Add RM Make Image Batch node

Combines up to 5 images into a single batch, auto-resizing mismatched
dimensions to match the first image.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

__init__.py

@@ -29,6 +29,8 @@ from .nodes.multi_detect_crop import NODE_CLASS_MAPPINGS as MULTI_DETECT_MAPPING
 from .nodes.multi_detect_crop import NODE_DISPLAY_NAME_MAPPINGS as MULTI_DETECT_DISPLAY
 from .nodes.math_expression import NODE_CLASS_MAPPINGS as MATH_EXPR_MAPPINGS
 from .nodes.math_expression import NODE_DISPLAY_NAME_MAPPINGS as MATH_EXPR_DISPLAY
+from .nodes.make_image_batch import NODE_CLASS_MAPPINGS as IMAGE_BATCH_MAPPINGS
+from .nodes.make_image_batch import NODE_DISPLAY_NAME_MAPPINGS as IMAGE_BATCH_DISPLAY
 
 # Combine all mappings
 NODE_CLASS_MAPPINGS = {
@@ -45,6 +47,7 @@ NODE_CLASS_MAPPINGS = {
     **FACE_DETAILER_MAPPINGS,
     **MULTI_DETECT_MAPPINGS,
     **MATH_EXPR_MAPPINGS,
+    **IMAGE_BATCH_MAPPINGS,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -61,6 +64,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     **FACE_DETAILER_DISPLAY,
     **MULTI_DETECT_DISPLAY,
     **MATH_EXPR_DISPLAY,
+    **IMAGE_BATCH_DISPLAY,
 }
 
 WEB_DIRECTORY = "./js"

nodes/make_image_batch.py

@@ -0,0 +1,59 @@
+"""
+RM Make Image Batch - Combine multiple images into a single batch.
+"""
+
+import torch
+
+
+class RMMakeImageBatch:
+    """Combine up to 5 images into a single image batch."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image1": ("IMAGE",),
+            },
+            "optional": {
+                "image2": ("IMAGE",),
+                "image3": ("IMAGE",),
+                "image4": ("IMAGE",),
+                "image5": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "process"
+    CATEGORY = "RMAutomation/Image"
+
+    def process(self, image1, image2=None, image3=None, image4=None, image5=None):
+        images = [image1]
+        for img in (image2, image3, image4, image5):
+            if img is not None:
+                images.append(img)
+
+        if len(images) == 1:
+            return (images[0],)
+
+        # Match dimensions to first image
+        target_h = images[0].shape[1]
+        target_w = images[0].shape[2]
+
+        result = []
+        for img in images:
+            if img.shape[1] != target_h or img.shape[2] != target_w:
+                img = torch.nn.functional.interpolate(
+                    img.movedim(-1, 1), size=(target_h, target_w), mode="bilinear"
+                ).movedim(1, -1)
+            result.append(img)
+
+        return (torch.cat(result, dim=0),)
+
+
+NODE_CLASS_MAPPINGS = {
+    "RMMakeImageBatch": RMMakeImageBatch,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "RMMakeImageBatch": "RM Make Image Batch",
+}