diff --git a/nodes/sampler.py b/nodes/sampler.py
index 2991253..1d89b84 100644
--- a/nodes/sampler.py
+++ b/nodes/sampler.py
@@ -20,6 +20,10 @@ class PrismAudioSampler:
                 "cfg_scale": ("FLOAT", {"default": 5.0, "min": 1.0, "max": 20.0, "step": 0.1, "tooltip": "Classifier-free guidance scale"}),
                 "seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFF}),
             },
+            "optional": {
+                "debug_zero_video": ("BOOLEAN", {"default": False, "tooltip": "Zero out video_features (keep text+sync) — isolates video feature issues"}),
+                "debug_zero_sync": ("BOOLEAN", {"default": False, "tooltip": "Zero out sync_features (keep text+video) — isolates sync feature issues"}),
+            },
         }
 
     RETURN_TYPES = ("AUDIO",)
@@ -27,7 +31,7 @@ class PrismAudioSampler:
     FUNCTION = "generate"
     CATEGORY = PRISMAUDIO_CATEGORY
 
-    def generate(self, model, features, duration, steps, cfg_scale, seed):
+    def generate(self, model, features, duration, steps, cfg_scale, seed, debug_zero_video=False, debug_zero_sync=False):
         device = get_device()
         dtype = model["dtype"]
         strategy = model["strategy"]
@@ -42,12 +46,30 @@ class PrismAudioSampler:
         # Determine if video features are present (not all zeros)
         has_video = features.get("video_features") is not None and features["video_features"].abs().sum() > 0
 
+        video_feat = features["video_features"].to(device, dtype=dtype)
+        sync_feat  = features["sync_features"].to(device, dtype=dtype)
+
+        if debug_zero_video:
+            print("[PrismAudio] DEBUG: zeroing video_features", flush=True)
+            video_feat = torch.zeros_like(video_feat)
+            has_video = False
+        if debug_zero_sync:
+            print("[PrismAudio] DEBUG: zeroing sync_features", flush=True)
+            sync_feat = torch.zeros(8, 768, device=device, dtype=dtype)
+
+        vf_stats = features["video_features"]
+        sf_stats = features["sync_features"]
+        tf_stats = features["text_features"]
+        print(f"[PrismAudio] feature stats — video: shape={tuple(vf_stats.shape)} mean={vf_stats.float().mean():.3f} std={vf_stats.float().std():.3f}", flush=True)
+        print(f"[PrismAudio] feature stats — sync:  shape={tuple(sf_stats.shape)} mean={sf_stats.float().mean():.3f} std={sf_stats.float().std():.3f}", flush=True)
+        print(f"[PrismAudio] feature stats — text:  shape={tuple(tf_stats.shape)} mean={tf_stats.float().mean():.3f} std={tf_stats.float().std():.3f}", flush=True)
+
         # Build metadata as a TUPLE of dicts (one per batch sample)
         # MultiConditioner.forward(batch_metadata: List[Dict]) iterates over this
         sample_meta = {
-            "video_features": features["video_features"].to(device, dtype=dtype),
-            "text_features": features["text_features"].to(device, dtype=dtype),
-            "sync_features": features["sync_features"].to(device, dtype=dtype),
+            "video_features": video_feat,
+            "text_features":  features["text_features"].to(device, dtype=dtype),
+            "sync_features":  sync_feat,
             "video_exist": torch.tensor(has_video),
         }
         metadata = (sample_meta,)