From db60a1e0e9b31df4e68fb1fc999710c732e80b18 Mon Sep 17 00:00:00 2001
From: bigcat88 <bigcat88@icloud.com>
Date: Tue, 30 Dec 2025 18:29:56 +0200
Subject: [PATCH] fix(LoadImage): ignore broken frames after first decoded
 image

---
 nodes.py | 48 ++++++++++++++++++++++++++----------------------
 1 file changed, 26 insertions(+), 22 deletions(-)

diff --git a/nodes.py b/nodes.py
index 7d83ecb21..7ae6f6bbb 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1665,32 +1665,36 @@ class LoadImage:
 
         excluded_formats = ['MPO']
 
-        for i in ImageSequence.Iterator(img):
-            i = node_helpers.pillow(ImageOps.exif_transpose, i)
+        try:
+            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 i.mode == 'I':
+                    i = i.point(lambda i: i * (1 / 255))
+                image = i.convert("RGB")
 
-            if len(output_images) == 0:
-                w = image.size[0]
-                h = image.size[1]
+                if len(output_images) == 0:
+                    w = image.size[0]
+                    h = image.size[1]
 
-            if image.size[0] != w or image.size[1] != h:
-                continue
+                if image.size[0] != w or image.size[1] != h:
+                    continue
 
-            image = np.array(image).astype(np.float32) / 255.0
-            image = torch.from_numpy(image)[None,]
-            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")
-            output_images.append(image)
-            output_masks.append(mask.unsqueeze(0))
+                image = np.array(image).astype(np.float32) / 255.0
+                image = torch.from_numpy(image)[None,]
+                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")
+                output_images.append(image)
+                output_masks.append(mask.unsqueeze(0))
+        except ValueError:
+            if img.format != "MPO" or len(output_images) == 0:
+                raise
 
         if len(output_images) > 1 and img.format not in excluded_formats:
             output_image = torch.cat(output_images, dim=0)