From 56196ab0f72c8f671bd85b425744f80f02c823ea Mon Sep 17 00:00:00 2001
From: EllangoK <karun.ellango7@gmail.com>
Date: Tue, 4 Apr 2023 10:57:34 -0400
Subject: [PATCH] use common_upcale in blend

---
 comfy_extras/nodes_post_processing.py | 29 +++++----------------------
 1 file changed, 5 insertions(+), 24 deletions(-)

diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py
index 322f3ca8..703deaab 100644
--- a/comfy_extras/nodes_post_processing.py
+++ b/comfy_extras/nodes_post_processing.py
@@ -3,6 +3,8 @@ import torch
 import torch.nn.functional as F
 from PIL import Image
 
+import comfy.utils
+
 
 class Blend:
     def __init__(self):
@@ -31,7 +33,9 @@ class Blend:
 
     def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
         if image1.shape != image2.shape:
-            image2 = self.crop_and_resize(image2, image1.shape)
+            image2 = image2.permute(0, 3, 1, 2)
+            image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center')
+            image2 = image2.permute(0, 2, 3, 1)
 
         blended_image = self.blend_mode(image1, image2, blend_mode)
         blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor
@@ -55,29 +59,6 @@ class Blend:
     def g(self, x):
         return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
 
-    def crop_and_resize(self, img: torch.Tensor, target_shape: tuple):
-        batch_size, img_h, img_w, img_c = img.shape
-        _, target_h, target_w, _ = target_shape
-        img_aspect_ratio = img_w / img_h
-        target_aspect_ratio = target_w / target_h
-
-        # Crop center of the image to the target aspect ratio
-        if img_aspect_ratio > target_aspect_ratio:
-            new_width = int(img_h * target_aspect_ratio)
-            left = (img_w - new_width) // 2
-            img = img[:, :, left:left + new_width, :]
-        else:
-            new_height = int(img_w / target_aspect_ratio)
-            top = (img_h - new_height) // 2
-            img = img[:, top:top + new_height, :, :]
-
-        # Resize to target size
-        img = img.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
-        img = F.interpolate(img, size=(target_h, target_w), mode='bilinear', align_corners=False)
-        img = img.permute(0, 2, 3, 1)
-
-        return img
-
 class Blur:
     def __init__(self):
         pass