From ef6722f6be7bf073d225d21da47354905a6abd2b Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Sat, 2 May 2026 17:34:27 -0700
Subject: [PATCH] Some cleanups to the load image node. (#13677)

---
 nodes.py | 29 ++++++++++-------------------
 1 file changed, 10 insertions(+), 19 deletions(-)

diff --git a/nodes.py b/nodes.py
index 99dc07227..710cccffe 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1694,26 +1694,27 @@ class LoadImage:
 
     RETURN_TYPES = ("IMAGE", "MASK")
     FUNCTION = "load_image"
+
     def load_image(self, image):
         image_path = folder_paths.get_annotated_filepath(image)
 
+        dtype = comfy.model_management.intermediate_dtype()
+        device = comfy.model_management.intermediate_device()
+
         components = InputImpl.VideoFromFile(image_path).get_components()
         if components.images.shape[0] > 0:
-            return (components.images, 1.0 - components.alpha[..., -1] if components.alpha is not None else torch.zeros((components.images.shape[0], 64, 64), dtype=torch.float32, device="cpu"))
+            return (components.images.to(device=device, dtype=dtype), (1.0 - components.alpha[..., -1]).to(device=device, dtype=dtype) if components.alpha is not None else torch.zeros((components.images.shape[0], 64, 64), dtype=dtype, device=device))
 
+        # This code is left here to handle animated webp which pyav does not support loading
         img = node_helpers.pillow(Image.open, image_path)
 
         output_images = []
         output_masks = []
         w, h = None, None
 
-        dtype = comfy.model_management.intermediate_dtype()
-
         for i in ImageSequence.Iterator(img):
             i = node_helpers.pillow(ImageOps.exif_transpose, i)
 
-            if i.mode == 'I':
-                i = i.point(lambda i: i * (1 / 255))
             image = i.convert("RGB")
 
             if len(output_images) == 0:
@@ -1728,25 +1729,15 @@ class LoadImage:
             if 'A' in i.getbands():
                 mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
                 mask = 1. - torch.from_numpy(mask)
-            elif i.mode == 'P' and 'transparency' in i.info:
-                mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
-                mask = 1. - torch.from_numpy(mask)
             else:
-                mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
             output_images.append(image.to(dtype=dtype))
             output_masks.append(mask.unsqueeze(0).to(dtype=dtype))
 
-            if img.format == "MPO":
-                break  # ignore all frames except the first one for MPO format
+        output_image = torch.cat(output_images, dim=0)
+        output_mask = torch.cat(output_masks, dim=0)
 
-        if len(output_images) > 1:
-            output_image = torch.cat(output_images, dim=0)
-            output_mask = torch.cat(output_masks, dim=0)
-        else:
-            output_image = output_images[0]
-            output_mask = output_masks[0]
-
-        return (output_image, output_mask)
+        return (output_image.to(device=device, dtype=dtype), output_mask.to(device=device, dtype=dtype))
 
     @classmethod
     def IS_CHANGED(s, image):