feat: 🎨 add support for image.size(0) == 0

Attempt at ignoring a branch if the image size is 0.

nodes/image_interpolation.py

@@ -82,6 +82,10 @@ def do_interpolation(
         film_model: interpolator.Interpolator,
     ):
         n = images.size(0)
+        # check if images is an empty tensor and return it...
+        if n == 0:
+            return (images,)
+
         # check if tensorflow GPU is available
         available_gpus = tf.config.list_physical_devices("GPU")
         if not len(available_gpus):
@@ -184,6 +188,8 @@ def export_prores(
         fps: float,
         prefix: str,
     ):
+        if images.size(0) == 0:
+            return ("",)
         output_dir = Path(folder_paths.get_output_directory())
         id = f"{prefix}_{uuid.uuid4()}.mov"
 

utils.py

@@ -5,6 +5,7 @@
 import sys
 
 from typing import Union, List
+from .log import log
 
 
 def add_path(path, prepend=False):
@@ -45,16 +46,21 @@ def add_path(path, prepend=False):
 add_path((comfy_dir / "custom_nodes"))
 
 
-def tensor2pil(image: torch.Tensor) -> Union[Image.Image, List[Image.Image]]:
+def tensor2pil(image: torch.Tensor) -> List[Image.Image]:
     batch_count = 1
     if len(image.shape) > 3:
         batch_count = image.size(0)
 
-    if batch_count == 1:
-        return Image.fromarray(
+    if batch_count > 1:
+        out = []
+        out.extend([tensor2pil(image[i]) for i in range(batch_count)])
+        return out
+
+    return [
+        Image.fromarray(
             np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
         )
-    return [tensor2pil(image[i]) for i in range(batch_count)]
+    ]
 
 
 def pil2tensor(image: Image.Image | List[Image.Image]) -> torch.Tensor:
@@ -76,5 +82,9 @@ def tensor2np(tensor: torch.Tensor) -> Union[np.ndarray, List[np.ndarray]]:
     if len(tensor.shape) > 3:
         batch_count = tensor.size(0)
     if batch_count > 1:
-        return [tensor2np(tensor[i]) for i in range(batch_count)]
-    return np.clip(255.0 * tensor.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+        out = []
+        out.extend([tensor2np(tensor[i]) for i in range(batch_count)])
+        return out
+
+    return [np.clip(255.0 * tensor.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)]
+