Ethanfel
9c784b4bdb
Spectral-loss-only fine-tuning of the BigVGAN vocoder (mel→waveform)
on BJ audio clips. DiT and VAE are completely frozen. Losses: mel L1
reconstruction + multi-resolution STFT magnitude L1 (same three
resolutions as the BigVGAN discriminator config). Saves in
{'generator': state_dict} format compatible with the original BigVGAN
checkpoint. Loader replaces vocoder weights in the loaded SELVA_MODEL
in-place so no full model reload is needed.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
65 lines
2.2 KiB
Python
65 lines
2.2 KiB
Python
"""SelVA BigVGAN Loader.
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Loads a fine-tuned BigVGAN vocoder checkpoint produced by SelVA BigVGAN Trainer
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and replaces the vocoder weights in the loaded SELVA_MODEL in-place.
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The model is modified in-place so ComfyUI's model cache is updated — no need to
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reload the full SelVA model. Subsequent Sampler runs will use the fine-tuned vocoder.
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"""
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from pathlib import Path
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import torch
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import folder_paths
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from .utils import SELVA_CATEGORY
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class SelvaBigvganLoader:
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CATEGORY = SELVA_CATEGORY
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FUNCTION = "load"
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RETURN_TYPES = ("SELVA_MODEL",)
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RETURN_NAMES = ("model",)
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OUTPUT_TOOLTIPS = ("SELVA_MODEL with the fine-tuned BigVGAN vocoder injected.",)
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DESCRIPTION = (
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"Loads a fine-tuned BigVGAN vocoder checkpoint from SelVA BigVGAN Trainer "
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"and replaces the vocoder weights in the SELVA_MODEL. "
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"Connect the output to SelVA Sampler instead of the base model loader."
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)
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model": ("SELVA_MODEL",),
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"path": ("STRING", {
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"default": "bigvgan_bj.pt",
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"tooltip": "Path to fine-tuned vocoder checkpoint (.pt). "
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"Relative paths resolve to ComfyUI output directory.",
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}),
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},
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}
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def load(self, model, path):
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p = Path(path.strip())
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if not p.is_absolute():
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p = Path(folder_paths.get_output_directory()) / p
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if not p.exists():
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raise FileNotFoundError(f"[BigVGAN] Checkpoint not found: {p}")
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if model["mode"] != "16k":
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raise NotImplementedError(
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"[BigVGAN] Fine-tuned loader only supports 16k mode."
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)
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ckpt = torch.load(str(p), map_location="cpu", weights_only=False)
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if "generator" not in ckpt:
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raise ValueError(f"[BigVGAN] Expected {{'generator': ...}} in checkpoint, got keys: {list(ckpt.keys())}")
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vocoder = model["feature_utils"].tod.vocoder.vocoder
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vocoder.load_state_dict(ckpt["generator"])
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vocoder.eval()
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print(f"[BigVGAN] Loaded fine-tuned vocoder from: {p}", flush=True)
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return (model,)
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