Add a center crop option to latent upscale node.

nodes.py

@@ -115,17 +115,33 @@ class EmptyLatentImage:
 
 class LatentUpscale:
     upscale_methods = ["nearest-exact", "bilinear", "area"]
+    crop_methods = ["disabled", "center"]
 
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,),
                               "width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
-                              "height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),}}
+                              "height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
+                              "crop": (s.crop_methods,)}}
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "upscale"
 
-    def upscale(self, samples, upscale_method, width, height):
-        s = torch.nn.functional.interpolate(samples, size=(height // 8, width // 8), mode=upscale_method)
+    def upscale(self, samples, upscale_method, width, height, crop):
+        if crop == "center":
+            old_width = samples.shape[3]
+            old_height = samples.shape[2]
+            old_aspect = old_width / old_height
+            new_aspect = width / height
+            x = 0
+            y = 0
+            if old_aspect > new_aspect:
+                x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
+            elif old_aspect < new_aspect:
+                y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
+            s = samples[:,:,y:old_height-y,x:old_width-x]
+        else:
+            s = samples
+        s = torch.nn.functional.interpolate(s, size=(height // 8, width // 8), mode=upscale_method)
         return (s,)
 
 class KSampler: