Upload webp_test.py

webp_test.py

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+import os
+from PIL import Image
+import numpy as np
+import torch
+
+class LoadWebPAnimation:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "path": ("STRING", {"default": ""}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "load_animation"
+
+    def load_animation(self, path):
+        # Check if the file exists
+        if not os.path.exists(path):
+            raise FileNotFoundError(f"File not found: {path}")
+
+        # Open the .webp file
+        with Image.open(path) as img:
+            # Check if it's an animation
+            if hasattr(img, 'is_animated') and img.is_animated:
+                frames = []
+                # Extract each frame
+                for i in range(img.n_frames):
+                    img.seek(i)
+                    frame = img.copy().convert('RGB')  # Ensure 3 channels (RGB)
+                    frames.append(np.array(frame))
+                batch = np.stack(frames, axis=0)  # Shape: (num_frames, height, width, 3)
+            else:
+                # Single image case
+                frame = img.convert('RGB')
+                batch = np.expand_dims(np.array(frame), axis=0)  # Shape: (1, height, width, 3)
+
+        # Convert to float32 and normalize to [0, 1]
+        batch = batch.astype(np.float32) / 255.0
+        
+        # Convert to PyTorch tensor without permuting
+        batch = torch.from_numpy(batch)  # Shape: (num_frames, height, width, 3)
+        
+        return (batch,)  # Return as a tuple
+
+# Register the node with ComfyUI
+NODE_CLASS_MAPPINGS = {
+    "LoadWebPAnimation": LoadWebPAnimation
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LoadWebPAnimation": "Load WebP Animation"
+}