Add ImagePrepForICLora, ImageCropByMask

__init__.py

@@ -60,6 +60,7 @@ NODE_CONFIG = {
     "ImageConcanate": {"class": ImageConcanate, "name": "Image Concatenate"},
     "ImageConcatFromBatch": {"class": ImageConcatFromBatch, "name": "Image Concatenate From Batch"},
     "ImageConcatMulti": {"class": ImageConcatMulti, "name": "Image Concatenate Multi"},
+    "ImageCropByMask": {"class": ImageCropByMask, "name": "Image Crop By Mask"},
     "ImageCropByMaskAndResize": {"class": ImageCropByMaskAndResize, "name": "Image Crop By Mask And Resize"},
     "ImageCropByMaskBatch": {"class": ImageCropByMaskBatch, "name": "Image Crop By Mask Batch"},
     "ImageUncropByMask": {"class": ImageUncropByMask, "name": "Image Uncrop By Mask"},
@@ -72,6 +73,7 @@ NODE_CONFIG = {
     "ImagePass": {"class": ImagePass},
     "ImagePadForOutpaintMasked": {"class": ImagePadForOutpaintMasked, "name": "Image Pad For Outpaint Masked"},
     "ImagePadForOutpaintTargetSize": {"class": ImagePadForOutpaintTargetSize, "name": "Image Pad For Outpaint Target Size"},
+    "ImagePrepForICLora": {"class": ImagePrepForICLora, "name": "Image Prep For ICLora"},
     "ImageResizeKJ": {"class": ImageResizeKJ, "name": "Resize Image"},
     "ImageUpscaleWithModelBatched": {"class": ImageUpscaleWithModelBatched, "name": "Image Upscale With Model Batched"},
     "InsertImagesToBatchIndexed": {"class": InsertImagesToBatchIndexed, "name": "Insert Images To Batch Indexed"},

nodes/image_nodes.py

@@ -1092,7 +1092,66 @@ class ImagePadForOutpaintTargetSize:
 
         # Now call the original expand_image with the calculated padding
         return ImagePadForOutpaintMasked.expand_image(self, image_scaled, pad_left, pad_top, pad_right, pad_bottom, feathering, mask_scaled)
-    
+
+class ImagePrepForICLora:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "output_width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
+                "output_height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
+                "border_width": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1}),
+            },
+            "optional": {
+                "mask": ("MASK",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    FUNCTION = "expand_image"
+
+    CATEGORY = "image"
+
+    def expand_image(self, image, output_width, output_height, border_width, mask=None):
+        if mask is not None:
+            if torch.allclose(mask, torch.zeros_like(mask)):
+                    print("Warning: The incoming mask is fully black. Handling it as None.")
+                    mask = None
+        B, H, W, C = image.size()
+
+        # Handle mask
+        if mask is not None:
+            resized_mask = torch.nn.functional.interpolate(
+                mask.unsqueeze(1), 
+                size=(image.shape[1], image.shape[2]), 
+                mode='nearest'
+            ).squeeze(1)
+            print(resized_mask.shape)
+            image = image * resized_mask.unsqueeze(-1)
+
+        # Calculate new width maintaining aspect ratio
+        new_width = int((W / H) * output_height)
+        
+        # Resize image to new height while maintaining aspect ratio
+        resized_image = common_upscale(image.movedim(-1,1), new_width, output_height, "lanczos", "disabled").movedim(1,-1)
+
+        # Create padded image
+        empty_image = torch.zeros((B, output_height, output_width, C), device=image.device)
+
+        if border_width > 0:
+            border = torch.zeros((B, output_height, border_width, C), device=image.device)
+            padded_image = torch.cat((resized_image, border, empty_image), dim=2)
+            padded_mask = torch.ones((B, padded_image.shape[1], padded_image.shape[2]), device=image.device)
+            padded_mask[:, :, :new_width + border_width] = 0
+        else:
+            padded_image = torch.cat((resized_image, empty_image), dim=2)
+            padded_mask = torch.ones((B, padded_image.shape[1], padded_image.shape[2]), device=image.device)
+            padded_mask[:, :, :new_width] = 0
+
+        return (padded_image, padded_mask)
+        
+
 class ImageAndMaskPreview(SaveImage):
     def __init__(self):
         self.output_dir = folder_paths.get_temp_directory()
@@ -2789,6 +2848,48 @@ class ImageCropByMaskAndResize:
 
         return (torch.stack(image_list), torch.stack(mask_list), bbox_list)
     
+class ImageCropByMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE", ),
+                "mask": ("MASK", ),           
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", )
+    RETURN_NAMES = ("image", )
+    FUNCTION = "crop"
+    CATEGORY = "KJNodes/image"
+
+    def crop(self, image, mask):
+        B, H, W, C = image.shape
+        mask = mask.round()
+        
+        # Find bounding box for each batch
+        crops = []
+        
+        for b in range(B):
+            # Get coordinates of non-zero elements
+            rows = torch.any(mask[min(b, mask.shape[0]-1)] > 0, dim=1)
+            cols = torch.any(mask[min(b, mask.shape[0]-1)] > 0, dim=0)
+            
+            # Find boundaries
+            y_min, y_max = torch.where(rows)[0][[0, -1]]
+            x_min, x_max = torch.where(cols)[0][[0, -1]]
+            
+            # Crop image and mask
+            crop = image[b:b+1, y_min:y_max+1, x_min:x_max+1, :]            
+            crops.append(crop)
+        
+        # Stack results back together
+        cropped_images = torch.cat(crops, dim=0)
+        
+        return (cropped_images, )
+
+       
+    
 class ImageUncropByMask:
 
     @classmethod