feat: prompt entered once in SelvaFeatureExtractor, reused by SelvaSampler

SelvaFeatureExtractor now stores the prompt in SELVA_FEATURES (both in the
returned dict and the .npz cache). SelvaSampler's prompt is now optional —
when left empty it falls back to the prompt stored in features. A non-empty
override can still be passed when CLIP text should differ from the sync text.

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

nodes/selva_feature_extractor.py

@@ -150,6 +150,7 @@ class SelvaFeatureExtractor:
             clip_features=clip_features.cpu().float().numpy(),
             sync_features=sync_features.cpu().float().numpy(),
             duration=float(duration),
+            prompt=np.array(prompt),
         )
         print(f"[SelVA] Features cached: {cached_path}", flush=True)
 
@@ -157,13 +158,17 @@ class SelvaFeatureExtractor:
             "clip_features": clip_features.cpu(),
             "sync_features": sync_features.cpu(),
             "duration": float(duration),
+            "prompt": prompt,
         }, float(fps))
 
 
 def _load_cached(path):
     data = np.load(path, allow_pickle=False)
-    return {
+    features = {
         "clip_features": torch.from_numpy(data["clip_features"]),
         "sync_features": torch.from_numpy(data["sync_features"]),
         "duration": float(data["duration"]),
     }
+    if "prompt" in data:
+        features["prompt"] = str(data["prompt"])
+    return features

nodes/selva_sampler.py

@@ -11,10 +11,6 @@ class SelvaSampler:
             "required": {
                 "model":    ("SELVA_MODEL",),
                 "features": ("SELVA_FEATURES",),
-                "prompt":   ("STRING", {
-                    "default": "", "multiline": True,
-                    "tooltip": "Should match the prompt used in SelvaFeatureExtractor.",
-                }),
                 "negative_prompt": ("STRING", {
                     "default": "", "multiline": True,
                     "tooltip": "Sounds to steer away from, e.g. 'wind noise, background music'.",
@@ -29,6 +25,12 @@ class SelvaSampler:
                                            "tooltip": "CFG scale (SelVA default is 4.5)."}),
                 "seed":     ("INT",   {"default": 0,   "min": 0,   "max": 0xFFFFFFFF}),
             },
+            "optional": {
+                "prompt":   ("STRING", {
+                    "default": "", "multiline": True,
+                    "tooltip": "CLIP text for audio generation. Leave empty to reuse the prompt from SelvaFeatureExtractor.",
+                }),
+            },
         }
 
     RETURN_TYPES = ("AUDIO",)
@@ -36,7 +38,7 @@ class SelvaSampler:
     FUNCTION = "generate"
     CATEGORY = PRISMAUDIO_CATEGORY
 
-    def generate(self, model, features, prompt, negative_prompt, duration, steps, cfg_strength, seed):
+    def generate(self, model, features, negative_prompt, duration, steps, cfg_strength, seed, prompt=None):
         from selva_core.model.flow_matching import FlowMatching
         from selva_core.model.sequence_config import SequenceConfig
 
@@ -47,6 +49,14 @@ class SelvaSampler:
         feature_utils = model["feature_utils"]
         mode          = model["mode"]
 
+        # Resolve prompt: use override if given, otherwise fall back to features prompt
+        if not prompt or not prompt.strip():
+            prompt = features.get("prompt", "")
+            if prompt:
+                print(f"[SelVA] Using prompt from features: '{prompt[:60]}'", flush=True)
+            else:
+                print("[SelVA] Warning: no prompt in features or sampler — CLIP text conditioning will be empty.", flush=True)
+
         # Resolve duration
         if duration <= 0:
             if "duration" not in features: