Added Image Aspect Ratio node

__init__.py

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+# from .mine_nodes import *
+from .cfe_nodes import *
+
+
+# A dictionary that contains all nodes you want to export with their names
+# NOTE: names should be globally unique
+NODE_CLASS_MAPPINGS = {
+    "CFE_Aspect_Ratio" : CFE_Aspect_Ratio,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "CFE_Aspect_Ratio":"CFE Aspect Ratio"
+}

cfe_nodes.py

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+import math
+import torch
+import comfy
+
+   
+
+class CFE_Aspect_Ratio:
+    """
+        Creates an empty latent image that will more easily characterize the resolution process for SD1.5, SDXL, and FLUX
+    """
+    def __init__(self):
+        self.device = comfy.model_management.intermediate_device()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "type" : (["square", "portrait (w:h)", "landscape (h:w)"],),
+                "resolution": ([
+                    "1:1",
+                    "2:3", 
+                    "3:4", 
+                    "4:7",
+                    "9:7",
+                    "9:16",
+                    "11:21",
+                    "13:18",
+                    "13:19",
+                    "14:17",
+                    "15:17",
+                    "15:16",
+                    "19:10",
+                ],),
+                "megapixel": ([".5 MP (SD1.5)", "1 MP (SDXL)", "2 MP (FLUX)"],),
+                "clip_size": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 1.0,
+                    "max": 10.0,
+                    "step": 0.5,
+                    "tooltip": "Multiplies the output width and height values",
+                    "lazy" : True
+                }),
+                "batch_size": ("INT", {
+                    "default": 1, 
+                    "min": 1, 
+                    "max": 4096, 
+                    "tooltip": "The number of latent images in the batch.",
+                    "lazy" : True
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("LATENT","INT","INT")
+    RETURN_NAMES = ("latent", "clip_width","clip_height")
+
+    FUNCTION = "calculate_resolution"
+
+    CATEGORY = "CFE"
+
+    def calculate_resolution(self, type, resolution, megapixel, clip_size, batch_size):
+
+        if type == "square":
+            ratio = 1
+        else:
+            colon = resolution.find(":")
+            res0 = float(resolution[:colon])
+            res1 = float(resolution[colon + 1:])
+            if type.find("portrait") != -1:
+                ratio = res0 / res1
+            else:
+                ratio = res1 / res0
+        
+        mp_mark = megapixel[:1]
+
+        if mp_mark == "1":
+            mp = 1024 * 1024
+        elif mp_mark == "2":
+            mp = 2 * 1024 * 1024
+        else:
+            mp = 512 * 512
+
+        clip_width = int(math.sqrt(math.floor(ratio * mp)))
+        clip_height = int(math.sqrt(math.floor((1.0/ratio) * mp)))
+
+        latent = torch.zeros([batch_size, 4, clip_height // 8, clip_width // 8], device=self.device)
+
+        clip_width = math.floor(clip_width * clip_size)
+        clip_height = math.floor(clip_height * clip_size)
+
+        return ({"samples":latent}, clip_width, clip_height)
+