feat: 💄 add a few more batch nodes

node_list.json

@@ -2,6 +2,9 @@
     "Animation Builder (mtb)": "Convenient way to manage basic animation maths at the core of many of my workflows",
     "Any To String (mtb)": "Tries to take any input and convert it to a string",
     "Batch Float (mtb)": "Generates a batch of float values with interpolation",
+    "Batch Float Assemble (mtb)": "Assembles mutiple batches of floats into a single stream (batch)",
+    "Batch Float Fill (mtb)": "Fills a batch float with a single value until it reaches the target length",
+    "Batch Make (mtb)": "Simply duplicates the input frame as a batch",
     "Batch Shape (mtb)": "Generates a batch of 2D shapes with optional shading (experimental)",
     "Batch Transform (mtb)": "Transform a batch of images using a batch of keyframes",
     "Bbox (mtb)": "The bounding box (BBOX) custom type used by other nodes",

nodes/batch.py

@@ -19,6 +19,29 @@ def hex_to_rgb(hex_color, bgr=False):
     return tuple(int(hex_color[i : i + 2], 16) for i in (0, 2, 4))
 
 
+class BatchMake:
+    """Simply duplicates the input frame as a batch"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "count": ("INT", {"default": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "generate_batch"
+    CATEGORY = "mtb/batch"
+
+    def generate_batch(self, image: torch.Tensor, count):
+        if len(image.shape) == 3:
+            image = image.unsqueeze(0)
+
+        return (image.repeat(count, 1, 1, 1),)
+
+
 class BatchShape:
     """Generates a batch of 2D shapes with optional shading (experimental)"""
 
@@ -112,6 +135,60 @@ def generate_shapes(
         return (pil2tensor(res),)
 
 
+class BatchFloatFill:
+    """Fills a batch float with a single value until it reaches the target length"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "floats": ("FLOATS",),
+                "direction": (["head", "tail"], {"default": "tail"}),
+                "value": ("FLOAT", {"default": 0.0}),
+                "count": ("INT", {"default": 1}),
+            }
+        }
+
+    FUNCTION = "fill_floats"
+    RETURN_TYPES = ("FLOATS",)
+    CATEGORY = "mtb/batch"
+
+    def fill_floats(self, floats, direction, value, count):
+        size = len(floats)
+        if size > count:
+            raise ValueError(f"Size ({size}) is less then target count ({count})")
+
+        rem = count - size
+        if direction == "tail":
+            floats = floats + [value] * rem
+        else:
+            floats = [value] * rem + floats
+        return (floats,)
+
+
+class BatchFloatAssemble:
+    """Assembles mutiple batches of floats into a single stream (batch)"""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {"reverse": ("BOOLEAN", {"default": False})}}
+
+    FUNCTION = "assemble_floats"
+    RETURN_TYPES = ("FLOATS",)
+    CATEGORY = "mtb/batch"
+
+    def assemble_floats(self, reverse, **kwargs):
+        res = []
+        if reverse:
+            for x in reversed(kwargs.values()):
+                res += x
+        else:
+            for x in kwargs.values():
+                res += x
+
+        return (res,)
+
+
 class BatchFloat:
     """Generates a batch of float values with interpolation"""
 
@@ -257,4 +334,11 @@ def transform_batch(
         return (torch.cat(res, dim=0),)
 
 
-__nodes__ = [BatchFloat, Batch2dTransform, BatchShape]
+__nodes__ = [
+    BatchFloat,
+    Batch2dTransform,
+    BatchShape,
+    BatchMake,
+    BatchFloatAssemble,
+    BatchFloatFill,
+]

web/comfy_shared.js

@@ -92,7 +92,27 @@ export function getWidgetType(config) {
   }
   return { type, linkType }
 }
+export const setupDynamicConnections = (nodeType, prefix, inputType) => {
+  const onNodeCreated = nodeType.prototype.onNodeCreated
+  nodeType.prototype.onNodeCreated = function () {
+    const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined
+    this.addInput(`${prefix}_1`, inputType)
+    return r
+  }
 
+  const onConnectionsChange = nodeType.prototype.onConnectionsChange
+  nodeType.prototype.onConnectionsChange = function (
+    type,
+    index,
+    connected,
+    link_info
+  ) {
+    const r = onConnectionsChange
+      ? onConnectionsChange.apply(this, arguments)
+      : undefined
+    dynamic_connection(this, index, connected, `${prefix}_`, inputType)
+  }
+}
 export const dynamic_connection = (
   node,
   index,

web/mtb_widgets.js

@@ -878,27 +878,17 @@ const mtb_widgets = {
         break
       }
       case 'Stack Images (mtb)': {
-        const onNodeCreated = nodeType.prototype.onNodeCreated
-        nodeType.prototype.onNodeCreated = function () {
-          const r = onNodeCreated
-            ? onNodeCreated.apply(this, arguments)
-            : undefined
-          this.addInput(`image_1`, 'IMAGE')
-          return r
-        }
+        shared.setupDynamicConnections(nodeType, 'image', 'IMAGE')
+
+        break
+      }
+      case 'Batch Float Assemble (mtb)': {
+        shared.setupDynamicConnections(nodeType, 'floats', 'FLOATS')
+        break
+      }
+      case 'Batch Merge (mtb)': {
+        shared.setupDynamicConnections(nodeType, 'batches', 'IMAGE')
 
-        const onConnectionsChange = nodeType.prototype.onConnectionsChange
-        nodeType.prototype.onConnectionsChange = function (
-          type,
-          index,
-          connected,
-          link_info
-        ) {
-          const r = onConnectionsChange
-            ? onConnectionsChange.apply(this, arguments)
-            : undefined
-          shared.dynamic_connection(this, index, connected, 'image_', 'IMAGE')
-        }
         break
       }
       case 'Save Tensors (mtb)': {