From 3f35aa39f29569a7ef9af07e3f62ce8bba23e9fc Mon Sep 17 00:00:00 2001
From: Ethanfel <ethan.fel@ts-pc.fr>
Date: Fri, 27 Mar 2026 18:04:32 +0100
Subject: [PATCH] feat: PrismAudioFeatureLoader node for pre-computed .npz
 files

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 nodes/feature_loader.py | 53 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 nodes/feature_loader.py

diff --git a/nodes/feature_loader.py b/nodes/feature_loader.py
new file mode 100644
index 0000000..6c57901
--- /dev/null
+++ b/nodes/feature_loader.py
@@ -0,0 +1,53 @@
+import os
+import numpy as np
+import torch
+from .utils import PRISMAUDIO_CATEGORY
+
+# Keys consumed by the conditioners (video_features, text_features, sync_features)
+# global_video_features and global_text_features are NOT consumed by any conditioner
+# in the prismaudio.json config — they are unused.
+REQUIRED_KEYS = [
+    "video_features",
+    "text_features",
+    "sync_features",
+]
+
+
+class PrismAudioFeatureLoader:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "npz_path": ("STRING", {"default": "", "tooltip": "Path to pre-computed .npz feature file"}),
+            },
+        }
+
+    RETURN_TYPES = ("PRISMAUDIO_FEATURES",)
+    RETURN_NAMES = ("features",)
+    FUNCTION = "load_features"
+    CATEGORY = PRISMAUDIO_CATEGORY
+
+    def load_features(self, npz_path):
+        if not os.path.exists(npz_path):
+            raise FileNotFoundError(f"[PrismAudio] Feature file not found: {npz_path}")
+
+        data = np.load(npz_path, allow_pickle=True)
+
+        features = {}
+        for key in REQUIRED_KEYS:
+            if key in data:
+                features[key] = torch.from_numpy(data[key]).float()
+            else:
+                print(f"[PrismAudio] Warning: key '{key}' not found in {npz_path}, using zeros")
+                # Provide zero tensor rather than None — Cond_MLP/Sync_MLP crash on None
+                # Sync_MLP requires length divisible by 8 (segments of 8 frames)
+                if key == "sync_features":
+                    features[key] = torch.zeros(8, 768)
+                else:
+                    features[key] = torch.zeros(1, 1024)
+
+        # Load duration if present
+        if "duration" in data:
+            features["duration"] = float(data["duration"])
+
+        return (features,)