154 lines
6.1 KiB
Python
154 lines
6.1 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 typing import List, Optional, Tuple, Union
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import torch
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from ...utils import logging, randn_tensor
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from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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class DanceDiffusionPipeline(DiffusionPipeline):
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r"""
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Pipeline for audio generation.
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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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unet ([`UNet1DModel`]):
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A `UNet1DModel` to denoise the encoded audio.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of
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[`IPNDMScheduler`].
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"""
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def __init__(self, unet, scheduler):
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super().__init__()
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self.register_modules(unet=unet, scheduler=scheduler)
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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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num_inference_steps: int = 100,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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audio_length_in_s: Optional[float] = None,
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return_dict: bool = True,
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) -> Union[AudioPipelineOutput, Tuple]:
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r"""
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The call function to the pipeline for generation.
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Args:
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batch_size (`int`, *optional*, defaults to 1):
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The number of audio samples to generate.
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num_inference_steps (`int`, *optional*, defaults to 50):
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The number of denoising steps. More denoising steps usually lead to a higher-quality audio sample at
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the expense of slower inference.
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generator (`torch.Generator`, *optional*):
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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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audio_length_in_s (`float`, *optional*, defaults to `self.unet.config.sample_size/self.unet.config.sample_rate`):
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The length of the generated audio sample in seconds.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple.
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Example:
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```py
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from diffusers import DiffusionPipeline
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from scipy.io.wavfile import write
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model_id = "harmonai/maestro-150k"
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pipe = DiffusionPipeline.from_pretrained(model_id)
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pipe = pipe.to("cuda")
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audios = pipe(audio_length_in_s=4.0).audios
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# To save locally
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for i, audio in enumerate(audios):
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write(f"maestro_test_{i}.wav", pipe.unet.sample_rate, audio.transpose())
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# To dislay in google colab
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import IPython.display as ipd
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for audio in audios:
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display(ipd.Audio(audio, rate=pipe.unet.sample_rate))
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```
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Returns:
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[`~pipelines.AudioPipelineOutput`] or `tuple`:
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If `return_dict` is `True`, [`~pipelines.AudioPipelineOutput`] is returned, otherwise a `tuple` is
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returned where the first element is a list with the generated audio.
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"""
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if audio_length_in_s is None:
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audio_length_in_s = self.unet.config.sample_size / self.unet.config.sample_rate
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sample_size = audio_length_in_s * self.unet.config.sample_rate
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down_scale_factor = 2 ** len(self.unet.up_blocks)
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if sample_size < 3 * down_scale_factor:
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raise ValueError(
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f"{audio_length_in_s} is too small. Make sure it's bigger or equal to"
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f" {3 * down_scale_factor / self.unet.config.sample_rate}."
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)
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original_sample_size = int(sample_size)
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if sample_size % down_scale_factor != 0:
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sample_size = (
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(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
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) * down_scale_factor
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logger.info(
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f"{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"
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f" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"
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" process."
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)
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sample_size = int(sample_size)
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dtype = next(self.unet.parameters()).dtype
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shape = (batch_size, self.unet.config.in_channels, sample_size)
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if isinstance(generator, list) and len(generator) != batch_size:
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raise ValueError(
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f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
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f" size of {batch_size}. Make sure the batch size matches the length of the generators."
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)
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audio = randn_tensor(shape, generator=generator, device=self._execution_device, dtype=dtype)
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# set step values
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self.scheduler.set_timesteps(num_inference_steps, device=audio.device)
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self.scheduler.timesteps = self.scheduler.timesteps.to(dtype)
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for t in self.progress_bar(self.scheduler.timesteps):
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# 1. predict noise model_output
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model_output = self.unet(audio, t).sample
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# 2. compute previous audio sample: x_t -> t_t-1
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audio = self.scheduler.step(model_output, t, audio).prev_sample
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audio = audio.clamp(-1, 1).float().cpu().numpy()
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audio = audio[:, :, :original_sample_size]
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if not return_dict:
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return (audio,)
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return AudioPipelineOutput(audios=audio)
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