update

__init__.py

@@ -1,16 +1,13 @@
 from .nodes.load_dilight_controlnet_node import LoadDiLightControlNetNode
 from .nodes.prepare_dilight_cond_node import PrepareDiLightCondNode
-from .nodes.load_dilight_image_node import LoadDiLightImageNode
 
 
 NODE_CLASS_MAPPINGS = {
     "LoadDiLightControlNet": LoadDiLightControlNetNode,
     "PrepareDiLightCond": PrepareDiLightCondNode,
-    "LoadDiLightImage": LoadDiLightImageNode,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "LoadDiLightControlNet": "Load DiLight ControlNet",
     "PrepareDiLightCond": "Prepare DiLight CN Image",
-    "LoadDiLightImage": "Load DiLight Image",
 }

nodes/load_dilight_image_node.py

@@ -1,72 +0,0 @@
-import os
-
-import torch
-import numpy as np
-from PIL import Image, ImageOps, ImageSequence
-
-import folder_paths
-import node_helpers
-
-
-class LoadDiLightImageNode:
-    @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 = "image"
-
-    RETURN_TYPES = ("IMAGE", "MASK")
-    FUNCTION = "load_image"
-    def load_image(self, image):
-        image_path = folder_paths.get_annotated_filepath(image)
-        
-        img = node_helpers.pillow(Image.open, image_path)
-        
-        output_images = []
-        output_masks = []
-        w, h = None, None
-
-        excluded_formats = ['MPO']
-        
-        for i in ImageSequence.Iterator(img):
-            i = node_helpers.pillow(ImageOps.exif_transpose, i)
-
-            if i.mode == 'I':
-                i = i.point(lambda i: i * (1 / 255))
-            if 'A' in i.getbands():
-                image = i.convert("RGBA")
-            else:
-                image = i.convert("RGB")
-
-            if len(output_images) == 0:
-                w = image.size[0]
-                h = image.size[1]
-            
-            if image.size[0] != w or image.size[1] != h:
-                continue
-            
-            image = np.array(image).astype(np.float32) / 255.0
-            image = torch.from_numpy(image)[None,]
-            if 'A' in i.getbands():
-                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
-                mask = 1. - torch.from_numpy(mask)
-            else:
-                mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
-            output_images.append(image)
-            output_masks.append(mask.unsqueeze(0))
-
-        if len(output_images) > 1 and img.format not in excluded_formats:
-            output_image = torch.cat(output_images, dim=0)
-            output_mask = torch.cat(output_masks, dim=0)
-        else:
-            output_image = output_images[0]
-            output_mask = output_masks[0]
-
-        if output_image.shape[-1] == 4:
-            output_image = output_image[..., :3] * output_image[..., 3:]
-
-        return (output_image, output_mask)