Add PadImageBatchInterleaved

__init__.py

@@ -82,6 +82,7 @@ NODE_CONFIG = {
     "LoadAndResizeImage": {"class": LoadAndResizeImage, "name": "Load & Resize Image"},
     "LoadImagesFromFolderKJ": {"class": LoadImagesFromFolderKJ, "name": "Load Images From Folder (KJ)"},
     "MergeImageChannels": {"class": MergeImageChannels, "name": "Merge Image Channels"},
+    "PadImageBatchInterleaved": {"class": PadImageBatchInterleaved, "name": "Pad Image Batch Interleaved"},
     "PreviewAnimation": {"class": PreviewAnimation, "name": "Preview Animation"},
     "RemapImageRange": {"class": RemapImageRange, "name": "Remap Image Range"},
     "ReverseImageBatch": {"class": ReverseImageBatch, "name": "Reverse Image Batch"},

nodes/image_nodes.py

@@ -1122,6 +1122,9 @@ class ImagePrepForICLora:
                     print("Warning: The incoming mask is fully black. Handling it as None.")
                     reference_mask = None
         image = reference_image
+        if latent_image is not None:
+            if image.shape[0] != latent_image.shape[0]:
+                image = image.repeat(latent_image.shape[0], 1, 1, 1)
         B, H, W, C = image.size()
 
         # Handle mask
@@ -1849,6 +1852,52 @@ Inserts images at the specified indices into the original image batch.
         
         return (original_images,)
 
+class PadImageBatchInterleaved:
+    
+    RETURN_TYPES = ("IMAGE", "MASK",)
+    RETURN_NAMES = ("images", "masks",)
+    FUNCTION = "pad"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Inserts empty frames between the images in a batch.
+"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "empty_frames_per_image": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
+            },
+        }
+    
+    def pad(self, images, empty_frames_per_image):
+        B, H, W, C = images.shape
+        
+        if B == 1 or empty_frames_per_image == 0:
+            return (images,)
+        
+        # Original B images + (B-1) sets of empty frames between them
+        total_frames = B + (B-1) * empty_frames_per_image
+        
+        # Create new tensor with zeros (empty frames)
+        padded_batch = torch.zeros((total_frames, H, W, C), 
+                                dtype=images.dtype, 
+                                device=images.device)
+        # Create mask tensor (1 for original frames, 0 for empty frames)
+        mask = torch.zeros((total_frames, H, W), 
+                        dtype=images.dtype, 
+                        device=images.device)
+        
+        # Fill in original images at their new positions
+        for i in range(B):
+            # Each image is separated by empty_frames_per_image blank frames
+            new_pos = i * (empty_frames_per_image + 1)
+            padded_batch[new_pos] = images[i]
+            mask[new_pos] = 1.0  # Mark this as an original frame
+        
+        return (padded_batch, mask)
+
 class ReplaceImagesInBatch:
     
     RETURN_TYPES = ("IMAGE",)
@@ -3147,11 +3196,14 @@ class ImagePadKJ:
                 out_image[b, :, :, :] = bg_color.unsqueeze(0).unsqueeze(0)  # Expand for H and W dimensions
                 out_image[b, pad_top:pad_top+H, pad_left:pad_left+W, :] = image[b]
 
+        
         if mask is not None:
             out_masks = torch.zeros((BM, padded_height, padded_width), dtype=mask.dtype, device=mask.device)
             for m in range(BM):
                 out_masks[m, pad_top:pad_top+H, pad_left:pad_left+W] = mask[m]
         else:
-            out_masks = torch.zeros((1, padded_height, padded_width), dtype=image.dtype, device=image.device)
+            out_masks = torch.ones((B, padded_height, padded_width), dtype=image.dtype, device=image.device)
+            for m in range(B):
+                out_masks[m, pad_top:pad_top+H, pad_left:pad_left+W] = 0.0
 
         return (out_image, out_masks)