Initial commit

multi_image_loader.py

@@ -0,0 +1,98 @@
+import torch
+import numpy as np
+from PIL import Image, ImageOps
+import os
+import folder_paths
+import io
+
+class MultiImageLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image_paths": ("STRING", {"default": "", "multiline": True}),
+                "width": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 1}),
+                "height": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 1}),
+                "upscale_method": (["lanczos", "bilinear", "nearest-exact"],),
+                "divisible_by": ("INT", {"default": 32, "min": 1, "max": 512, "step": 1}),
+                "img_compression": ("INT", {"default": 18, "min": 0, "max": 100, "step": 1}),
+            },
+        }
+
+    # Added "IMAGE" at the beginning for multi_output + 50 individual outputs = 51 outputs
+    RETURN_TYPES = ("IMAGE",) * 51
+    RETURN_NAMES = ("multi_output",) + tuple(f"image_{i+1}" for i in range(50))
+    FUNCTION = "load_images"
+    CATEGORY = "image"
+
+    def load_images(self, image_paths, width, height, upscale_method, divisible_by, img_compression):
+        results = []
+        valid_paths = [p.strip() for p in image_paths.split("\n") if p.strip()]
+
+        # Track the dimensions of the first processed image
+        first_target_w, first_target_h = None, None
+
+        for path in valid_paths:
+            try:
+                # Resolve full path
+                full_path = path
+                if not os.path.exists(full_path):
+                    full_path = os.path.join(folder_paths.get_input_directory(), path)
+                    
+                if not os.path.exists(full_path):
+                    print(f"Warning: Image path not found: {path}")
+                    continue
+
+                image = Image.open(full_path)
+                image = ImageOps.exif_transpose(image)
+                image = image.convert("RGB")
+
+                orig_w, orig_h = image.size
+                target_w, target_h = width, height
+
+                if target_w == 0 and target_h == 0:
+                    target_w, target_h = orig_w, orig_h
+                elif target_w == 0:
+                    target_w = int(orig_w * (target_h / orig_h))
+                elif target_h == 0:
+                    target_h = int(orig_h * (target_w / orig_w))
+
+                # Divisible by constraint
+                target_w = (target_w // divisible_by) * divisible_by
+                target_h = (target_h // divisible_by) * divisible_by
+                
+                # To prevent torch.cat errors, ALL images in the batch must match the dimensions 
+                # of the first successfully loaded image.
+                if first_target_w is None:
+                    first_target_w, first_target_h = target_w, target_h
+                else:
+                    target_w, target_h = first_target_w, first_target_h
+
+                if target_w != orig_w or target_h != orig_h:
+                    resample = Image.LANCZOS if upscale_method == "lanczos" else Image.BILINEAR
+                    image = image.resize((target_w, target_h), resample=resample)
+
+                # Compression
+                if img_compression > 0:
+                    img_byte_arr = io.BytesIO()
+                    image.save(img_byte_arr, format="JPEG", quality=max(1, 100 - img_compression))
+                    image = Image.open(img_byte_arr)
+
+                image_np = np.array(image).astype(np.float32) / 255.0
+                results.append(torch.from_numpy(image_np)[None,])
+            except Exception as e:
+                print(f"Error loading {path}: {e}")
+
+        # Combine all successfully loaded images into a single batched tensor for multi_output
+        if len(results) > 0:
+            multi_output = torch.cat(results, dim=0)
+        else:
+            # Fallback empty tensor if no valid paths
+            multi_output = torch.zeros((1, 64, 64, 3))
+            results = [multi_output]
+
+        # Pad individual outputs exactly to length 50 as defined in RETURN_TYPES
+        padded_results = results + [torch.zeros((1, 64, 64, 3))] * (50 - len(results))
+
+        # Return the multi batch output first, followed by the individual padded items
+        return (multi_output, *padded_results[:50])