LoadImagesFromFolderKJ: resize batch to largest image in the folder

nodes/image_nodes.py

@@ -2182,8 +2182,8 @@ class LoadImagesFromFolderKJ:
     RETURN_TYPES = ("IMAGE", "MASK", "INT", "STRING",)
     RETURN_NAMES = ("image", "mask", "count", "image_path",)
     FUNCTION = "load_images"
-
-    CATEGORY = "image"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """Loads images from a folder into a batch, images are resized to match the largest image in the folder."""
 
     def load_images(self, folder, image_load_cap, start_index):
         if not os.path.isdir(folder):
@@ -2202,6 +2202,17 @@ class LoadImagesFromFolderKJ:
         # start at start_index
         dir_files = dir_files[start_index:]
 
+        # First pass - find maximum dimensions
+        max_width = 0
+        max_height = 0
+        for image_path in dir_files:
+            if os.path.isdir(image_path):
+                continue
+            with Image.open(image_path) as img:
+                width, height = img.size
+                max_width = max(max_width, width)
+                max_height = max(max_height, height)
+
         images = []
         masks = []
         image_path_list = []
@@ -2211,24 +2222,33 @@ class LoadImagesFromFolderKJ:
             limit_images = True
         image_count = 0
 
-        has_non_empty_mask = False
-
         for image_path in dir_files:
-            if os.path.isdir(image_path) and os.path.ex:
+            if os.path.isdir(image_path):
                 continue
             if limit_images and image_count >= image_load_cap:
                 break
             i = Image.open(image_path)
             i = ImageOps.exif_transpose(i)
+            
+            # Resize image to maximum dimensions
+            if i.size != (max_width, max_height):
+                i = i.resize((max_width, max_height), Image.Resampling.LANCZOS)
+            
             image = i.convert("RGB")
             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)
-                has_non_empty_mask = True
+                if mask.shape != (max_height, max_width):
+                    mask = torch.nn.functional.interpolate(mask.unsqueeze(0).unsqueeze(0), 
+                                                         size=(max_height, max_width), 
+                                                         mode='bilinear', 
+                                                         align_corners=False).squeeze()
             else:
-                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+                mask = torch.zeros((max_height, max_width), dtype=torch.float32, device="cpu")
+            
             images.append(image)
             masks.append(mask)
             image_path_list.append(image_path)
@@ -2236,30 +2256,16 @@ class LoadImagesFromFolderKJ:
 
         if len(images) == 1:
             return (images[0], masks[0], 1, image_path_list)
-
+        
         elif len(images) > 1:
             image1 = images[0]
-            mask1 = None
+            mask1 = masks[0].unsqueeze(0)
 
             for image2 in images[1:]:
-                if image1.shape[1:] != image2.shape[1:]:
-                    image2 = common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1, -1)
                 image1 = torch.cat((image1, image2), dim=0)
 
             for mask2 in masks[1:]:
-                if has_non_empty_mask:
-                    if image1.shape[1:3] != mask2.shape:
-                        mask2 = torch.nn.functional.interpolate(mask2.unsqueeze(0).unsqueeze(0), size=(image1.shape[2], image1.shape[1]), mode='bilinear', align_corners=False)
-                        mask2 = mask2.squeeze(0)
-                    else:
-                        mask2 = mask2.unsqueeze(0)
-                else:
-                    mask2 = mask2.unsqueeze(0)
-
-                if mask1 is None:
-                    mask1 = mask2
-                else:
-                    mask1 = torch.cat((mask1, mask2), dim=0)
+                mask1 = torch.cat((mask1, mask2.unsqueeze(0)), dim=0)
 
             return (image1, mask1, len(images), image_path_list)
 

nodes/nodes.py

@@ -166,10 +166,11 @@ Selects and returns the latents at the specified indices as an latent batch.
             "required": {
                  "latents": ("LATENT",),
                  "indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}),
+                 "latent_format": (["BCHW", "BTCHW", "BCTHW"], {"default": "BCHW"}),
         },
     } 
     
-    def indexedlatentsfrombatch(self, latents, indexes):
+    def indexedlatentsfrombatch(self, latents, indexes, latent_format):
         
         samples = latents.copy()
         latent_samples = samples["samples"] 
@@ -181,7 +182,12 @@ Selects and returns the latents at the specified indices as an latent batch.
         indices_tensor = torch.tensor(index_list, dtype=torch.long)
         
         # Select the latents at the specified indices
-        chosen_latents = latent_samples[indices_tensor]
+        if latent_format == "BCHW":
+            chosen_latents = latent_samples[indices_tensor]
+        elif latent_format == "BTCHW":
+            chosen_latents = latent_samples[:, indices_tensor]
+        elif latent_format == "BCTHW":
+            chosen_latents = latent_samples[:, :, indices_tensor]
 
         samples["samples"] = chosen_latents
         return (samples,)
@@ -2216,4 +2222,5 @@ Concatenates the audio1 to audio2 in the specified direction.
             concatenated_audio = torch.cat((waveform_1, waveform_2), dim=2)  # Concatenate along width
         elif direction == 'left':
             concatenated_audio= torch.cat((waveform_2, waveform_1), dim=2)  # Concatenate along width
-        return ({"waveform": concatenated_audio, "sample_rate": sample_rate_1},)
+        return ({"waveform": concatenated_audio, "sample_rate": sample_rate_1},)
+    

web/js/contextmenu.js

@@ -95,7 +95,6 @@ app.registerExtension({
 			app.ui.settings.addSetting({
 				id: "KJNodes.nodeAutoColor",
 				name: "KJNodes: Automatically set node colors",
-				defaultValue: true,
 				type: "boolean",
 				defaultValue: true,
 			});