feat: SelvaModelLoader node — loads TextSynch + MMAudio + FeaturesUtils

Resolves weights from models/selva/. Reuses synchformer_state_dict.pth from
models/prismaudio/ (no duplicate download). Supports four variants:
small_16k / small_44k / medium_44k / large_44k.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

nodes/__init__.py

@@ -7,6 +7,9 @@ _NODES = {
     "PrismAudioFeatureExtractor": (".feature_extractor", "PrismAudioFeatureExtractor", "PrismAudio Feature Extractor"),
     "PrismAudioSampler": (".sampler", "PrismAudioSampler", "PrismAudio Sampler"),
     "PrismAudioTextOnly": (".text_only", "PrismAudioTextOnly", "PrismAudio Text Only"),
+    "SelvaModelLoader":       (".selva_model_loader",       "SelvaModelLoader",       "SelVA Model Loader"),
+    "SelvaFeatureExtractor":  (".selva_feature_extractor",  "SelvaFeatureExtractor",  "SelVA Feature Extractor"),
+    "SelvaSampler":           (".selva_sampler",            "SelvaSampler",           "SelVA Sampler"),
 }
 
 for key, (module_path, class_name, display_name) in _NODES.items():

nodes/selva_model_loader.py

@@ -0,0 +1,119 @@
+import os
+import torch
+import folder_paths
+
+from .utils import PRISMAUDIO_CATEGORY, get_offload_device, determine_offload_strategy
+
+# Variant → (generator filename, mode, has_bigvgan)
+_VARIANTS = {
+    "small_16k":  ("generator_small_16k_sup_5.pth",  "16k", True),
+    "small_44k":  ("generator_small_44k_sup_5.pth",  "44k", False),
+    "medium_44k": ("generator_medium_44k_sup_5.pth", "44k", False),
+    "large_44k":  ("generator_large_44k_sup_5.pth",  "44k", False),
+}
+
+_SELVA_DIR = os.path.join(folder_paths.models_dir, "selva")
+
+
+def _selva_path(*parts):
+    return os.path.join(_SELVA_DIR, *parts)
+
+
+def _require(path, hint):
+    if not os.path.exists(path):
+        raise RuntimeError(f"[SelVA] Missing: {path}\n{hint}")
+    return path
+
+
+class SelvaModelLoader:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "variant": (list(_VARIANTS.keys()),),
+                "precision": (["bf16", "fp16", "fp32"],),
+                "offload_strategy": (["auto", "keep_in_vram", "offload_to_cpu"],),
+            }
+        }
+
+    RETURN_TYPES = ("SELVA_MODEL",)
+    RETURN_NAMES = ("model",)
+    FUNCTION = "load_model"
+    CATEGORY = PRISMAUDIO_CATEGORY
+
+    def load_model(self, variant, precision, offload_strategy):
+        from selva_core.model.networks_generator import get_my_mmaudio
+        from selva_core.model.networks_video_enc import get_my_textsynch
+        from selva_core.model.utils.features_utils import FeaturesUtils
+        from selva_core.model.sequence_config import CONFIG_16K, CONFIG_44K
+
+        gen_filename, mode, has_bigvgan = _VARIANTS[variant]
+
+        dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
+        strategy = determine_offload_strategy(offload_strategy)
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        # Resolve weight paths
+        video_enc_path = _require(
+            _selva_path("video_enc_sup_5.pth"),
+            "Download from https://huggingface.co/jnwnlee/selva and place in models/selva/"
+        )
+        gen_path = _require(
+            _selva_path(gen_filename),
+            f"Download {gen_filename} from https://huggingface.co/jnwnlee/selva and place in models/selva/"
+        )
+        vae_path = _require(
+            _selva_path("ext", f"v1-{mode}.pth"),
+            f"Download v1-{mode}.pth from MMAudio/SelVA release and place in models/selva/ext/"
+        )
+        synch_path = _require(
+            os.path.join(folder_paths.models_dir, "prismaudio", "synchformer_state_dict.pth"),
+            "Synchformer checkpoint missing from models/prismaudio/ — download from FunAudioLLM/PrismAudio"
+        )
+        bigvgan_path = None
+        if has_bigvgan:
+            bigvgan_path = _require(
+                _selva_path("ext", "best_netG.pt"),
+                "Download best_netG.pt (BigVGAN 16k vocoder) from MMAudio release and place in models/selva/ext/"
+            )
+
+        print(f"[SelVA] Loading TextSynch from {video_enc_path}", flush=True)
+        net_video_enc = get_my_textsynch("depth1").to(device, dtype).eval()
+        net_video_enc.load_weights(
+            torch.load(video_enc_path, map_location="cpu", weights_only=True)
+        )
+
+        print(f"[SelVA] Loading MMAudio ({variant}) from {gen_path}", flush=True)
+        seq_cfg = CONFIG_16K if mode == "16k" else CONFIG_44K
+        net_generator = get_my_mmaudio(variant).to(device, dtype).eval()
+        net_generator.load_weights(
+            torch.load(gen_path, map_location="cpu", weights_only=True)
+        )
+
+        print("[SelVA] Loading FeaturesUtils (CLIP + T5 + Synchformer + VAE)...", flush=True)
+        feature_utils = FeaturesUtils(
+            tod_vae_ckpt=vae_path,
+            synchformer_ckpt=synch_path,
+            enable_conditions=True,
+            mode=mode,
+            bigvgan_vocoder_ckpt=bigvgan_path,
+            need_vae_encoder=False,
+        ).to(device, dtype).eval()
+
+        if strategy == "offload_to_cpu":
+            net_generator.to(get_offload_device())
+            net_video_enc.to(get_offload_device())
+            feature_utils.to(get_offload_device())
+
+        print(f"[SelVA] Model ready: variant={variant} dtype={dtype} strategy={strategy}", flush=True)
+
+        return ({
+            "generator":     net_generator,
+            "video_enc":     net_video_enc,
+            "feature_utils": feature_utils,
+            "variant":       variant,
+            "mode":          mode,
+            "strategy":      strategy,
+            "dtype":         dtype,
+            "seq_cfg":       seq_cfg,
+        },)