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load_images_from_folder.py

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+import os
+import numpy as np
+from PIL import Image, ImageSequence, ImageOps
+import torch
+
+class LoadImagesFromSelectedFolder:
+    @classmethod
+    def INPUT_TYPES(cls):
+        # Get the directory where this script is located
+        script_dir = os.path.dirname(os.path.abspath(__file__))
+        comfyui_root = os.path.abspath(os.path.join(script_dir, '..', '..'))
+        output_dir = os.path.join(comfyui_root, 'output')
+        
+        def count_images(folder_path):
+            # Count the number of image files in the folder
+            return len([f for f in os.listdir(folder_path) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp'))])
+        
+        folders = []
+        for root, dirs, files in os.walk(output_dir):
+            rel_path = os.path.relpath(root, output_dir)
+            if rel_path == '.':
+                continue
+            image_count = count_images(root)
+            if image_count > 0:
+                folder_name = f"{rel_path} ({image_count} images)"
+                folders.append((folder_name, rel_path))
+        
+        # Sort folders alphabetically, case-insensitive
+        folders.sort(key=lambda x: x[0].lower())
+
+        return {
+            "required": {
+                "selected_folder": ([folder[0] for folder in folders],),
+            }
+        }
+    
+    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "IMAGE")
+    RETURN_NAMES = ("Images resolution 1", "Images resolution 2", "Images resolution 3", "Images resolution 4")
+    FUNCTION = "load_images_from_selected_folder"
+    CATEGORY = "Bjornulf"
+
+    def load_images_from_selected_folder(self, selected_folder):
+        script_dir = os.path.dirname(os.path.abspath(__file__))
+        comfyui_root = os.path.abspath(os.path.join(script_dir, '..', '..'))
+        output_dir = os.path.join(comfyui_root, 'output')
+        folder_path = os.path.join(output_dir, selected_folder.split(" (")[0])
+        
+        images_by_resolution = {}
+
+        # Check if the folder exists and contains images
+        if not os.path.exists(folder_path):
+            print(f"Folder {folder_path} does not exist.")
+            return (None, None, None)
+
+        image_files = [f for f in os.listdir(folder_path) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp'))]
+        if not image_files:
+            print(f"No images found in folder {folder_path}.")
+            return (None, None, None)
+
+        for image_file in image_files:
+            image_path = os.path.join(folder_path, image_file)
+            img = Image.open(image_path)
+            
+            for i in ImageSequence.Iterator(img):
+                i = ImageOps.exif_transpose(i)
+
+                if i.mode == 'I':
+                    i = i.point(lambda i: i * (1 / 255))
+                image = i.convert("RGB")
+                
+                resolution = image.size
+                
+                image = np.array(image).astype(np.float32) / 255.0
+                image = torch.from_numpy(image)[None,]
+                
+                if resolution not in images_by_resolution:
+                    images_by_resolution[resolution] = []
+                
+                images_by_resolution[resolution].append(image)
+
+        # Sort resolutions by total pixel count (width * height)
+        sorted_resolutions = sorted(images_by_resolution.keys(), key=lambda r: r[0] * r[1], reverse=True)
+
+        outputs = []
+        for i in range(4):  # Return up to 4 different resolutions
+            if i < len(sorted_resolutions):
+                resolution = sorted_resolutions[i]
+                output_image = torch.cat(images_by_resolution[resolution], dim=0)
+                outputs.append(output_image)
+            else:
+                # Create a placeholder tensor filled with 11111111111111111111111
+                H, W, C = 64, 64, 3 
+                placeholder_image = torch.ones((1, H, W, C), dtype=torch.float32)
+                outputs.append(placeholder_image)
+
+        return tuple(outputs)