fix: bug sweep and improvements

- nodes/__init__.py: fix [PrismAudio] leftover label in error print
- selva_feature_extractor: hash beginning, middle and end of video tensor
  instead of just first 1MB, avoiding collisions on videos with same opening frames
- selva_sampler: derive SequenceConfig from model template via dataclasses.replace
  instead of hardcoding sampling_rate/spectrogram_frame_rate per mode

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

nodes/__init__.py

@@ -14,4 +14,4 @@ for key, (module_path, class_name, display_name) in _NODES.items():
         NODE_CLASS_MAPPINGS[key] = getattr(mod, class_name)
         NODE_DISPLAY_NAME_MAPPINGS[key] = display_name
     except (ImportError, AttributeError) as e:
-        print(f"[PrismAudio] Skipping {key}: {e}")
+        print(f"[SelVA] Skipping {key}: {e}")

nodes/selva_feature_extractor.py

@@ -36,7 +36,12 @@ def _resize_frames(frames, size):
 
 def _hash_inputs(video_tensor, prompt, fps, duration, variant):
     h = hashlib.sha256()
-    h.update(video_tensor.cpu().numpy().tobytes()[:1024 * 1024])
+    raw = video_tensor.cpu().numpy().tobytes()
+    n = len(raw)
+    chunk = 512 * 1024  # 512 KB per sample
+    h.update(raw[:chunk])
+    h.update(raw[n // 2: n // 2 + chunk])
+    h.update(raw[max(0, n - chunk):])
     h.update(prompt.encode())
     h.update(str(fps).encode())
     h.update(str(round(duration, 3)).encode())  # resolved duration affects frame count

nodes/selva_sampler.py

@@ -38,8 +38,8 @@ class SelvaSampler:
     CATEGORY = SELVA_CATEGORY
 
     def generate(self, model, features, prompt, negative_prompt, duration, steps, cfg_strength, seed):
+        import dataclasses
         from selva_core.model.flow_matching import FlowMatching
-        from selva_core.model.sequence_config import SequenceConfig
 
         device   = get_device()
         dtype    = model["dtype"]
@@ -63,11 +63,8 @@ class SelvaSampler:
             duration = features["duration"]
             print(f"[SelVA] Using video duration from features: {duration:.2f}s", flush=True)
 
-        # Compute sequence config for this duration
+        # Derive sequence config for this duration from the model's mode template
-        if mode == "16k":
+        seq_cfg = dataclasses.replace(model["seq_cfg"], duration=duration)
-            seq_cfg = SequenceConfig(duration=duration, sampling_rate=16000, spectrogram_frame_rate=256)
-        else:
-            seq_cfg = SequenceConfig(duration=duration, sampling_rate=44100, spectrogram_frame_rate=512)
         sample_rate = seq_cfg.sampling_rate
 
         if strategy == "offload_to_cpu":