feat: sync_strength control and temporal coverage diagnostic in sampler

Adds sync_strength (0.0–3.0, default 1.0) to PrismAudioSampler.
The scale is applied post-conditioner (after Sync_MLP) to the conditioning
tensor before it enters the DiT. Since CFG always uses zeros as the null
sync embedding, this cleanly scales the sync guidance signal:
  effective_sync_guidance = cfg_scale * (sync_strength * cond - 0)
Higher values tighten temporal audio-video alignment; 0.0 disables sync
guidance entirely (audio conditioned only by video + text features).
Not applied in T2A mode where sync is replaced by the learned empty_sync_feat.

Also logs sync temporal coverage vs audio target duration, with a warning
when they differ by more than 0.5s (stale or mismatched features).

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

nodes/sampler.py

@@ -18,6 +18,7 @@ class PrismAudioSampler:
                 "duration": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 30.0, "step": 0.1, "tooltip": "Audio duration in seconds. Set to 0 to use the video duration from features automatically."}),
                 "steps": ("INT", {"default": 100, "min": 1, "max": 100, "tooltip": "Number of sampling steps"}),
                 "cfg_scale": ("FLOAT", {"default": 7.0, "min": 1.0, "max": 20.0, "step": 0.1, "tooltip": "Classifier-free guidance scale"}),
+                "sync_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.05, "tooltip": "Scale factor for sync conditioning. Higher values tighten audio-visual sync at the cost of audio naturalness; 0.0 disables sync guidance entirely."}),
                 "seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFF}),
             },
         }
@@ -27,7 +28,7 @@ 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, sync_strength, seed):
         device = get_device()
         dtype = model["dtype"]
         strategy = model["strategy"]
@@ -43,6 +44,16 @@ class PrismAudioSampler:
         # Compute latent dimensions
         latent_length = round(SAMPLE_RATE * duration / DOWNSAMPLING_RATIO)
 
+        # Sync temporal coverage diagnostic
+        sync_frames = features["sync_features"].shape[0]
+        sync_duration_covered = sync_frames / 25.0  # Synchformer always extracts at 25fps
+        print(f"[PrismAudio] sync: {sync_frames} frames @ 25fps = {sync_duration_covered:.2f}s | "
+              f"audio target: {latent_length} latent frames = {duration:.2f}s", flush=True)
+        if abs(sync_duration_covered - duration) > 0.5:
+            print(f"[PrismAudio] Warning: sync coverage ({sync_duration_covered:.2f}s) differs from "
+                  f"audio duration ({duration:.2f}s) by more than 0.5s — consider re-extracting features "
+                  f"with the correct video duration.", flush=True)
+
         # Note: no seq length config needed — the model adapts to input tensor shapes
         # dynamically via its transformer architecture.
 
@@ -76,6 +87,13 @@ class PrismAudioSampler:
             if not has_video:
                 _substitute_empty_features(diffusion, conditioning, device, dtype)
 
+            # Scale sync conditioning after the conditioner MLP (clean linear scale,
+            # avoids SiLU nonlinearity in Sync_MLP). The CFG null path always uses zeros,
+            # so this directly scales the sync guidance magnitude: cfg_scale * (strength*cond - 0).
+            # Only applied when video is present — T2A uses learned empty_sync_feat, not raw sync.
+            if has_video and sync_strength != 1.0 and 'sync_features' in conditioning:
+                conditioning['sync_features'][0] = conditioning['sync_features'][0] * sync_strength
+
             # Assemble conditioning inputs for the DiT
             cond_inputs = diffusion.get_conditioning_inputs(conditioning)