diff --git a/inpaint_cropandstitch.py b/inpaint_cropandstitch.py
index b0c0ba3..2d8af26 100644
--- a/inpaint_cropandstitch.py
+++ b/inpaint_cropandstitch.py
@@ -601,44 +601,41 @@ class GPUProcessorLogic(ProcessorLogic):
         # samples shape: [B, H, W, C]
         mode = algorithm.lower()
         
-        # For algorithms not supported by torch.interpolate, use PIL on CPU
-        if mode in ('lanczos', 'box', 'hamming'):
-            original_device = samples.device
-            samples = samples.movedim(-1, 1)  # [B, C, H, W]
-            algorithm_enum = getattr(Image, algorithm.upper())
-            results = []
-            for i in range(samples.shape[0]):
-                samples_pil: Image.Image = F.to_pil_image(samples[i].cpu()).resize((width, height), algorithm_enum)
-                results.append(F.to_tensor(samples_pil))
-            samples = torch.stack(results, dim=0).to(original_device)
-            samples = samples.movedim(1, -1)
-            return samples
-        
+        # CPU works better, fallback to CPU for rescaling
+        original_device = samples.device
         samples = samples.movedim(-1, 1)  # [B, C, H, W]
-        samples = TF.interpolate(samples, size=(height, width), mode=mode, align_corners=False if mode not in ['nearest', 'area'] else None)
+        algorithm_enum = getattr(Image, algorithm.upper())
+        results = []
+        for i in range(samples.shape[0]):
+            samples_pil: Image.Image = F.to_pil_image(samples[i].cpu()).resize((width, height), algorithm_enum)
+            results.append(F.to_tensor(samples_pil))
+        samples = torch.stack(results, dim=0).to(original_device)
         samples = samples.movedim(1, -1)
         return samples
+        
+        #samples = samples.movedim(-1, 1)  # [B, C, H, W]
+        #samples = TF.interpolate(samples, size=(height, width), mode=mode, align_corners=False if mode not in ['nearest', 'area'] else None)
+        #samples = samples.movedim(1, -1)
+        #return samples
 
     def rescale_m(self, samples, width, height, algorithm: str):
         # samples shape: [B, H, W]
         mode = algorithm.lower()
         
-        # For algorithms not supported by torch.interpolate, use PIL on CPU
-        if mode in ('lanczos', 'box', 'hamming'):
-            original_device = samples.device
-            algorithm_enum = getattr(Image, algorithm.upper())
-            results = []
-            for i in range(samples.shape[0]):
-                samples_pil: Image.Image = F.to_pil_image(samples[i].cpu()).resize((width, height), algorithm_enum)
-                results.append(F.to_tensor(samples_pil).squeeze(0))
-            samples = torch.stack(results, dim=0).to(original_device)
-            return samples
-        
-        # For torch-supported modes, use GPU interpolate
-        samples = samples.unsqueeze(1)  # [B, H, W] -> [B, 1, H, W]
-        samples = TF.interpolate(samples, size=(height, width), mode=mode, align_corners=False if mode not in ['nearest', 'area'] else None)
-        samples = samples.squeeze(1)
+        # CPU works better, fallback to CPU for rescaling
+        original_device = samples.device
+        algorithm_enum = getattr(Image, algorithm.upper())
+        results = []
+        for i in range(samples.shape[0]):
+            samples_pil: Image.Image = F.to_pil_image(samples[i].cpu()).resize((width, height), algorithm_enum)
+            results.append(F.to_tensor(samples_pil).squeeze(0))
+        samples = torch.stack(results, dim=0).to(original_device)
         return samples
+        
+        #samples = samples.unsqueeze(1)  # [B, H, W] -> [B, 1, H, W]
+        #samples = TF.interpolate(samples, size=(height, width), mode=mode, align_corners=False if mode not in ['nearest', 'area'] else None)
+        #samples = samples.squeeze(1)
+        #return samples
 
     def fillholes_iterative_hipass_fill_m(self, samples):
         # samples shape: [B, H, W]
@@ -1234,7 +1231,7 @@ class InpaintCropImproved:
     DESCRIPTION = "Crops an image around a mask for inpainting, the optional context mask defines an extra area to keep for the context."
 
     # Remove the following # to turn on debug mode (extra outputs, print statements)
-    #'''
+    '''
     DEBUG_MODE = False
     RETURN_TYPES = ("STITCHER", "IMAGE", "MASK")
     RETURN_NAMES = ("stitcher", "cropped_image", "cropped_mask")
diff --git a/pyproject.toml b/pyproject.toml
index 95cac75..a41a85b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-inpaint-cropandstitch"
 description = "The '✂️ Inpaint Crop' and '✂️ Inpaint Stitch' nodes enable inpainting only on masked area very easily: crop the image around the masked area with the Crop node, then use any standard workflow for sampling, then connect the sampled image to the Stitch node, which will put it back in place in the original image. These nodes enable faster sampling of smaller areas and take care of downsampling and upsampling to fit specific model and resource needs."
-version = "3.0.3"
+version = "3.0.4"
 license = { file = "LICENSE" }
 
 [project.urls]