Ethanfel
db112394e8
Implements AF-Vocoder GAFilter (Interspeech 2025): learnable per-channel depthwise FIR filter inserted after each Snake/Activation1d in BigVGAN residual blocks. Initialized as identity so training starts from pretrained behaviour. - inject_gafilters() walks resblocks.*.activations and wraps each Activation1d with _ActivationWithGAFilter — weights appear in vocoder.state_dict() automatically - Trained alongside Snake alphas in snake_alpha_only mode - Checkpoint saves has_gafilter + gafilter_kernel_size metadata - Loader detects metadata and injects before load_state_dict so weights populate correctly - Controlled by use_gafilter (default True) and gafilter_kernel_size (default 9) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
74 lines
2.7 KiB
Python
74 lines
2.7 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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from .selva_bigvgan_trainer import inject_gafilters
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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/BigVGANv2 vocoder checkpoint from SelVA BigVGAN Trainer "
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"and replaces the vocoder weights in the SELVA_MODEL in-place. "
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"Supports both 16k and 44k models. "
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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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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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mode = model["mode"]
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if mode == "16k":
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vocoder = model["feature_utils"].tod.vocoder.vocoder # BigVGANVocoder
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elif mode == "44k":
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vocoder = model["feature_utils"].tod.vocoder # BigVGANv2 directly
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else:
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raise ValueError(f"[BigVGAN] Unknown mode: {mode}")
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if ckpt.get("has_gafilter", False):
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kernel_size = ckpt.get("gafilter_kernel_size", 9)
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n_gaf = inject_gafilters(vocoder, kernel_size)
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print(f"[BigVGAN] GAFilter injected: {n_gaf} filters kernel={kernel_size}", flush=True)
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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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