MultiAlphaComposite

__init__.py

@@ -9,6 +9,7 @@ from .ksampler_fun import KSampler
 from .fal_recraft_upscale import FalAPI_recraft_upscale
 from .fal_kling_video import FalAPI_kling_video
 from .string_lower import StringLower
+from .multi_alpha_composite import MultiAlphaComposite
 WEB_DIRECTORY = "./js"
 
 NODE_CLASS_MAPPINGS = {
@@ -21,7 +22,8 @@ NODE_CLASS_MAPPINGS = {
     "Fun KSampler": KSampler,
     "FalAPI_recraft_upscale": FalAPI_recraft_upscale,
     "FalAPI_kling_video": FalAPI_kling_video,
-    "StringLower": StringLower
+    "StringLower": StringLower,
+    "MultiAlphaComposite": MultiAlphaComposite
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
     "LayeredInfiniteZoom": "Layered Infinite Zoom",
@@ -33,7 +35,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "Fun KSampler": "Fun KSampler",
     "FalAPI_recraft_upscale": "Fal API Recraft Upscale",
     "FalAPI_kling_video": "Fal API Kling Video",
-    "StringLower": "String to Lowercase"
+    "StringLower": "String to Lowercase",
+    "MultiAlphaComposite": "Multi Alpha Composite"
 }
 
 __all__ = [

multi_alpha_composite.py

@@ -0,0 +1,69 @@
+from PIL import Image
+import numpy as np
+import torch
+
+def tensor_to_pil(tensor):
+    """Convert ComfyUI tensor to PIL Image"""
+    # ComfyUI tensors are typically in format [batch, height, width, channels]
+    if len(tensor.shape) == 4:
+        tensor = tensor.squeeze(0)  # Remove batch dimension
+    
+    # Convert from tensor to numpy array and scale to 0-255
+    np_image = (tensor.cpu().numpy() * 255).astype(np.uint8)
+    return Image.fromarray(np_image, 'RGB')
+
+def pil_to_tensor(pil_image):
+    """Convert PIL Image to ComfyUI tensor"""
+    np_image = np.array(pil_image).astype(np.float32) / 255.0
+    tensor = torch.from_numpy(np_image)
+    return tensor.unsqueeze(0)  # Add batch dimension
+
+class MultiAlphaComposite:
+    CATEGORY = "Image/Composite"
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "num_images": ("INT", {"default": 2, "min": 2, "max": 8, "step": 1}),
+                "image1": ("IMAGE",),
+                "image2": ("IMAGE",),
+            },
+            "optional": {
+                "image3": ("IMAGE",),
+                "image4": ("IMAGE",),
+                "image5": ("IMAGE",),
+                "image6": ("IMAGE",),
+                "image7": ("IMAGE",),
+                "image8": ("IMAGE",),
+            }
+        }
+    
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "composite"
+
+    def composite(self, num_images, image1, image2, image3=None, image4=None, image5=None, image6=None, image7=None, image8=None):
+        # Collect all provided images up to num_images
+        images = [image1, image2, image3, image4, image5, image6, image7, image8]
+        valid_images = [img for img in images[:num_images] if img is not None]
+        
+        if len(valid_images) < 2:
+            raise ValueError("At least 2 images are required for compositing")
+        
+        # Convert ComfyUI tensors to PIL Images
+        pil_images = [tensor_to_pil(img) for img in valid_images]
+        
+        # Convert to RGBA for alpha compositing
+        layers = [img.convert("RGBA") for img in pil_images]
+        
+        # Composite layers
+        base = layers[0]
+        for layer in layers[1:]:
+            # Resize layer to match base if needed
+            if layer.size != base.size:
+                layer = layer.resize(base.size, Image.LANCZOS)
+            base = Image.alpha_composite(base, layer)
+        
+        # Convert back to ComfyUI tensor format
+        result_tensor = pil_to_tensor(base.convert("RGB"))
+        return (result_tensor,)