wehub-resource-sync
510 lines
21 KiB
Python
510 lines
21 KiB
Python
from __future__ import annotations
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from collections import deque
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from functools import partial
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from typing import List, Tuple
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import numpy as np
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import torch
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from torch import nn
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from tqdm import tqdm
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from modules.backbones import build_backbone
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from utils.hparams import hparams
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def extract(a, t, x_shape):
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b, *_ = t.shape
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out = a.gather(-1, t)
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return out.reshape(b, *((1,) * (len(x_shape) - 1)))
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def noise_like(shape, device, repeat=False):
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repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
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noise = lambda: torch.randn(shape, device=device)
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return repeat_noise() if repeat else noise()
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def linear_beta_schedule(timesteps, max_beta=0.01):
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"""
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linear schedule
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"""
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betas = np.linspace(1e-4, max_beta, timesteps)
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return betas
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def cosine_beta_schedule(timesteps, s=0.008):
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"""
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cosine schedule
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as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
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"""
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steps = timesteps + 1
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x = np.linspace(0, steps, steps)
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alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2
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alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
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betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
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return np.clip(betas, a_min=0, a_max=0.999)
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beta_schedule = {
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"cosine": cosine_beta_schedule,
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"linear": linear_beta_schedule,
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}
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class GaussianDiffusion(nn.Module):
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def __init__(self, out_dims, num_feats=1, timesteps=1000, k_step=1000,
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backbone_type=None, backbone_args=None, betas=None,
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spec_min=None, spec_max=None):
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super().__init__()
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self.denoise_fn: nn.Module = build_backbone(out_dims, num_feats, backbone_type, backbone_args)
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self.out_dims = out_dims
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self.num_feats = num_feats
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if betas is not None:
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betas = betas.detach().cpu().numpy() if isinstance(betas, torch.Tensor) else betas
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else:
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schedule_args = {}
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if hparams['schedule_type'] == 'linear':
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schedule_args['max_beta'] = hparams.get('max_beta', 0.01)
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betas = beta_schedule[hparams['schedule_type']](timesteps, **schedule_args)
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alphas = 1. - betas
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alphas_cumprod = np.cumprod(alphas, axis=0)
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alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
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self.use_shallow_diffusion = hparams.get('use_shallow_diffusion', False)
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if self.use_shallow_diffusion:
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assert k_step <= timesteps, 'K_step should not be larger than timesteps.'
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self.timesteps = timesteps
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self.k_step = k_step if self.use_shallow_diffusion else timesteps
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self.noise_list = deque(maxlen=4)
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to_torch = partial(torch.tensor, dtype=torch.float32)
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self.register_buffer('betas', to_torch(betas))
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self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
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self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
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# calculations for diffusion q(x_t | x_{t-1}) and others
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self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
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self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
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self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
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self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
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self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
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# calculations for posterior q(x_{t-1} | x_t, x_0)
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posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
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# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
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self.register_buffer('posterior_variance', to_torch(posterior_variance))
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# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
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self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
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self.register_buffer('posterior_mean_coef1', to_torch(
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betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
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self.register_buffer('posterior_mean_coef2', to_torch(
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(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
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# spec: [B, T, M] or [B, F, T, M]
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# spec_min and spec_max: [1, 1, M] or [1, 1, F, M] => transpose(-3, -2) => [1, 1, M] or [1, F, 1, M]
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spec_min = torch.FloatTensor(spec_min)[None, None, :out_dims].transpose(-3, -2)
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spec_max = torch.FloatTensor(spec_max)[None, None, :out_dims].transpose(-3, -2)
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self.register_buffer('spec_min', spec_min)
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self.register_buffer('spec_max', spec_max)
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# for compatibility with ONNX continuous acceleration
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self.time_scale_factor = self.timesteps
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self.t_start = 1 - self.k_step / self.timesteps
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factors = torch.LongTensor([i for i in range(1, self.timesteps + 1) if self.timesteps % i == 0])
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self.register_buffer('timestep_factors', factors, persistent=False)
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def q_mean_variance(self, x_start, t):
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mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
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variance = extract(1. - self.alphas_cumprod, t, x_start.shape)
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log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
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return mean, variance, log_variance
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def predict_start_from_noise(self, x_t, t, noise):
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return (
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extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
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extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
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)
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def q_posterior(self, x_start, x_t, t):
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posterior_mean = (
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extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
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extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
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)
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posterior_variance = extract(self.posterior_variance, t, x_t.shape)
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posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
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return posterior_mean, posterior_variance, posterior_log_variance_clipped
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def p_mean_variance(self, x, t, cond):
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noise_pred = self.denoise_fn(x, t, cond=cond)
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x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred)
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# This is previously inherited from original DiffSinger repository
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# and disabled due to some loudness issues when speedup = 1.
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# x_recon.clamp_(-1., 1.)
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model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
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return model_mean, posterior_variance, posterior_log_variance
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@torch.no_grad()
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def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False):
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b, *_, device = *x.shape, x.device
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model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond)
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noise = noise_like(x.shape, device, repeat_noise)
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# no noise when t == 0
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nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
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return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
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@torch.no_grad()
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def p_sample_ddim(self, x, t, interval, cond):
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a_t = extract(self.alphas_cumprod, t, x.shape)
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a_prev = extract(self.alphas_cumprod, torch.max(t - interval, torch.zeros_like(t)), x.shape)
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noise_pred = self.denoise_fn(x, t, cond=cond)
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x_prev = a_prev.sqrt() * (
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x / a_t.sqrt() + (((1 - a_prev) / a_prev).sqrt() - ((1 - a_t) / a_t).sqrt()) * noise_pred
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)
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return x_prev
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@torch.no_grad()
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def p_sample_plms(self, x, t, interval, cond, clip_denoised=True, repeat_noise=False):
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"""
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Use the PLMS method from
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[Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778).
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"""
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def get_x_pred(x, noise_t, t):
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a_t = extract(self.alphas_cumprod, t, x.shape)
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a_prev = extract(self.alphas_cumprod, torch.max(t - interval, torch.zeros_like(t)), x.shape)
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a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt()
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x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x - 1 / (
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a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t)
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x_pred = x + x_delta
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return x_pred
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noise_list = self.noise_list
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noise_pred = self.denoise_fn(x, t, cond=cond)
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if len(noise_list) == 0:
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x_pred = get_x_pred(x, noise_pred, t)
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noise_pred_prev = self.denoise_fn(x_pred, max(t - interval, 0), cond=cond)
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noise_pred_prime = (noise_pred + noise_pred_prev) / 2
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elif len(noise_list) == 1:
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noise_pred_prime = (3 * noise_pred - noise_list[-1]) / 2
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elif len(noise_list) == 2:
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noise_pred_prime = (23 * noise_pred - 16 * noise_list[-1] + 5 * noise_list[-2]) / 12
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else:
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noise_pred_prime = (55 * noise_pred - 59 * noise_list[-1] + 37 * noise_list[-2] - 9 * noise_list[-3]) / 24
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x_prev = get_x_pred(x, noise_pred_prime, t)
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noise_list.append(noise_pred)
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return x_prev
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def q_sample(self, x_start, t, noise):
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return (
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extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
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extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
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)
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def p_losses(self, x_start, t, cond, noise=None):
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if noise is None:
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noise = torch.randn_like(x_start)
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x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
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x_recon = self.denoise_fn(x_noisy, t, cond)
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return x_recon, noise
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def inference(self, cond, b=1, x_start=None, device=None):
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depth = hparams.get('K_step_infer', self.k_step)
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speedup = hparams['diff_speedup']
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if speedup > 0:
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assert depth % speedup == 0, f'Acceleration ratio must be a factor of diffusion depth {depth}.'
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noise = torch.randn(b, self.num_feats, self.out_dims, cond.shape[2], device=device)
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if self.use_shallow_diffusion:
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t_max = min(depth, self.k_step)
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else:
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t_max = self.k_step
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if t_max >= self.timesteps:
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x = noise
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elif t_max > 0:
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assert x_start is not None, 'Missing shallow diffusion source.'
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x = self.q_sample(
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x_start, torch.full((b,), t_max - 1, device=device, dtype=torch.long), noise
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)
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else:
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assert x_start is not None, 'Missing shallow diffusion source.'
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x = x_start
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if speedup > 1 and t_max > 0:
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algorithm = hparams['diff_accelerator']
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if algorithm == 'dpm-solver':
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from inference.dpm_solver_pytorch import NoiseScheduleVP, model_wrapper, DPM_Solver
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# 1. Define the noise schedule.
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noise_schedule = NoiseScheduleVP(schedule='discrete', betas=self.betas[:t_max])
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# 2. Convert your discrete-time `model` to the continuous-time
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# noise prediction model. Here is an example for a diffusion model
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# `model` with the noise prediction type ("noise") .
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def my_wrapper(fn):
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def wrapped(x, t, **kwargs):
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ret = fn(x, t, **kwargs)
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self.bar.update(1)
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return ret
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return wrapped
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model_fn = model_wrapper(
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my_wrapper(self.denoise_fn),
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noise_schedule,
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model_type="noise", # or "x_start" or "v" or "score"
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model_kwargs={"cond": cond}
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)
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# 3. Define dpm-solver and sample by singlestep DPM-Solver.
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# (We recommend singlestep DPM-Solver for unconditional sampling)
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# You can adjust the `steps` to balance the computation
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# costs and the sample quality.
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dpm_solver = DPM_Solver(model_fn, noise_schedule, algorithm_type="dpmsolver++")
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steps = t_max // hparams["diff_speedup"]
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self.bar = tqdm(desc="sample time step", total=steps, disable=not hparams['infer'], leave=False)
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x = dpm_solver.sample(
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x,
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steps=steps,
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order=2,
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skip_type="time_uniform",
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method="multistep",
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)
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self.bar.close()
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elif algorithm == 'unipc':
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from inference.uni_pc import NoiseScheduleVP, model_wrapper, UniPC
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# 1. Define the noise schedule.
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noise_schedule = NoiseScheduleVP(schedule='discrete', betas=self.betas[:t_max])
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# 2. Convert your discrete-time `model` to the continuous-time
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# noise prediction model. Here is an example for a diffusion model
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# `model` with the noise prediction type ("noise") .
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def my_wrapper(fn):
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def wrapped(x, t, **kwargs):
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ret = fn(x, t, **kwargs)
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self.bar.update(1)
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return ret
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return wrapped
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model_fn = model_wrapper(
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my_wrapper(self.denoise_fn),
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noise_schedule,
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model_type="noise", # or "x_start" or "v" or "score"
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model_kwargs={"cond": cond}
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)
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# 3. Define uni_pc and sample by multistep UniPC.
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# You can adjust the `steps` to balance the computation
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# costs and the sample quality.
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uni_pc = UniPC(model_fn, noise_schedule, variant='bh2')
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steps = t_max // hparams["diff_speedup"]
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self.bar = tqdm(desc="sample time step", total=steps, disable=not hparams['infer'], leave=False)
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x = uni_pc.sample(
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x,
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steps=steps,
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order=2,
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skip_type="time_uniform",
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method="multistep",
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)
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self.bar.close()
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elif algorithm == 'pndm':
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self.noise_list = deque(maxlen=4)
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iteration_interval = speedup
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for i in tqdm(
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reversed(range(0, t_max, iteration_interval)), desc='sample time step',
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total=t_max // iteration_interval, disable=not hparams['infer'], leave=False
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):
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x = self.p_sample_plms(
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x, torch.full((b,), i, device=device, dtype=torch.long),
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iteration_interval, cond=cond
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)
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elif algorithm == 'ddim':
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iteration_interval = speedup
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for i in tqdm(
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reversed(range(0, t_max, iteration_interval)), desc='sample time step',
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total=t_max // iteration_interval, disable=not hparams['infer'], leave=False
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):
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x = self.p_sample_ddim(
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x, torch.full((b,), i, device=device, dtype=torch.long),
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iteration_interval, cond=cond
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)
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else:
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raise ValueError(f"Unsupported acceleration algorithm for DDPM: {algorithm}.")
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else:
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for i in tqdm(reversed(range(0, t_max)), desc='sample time step', total=t_max,
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disable=not hparams['infer'], leave=False):
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x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond)
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x = x.transpose(2, 3).squeeze(1) # [B, F, M, T] => [B, T, M] or [B, F, T, M]
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return x
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def forward(self, condition, gt_spec=None, src_spec=None, infer=True):
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"""
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conditioning diffusion, use fastspeech2 encoder output as the condition
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"""
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cond = condition.transpose(1, 2)
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b, device = condition.shape[0], condition.device
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if not infer:
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# gt_spec: [B, T, M] or [B, F, T, M]
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spec = self.norm_spec(gt_spec).transpose(-2, -1) # [B, M, T] or [B, F, M, T]
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if self.num_feats == 1:
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spec = spec[:, None, :, :] # [B, F=1, M, T]
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t = torch.randint(0, self.k_step, (b,), device=device).long()
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x_recon, noise = self.p_losses(spec, t, cond=cond)
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return x_recon, noise
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else:
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# src_spec: [B, T, M] or [B, F, T, M]
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if src_spec is not None:
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spec = self.norm_spec(src_spec).transpose(-2, -1)
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if self.num_feats == 1:
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spec = spec[:, None, :, :]
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else:
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spec = None
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x = self.inference(cond, b=b, x_start=spec, device=device)
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return self.denorm_spec(x)
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def norm_spec(self, x):
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return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1
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def denorm_spec(self, x):
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return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min
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class RepetitiveDiffusion(GaussianDiffusion):
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def __init__(self, vmin: float | int | list, vmax: float | int | list,
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repeat_bins: int, timesteps=1000, k_step=1000,
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backbone_type=None, backbone_args=None,
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betas=None):
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assert (isinstance(vmin, (float, int)) and isinstance(vmax, (float, int))) or len(vmin) == len(vmax)
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num_feats = 1 if isinstance(vmin, (float, int)) else len(vmin)
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spec_min = [vmin] if num_feats == 1 else [[v] for v in vmin]
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spec_max = [vmax] if num_feats == 1 else [[v] for v in vmax]
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self.repeat_bins = repeat_bins
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super().__init__(
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out_dims=repeat_bins, num_feats=num_feats,
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|
timesteps=timesteps, k_step=k_step,
|
|
backbone_type=backbone_type, backbone_args=backbone_args,
|
|
betas=betas, spec_min=spec_min, spec_max=spec_max
|
|
)
|
|
|
|
def norm_spec(self, x):
|
|
"""
|
|
|
|
:param x: [B, T] or [B, F, T]
|
|
:return [B, T, R] or [B, F, T, R]
|
|
"""
|
|
if self.num_feats == 1:
|
|
repeats = [1, 1, self.repeat_bins]
|
|
else:
|
|
repeats = [1, 1, 1, self.repeat_bins]
|
|
return super().norm_spec(x.unsqueeze(-1).repeat(repeats))
|
|
|
|
def denorm_spec(self, x):
|
|
"""
|
|
|
|
:param x: [B, T, R] or [B, F, T, R]
|
|
:return [B, T] or [B, F, T]
|
|
"""
|
|
return super().denorm_spec(x).mean(dim=-1)
|
|
|
|
|
|
class PitchDiffusion(RepetitiveDiffusion):
|
|
def __init__(self, vmin: float, vmax: float,
|
|
cmin: float, cmax: float, repeat_bins,
|
|
timesteps=1000, k_step=1000,
|
|
backbone_type=None, backbone_args=None,
|
|
betas=None):
|
|
self.vmin = vmin # norm min
|
|
self.vmax = vmax # norm max
|
|
self.cmin = cmin # clip min
|
|
self.cmax = cmax # clip max
|
|
super().__init__(
|
|
vmin=vmin, vmax=vmax, repeat_bins=repeat_bins,
|
|
timesteps=timesteps, k_step=k_step,
|
|
backbone_type=backbone_type, backbone_args=backbone_args,
|
|
betas=betas
|
|
)
|
|
|
|
def norm_spec(self, x):
|
|
return super().norm_spec(x.clamp(min=self.cmin, max=self.cmax))
|
|
|
|
def denorm_spec(self, x):
|
|
return super().denorm_spec(x).clamp(min=self.cmin, max=self.cmax)
|
|
|
|
|
|
class MultiVarianceDiffusion(RepetitiveDiffusion):
|
|
def __init__(
|
|
self, ranges: List[Tuple[float, float]],
|
|
clamps: List[Tuple[float | None, float | None] | None],
|
|
repeat_bins, timesteps=1000, k_step=1000,
|
|
backbone_type=None, backbone_args=None,
|
|
betas=None
|
|
):
|
|
assert len(ranges) == len(clamps)
|
|
self.clamps = clamps
|
|
vmin = [r[0] for r in ranges]
|
|
vmax = [r[1] for r in ranges]
|
|
if len(vmin) == 1:
|
|
vmin = vmin[0]
|
|
if len(vmax) == 1:
|
|
vmax = vmax[0]
|
|
super().__init__(
|
|
vmin=vmin, vmax=vmax, repeat_bins=repeat_bins,
|
|
timesteps=timesteps, k_step=k_step,
|
|
backbone_type=backbone_type, backbone_args=backbone_args,
|
|
betas=betas
|
|
)
|
|
|
|
def clamp_spec(self, xs: list | tuple):
|
|
clamped = []
|
|
for x, c in zip(xs, self.clamps):
|
|
if c is None:
|
|
clamped.append(x)
|
|
continue
|
|
clamped.append(x.clamp(min=c[0], max=c[1]))
|
|
return clamped
|
|
|
|
def norm_spec(self, xs: list | tuple):
|
|
"""
|
|
|
|
:param xs: sequence of [B, T]
|
|
:return: [B, F, T] => super().norm_spec(xs) => [B, F, T, R]
|
|
"""
|
|
assert len(xs) == self.num_feats
|
|
clamped = self.clamp_spec(xs)
|
|
xs = torch.stack(clamped, dim=1) # [B, F, T]
|
|
if self.num_feats == 1:
|
|
xs = xs.squeeze(1) # [B, T]
|
|
return super().norm_spec(xs)
|
|
|
|
def denorm_spec(self, xs):
|
|
"""
|
|
|
|
:param xs: [B, T, R] or [B, F, T, R] => super().denorm_spec(xs) => [B, T] or [B, F, T]
|
|
:return: sequence of [B, T]
|
|
"""
|
|
xs = super().denorm_spec(xs)
|
|
if self.num_feats == 1:
|
|
xs = [xs]
|
|
else:
|
|
xs = xs.unbind(dim=1)
|
|
assert len(xs) == self.num_feats
|
|
return self.clamp_spec(xs)
|
|
|