feat: add BigVGAN vocoder fine-tuner and loader nodes

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>

nodes/selva_bigvgan_loader.py

@@ -0,0 +1,64 @@
+"""SelVA BigVGAN Loader.
+
+Loads a fine-tuned BigVGAN vocoder checkpoint produced by SelVA BigVGAN Trainer
+and replaces the vocoder weights in the loaded SELVA_MODEL in-place.
+
+The model is modified in-place so ComfyUI's model cache is updated — no need to
+reload the full SelVA model. Subsequent Sampler runs will use the fine-tuned vocoder.
+"""
+
+from pathlib import Path
+
+import torch
+import folder_paths
+
+from .utils import SELVA_CATEGORY
+
+
+class SelvaBigvganLoader:
+    CATEGORY    = SELVA_CATEGORY
+    FUNCTION    = "load"
+    RETURN_TYPES  = ("SELVA_MODEL",)
+    RETURN_NAMES  = ("model",)
+    OUTPUT_TOOLTIPS = ("SELVA_MODEL with the fine-tuned BigVGAN vocoder injected.",)
+    DESCRIPTION = (
+        "Loads a fine-tuned BigVGAN vocoder checkpoint from SelVA BigVGAN Trainer "
+        "and replaces the vocoder weights in the SELVA_MODEL. "
+        "Connect the output to SelVA Sampler instead of the base model loader."
+    )
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model": ("SELVA_MODEL",),
+                "path": ("STRING", {
+                    "default": "bigvgan_bj.pt",
+                    "tooltip": "Path to fine-tuned vocoder checkpoint (.pt). "
+                               "Relative paths resolve to ComfyUI output directory.",
+                }),
+            },
+        }
+
+    def load(self, model, path):
+        p = Path(path.strip())
+        if not p.is_absolute():
+            p = Path(folder_paths.get_output_directory()) / p
+        if not p.exists():
+            raise FileNotFoundError(f"[BigVGAN] Checkpoint not found: {p}")
+
+        if model["mode"] != "16k":
+            raise NotImplementedError(
+                "[BigVGAN] Fine-tuned loader only supports 16k mode."
+            )
+
+        ckpt = torch.load(str(p), map_location="cpu", weights_only=False)
+        if "generator" not in ckpt:
+            raise ValueError(f"[BigVGAN] Expected {{'generator': ...}} in checkpoint, got keys: {list(ckpt.keys())}")
+
+        vocoder = model["feature_utils"].tod.vocoder.vocoder
+        vocoder.load_state_dict(ckpt["generator"])
+        vocoder.eval()
+
+        print(f"[BigVGAN] Loaded fine-tuned vocoder from: {p}", flush=True)
+        return (model,)