fix: two bugs in SelVA nodes

- selva_feature_extractor: cache hash now includes resolved duration;
  same video + different duration override no longer returns stale features
- selva_sampler: MPS-safe noise generation (torch.Generator on CPU then
  move to device, same pattern as PrismAudioSampler)

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

nodes/selva_feature_extractor.py

@@ -34,11 +34,12 @@ def _resize_frames(frames, size):
     return x.clamp(0.0, 1.0)  # [N, C, H, W]
 
 
-def _hash_inputs(video_tensor, prompt, fps, variant):
+def _hash_inputs(video_tensor, prompt, fps, duration, variant):
     h = hashlib.sha256()
     h.update(video_tensor.cpu().numpy().tobytes()[:1024 * 1024])
     h.update(prompt.encode())
     h.update(str(fps).encode())
+    h.update(str(round(duration, 3)).encode())  # resolved duration affects frame count
     h.update(variant.encode())
     return h.hexdigest()[:16]
 
@@ -86,7 +87,7 @@ class SelvaFeatureExtractor:
         if not cache_dir:
             cache_dir = os.path.join(tempfile.gettempdir(), "selva_features")
         os.makedirs(cache_dir, exist_ok=True)
-        cache_key = _hash_inputs(video, prompt, fps, model["variant"])
+        cache_key = _hash_inputs(video, prompt, fps, duration, model["variant"])
         cached_path = os.path.join(cache_dir, f"{cache_key}.npz")
 
         if os.path.exists(cached_path):

nodes/selva_sampler.py

@@ -93,12 +93,13 @@ class SelvaSampler:
                 bs=1, negative_text_features=neg_text_clip
             )
 
-            # Initial noise
-            rng = torch.Generator(device=device).manual_seed(seed)
+            # Initial noise (MPS doesn't support torch.Generator on device)
+            gen_device = "cpu" if device.type == "mps" else device
+            rng = torch.Generator(device=gen_device).manual_seed(seed)
             x0 = torch.randn(
                 1, seq_cfg.latent_seq_len, net_generator.latent_dim,
-                device=device, dtype=dtype, generator=rng,
-            )
+                device=gen_device, dtype=dtype, generator=rng,
+            ).to(device)
 
             # Flow matching ODE (Euler)
             fm = FlowMatching(min_sigma=0, inference_mode="euler", num_steps=steps)