Update

__init__.py

@@ -26,6 +26,8 @@
 from .classes.DATASET_xCopy import N_CLASS_MAPPINGS as xCopyMappings, N_DISPLAY_NAME_MAPPINGS as xCopyNameMappings
 from .classes.DATASET_OpenAIChat import N_CLASS_MAPPINGS as OpenAIChatMappings, N_DISPLAY_NAME_MAPPINGS as OpenAIChatNameMappings
 from .classes.DATASET_OpenAIChatImage import N_CLASS_MAPPINGS as OpenAIChatImageMappings, N_DISPLAY_NAME_MAPPINGS as OpenAIChatImageNameMappings
+from .classes.DATASET_LoadImage import N_CLASS_MAPPINGS as LoadImageMappings, N_DISPLAY_NAME_MAPPINGS as LoadImageNameMappings
+from .classes.DATASET_SaveImage import N_CLASS_MAPPINGS as SaveImageMappings, N_DISPLAY_NAME_MAPPINGS as SaveImageNameMappings
 
 NODE_CLASS_MAPPINGS = {}
 NODE_CLASS_MAPPINGS.update(TXTFileSaverMappings)
@@ -36,6 +38,8 @@
 NODE_CLASS_MAPPINGS.update(xCopyMappings)
 NODE_CLASS_MAPPINGS.update(OpenAIChatMappings)
 NODE_CLASS_MAPPINGS.update(OpenAIChatImageMappings)
+NODE_CLASS_MAPPINGS.update(LoadImageMappings)
+NODE_CLASS_MAPPINGS.update(SaveImageMappings)
 
 NODE_DISPLAY_NAME_MAPPINGS = {}
 NODE_DISPLAY_NAME_MAPPINGS.update(TXTFileSaverNameMappings)
@@ -46,5 +50,7 @@
 NODE_DISPLAY_NAME_MAPPINGS.update(xCopyNameMappings)
 NODE_DISPLAY_NAME_MAPPINGS.update(OpenAIChatNameMappings)
 NODE_DISPLAY_NAME_MAPPINGS.update(OpenAIChatImageNameMappings)
+NODE_DISPLAY_NAME_MAPPINGS.update(LoadImageNameMappings)
+NODE_DISPLAY_NAME_MAPPINGS.update(SaveImageNameMappings)
 
 WEB_DIRECTORY = "./web"

classes/DATASET_LoadImage.py

@@ -0,0 +1,97 @@
+import folder_paths
+import os
+import node_helpers
+import numpy as np
+import torch
+import hashlib
+from PIL import Image, ImageOps, ImageSequence, ImageFile
+
+#code credit: nodes.py comfui 
+class DATASET_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 = "image"
+
+ RETURN_TYPES = ("IMAGE", "MASK", "STRING", "STRING", "STRING", "STRING")
+ RETURN_NAMES = ("image", "image_mask", "image_name", "image_name_without_extension", "image_path", "image_directory_path")
+ 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))
+ 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]
+
+ image_name = os.path.basename(image_path)
+ image_dir = os.path.dirname(image_path)
+ image_name_without_extension = os.path.splitext(image_name)[0]
+
+ return (output_image, output_mask, image_name, image_name_without_extension, image_path, image_dir)
+
+ @classmethod
+ def IS_CHANGED(s, image):
+ image_path = folder_paths.get_annotated_filepath(image)
+ m = hashlib.sha256()
+ with open(image_path, 'rb') as f:
+ m.update(f.read())
+ return m.digest().hex()
+
+ @classmethod
+ def VALIDATE_INPUTS(s, image):
+ if not folder_paths.exists_annotated_filepath(image):
+ return "Invalid image file: {}".format(image)
+
+ return True
+
+
+N_CLASS_MAPPINGS = {
+ "DATASET_LoadImage": DATASET_LoadImage,
+}
+
+N_DISPLAY_NAME_MAPPINGS = {
+ "DATASET_LoadImage": "DATASET_LoadImage",
+}

classes/DATASET_SaveImage.py

@@ -0,0 +1,69 @@
+import os
+import json
+import json
+import numpy as np
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+from comfy.cli_args import args
+
+class DATASET_SaveImage:
+
+ def __init__(self):
+ self.compression = 4
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "Images": ("IMAGE",),
+ "Directory": ("STRING", {}),
+ "Filename": ("STRING", {"default": "Image"}),
+ },
+ "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+ }
+
+ RETURN_TYPES = ()
+ FUNCTION = "BatchSave"
+ OUTPUT_NODE = True
+ CATEGORY = "🔵 JDCN 🔵"
+
+ def BatchSave(self, Images, Directory, Filename, prompt=None, extra_pnginfo=None):
+
+ try:
+
+ Directory = Directory
+ Filename = Filename
+
+ if not os.path.exists(Directory):
+ os.makedirs(Directory)
+
+ for image in Images: 
+
+ image = image.cpu().numpy()
+ image = (image * 255).astype(np.uint8)
+ img = Image.fromarray(image)
+ metadata = None
+ if not args.disable_metadata:
+ metadata = PngInfo()
+ if prompt is not None:
+ metadata.add_text("prompt", json.dumps(prompt))
+ if extra_pnginfo is not None:
+ for x in extra_pnginfo:
+ metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+ file_path = os.path.join(Directory,Filename)
+ img.save(file_path, pnginfo=metadata, compress_level=self.compression)
+
+ except Exception as e:
+ print(f"Error saving image: {e}")
+
+ return ()
+
+
+N_CLASS_MAPPINGS = {
+ "DATASET_SaveImage": DATASET_SaveImage,
+}
+
+N_DISPLAY_NAME_MAPPINGS = {
+ "DATASET_SaveImage": "DATASET_SaveImage",
+}