134 lines
5.9 KiB
Python
134 lines
5.9 KiB
Python
# Copyright 2022 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 ...pipeline_utils import AudioPipelineOutput, DiffusionPipeline
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from ...utils import logging
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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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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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Parameters:
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unet ([`UNet1DModel`]): U-Net architecture to denoise the encoded image.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image. 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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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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One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
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to make 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. Note that the output of the pipeline, *i.e.*
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`sample_size`, will be `audio_length_in_s` * `self.unet.sample_rate`.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipeline_utils.AudioPipelineOutput`] instead of a plain tuple.
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Returns:
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[`~pipeline_utils.AudioPipelineOutput`] or `tuple`: [`~pipelines.utils.AudioPipelineOutput`] if
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`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
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generated images.
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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.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.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 = ((audio_length_in_s * self.unet.sample_rate) // down_scale_factor + 1) * 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.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.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(iter(self.unet.parameters())).dtype
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shape = (batch_size, self.unet.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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rand_device = "cpu" if self.device.type == "mps" else self.device
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if isinstance(generator, list):
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shape = (1,) + shape[1:]
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audio = [
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torch.randn(shape, generator=generator[i], device=rand_device, dtype=self.unet.dtype)
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for i in range(batch_size)
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]
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audio = torch.cat(audio, dim=0).to(self.device)
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else:
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audio = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(self.device)
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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 image: 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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