chore: import upstream snapshot with attribution
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wehub-resource-sync
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from __future__ import annotations
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from typing import List, Tuple
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import torch
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import torch.nn as 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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class RectifiedFlow(nn.Module):
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def __init__(self, out_dims, num_feats=1, t_start=0., time_scale_factor=1000,
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backbone_type=None, backbone_args=None,
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spec_min=None, spec_max=None):
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super().__init__()
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self.velocity_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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self.use_shallow_diffusion = hparams.get('use_shallow_diffusion', False)
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if self.use_shallow_diffusion:
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assert 0. <= t_start <= 1., 'T_start should be in [0, 1].'
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else:
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t_start = 0.
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self.t_start = t_start
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self.time_scale_factor = time_scale_factor
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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, persistent=False)
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self.register_buffer('spec_max', spec_max, persistent=False)
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def p_losses(self, x_end, t, cond):
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x_start = torch.randn_like(x_end)
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x_t = x_start + t[:, None, None, None] * (x_end - x_start)
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v_pred = self.velocity_fn(x_t, t * self.time_scale_factor, cond)
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return v_pred, x_end - x_start
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def forward(self, condition, gt_spec=None, src_spec=None, infer=True):
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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 = self.t_start + (1.0 - self.t_start) * torch.rand((b,), device=device)
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v_pred, v_gt = self.p_losses(spec, t, cond=cond)
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return v_pred, v_gt, t
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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_end=spec, device=device)
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return self.denorm_spec(x)
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@torch.no_grad()
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def sample_euler(self, x, t, dt, cond):
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x += self.velocity_fn(x, self.time_scale_factor * t, cond) * dt
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t += dt
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return x, t
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@torch.no_grad()
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def sample_rk2(self, x, t, dt, cond):
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k_1 = self.velocity_fn(x, self.time_scale_factor * t, cond)
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k_2 = self.velocity_fn(x + 0.5 * k_1 * dt, self.time_scale_factor * (t + 0.5 * dt), cond)
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x += k_2 * dt
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t += dt
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return x, t
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@torch.no_grad()
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def sample_rk4(self, x, t, dt, cond):
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k_1 = self.velocity_fn(x, self.time_scale_factor * t, cond)
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k_2 = self.velocity_fn(x + 0.5 * k_1 * dt, self.time_scale_factor * (t + 0.5 * dt), cond)
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k_3 = self.velocity_fn(x + 0.5 * k_2 * dt, self.time_scale_factor * (t + 0.5 * dt), cond)
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k_4 = self.velocity_fn(x + k_3 * dt, self.time_scale_factor * (t + dt), cond)
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x += (k_1 + 2 * k_2 + 2 * k_3 + k_4) * dt / 6
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t += dt
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return x, t
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@torch.no_grad()
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def sample_rk5(self, x, t, dt, cond):
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k_1 = self.velocity_fn(x, self.time_scale_factor * t, cond)
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k_2 = self.velocity_fn(x + 0.25 * k_1 * dt, self.time_scale_factor * (t + 0.25 * dt), cond)
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k_3 = self.velocity_fn(x + 0.125 * (k_2 + k_1) * dt, self.time_scale_factor * (t + 0.25 * dt), cond)
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k_4 = self.velocity_fn(x + 0.5 * (-k_2 + 2 * k_3) * dt, self.time_scale_factor * (t + 0.5 * dt), cond)
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k_5 = self.velocity_fn(x + 0.0625 * (3 * k_1 + 9 * k_4) * dt, self.time_scale_factor * (t + 0.75 * dt), cond)
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k_6 = self.velocity_fn(x + (-3 * k_1 + 2 * k_2 + 12 * k_3 - 12 * k_4 + 8 * k_5) * dt / 7,
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self.time_scale_factor * (t + dt),
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cond)
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x += (7 * k_1 + 32 * k_3 + 12 * k_4 + 32 * k_5 + 7 * k_6) * dt / 90
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t += dt
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return x, t
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@torch.no_grad()
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def inference(self, cond, b=1, x_end=None, device=None):
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noise = torch.randn(b, self.num_feats, self.out_dims, cond.shape[2], device=device)
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t_start = hparams.get('T_start_infer', self.t_start)
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if self.use_shallow_diffusion and t_start > 0:
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assert x_end is not None, 'Missing shallow diffusion source.'
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if t_start >= 1.:
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t_start = 1.
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x = x_end
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else:
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x = t_start * x_end + (1 - t_start) * noise
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else:
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t_start = 0.
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x = noise
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algorithm = hparams['sampling_algorithm']
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infer_step = hparams['sampling_steps']
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if t_start < 1:
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dt = (1.0 - t_start) / max(1, infer_step)
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algorithm_fn = {
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'euler': self.sample_euler,
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'rk2': self.sample_rk2,
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'rk4': self.sample_rk4,
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'rk5': self.sample_rk5,
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}.get(algorithm)
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if algorithm_fn is None:
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raise ValueError(f'Unsupported algorithm for Rectified Flow: {algorithm}.')
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dts = torch.tensor([dt]).to(x)
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for i in tqdm(range(infer_step), desc='sample time step', total=infer_step,
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disable=not hparams['infer'], leave=False):
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x, _ = algorithm_fn(x, t_start + i * dts, dt, cond)
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x = x.float()
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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 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 RepetitiveRectifiedFlow(RectifiedFlow):
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def __init__(self, vmin: float | int | list, vmax: float | int | list,
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repeat_bins: int, time_scale_factor=1000,
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backbone_type=None, backbone_args=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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time_scale_factor=time_scale_factor,
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backbone_type=backbone_type, backbone_args=backbone_args,
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spec_min=spec_min, spec_max=spec_max
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)
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def norm_spec(self, x):
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"""
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:param x: [B, T] or [B, F, T]
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:return [B, T, R] or [B, F, T, R]
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"""
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if self.num_feats == 1:
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repeats = [1, 1, self.repeat_bins]
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else:
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repeats = [1, 1, 1, self.repeat_bins]
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return super().norm_spec(x.unsqueeze(-1).repeat(repeats))
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def denorm_spec(self, x):
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"""
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:param x: [B, T, R] or [B, F, T, R]
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:return [B, T] or [B, F, T]
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"""
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return super().denorm_spec(x).mean(dim=-1)
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class PitchRectifiedFlow(RepetitiveRectifiedFlow):
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def __init__(self, vmin: float, vmax: float,
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cmin: float, cmax: float, repeat_bins,
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time_scale_factor=1000,
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backbone_type=None, backbone_args=None):
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self.vmin = vmin # norm min
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self.vmax = vmax # norm max
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self.cmin = cmin # clip min
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self.cmax = cmax # clip max
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super().__init__(
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vmin=vmin, vmax=vmax, repeat_bins=repeat_bins,
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time_scale_factor=time_scale_factor,
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backbone_type=backbone_type, backbone_args=backbone_args
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)
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def norm_spec(self, x):
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return super().norm_spec(x.clamp(min=self.cmin, max=self.cmax))
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def denorm_spec(self, x):
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return super().denorm_spec(x).clamp(min=self.cmin, max=self.cmax)
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class MultiVarianceRectifiedFlow(RepetitiveRectifiedFlow):
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def __init__(
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self, ranges: List[Tuple[float, float]],
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clamps: List[Tuple[float | None, float | None] | None],
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repeat_bins, time_scale_factor=1000,
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backbone_type=None, backbone_args=None
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):
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assert len(ranges) == len(clamps)
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self.clamps = clamps
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vmin = [r[0] for r in ranges]
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vmax = [r[1] for r in ranges]
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if len(vmin) == 1:
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vmin = vmin[0]
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if len(vmax) == 1:
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vmax = vmax[0]
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super().__init__(
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vmin=vmin, vmax=vmax, repeat_bins=repeat_bins,
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time_scale_factor=time_scale_factor,
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backbone_type=backbone_type, backbone_args=backbone_args
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)
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def clamp_spec(self, xs: list | tuple):
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clamped = []
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for x, c in zip(xs, self.clamps):
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if c is None:
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clamped.append(x)
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continue
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clamped.append(x.clamp(min=c[0], max=c[1]))
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return clamped
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def norm_spec(self, xs: list | tuple):
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"""
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:param xs: sequence of [B, T]
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:return: [B, F, T] => super().norm_spec(xs) => [B, F, T, R]
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"""
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assert len(xs) == self.num_feats
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clamped = self.clamp_spec(xs)
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xs = torch.stack(clamped, dim=1) # [B, F, T]
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if self.num_feats == 1:
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xs = xs.squeeze(1) # [B, T]
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return super().norm_spec(xs)
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def denorm_spec(self, xs):
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"""
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:param xs: [B, T, R] or [B, F, T, R] => super().denorm_spec(xs) => [B, T] or [B, F, T]
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:return: sequence of [B, T]
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"""
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xs = super().denorm_spec(xs)
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if self.num_feats == 1:
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xs = [xs]
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else:
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xs = xs.unbind(dim=1)
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assert len(xs) == self.num_feats
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return self.clamp_spec(xs)
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