48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
# Copyright (c) 2023 Amphion.
|
|
#
|
|
# This source code is licensed under the MIT license found in the
|
|
# LICENSE file in the root directory of this source tree.
|
|
|
|
import torch
|
|
from diffusers import DiffusionPipeline
|
|
|
|
|
|
class DiffusionInferencePipeline(DiffusionPipeline):
|
|
def __init__(self, network, scheduler, num_inference_timesteps=1000):
|
|
super().__init__()
|
|
|
|
self.register_modules(network=network, scheduler=scheduler)
|
|
self.num_inference_timesteps = num_inference_timesteps
|
|
|
|
@torch.inference_mode()
|
|
def __call__(
|
|
self,
|
|
initial_noise: torch.Tensor,
|
|
conditioner: torch.Tensor = None,
|
|
):
|
|
r"""
|
|
Args:
|
|
initial_noise: The initial noise to be denoised.
|
|
conditioner:The conditioner.
|
|
n_inference_steps: The number of denoising steps. More denoising steps
|
|
usually lead to a higher quality at the expense of slower inference.
|
|
"""
|
|
|
|
mel = initial_noise
|
|
batch_size = mel.size(0)
|
|
self.scheduler.set_timesteps(self.num_inference_timesteps)
|
|
|
|
for t in self.progress_bar(self.scheduler.timesteps):
|
|
timestep = torch.full((batch_size,), t, device=mel.device, dtype=torch.long)
|
|
|
|
# 1. predict noise model_output
|
|
model_output = self.network(mel, timestep, conditioner)
|
|
|
|
# 2. denoise, compute previous step: x_t -> x_t-1
|
|
mel = self.scheduler.step(model_output, t, mel).prev_sample
|
|
|
|
# 3. clamp
|
|
mel = mel.clamp(-1.0, 1.0)
|
|
|
|
return mel
|