Fix typo

modules/shared.py

@@ -22,3 +22,4 @@ actual_upscaler = None
 
 # Batch of images to upscale
 batch = None
+batch_as_tensor = None

modules/upscaler.py

@@ -9,18 +9,13 @@ if (not hasattr(Image, 'Resampling')):  # For older versions of Pillow
 
 class Upscaler:
 
-    def _upscale(self, img: Image, scale):
+    def upscale(self, img: Image, scale, selected_model: str = None):
         if scale == 1.0:
             return img
         if (shared.actual_upscaler is None):
-            return img.resize((img.width * scale, img.height * scale), Image.Resampling.NEAREST)
-        tensor = pil_to_tensor(img)
-        image_upscale_node = ImageUpscaleWithModel()
-        (upscaled,) = image_upscale_node.upscale(shared.actual_upscaler, tensor.contiguous())  # Tensor may not be contiguous on Mac
-        return tensor_to_pil(upscaled)
-
-    def upscale(self, img: Image, scale, selected_model: str = None):
-        shared.batch = [self._upscale(img, scale) for img in shared.batch]
+            return img.resize((img.width * scale, img.height * scale), Image.Resampling.LANCZOS)
+        (upscaled, ) = ImageUpscaleWithModel().upscale(shared.actual_upscaler, shared.batch_as_tensor)
+        shared.batch = [tensor_to_pil(upscaled, i) for i in range(len(upscaled))]
         return shared.batch[0]
 
 

nodes.py

@@ -130,11 +130,12 @@ class UltimateSDUpscale:
         shared.actual_upscaler = upscale_model
 
         # Set the batch of images
-        shared.batch = [tensor_to_pil(image, i) for i in range(len(image))]
+        shared.batch = [tensor_to_pil(image, 0)]  # Script only works with one image, we will do the batch processing ourselves
+        shared.batch_as_tensor = image
 
         # Processing
         sdprocessing = StableDiffusionProcessing(
-            tensor_to_pil(image), model, positive, negative, vae,
+            shared.batch[0], model, positive, negative, vae,
             seed, steps, cfg, sampler_name, scheduler, denoise, upscale_by, force_uniform_tiles, tiled_decode,
             tile_width, tile_height, MODES[self.mode_type], SEAM_FIX_MODES[self.seam_fix_mode],
             custom_sampler, custom_sigmas,