Added support for Porter-Duff image compositing

comfy_extras/nodes_compositing.py

+import numpy as np
+import torch
+import comfy.utils
+from enum import Enum
+
+
+class PorterDuffMode(Enum):
+    ADD = 0
+    CLEAR = 1
+    DARKEN = 2
+    DST = 3
+    DST_ATOP = 4
+    DST_IN = 5
+    DST_OUT = 6
+    DST_OVER = 7
+    LIGHTEN = 8
+    MULTIPLY = 9
+    OVERLAY = 10
+    SCREEN = 11
+    SRC = 12
+    SRC_ATOP = 13
+    SRC_IN = 14
+    SRC_OUT = 15
+    SRC_OVER = 16
+    XOR = 17
+
+
+def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode):
+    if mode == PorterDuffMode.ADD:
+        out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1)
+        out_image = torch.clamp(src_image + dst_image, 0, 1)
+    elif mode == PorterDuffMode.CLEAR:
+        out_alpha = torch.zeros_like(dst_alpha)
+        out_image = torch.zeros_like(dst_image)
+    elif mode == PorterDuffMode.DARKEN:
+        out_alpha = src_alpha + dst_alpha  - src_alpha * dst_alpha
+        out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image)
+    elif mode == PorterDuffMode.DST:
+        out_alpha = dst_alpha
+        out_image = dst_image
+    elif mode == PorterDuffMode.DST_ATOP:
+        out_alpha = src_alpha
+        out_image = src_alpha * dst_image + (1 - dst_alpha) * src_image
+    elif mode == PorterDuffMode.DST_IN:
+        out_alpha = src_alpha * dst_alpha
+        out_image = dst_image * src_alpha
+    elif mode == PorterDuffMode.DST_OUT:
+        out_alpha = (1 - src_alpha) * dst_alpha
+        out_image = (1 - src_alpha) * dst_image
+    elif mode == PorterDuffMode.DST_OVER:
+        out_alpha = dst_alpha + (1 - dst_alpha) * src_alpha
+        out_image = dst_image + (1 - dst_alpha) * src_image
+    elif mode == PorterDuffMode.LIGHTEN:
+        out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
+        out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.max(src_image, dst_image)
+    elif mode == PorterDuffMode.MULTIPLY:
+        out_alpha = src_alpha * dst_alpha
+        out_image = src_image * dst_image
+    elif mode == PorterDuffMode.OVERLAY:
+        out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
+        out_image = torch.where(2 * dst_image < dst_alpha, 2 * src_image * dst_image,
+            src_alpha * dst_alpha - 2 * (dst_alpha - src_image) * (src_alpha - dst_image))
+    elif mode == PorterDuffMode.SCREEN:
+        out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
+        out_image = src_image + dst_image - src_image * dst_image
+    elif mode == PorterDuffMode.SRC:
+        out_alpha = src_alpha
+        out_image = src_image
+    elif mode == PorterDuffMode.SRC_ATOP:
+        out_alpha = dst_alpha
+        out_image = dst_alpha * src_image + (1 - src_alpha) * dst_image
+    elif mode == PorterDuffMode.SRC_IN:
+        out_alpha = src_alpha * dst_alpha
+        out_image = src_image * dst_alpha
+    elif mode == PorterDuffMode.SRC_OUT:
+        out_alpha = (1 - dst_alpha) * src_alpha
+        out_image = (1 - dst_alpha) * src_image
+    elif mode == PorterDuffMode.SRC_OVER:
+        out_alpha = src_alpha + (1 - src_alpha) * dst_alpha
+        out_image = src_image + (1 - src_alpha) * dst_image
+    elif mode == PorterDuffMode.XOR:
+        out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha
+        out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image
+    else:
+        out_alpha = None
+        out_image = None
+    return out_image, out_alpha
+
+
+class PorterDuffImageComposite:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "source": ("IMAGE",),
+                "source_alpha": ("ALPHA",),
+                "destination": ("IMAGE",),
+                "destination_alpha": ("ALPHA",),
+                "mode": ([mode.name for mode in PorterDuffMode], {"default": PorterDuffMode.DST.name}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "ALPHA")
+    FUNCTION = "composite"
+    CATEGORY = "compositing"
+
+    def composite(self, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode):
+        batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha))
+        out_images = []
+        out_alphas = []
+
+        for i in range(batch_size):
+            src_image = source[i]
+            dst_image = destination[i]
+
+            src_alpha = source_alpha[i].unsqueeze(2)
+            dst_alpha = destination_alpha[i].unsqueeze(2)
+
+            if dst_alpha.shape != dst_image.shape:
+                upscale_input = dst_alpha[None,:,:,:].permute(0, 3, 1, 2)
+                upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
+                dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
+            if src_image.shape != dst_image.shape:
+                upscale_input = src_image[None,:,:,:].permute(0, 3, 1, 2)
+                upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
+                src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0)
+            if src_alpha.shape != dst_alpha.shape:
+                upscale_input = src_alpha[None,:,:,:].permute(0, 3, 1, 2)
+                upscale_output = comfy.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center')
+                src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
+
+            out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode])
+
+            out_images.append(out_image)
+            out_alphas.append(out_alpha.squeeze(2))
+
+        result = (torch.stack(out_images), torch.stack(out_alphas))
+        return result
+
+
+class SplitImageWithAlpha:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "image": ("IMAGE",),
+                }
+        }
+
+    CATEGORY = "compositing"
+    RETURN_TYPES = ("IMAGE", "ALPHA")
+    FUNCTION = "split_image_with_alpha"
+
+    def split_image_with_alpha(self, image: torch.Tensor):
+        out_images = [i[:,:,:3] for i in image]
+        out_alphas = [i[:,:,3] for i in image]
+        result = (torch.stack(out_images), torch.stack(out_alphas))
+        return result
+
+
+class JoinImageWithAlpha:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "image": ("IMAGE",),
+                    "alpha": ("ALPHA",),
+                }
+        }
+
+    CATEGORY = "compositing"
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "join_image_with_alpha"
+
+    def join_image_with_alpha(self, image: torch.Tensor, alpha: torch.Tensor):
+        batch_size = min(len(image), len(alpha))
+        out_images = []
+
+        for i in range(batch_size):
+           out_images.append(torch.cat((image[i], alpha[i].unsqueeze(2)), dim=2))
+
+        result = (torch.stack(out_images),)
+        return result
+
+
+class ConvertAlphaToImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "alpha": ("ALPHA",),
+                }
+        }
+
+    CATEGORY = "compositing"
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "alpha_to_image"
+
+    def alpha_to_image(self, alpha):
+        result = alpha.reshape((-1, 1, alpha.shape[-2], alpha.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
+        return (result,)
+
+
+class ConvertImageToAlpha:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "image": ("IMAGE",),
+                    "channel": (["red", "green", "blue", "alpha"],),
+                }
+        }
+
+    CATEGORY = "compositing"
+    RETURN_TYPES = ("ALPHA",)
+    FUNCTION = "image_to_alpha"
+
+    def image_to_alpha(self, image, channel):
+        channels = ["red", "green", "blue", "alpha"]
+        alpha = image[0, :, :, channels.index(channel)]
+        return (alpha,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "PorterDuffImageComposite": PorterDuffImageComposite,
+    "SplitImageWithAlpha": SplitImageWithAlpha,
+    "JoinImageWithAlpha": JoinImageWithAlpha,
+    "ConvertAlphaToImage": ConvertAlphaToImage,
+    "ConvertImageToAlpha": ConvertImageToAlpha,
+}
+
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "PorterDuffImageComposite": "Porter-Duff Image Composite",
+    "SplitImageWithAlpha": "Split Image with Alpha",
+    "JoinImageWithAlpha": "Join Image with Alpha",
+    "ConvertAlphaToImage": "Convert Alpha to Image",
+    "ConvertImageToAlpha": "Convert Image to Alpha",
+}

nodes.py

@@ -1372,6 +1372,31 @@ class LoadImage:
 
         return True
 
+class LoadImageWithAlpha(LoadImage):
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
+        return {"required":
+                    {"image": (sorted(files), {"image_upload": True})},
+                }
+
+    CATEGORY = "compositing"
+
+    RETURN_TYPES = ("IMAGE", "ALPHA")
+
+    FUNCTION = "load_image"
+    def load_image(self, image):
+        image_path = folder_paths.get_annotated_filepath(image)
+        i = Image.open(image_path)
+        i = ImageOps.exif_transpose(i)
+        image = i.convert("RGBA")
+        alpha = np.array(image.getchannel("A")).astype(np.float32) / 255.0
+        alpha = torch.from_numpy(alpha)[None,]
+        image = np.array(image).astype(np.float32) / 255.0
+        image = torch.from_numpy(image)[None,]
+        return (image, alpha)
+
 class LoadImageMask:
     _color_channels = ["alpha", "red", "green", "blue"]
     @classmethod
@@ -1606,6 +1631,7 @@ NODE_CLASS_MAPPINGS = {
     "SaveImage": SaveImage,
     "PreviewImage": PreviewImage,
     "LoadImage": LoadImage,
+    "LoadImageWithAlpha": LoadImageWithAlpha,
     "LoadImageMask": LoadImageMask,
     "ImageScale": ImageScale,
     "ImageScaleBy": ImageScaleBy,
@@ -1702,6 +1728,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "SaveImage": "Save Image",
     "PreviewImage": "Preview Image",
     "LoadImage": "Load Image",
+    "LoadImageWithAlpha": "Load Image with Alpha",
     "LoadImageMask": "Load Image (as Mask)",
     "ImageScale": "Upscale Image",
     "ImageScaleBy": "Upscale Image By",
@@ -1788,6 +1815,7 @@ def init_custom_nodes():
         "nodes_upscale_model.py",
         "nodes_post_processing.py",
         "nodes_mask.py",
+        "nodes_compositing.py",
         "nodes_rebatch.py",
         "nodes_model_merging.py",
         "nodes_tomesd.py",