debug: log weight load stats for diffusion and VAE checkpoints

Print key counts, missing/unexpected keys, and sample key names to
diagnose whether weights are actually loading correctly (strict=False
silently hides mismatches that would cause garbage audio output).

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

nodes/model_loader.py

@@ -91,12 +91,25 @@ class PrismAudioModelLoader:
         # Handle wrapped state dicts: some ckpts wrap in {"state_dict": ...}
         if "state_dict" in diffusion_state:
             diffusion_state = diffusion_state["state_dict"]
-        model.load_state_dict(diffusion_state, strict=False)
+        diff_result = model.load_state_dict(diffusion_state, strict=False)
+        print(f"[PrismAudio] Diffusion ckpt: {len(diffusion_state)} keys in file", flush=True)
+        print(f"[PrismAudio] Diffusion load: missing={len(diff_result.missing_keys)}, unexpected={len(diff_result.unexpected_keys)}", flush=True)
+        if diff_result.missing_keys:
+            print(f"[PrismAudio]   missing (first 10): {diff_result.missing_keys[:10]}", flush=True)
+        if diff_result.unexpected_keys:
+            print(f"[PrismAudio]   unexpected (first 5): {diff_result.unexpected_keys[:5]}", flush=True)
+        # Sample a few ckpt keys to verify prefix alignment
+        sample_keys = list(diffusion_state.keys())[:5]
+        print(f"[PrismAudio]   ckpt key samples: {sample_keys}", flush=True)
 
         # Load VAE weights separately
         # Use comfy.utils.load_torch_file for consistency and PyTorch 2.6+ compat
         vae_path = os.path.join(model_dir, REQUIRED_FILES["vae"])
         vae_full_state = comfy.utils.load_torch_file(vae_path)
+        print(f"[PrismAudio] VAE ckpt: {len(vae_full_state)} keys in file", flush=True)
+        # Sample raw keys to see actual prefix
+        vae_sample_keys = list(vae_full_state.keys())[:8]
+        print(f"[PrismAudio]   VAE raw key samples: {vae_sample_keys}", flush=True)
         # Strip "autoencoder." prefix from keys
         vae_state = {}
         prefix = "autoencoder."
@@ -105,10 +118,17 @@ class PrismAudioModelLoader:
                 vae_state[k[len(prefix):]] = v
             else:
                 vae_state[k] = v
+        print(f"[PrismAudio]   VAE after strip: {len(vae_state)} keys", flush=True)
+        # Sample model keys to compare
+        model_vae_keys = list(model.pretransform.state_dict().keys())[:5]
+        print(f"[PrismAudio]   pretransform model key samples: {model_vae_keys}", flush=True)
         # strict=False: vae.ckpt is a training checkpoint that also contains
         # discriminator, loss modules, and EMA wrappers not present in the
         # inference AudioAutoencoder — ignore those extra keys.
-        model.pretransform.load_state_dict(vae_state, strict=False)
+        vae_result = model.pretransform.load_state_dict(vae_state, strict=False)
+        print(f"[PrismAudio] VAE load: missing={len(vae_result.missing_keys)}, unexpected={len(vae_result.unexpected_keys)}", flush=True)
+        if vae_result.missing_keys:
+            print(f"[PrismAudio]   VAE missing (first 10): {vae_result.missing_keys[:10]}", flush=True)
 
         # Apply precision: DiT + conditioners in user-selected dtype,
         # but keep VAE (pretransform) in fp32 to avoid NaN from snake activations in fp16