Add ImageBatchFilter

Node that removes "empty" frames from a batch, empty being a single color with threshold

__init__.py

@@ -52,6 +52,7 @@ NODE_CONFIG = {
     "GetLatentRangeFromBatch": {"class": GetLatentRangeFromBatch, "name": "Get Latent Range From Batch"},
     "GetImageSizeAndCount": {"class": GetImageSizeAndCount, "name": "Get Image Size & Count"},
     "FastPreview": {"class": FastPreview, "name": "Fast Preview"},
+    "ImageBatchFilter": {"class": ImageBatchFilter, "name": "Image Batch Filter"},
     "ImageAndMaskPreview": {"class": ImageAndMaskPreview},
     "ImageAddMulti": {"class": ImageAddMulti, "name": "Image Add Multi"},
     "ImageBatchMulti": {"class": ImageBatchMulti, "name": "Image Batch Multi"},

nodes/image_nodes.py

@@ -1790,6 +1790,51 @@ Inserts a latent at the specified index into the original latent batch.
             ], dim=2)
 
         return ({"samples": joined_latents,},)
+
+class ImageBatchFilter:
+    
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "filter"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = "Removes empty images from a batch"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "images": ("IMAGE",),
+                 "empty_color": ("STRING", {"default": "0, 0, 0"}),
+                "empty_threshold": ("FLOAT", {"default": 0.01,"min": 0.0, "max": 1.0, "step": 0.01}),
+        },
+        "optional": {
+            "replacement_image": ("IMAGE",),
+        }
+    } 
+    
+    def filter(self, images, empty_color, empty_threshold, replacement_image=None):
+        B, H, W, C = images.shape
+
+        input_images = images.clone()
+
+        empty_color_list = [int(color.strip()) for color in empty_color.split(',')]
+        empty_color_tensor = torch.tensor(empty_color_list, dtype=torch.float32).to(input_images.device)
+        
+        color_diff = torch.abs(input_images - empty_color_tensor)
+        mean_diff = color_diff.mean(dim=(1, 2, 3))
+
+        empty_indices = mean_diff <= empty_threshold
+        
+        if replacement_image is not None:
+            B_rep, H_rep, W_rep, C_rep = replacement_image.shape
+            replacement = replacement_image.clone()
+            if (H_rep != images.shape[1]) or (W_rep != images.shape[2]) or (C_rep != images.shape[3]):
+                replacement = common_upscale(replacement.movedim(-1, 1), W, H, "lanczos", "center").movedim(1, -1)
+            input_images[empty_indices] = replacement[0]
+
+            return (input_images,)
+        else:
+            non_empty_images = input_images[~empty_indices]
+            return (non_empty_images,)
     
 class GetImagesFromBatchIndexed: