add reference code

2025-06-23 20:42:49 +08:00
parent 1c44578d9a
commit ca2c13e0ef
131 changed files with 18966 additions and 0 deletions
@@ -0,0 +1,86 @@
+import torch
+from PIL import Image
+from .imagefunc import log, pil2tensor, tensor2pil, image2mask, mask2image, chop_image, chop_mode
+
+
+
+class ImageBlend:
+
+    def __init__(self):
+        self.NODE_NAME = 'ImageBlend'
+
+    @classmethod
+    def INPUT_TYPES(self):
+
+        return {
+            "required": {
+                "background_image": ("IMAGE", ),  #
+                "layer_image": ("IMAGE",),  #
+                "invert_mask": ("BOOLEAN", {"default": True}),  # 反转mask
+                "blend_mode": (chop_mode,),  # 混合模式
+                "opacity": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}),  # 透明度
+            },
+            "optional": {
+                "layer_mask": ("MASK",),  #
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = 'image_blend'
+    CATEGORY = '😺dzNodes/LayerUtility'
+
+    def image_blend(self, background_image, layer_image,
+                  invert_mask, blend_mode, opacity,
+                  layer_mask=None
+                  ):
+
+        b_images = []
+        l_images = []
+        l_masks = []
+        ret_images = []
+        for b in background_image:
+            b_images.append(torch.unsqueeze(b, 0))
+        for l in layer_image:
+            l_images.append(torch.unsqueeze(l, 0))
+            m = tensor2pil(l)
+            if m.mode == 'RGBA':
+                l_masks.append(m.split()[-1])
+            else:
+                l_masks.append(Image.new('L', m.size, 'white'))
+        if layer_mask is not None:
+            if layer_mask.dim() == 2:
+                layer_mask = torch.unsqueeze(layer_mask, 0)
+            l_masks = []
+            for m in layer_mask:
+                if invert_mask:
+                    m = 1 - m
+                l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
+        max_batch = max(len(b_images), len(l_images), len(l_masks))
+        for i in range(max_batch):
+            background_image = b_images[i] if i < len(b_images) else b_images[-1]
+            layer_image = l_images[i] if i < len(l_images) else l_images[-1]
+            _mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
+
+            _canvas = tensor2pil(background_image).convert('RGB')
+            _layer = tensor2pil(layer_image).convert('RGB')
+
+            if _mask.size != _layer.size:
+                _mask = Image.new('L', _layer.size, 'white')
+                log(f"Warning: {self.NODE_NAME} mask mismatch, dropped!", message_type='warning')
+
+            # 合成layer
+            _comp = chop_image(_canvas, _layer, blend_mode, opacity)
+            _canvas.paste(_comp, mask=_mask)
+
+            ret_images.append(pil2tensor(_canvas))
+        log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
+        return (torch.cat(ret_images, dim=0),)
+
+NODE_CLASS_MAPPINGS = {
+    "LayerUtility: ImageBlend": ImageBlend
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LayerUtility: ImageBlend": "LayerUtility: ImageBlend"
+}