first commit

__init__.py

@@ -1,7 +1,7 @@
 from .nodes import (
-    image_crop_circle,
+    agenticflow_ai_variable,
 )
 
 NODE_CLASS_MAPPINGS = {
-    **image_crop_circle.NODE_CLASS_MAPPINGS,
+    **agenticflow_ai_variable.NODE_CLASS_MAPPINGS,
 }

nodes/agenticflow_ai_variable.py

@@ -0,0 +1,77 @@
+import numpy as np
+import torch
+
+
+class AgenticflowAIVariable:
+    """A node that handles variable values and provides type conversions.
+
+    This node takes a variable input and a fallback value, attempting to convert the input
+    into multiple data types (string, integer, and float). If the variable is empty or
+    wrapped in curly braces (indicating an unresolved variable), it uses the fallback value.
+
+    Attributes:
+        INPUT_TYPES: Defines the input parameters - variable and fallback string
+        RETURN_TYPES: Returns the value as (string, integer, float) tuple
+        FUNCTION: The main processing function name
+        CATEGORY: Node category in the ComfyUI interface
+    """
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "variable": (
+                    [
+                        "",
+                    ],
+                ),
+                "fallback": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "single_line": True,
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("STRING", "INT", "FLOAT")
+    FUNCTION = "do_it"
+
+    CATEGORY = "agenticflowai/variables"
+
+    # @classmethod
+    # def VALIDATE_INPUTS(cls, variable: str, fallback: str):
+    #     # Since we populate dynamically, comfy will report invalid inputs. Override to always return True
+    #     return True
+
+    def do_it(self, variable: str, fallback: str):
+        variable = variable.strip()
+        fallback = fallback.strip()
+        if variable == "" or (variable.startswith("{") and variable.endswith("}")):
+            variable = fallback
+
+        int_val = 0
+        float_val = 0.0
+        string_val = f"{variable}"
+        try:
+            int_val = int(variable)
+        except Exception as _:
+            pass
+        try:
+            float_val = float(variable)
+        except Exception as _:
+            pass
+        return (string_val, int_val, float_val)
+
+
+# A dictionary that contains all nodes you want to export with their names
+# NOTE: names should be globally unique
+NODE_CLASS_MAPPINGS = {
+    "AgenticflowAIVariable": AgenticflowAIVariable,
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "AgenticflowAIVariable": "AgenticflowAIVariable",
+}

nodes/image_crop_circle.py

@@ -1,58 +0,0 @@
-import numpy as np
-import torch
-
-
-class ImageCropCircle:
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "crop_circle"
-
-    CATEGORY = "image/crop"
-
-    def crop_circle(self, image):
-        # Get dimensions from the image
-        _, h, w, _ = image.shape
-
-        # Determine the size of the circle (use the smaller dimension)
-        size = min(h, w)
-
-        # Calculate the crop coordinates to center the circle
-        x = (w - size) // 2
-        y = (h - size) // 2
-
-        # Crop the image to a square
-        cropped = image[:, y : y + size, x : x + size, :]
-
-        # Create a circular mask
-        center = (size // 2, size // 2)
-        radius = size // 2
-        Y, X = np.ogrid[:size, :size]
-        dist_from_center = np.sqrt((X - center[0]) ** 2 + (Y - center[1]) ** 2)
-        mask = dist_from_center <= radius
-
-        # Apply the mask
-        mask = torch.from_numpy(mask).float().unsqueeze(0).unsqueeze(-1)
-        mask = mask.expand_as(cropped)
-        result = cropped * mask
-
-        return (result,)
-
-
-# A dictionary that contains all nodes you want to export with their names
-# NOTE: names should be globally unique
-NODE_CLASS_MAPPINGS = {
-    "ImageCropCircle": ImageCropCircle,
-}
-
-# A dictionary that contains the friendly/humanly readable titles for the nodes
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "ImageCropCircle": "ImageCropCircle",
-}

requirements.txt

@@ -1,3 +1,4 @@
 diffusers
 tqdm
-boto3
+boto3
+diffusers==0.24.0