_prod_t = 1 - alpha_prod_t
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model_output = self.unet(sample, t)["sample"]
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pred_sample_direction = (1 - alpha_prod_t_prev) ** (0.5) * model_output
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sample = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
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sample = sample * alpha_prod_t ** (0.5) + beta_prod_t ** (0.5) * model_output
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return sample
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@staticmethod
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def slerp(x0: torch.Tensor, x1: torch.Tensor, alpha: float) -> torch.Tensor:
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"""Spherical Linear intERPolation.
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Args:
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x0 (`torch.Tensor`):
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The first tensor to interpolate between.
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x1 (`torch.Tensor`):
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Second tensor to interpolate between.
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alpha (`float`):
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Interpolation between 0 and 1
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Returns:
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`torch.Tensor`:
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The interpolated tensor.
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"""
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theta = acos(torch.dot(torch.flatten(x0), torch.flatten(x1)) / torch.norm(x0) / torch.norm(x1))
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return sin((1 - alpha) * theta) * x0 / sin(theta) + sin(alpha * theta) * x1 / sin(theta)
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