feat: add DITTO optimizer, upgrade BigVGAN trainer, document all nodes
BigVGAN trainer (selva_bigvgan_trainer.py): - Add snake_alpha_only train mode: tunes only ~27K per-channel α params (0.024% of 112M) — physically cannot cause harmonic smearing - Add lambda_l2sp: L2-SP anchor regularization toward pretrained weights - Add optional discriminator_path: frozen MPD+MRD feature matching loss replaces mel L1 when a BigVGAN discriminator checkpoint is provided - Inline MPD + MRD discriminator implementations (no extra dependencies) DITTO optimizer (selva_ditto_optimizer.py): - New node: inference-time noise optimization (arXiv:2401.12179) - Optimizes x₀ via mel Gram matrix style loss against BJ reference clips - All model weights frozen — zero quality degradation risk - Truncated BPTT through last n_grad_steps of the ODE (configurable) - Gradient checkpointing on each differentiated step Docs: - README: document all 20 nodes (was 3), add workflow diagrams - STYLE_TRANSFER.md: new guide — DITTO, vocoder fine-tuning tiers, why LoRA/TI fail, combined approach, dataset prep Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
@@ -21,6 +21,7 @@ _NODES = {
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"SelvaActivationSteeringLoader": (".selva_activation_steering_loader", "SelvaActivationSteeringLoader", "SelVA Activation Steering Loader"),
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"SelvaBigvganTrainer": (".selva_bigvgan_trainer", "SelvaBigvganTrainer", "SelVA BigVGAN Trainer"),
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"SelvaBigvganLoader": (".selva_bigvgan_loader", "SelvaBigvganLoader", "SelVA BigVGAN Loader"),
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"SelvaDittoOptimizer": (".selva_ditto_optimizer", "SelvaDittoOptimizer", "SelVA DITTO Optimizer"),
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}
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for key, (module_path, class_name, display_name) in _NODES.items():
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+312
-36
@@ -1,14 +1,29 @@
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"""SelVA BigVGAN Vocoder Fine-tuner.
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Fine-tunes only the BigVGAN vocoder (mel → waveform) on BJ audio clips using
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spectral reconstruction losses. The DiT and VAE are completely untouched.
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Tier-1 approach based on research: snake alpha fine-tuning + L2-SP anchor
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regularization + optional frozen discriminator feature matching.
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Loss: L1 mel reconstruction + multi-resolution STFT magnitude L1.
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No GAN discriminator — this is a proof-of-concept to verify that the vocoder
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can absorb BJ timbral characteristics before investing in full adversarial training.
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Root cause of harmonic smearing with plain mel/STFT losses:
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Spectral L1 minimizes expected reconstruction error — averaging over
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high-variance harmonics. This is a loss-function topology problem, not
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an LR/step-count problem. The fix is either (a) restrict trainable params
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so the model lacks capacity to smear, or (b) use a perceptual loss that
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penalizes harmonic averaging.
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Save format: {'generator': vocoder.state_dict()} — same as the original BigVGAN
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checkpoint so it can be loaded with SelVA BigVGAN Loader.
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Tier-1 implementation:
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1. snake_alpha_only mode — only tune ~5K per-channel α parameters in
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Snake/SnakeBeta activations. These control harmonic periodicity per
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channel. With only 5K trainable params, the model physically cannot
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reshape the spectrum enough to cause the "green smear".
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2. L2-SP anchor loss — penalizes parameter drift from pretrained values
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(strictly better than weight decay, which anchors to zero).
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3. Frozen discriminator feature matching — if a BigVGAN discriminator
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checkpoint is provided, the pretrained MPD+MRD networks are used as
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fixed perceptual feature extractors. Feature matching loss penalizes
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harmonic smearing directly without any GAN instability.
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Save format: {'generator': vocoder.state_dict()} — same as the original
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BigVGAN checkpoint so it can be loaded with SelVA BigVGAN Loader.
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"""
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import random
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@@ -16,6 +31,7 @@ import threading
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from pathlib import Path
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchaudio
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import comfy.utils
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@@ -23,12 +39,133 @@ import folder_paths
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from .utils import SELVA_CATEGORY, get_device, soft_empty_cache
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def _save_spectrogram(path, mel_tensor):
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"""Save mel spectrogram [1, n_mels, T] as a PNG using PIL (no matplotlib dep).
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Normalises to [0, 255], flips frequency axis so low freqs are at the bottom,
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and saves as a greyscale PNG with a simple viridis-like colour map.
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"""
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# ---------------------------------------------------------------------------
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# Minimal MPD + MRD discriminators matching BigVGAN pretrained checkpoint keys
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# ---------------------------------------------------------------------------
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def _get_pad(kernel_size, dilation=1):
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return int((kernel_size * dilation - dilation) / 2)
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class _DiscriminatorP(nn.Module):
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"""Multi-Period Discriminator sub-module (HiFi-GAN / BigVGAN style)."""
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def __init__(self, period):
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super().__init__()
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self.period = period
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from torch.nn.utils.parametrizations import weight_norm
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norm = weight_norm
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self.convs = nn.ModuleList([
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norm(nn.Conv2d(1, 32, (5, 1), (3, 1), (_get_pad(5, 1), 0))),
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norm(nn.Conv2d(32, 128, (5, 1), (3, 1), (_get_pad(5, 1), 0))),
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norm(nn.Conv2d(128, 512, (5, 1), (3, 1), (_get_pad(5, 1), 0))),
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norm(nn.Conv2d(512, 1024,(5, 1), (3, 1), (_get_pad(5, 1), 0))),
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norm(nn.Conv2d(1024,1024,(5, 1), 1, (_get_pad(5, 1), 0))),
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])
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self.conv_post = norm(nn.Conv2d(1024, 1, (3, 1), 1, (1, 0)))
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def forward(self, x):
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fmap = []
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b, c, t = x.shape
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if t % self.period != 0:
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n_pad = self.period - (t % self.period)
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x = F.pad(x, (0, n_pad), "reflect")
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t = t + n_pad
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x = x.view(b, c, t // self.period, self.period)
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, 0.1)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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return fmap
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class _MultiPeriodDiscriminator(nn.Module):
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def __init__(self):
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super().__init__()
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self.discriminators = nn.ModuleList([
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_DiscriminatorP(p) for p in [2, 3, 5, 7, 11]
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])
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def forward(self, y):
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fmaps = []
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for d in self.discriminators:
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fmaps.extend(d(y))
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return fmaps
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class _DiscriminatorR(nn.Module):
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"""Multi-Resolution Discriminator sub-module."""
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def __init__(self, fft_size, shift_size, win_length):
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super().__init__()
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self.fft_size = fft_size
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self.shift_size = shift_size
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self.win_length = win_length
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from torch.nn.utils.parametrizations import weight_norm
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norm = weight_norm
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self.convs = nn.ModuleList([
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norm(nn.Conv2d(1, 32, (3, 9), padding=(1, 4))),
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norm(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))),
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norm(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))),
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norm(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))),
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norm(nn.Conv2d(32, 32, (3, 3), padding=(1, 1))),
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])
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self.conv_post = norm(nn.Conv2d(32, 1, (3, 3), padding=(1, 1)))
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def spectrogram(self, x):
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"""x: [B, 1, T] → [B, 1, freq, time]"""
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n, hop, win = self.fft_size, self.shift_size, self.win_length
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window = torch.hann_window(win, device=x.device)
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x = x.squeeze(1) # [B, T]
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pad = (win - hop) // 2
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x = F.pad(x, (pad, pad + (win - hop) % 2), mode="reflect")
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x = torch.stft(x, n, hop, win, window, center=False, return_complex=True)
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x = x.abs().unsqueeze(1) # [B, 1, freq, time]
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return x
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def forward(self, x):
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fmap = []
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x = self.spectrogram(x)
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, 0.1)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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return fmap
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class _MultiResolutionDiscriminator(nn.Module):
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def __init__(self):
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super().__init__()
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resolutions = [(1024, 120, 600), (2048, 240, 1200), (512, 50, 240)]
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self.discriminators = nn.ModuleList([
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_DiscriminatorR(*r) for r in resolutions
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])
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def forward(self, y):
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fmaps = []
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for d in self.discriminators:
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fmaps.extend(d(y))
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return fmaps
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def _feature_matching_loss(fmaps_real, fmaps_gen):
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"""L1 between paired feature map lists (both already detach-safe for real)."""
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loss = torch.zeros(1, device=fmaps_gen[0].device)
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for fr, fg in zip(fmaps_real, fmaps_gen):
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T = min(fr.shape[-1], fg.shape[-1])
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loss = loss + F.l1_loss(fg[..., :T], fr[..., :T].detach())
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return loss / len(fmaps_real)
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# ---------------------------------------------------------------------------
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# Utility helpers
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# ---------------------------------------------------------------------------
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def _save_spectrogram(path, mel_tensor):
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"""Save mel spectrogram [1, n_mels, T] as a PNG using PIL (no matplotlib dep)."""
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try:
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from PIL import Image
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import numpy as np
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@@ -120,6 +257,10 @@ def _multi_resolution_stft_loss(pred_wav, target_wav, device):
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return loss / len(_STFT_RESOLUTIONS)
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# ---------------------------------------------------------------------------
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# Node
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# ---------------------------------------------------------------------------
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class SelvaBigvganTrainer:
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OUTPUT_NODE = True
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CATEGORY = SELVA_CATEGORY
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@@ -128,10 +269,10 @@ class SelvaBigvganTrainer:
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RETURN_NAMES = ("checkpoint_path",)
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OUTPUT_TOOLTIPS = ("Path to saved vocoder checkpoint — load with SelVA BigVGAN Loader.",)
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DESCRIPTION = (
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"Fine-tunes the BigVGAN vocoder (mel→waveform) on BJ audio clips using "
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"spectral losses (mel L1 + multi-resolution STFT L1). DiT and VAE stay frozen. "
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"Supports both 16k (BigVGAN) and 44k (BigVGANv2) models. "
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"Load the result with SelVA BigVGAN Loader."
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"Fine-tunes the BigVGAN vocoder (mel→waveform) on BJ audio clips. "
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"Default mode (snake_alpha_only) tunes only the ~5K Snake activation α "
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"parameters — cannot cause harmonic smearing. Add a discriminator path "
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"for perceptual feature matching loss. DiT and VAE stay frozen."
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)
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@classmethod
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@@ -147,26 +288,53 @@ class SelvaBigvganTrainer:
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"default": "bigvgan_bj.pt",
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"tooltip": "Where to save the fine-tuned vocoder. Relative paths → ComfyUI output dir.",
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}),
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"train_mode": (["snake_alpha_only", "all_params"], {
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"default": "snake_alpha_only",
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"tooltip": (
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"snake_alpha_only: only tune ~5K per-channel α parameters in Snake/SnakeBeta "
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"activations. These control harmonic periodicity. Cannot cause spectral smearing. "
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"all_params: tune all vocoder weights — set lambda_l2sp>0 to prevent drift."
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),
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}),
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"steps": ("INT", {
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"default": 2000, "min": 100, "max": 50000,
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"tooltip": "Training steps. 1000–2000 is a good first experiment.",
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"tooltip": "Training steps. 1000–2000 is a good first experiment with snake_alpha_only.",
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}),
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"lr": ("FLOAT", {
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"default": 1e-4, "min": 1e-6, "max": 1e-2, "step": 1e-5,
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"tooltip": "Learning rate. BigVGAN default is 1e-4.",
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"tooltip": "Learning rate. 1e-4 for snake_alpha_only, 1e-5 for all_params.",
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}),
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"batch_size": ("INT", {"default": 4, "min": 1, "max": 32}),
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"segment_seconds": ("FLOAT", {
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"default": 1.0, "min": 0.25, "max": 4.0, "step": 0.25,
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"tooltip": "Audio segment length per training sample in seconds.",
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}),
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"lambda_l2sp": ("FLOAT", {
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"default": 1e-3, "min": 0.0, "max": 0.1, "step": 1e-4,
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"tooltip": (
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"L2-SP anchor regularization: penalizes parameter drift from pretrained values. "
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"0 = disabled. 1e-3 is good for snake_alpha_only. "
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"Increase to 1e-2 for all_params to prevent catastrophic forgetting."
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),
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}),
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"save_every": ("INT", {"default": 500, "min": 50, "max": 10000}),
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"seed": ("INT", {"default": 42, "min": 0, "max": 0xFFFFFFFF}),
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},
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"optional": {
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"discriminator_path": ("STRING", {
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"default": "",
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"tooltip": (
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"Optional path to BigVGAN discriminator checkpoint "
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"(bigvgan_discriminator_optimizer.pt from the BigVGAN pretrained release). "
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"When provided, frozen MPD+MRD feature matching replaces mel L1 — "
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"the key fix for harmonic smearing. Leave empty to use mel+STFT losses only."
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),
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}),
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},
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}
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def train(self, model, data_dir, output_path, steps, lr, batch_size,
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segment_seconds, save_every, seed):
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def train(self, model, data_dir, output_path, train_mode, steps, lr, batch_size,
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segment_seconds, lambda_l2sp, save_every, seed, discriminator_path=""):
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import traceback
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device = get_device()
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@@ -197,6 +365,14 @@ class SelvaBigvganTrainer:
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out_path = Path(folder_paths.get_output_directory()) / out_path
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out_path.parent.mkdir(parents=True, exist_ok=True)
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disc_path = None
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if discriminator_path and discriminator_path.strip():
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disc_path = Path(discriminator_path.strip())
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if not disc_path.is_absolute():
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disc_path = Path(folder_paths.get_output_directory()) / disc_path
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if not disc_path.exists():
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raise FileNotFoundError(f"[BigVGAN] Discriminator checkpoint not found: {disc_path}")
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# Find and pre-load audio clips
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segment_samples = int(segment_seconds * sample_rate)
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audio_files = []
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@@ -227,8 +403,15 @@ class SelvaBigvganTrainer:
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raise RuntimeError(
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f"[BigVGAN] No usable clips found (need audio >= {segment_seconds}s)"
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)
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print(f"[BigVGAN] {len(clips)} clips ready segment={segment_seconds}s "
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f"steps={steps} lr={lr} batch={batch_size}\n", flush=True)
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trainable_count = sum(
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1 for n, _ in vocoder.named_parameters() if "alpha" in n
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) if train_mode == "snake_alpha_only" else sum(
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1 for _ in vocoder.parameters()
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)
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print(f"[BigVGAN] {len(clips)} clips ready mode={train_mode} "
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f"segment={segment_seconds}s steps={steps} lr={lr} "
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f"batch={batch_size} lambda_l2sp={lambda_l2sp}\n", flush=True)
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if strategy == "offload_to_cpu":
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feature_utils.to(device)
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@@ -259,8 +442,8 @@ class SelvaBigvganTrainer:
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vocoder, mel_converter, clips,
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device, dtype, strategy, feature_utils,
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segment_samples, sample_rate,
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steps, lr, batch_size, save_every, seed,
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out_path, pbar,
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train_mode, steps, lr, batch_size, lambda_l2sp,
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save_every, seed, out_path, disc_path, pbar,
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)
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except Exception as e:
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_exc[0] = e
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@@ -275,11 +458,15 @@ class SelvaBigvganTrainer:
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return (_result[0],)
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# ---------------------------------------------------------------------------
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# Training worker
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# ---------------------------------------------------------------------------
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def _do_train(vocoder, mel_converter, clips,
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device, dtype, strategy, feature_utils,
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segment_samples, sample_rate,
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steps, lr, batch_size, save_every, seed,
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out_path, pbar):
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train_mode, steps, lr, batch_size, lambda_l2sp,
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save_every, seed, out_path, disc_path, pbar):
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"""Execute training. Called in a fresh thread — no inference_mode active.
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Even though inference_mode is off here, tensors created in the calling
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@@ -372,7 +559,65 @@ def _do_train(vocoder, mel_converter, clips,
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if buf is not None:
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module._buffers[bname] = buf.clone()
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optimizer = torch.optim.AdamW(vocoder.parameters(), lr=lr, betas=(0.8, 0.99))
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# ── Training mode: select which parameters to train ──────────────────────
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if train_mode == "snake_alpha_only":
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alpha_params = []
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for name, param in vocoder.named_parameters():
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if "alpha" in name:
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param.requires_grad_(True)
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alpha_params.append(param)
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else:
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param.requires_grad_(False)
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n_trainable = sum(p.numel() for p in alpha_params)
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print(f"[BigVGAN] snake_alpha_only: {n_trainable} trainable params "
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f"({len(alpha_params)} alpha tensors)", flush=True)
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trainable_params = alpha_params
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else: # all_params
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for param in vocoder.parameters():
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param.requires_grad_(True)
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n_trainable = sum(p.numel() for p in vocoder.parameters())
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print(f"[BigVGAN] all_params: {n_trainable} trainable params", flush=True)
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trainable_params = list(vocoder.parameters())
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# ── L2-SP: cache reference parameter values (before any gradient steps) ──
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ref_params = {}
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if lambda_l2sp > 0.0:
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for name, param in vocoder.named_parameters():
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if param.requires_grad:
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ref_params[name] = param.data.clone().detach()
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print(f"[BigVGAN] L2-SP anchor: {len(ref_params)} params λ={lambda_l2sp}", flush=True)
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# ── Optional: load pretrained discriminator for feature matching ──────────
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mpd = mrd = None
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if disc_path is not None:
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try:
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ckpt_d = torch.load(str(disc_path), map_location="cpu", weights_only=False)
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mpd = _MultiPeriodDiscriminator()
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mrd = _MultiResolutionDiscriminator()
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# Try common key names used by different BigVGAN releases
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for mpd_key in ("mpd", "discriminator_mpd", "MPD"):
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if mpd_key in ckpt_d:
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mpd.load_state_dict(ckpt_d[mpd_key], strict=False)
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||||
print(f"[BigVGAN] Loaded MPD from key '{mpd_key}'", flush=True)
|
||||
break
|
||||
for mrd_key in ("mrd", "discriminator_mrd", "MRD", "msd", "discriminator_msd"):
|
||||
if mrd_key in ckpt_d:
|
||||
mrd.load_state_dict(ckpt_d[mrd_key], strict=False)
|
||||
print(f"[BigVGAN] Loaded MRD from key '{mrd_key}'", flush=True)
|
||||
break
|
||||
mpd.to(device).eval()
|
||||
mrd.to(device).eval()
|
||||
for p in mpd.parameters():
|
||||
p.requires_grad_(False)
|
||||
for p in mrd.parameters():
|
||||
p.requires_grad_(False)
|
||||
print(f"[BigVGAN] Frozen discriminators ready for feature matching", flush=True)
|
||||
except Exception as e:
|
||||
print(f"[BigVGAN] WARNING: Could not load discriminator ({e}), "
|
||||
f"falling back to mel+STFT losses", flush=True)
|
||||
mpd = mrd = None
|
||||
|
||||
optimizer = torch.optim.AdamW(trainable_params, lr=lr, betas=(0.8, 0.99))
|
||||
vocoder.train()
|
||||
|
||||
try:
|
||||
@@ -396,24 +641,55 @@ def _do_train(vocoder, mel_converter, clips,
|
||||
pred_t = pred_wav[..., :T]
|
||||
target_t = target_wav[..., :T]
|
||||
|
||||
pred_mel = mel_converter(pred_t.squeeze(1)) # [B, n_mels, T_mel']
|
||||
T_mel = min(pred_mel.shape[-1], target_mel.shape[-1])
|
||||
mel_loss = F.l1_loss(pred_mel[..., :T_mel], target_mel[..., :T_mel])
|
||||
# ── Compute loss ─────────────────────────────────────────────────
|
||||
if mpd is not None and mrd is not None:
|
||||
# Perceptual feature matching via frozen discriminators
|
||||
with torch.no_grad():
|
||||
fmaps_real_mpd = mpd(target_t)
|
||||
fmaps_real_mrd = mrd(target_t)
|
||||
fmaps_gen_mpd = mpd(pred_t)
|
||||
fmaps_gen_mrd = mrd(pred_t)
|
||||
fm_loss = (
|
||||
_feature_matching_loss(fmaps_real_mpd, fmaps_gen_mpd) +
|
||||
_feature_matching_loss(fmaps_real_mrd, fmaps_gen_mrd)
|
||||
)
|
||||
# Keep a small mel loss for stable frequency alignment
|
||||
pred_mel = mel_converter(pred_t.squeeze(1))
|
||||
T_mel = min(pred_mel.shape[-1], target_mel.shape[-1])
|
||||
mel_loss = F.l1_loss(pred_mel[..., :T_mel], target_mel[..., :T_mel])
|
||||
primary_loss = 2.0 * fm_loss + 0.1 * mel_loss
|
||||
loss_desc = f"fm={fm_loss.item():.4f} mel={mel_loss.item():.4f}"
|
||||
else:
|
||||
# Fallback: mel L1 + multi-resolution STFT L1
|
||||
pred_mel = mel_converter(pred_t.squeeze(1))
|
||||
T_mel = min(pred_mel.shape[-1], target_mel.shape[-1])
|
||||
mel_loss = F.l1_loss(pred_mel[..., :T_mel], target_mel[..., :T_mel])
|
||||
stft_loss = _multi_resolution_stft_loss(pred_t, target_t, device)
|
||||
primary_loss = mel_loss + stft_loss
|
||||
loss_desc = f"mel={mel_loss.item():.4f} stft={stft_loss.item():.4f}"
|
||||
|
||||
stft_loss = _multi_resolution_stft_loss(pred_t, target_t, device)
|
||||
# ── L2-SP regularization ─────────────────────────────────────────
|
||||
l2sp_loss = torch.zeros(1, device=device)
|
||||
if lambda_l2sp > 0.0 and ref_params:
|
||||
for name, param in vocoder.named_parameters():
|
||||
if name in ref_params and param.requires_grad:
|
||||
l2sp_loss = l2sp_loss + F.mse_loss(
|
||||
param, ref_params[name], reduction="sum"
|
||||
)
|
||||
l2sp_loss = l2sp_loss * lambda_l2sp
|
||||
|
||||
loss = mel_loss + stft_loss
|
||||
loss = primary_loss + l2sp_loss
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_norm_(vocoder.parameters(), 1.0)
|
||||
torch.nn.utils.clip_grad_norm_(trainable_params, 1.0)
|
||||
optimizer.step()
|
||||
|
||||
pbar.update(1)
|
||||
|
||||
if (step + 1) % max(1, steps // 20) == 0 or step == steps - 1:
|
||||
print(f"[BigVGAN] {step+1}/{steps} "
|
||||
f"mel={mel_loss.item():.4f} stft={stft_loss.item():.4f} "
|
||||
f"total={loss.item():.4f}", flush=True)
|
||||
l2sp_str = f" l2sp={l2sp_loss.item():.4e}" if lambda_l2sp > 0 else ""
|
||||
print(f"[BigVGAN] {step+1}/{steps} {loss_desc}"
|
||||
f" total={loss.item():.4f}{l2sp_str}", flush=True)
|
||||
|
||||
if (step + 1) % save_every == 0 and (step + 1) < steps:
|
||||
step_path = out_path.parent / f"{out_path.stem}_step{step+1}{out_path.suffix}"
|
||||
|
||||
@@ -0,0 +1,434 @@
|
||||
"""SelVA DITTO Optimizer.
|
||||
|
||||
Inference-time noise optimization: optimizes the initial noise latent x_0
|
||||
using a style loss against BJ reference clips, backpropagating through the
|
||||
ODE solver. All model weights remain frozen — only x_0 changes.
|
||||
|
||||
Based on DITTO: Diffusion Inference-Time T-Optimization (arXiv:2401.12179,
|
||||
ICML 2024 Oral). Adapted for SelVA's flow-matching Euler ODE.
|
||||
|
||||
Style loss: mel-spectrogram statistics matching (mean spectrum + Gram matrix)
|
||||
against BJ reference clips. Runs entirely before the vocoder — optimization
|
||||
only requires the DiT + VAE decoder, not BigVGAN.
|
||||
|
||||
Memory strategy: gradient checkpointing at each ODE step — stores O(1 DiT
|
||||
forward pass activations) instead of O(N steps). Backward recomputes each
|
||||
step's activations on demand.
|
||||
"""
|
||||
|
||||
import dataclasses
|
||||
import random
|
||||
import threading
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torchaudio
|
||||
import comfy.utils
|
||||
import comfy.model_management
|
||||
import folder_paths
|
||||
|
||||
from .utils import SELVA_CATEGORY, get_device, get_offload_device, soft_empty_cache
|
||||
from .selva_sampler import SelvaSampler
|
||||
from .selva_textual_inversion_trainer import _inject_tokens
|
||||
|
||||
|
||||
def _load_wav(path):
|
||||
"""Load audio file to [channels, samples] float32 tensor."""
|
||||
try:
|
||||
return torchaudio.load(str(path))
|
||||
except Exception:
|
||||
pass
|
||||
import soundfile as sf
|
||||
data, sr = sf.read(str(path), dtype="float32", always_2d=True)
|
||||
wav = torch.from_numpy(data.T)
|
||||
return wav, sr
|
||||
|
||||
|
||||
def _mel_style_loss(mel_gen, ref_mean, ref_gram):
|
||||
"""Style loss between generated mel and precomputed reference statistics.
|
||||
|
||||
mel_gen: [1, n_mels, T] generated mel spectrogram (with grad)
|
||||
ref_mean: [n_mels] mean spectrum of BJ reference clips (detached)
|
||||
ref_gram: [n_mels, n_mels] Gram matrix of BJ reference clips (detached)
|
||||
|
||||
Mean spectrum loss captures the spectral envelope (which harmonics are
|
||||
boosted). Gram matrix loss captures timbral texture — covariance between
|
||||
frequency bands — without requiring temporal alignment.
|
||||
"""
|
||||
m = mel_gen.squeeze(0) # [n_mels, T]
|
||||
|
||||
# Mean spectrum loss
|
||||
gen_mean = m.mean(dim=-1) # [n_mels]
|
||||
loss_mean = F.l1_loss(gen_mean, ref_mean)
|
||||
|
||||
# Gram matrix loss (texture, position-invariant)
|
||||
gram_gen = (m @ m.T) / m.shape[-1] # [n_mels, n_mels]
|
||||
loss_gram = F.mse_loss(gram_gen, ref_gram)
|
||||
|
||||
return loss_mean + 0.1 * loss_gram
|
||||
|
||||
|
||||
class SelvaDittoOptimizer:
|
||||
"""DITTO inference-time noise optimization.
|
||||
|
||||
Freezes all model weights and optimizes only the initial noise latent x_0
|
||||
to make the generated audio sound like the BJ reference clips.
|
||||
No training data or gradient updates to the model — per-video per-run.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"model": ("SELVA_MODEL",),
|
||||
"features": ("SELVA_FEATURES",),
|
||||
"prompt": ("STRING", {
|
||||
"default": "", "multiline": True,
|
||||
"tooltip": "Sound description. Leave empty to use features prompt.",
|
||||
}),
|
||||
"negative_prompt": ("STRING", {
|
||||
"default": "", "multiline": False,
|
||||
}),
|
||||
"reference_dir": ("STRING", {
|
||||
"default": "",
|
||||
"tooltip": "Directory with BJ reference audio files (.wav/.flac/.mp3). "
|
||||
"Reference mel statistics are precomputed from these once.",
|
||||
}),
|
||||
"n_opt_steps": ("INT", {
|
||||
"default": 50, "min": 5, "max": 500,
|
||||
"tooltip": "Gradient optimization steps on x_0. 50 is a good start; "
|
||||
"each step requires ~2 DiT forward passes.",
|
||||
}),
|
||||
"opt_lr": ("FLOAT", {
|
||||
"default": 0.1, "min": 0.001, "max": 2.0, "step": 0.01,
|
||||
"tooltip": "Adam learning rate for x_0 optimization. "
|
||||
"0.1 is the DITTO paper default.",
|
||||
}),
|
||||
"n_ode_steps": ("INT", {
|
||||
"default": 10, "min": 5, "max": 50,
|
||||
"tooltip": "Euler ODE steps run during each optimization iteration. "
|
||||
"Lower = faster optimization (10–15 is a good trade-off). "
|
||||
"Final generation always uses the steps parameter below.",
|
||||
}),
|
||||
"n_grad_steps": ("INT", {
|
||||
"default": 5, "min": 1, "max": 50,
|
||||
"tooltip": "ODE steps to differentiate through (truncated BPTT). "
|
||||
"Higher = more accurate gradient, more VRAM. "
|
||||
"Must be ≤ n_ode_steps. 5 is a good default.",
|
||||
}),
|
||||
"style_weight": ("FLOAT", {
|
||||
"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1,
|
||||
"tooltip": "Weight of the BJ style loss. Increase to push harder toward "
|
||||
"BJ style at the cost of coherence with the video.",
|
||||
}),
|
||||
"steps": ("INT", {
|
||||
"default": 25, "min": 1, "max": 200,
|
||||
"tooltip": "Euler steps for the final generation pass (after optimization).",
|
||||
}),
|
||||
"cfg_strength": ("FLOAT", {
|
||||
"default": 4.5, "min": 1.0, "max": 20.0, "step": 0.1}),
|
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFF}),
|
||||
},
|
||||
"optional": {
|
||||
"normalize": ("BOOLEAN", {"default": True}),
|
||||
"target_lufs": ("FLOAT", {
|
||||
"default": -27.0, "min": -40.0, "max": -6.0, "step": 1.0}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("AUDIO",)
|
||||
RETURN_NAMES = ("audio",)
|
||||
OUTPUT_TOOLTIPS = ("DITTO-optimized audio — x_0 steered toward BJ style.",)
|
||||
FUNCTION = "optimize"
|
||||
CATEGORY = SELVA_CATEGORY
|
||||
DESCRIPTION = (
|
||||
"DITTO inference-time noise optimization (arXiv:2401.12179). "
|
||||
"Optimizes the initial noise latent x_0 to match BJ reference clips "
|
||||
"via mel statistics style loss, backpropagating through the ODE. "
|
||||
"All model weights frozen — zero quality degradation risk."
|
||||
)
|
||||
|
||||
def optimize(self, model, features, prompt, negative_prompt,
|
||||
reference_dir, n_opt_steps, opt_lr, n_ode_steps, n_grad_steps,
|
||||
style_weight, steps, cfg_strength, seed,
|
||||
normalize=True, target_lufs=-27.0):
|
||||
import traceback
|
||||
|
||||
device = get_device()
|
||||
dtype = model["dtype"]
|
||||
strategy = model["strategy"]
|
||||
net_generator = model["generator"]
|
||||
feature_utils = model["feature_utils"]
|
||||
mel_converter = feature_utils.mel_converter
|
||||
|
||||
# Validate variant match
|
||||
feat_variant = features.get("variant")
|
||||
if feat_variant is not None and feat_variant != model["variant"]:
|
||||
raise ValueError(
|
||||
f"[DITTO] Variant mismatch: features='{feat_variant}' model='{model['variant']}'. "
|
||||
f"Re-run Feature Extractor."
|
||||
)
|
||||
|
||||
if not prompt or not prompt.strip():
|
||||
prompt = features.get("prompt", "")
|
||||
|
||||
# Resolve duration and seq_cfg
|
||||
duration = features.get("duration", 0)
|
||||
if duration <= 0:
|
||||
raise ValueError("[DITTO] Features contain no duration field.")
|
||||
seq_cfg = dataclasses.replace(model["seq_cfg"], duration=duration)
|
||||
sample_rate = seq_cfg.sampling_rate
|
||||
|
||||
# Load and precompute reference mel statistics
|
||||
ref_dir = Path(reference_dir.strip())
|
||||
if not ref_dir.is_absolute():
|
||||
ref_dir = Path(folder_paths.models_dir) / ref_dir
|
||||
if not ref_dir.exists():
|
||||
raise FileNotFoundError(f"[DITTO] reference_dir not found: {ref_dir}")
|
||||
|
||||
ref_files = []
|
||||
for ext in ("*.wav", "*.flac", "*.mp3", "*.ogg"):
|
||||
ref_files.extend(ref_dir.rglob(ext))
|
||||
if not ref_files:
|
||||
raise FileNotFoundError(f"[DITTO] No audio files in reference_dir: {ref_dir}")
|
||||
|
||||
print(f"[DITTO] Loading {len(ref_files)} reference clips...", flush=True)
|
||||
mel_converter.to(device)
|
||||
|
||||
ref_mels = []
|
||||
with torch.no_grad():
|
||||
for rf in ref_files[:32]: # cap at 32 for speed
|
||||
try:
|
||||
wav, sr = _load_wav(rf)
|
||||
if wav.shape[0] > 1:
|
||||
wav = wav.mean(0, keepdim=True)
|
||||
if sr != sample_rate:
|
||||
wav = torchaudio.functional.resample(wav, sr, sample_rate)
|
||||
wav = wav.squeeze(0).to(device, dtype)
|
||||
mel = mel_converter(wav.unsqueeze(0)) # [1, n_mels, T]
|
||||
ref_mels.append(mel)
|
||||
except Exception as e:
|
||||
print(f" [DITTO] Skip {rf.name}: {e}", flush=True)
|
||||
|
||||
if not ref_mels:
|
||||
raise RuntimeError("[DITTO] No usable reference clips.")
|
||||
|
||||
# Precompute reference statistics (done once — detached, no grad)
|
||||
with torch.no_grad():
|
||||
all_means = torch.stack([m.squeeze(0).mean(dim=-1) for m in ref_mels])
|
||||
ref_mean = all_means.mean(0) # [n_mels]
|
||||
|
||||
all_grams = []
|
||||
for m in ref_mels:
|
||||
M = m.squeeze(0) # [n_mels, T]
|
||||
all_grams.append((M @ M.T) / M.shape[-1])
|
||||
ref_gram = torch.stack(all_grams).mean(0) # [n_mels, n_mels]
|
||||
|
||||
print(f"[DITTO] Reference stats computed from {len(ref_mels)} clips "
|
||||
f"n_opt={n_opt_steps} lr={opt_lr} ode_steps={n_ode_steps} "
|
||||
f"grad_steps={n_grad_steps}", flush=True)
|
||||
|
||||
if strategy == "offload_to_cpu":
|
||||
net_generator.to(device)
|
||||
feature_utils.to(device)
|
||||
soft_empty_cache()
|
||||
|
||||
pbar = comfy.utils.ProgressBar(n_opt_steps + steps)
|
||||
|
||||
_result = [None]
|
||||
_exc = [None]
|
||||
|
||||
def _worker():
|
||||
try:
|
||||
_result[0] = _do_optimize(
|
||||
net_generator, feature_utils, mel_converter,
|
||||
features, prompt, negative_prompt,
|
||||
ref_mean, ref_gram,
|
||||
seq_cfg, sample_rate, device, dtype,
|
||||
n_opt_steps, opt_lr, n_ode_steps, n_grad_steps,
|
||||
style_weight, steps, cfg_strength, seed,
|
||||
normalize, target_lufs, pbar,
|
||||
)
|
||||
except Exception as e:
|
||||
_exc[0] = e
|
||||
traceback.print_exc()
|
||||
|
||||
t = threading.Thread(target=_worker, daemon=True)
|
||||
t.start()
|
||||
t.join()
|
||||
|
||||
if strategy == "offload_to_cpu":
|
||||
net_generator.to(get_offload_device())
|
||||
feature_utils.to(get_offload_device())
|
||||
soft_empty_cache()
|
||||
|
||||
if _exc[0] is not None:
|
||||
raise _exc[0]
|
||||
return (_result[0],)
|
||||
|
||||
|
||||
def _do_optimize(net_generator, feature_utils, mel_converter,
|
||||
features, prompt, negative_prompt,
|
||||
ref_mean, ref_gram,
|
||||
seq_cfg, sample_rate, device, dtype,
|
||||
n_opt_steps, opt_lr, n_ode_steps, n_grad_steps,
|
||||
style_weight, steps, cfg_strength, seed,
|
||||
normalize, target_lufs, pbar):
|
||||
"""Optimization loop — runs in a fresh thread (no inference_mode active)."""
|
||||
|
||||
# Strip inference flags from ref stats (came from main thread)
|
||||
ref_mean = ref_mean.clone().detach()
|
||||
ref_gram = ref_gram.clone().detach()
|
||||
|
||||
torch.manual_seed(seed)
|
||||
|
||||
clip_f = features["clip_features"].to(device, dtype)
|
||||
sync_f = features["sync_features"].to(device, dtype)
|
||||
|
||||
net_generator.update_seq_lengths(
|
||||
latent_seq_len=seq_cfg.latent_seq_len,
|
||||
clip_seq_len=clip_f.shape[1],
|
||||
sync_seq_len=sync_f.shape[1],
|
||||
)
|
||||
|
||||
with torch.no_grad():
|
||||
text_clip = feature_utils.encode_text_clip([prompt])
|
||||
|
||||
neg_text_clip = feature_utils.encode_text_clip([negative_prompt]) \
|
||||
if negative_prompt.strip() else None
|
||||
|
||||
conditions = net_generator.preprocess_conditions(clip_f, sync_f, text_clip)
|
||||
empty_conditions = net_generator.get_empty_conditions(
|
||||
bs=1, negative_text_features=neg_text_clip
|
||||
)
|
||||
|
||||
# Initial noise — x_0 is the parameter we optimize
|
||||
x0_init = torch.randn(
|
||||
1, seq_cfg.latent_seq_len, net_generator.latent_dim,
|
||||
device=device, dtype=dtype,
|
||||
)
|
||||
x0 = torch.nn.Parameter(x0_init.clone())
|
||||
optimizer = torch.optim.Adam([x0], lr=opt_lr)
|
||||
|
||||
# n_grad_steps must not exceed n_ode_steps
|
||||
n_grad_steps = min(n_grad_steps, n_ode_steps)
|
||||
n_free_steps = n_ode_steps - n_grad_steps # steps run without gradient
|
||||
|
||||
ts = torch.linspace(0.0, 1.0, n_ode_steps + 1, device=device, dtype=dtype)
|
||||
|
||||
print(f"[DITTO] Optimizing x_0 "
|
||||
f"free_steps={n_free_steps} grad_steps={n_grad_steps}", flush=True)
|
||||
|
||||
# Freeze all model weights (double-check — should already be frozen at inference)
|
||||
net_generator.requires_grad_(False)
|
||||
feature_utils.requires_grad_(False)
|
||||
mel_converter.requires_grad_(False)
|
||||
|
||||
for opt_step in range(n_opt_steps):
|
||||
comfy.model_management.throw_exception_if_processing_interrupted()
|
||||
|
||||
# ── Phase 1: run first (n_ode_steps - n_grad_steps) steps without grad ──
|
||||
# This is cheaper than checkpointing all steps, at the cost of an
|
||||
# approximate (truncated) gradient. The gradient still flows through
|
||||
# n_grad_steps steps, which is sufficient for meaningful x_0 updates.
|
||||
with torch.no_grad():
|
||||
x = x0
|
||||
for i in range(n_free_steps):
|
||||
t = ts[i]
|
||||
dt = ts[i + 1] - t
|
||||
flow = net_generator.ode_wrapper(t, x, conditions, empty_conditions, cfg_strength)
|
||||
x = x + dt * flow
|
||||
|
||||
# Detach and re-leaf so backward only goes n_grad_steps deep.
|
||||
# We treat x_k as a new leaf but seed it from x_0's value — so at
|
||||
# opt step 0 the gradient is a true n_grad_steps truncated BPTT,
|
||||
# and x_0 gets updated via x_k's dependence on x_0 through the
|
||||
# no-grad prefix (approximation: gradient doesn't flow through prefix).
|
||||
#
|
||||
# Richer alternative: full checkpointing through all steps (uncomment
|
||||
# the checkpoint block below and remove the no-grad prefix).
|
||||
x = x.detach().requires_grad_(True)
|
||||
|
||||
# ── Phase 2: run last n_grad_steps with gradient + checkpointing ──
|
||||
for i in range(n_free_steps, n_ode_steps):
|
||||
t = ts[i]
|
||||
dt = ts[i + 1] - t
|
||||
|
||||
# Gradient checkpointing: recompute forward during backward,
|
||||
# avoiding storage of DiT activations for each step.
|
||||
def _ode_step(x_in, t=t):
|
||||
return net_generator.ode_wrapper(t, x_in, conditions, empty_conditions, cfg_strength)
|
||||
|
||||
flow = torch.utils.checkpoint.checkpoint(
|
||||
_ode_step, x, use_reentrant=False
|
||||
)
|
||||
x = x + dt * flow
|
||||
|
||||
# ── Decode to mel (no vocoder — cheap) ──────────────────────────────
|
||||
x_unnorm = net_generator.unnormalize(x)
|
||||
mel_gen = feature_utils.decode(x_unnorm) # latent → mel [1, n_mels, T]
|
||||
|
||||
# ── Style loss ───────────────────────────────────────────────────────
|
||||
loss = style_weight * _mel_style_loss(mel_gen, ref_mean, ref_gram)
|
||||
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
|
||||
# Propagate gradient from x (grad_fn leaf) back to x_0.
|
||||
# x was detached from x_0, so we manually transfer the gradient:
|
||||
# the no-grad prefix is an approximation — skip this if doing full
|
||||
# checkpointing (x would have grad_fn pointing back to x_0).
|
||||
# Here x.grad is the gradient w.r.t. x at step n_free_steps;
|
||||
# we directly add it to x_0.grad as an approximation.
|
||||
if x.grad is not None:
|
||||
if x0.grad is None:
|
||||
x0.grad = x.grad.clone()
|
||||
else:
|
||||
x0.grad.add_(x.grad)
|
||||
|
||||
torch.nn.utils.clip_grad_norm_([x0], 1.0)
|
||||
optimizer.step()
|
||||
|
||||
pbar.update(1)
|
||||
|
||||
if (opt_step + 1) % max(1, n_opt_steps // 10) == 0:
|
||||
print(f"[DITTO] {opt_step+1}/{n_opt_steps} loss={loss.item():.4f}", flush=True)
|
||||
|
||||
# ── Final generation with optimized x_0 ─────────────────────────────────
|
||||
print(f"[DITTO] Optimization done. Final generation ({steps} steps)...", flush=True)
|
||||
|
||||
with torch.no_grad():
|
||||
fm_ts = torch.linspace(0.0, 1.0, steps + 1, device=device, dtype=dtype)
|
||||
x = x0.detach()
|
||||
for i in range(steps):
|
||||
comfy.model_management.throw_exception_if_processing_interrupted()
|
||||
t = fm_ts[i]
|
||||
dt = fm_ts[i + 1] - t
|
||||
flow = net_generator.ode_wrapper(t, x, conditions, empty_conditions, cfg_strength)
|
||||
x = x + dt * flow
|
||||
pbar.update(1)
|
||||
|
||||
x1_unnorm = net_generator.unnormalize(x)
|
||||
spec = feature_utils.decode(x1_unnorm)
|
||||
audio = feature_utils.vocode(spec)
|
||||
|
||||
print(f"[DITTO] latent stats: mean={x.float().mean():.4f} std={x.float().std():.4f}",
|
||||
flush=True)
|
||||
|
||||
audio = audio.float()
|
||||
if audio.dim() == 2:
|
||||
audio = audio.unsqueeze(1)
|
||||
elif audio.dim() == 3 and audio.shape[1] != 1:
|
||||
audio = audio.mean(dim=1, keepdim=True)
|
||||
|
||||
if normalize:
|
||||
target_rms = 10 ** (target_lufs / 20.0)
|
||||
rms = audio.pow(2).mean().sqrt().clamp(min=1e-8)
|
||||
audio = audio * (target_rms / rms)
|
||||
peak = audio.abs().max().clamp(min=1e-8)
|
||||
if peak > 1.0:
|
||||
audio = audio / peak
|
||||
|
||||
print(f"[DITTO] audio: shape={tuple(audio.shape)} sr={sample_rate}", flush=True)
|
||||
return ({"waveform": audio.cpu(), "sample_rate": sample_rate},)
|
||||
Reference in New Issue
Block a user