diff --git a/comfy_extras/nodes/nodes_upscale_model.py b/comfy_extras/nodes/nodes_upscale_model.py
index 20f5b5eb5..3d157a2b0 100644
--- a/comfy_extras/nodes/nodes_upscale_model.py
+++ b/comfy_extras/nodes/nodes_upscale_model.py
@@ -73,11 +73,11 @@ class UpscaleModelManageable(ModelManageable):
     def model_size(self) -> int:
         model_params_size = sum(p.numel() * p.element_size() for p in self.model.parameters())
         dtype_size = torch.finfo(self.model_dtype()).bits // 8
-        input_size = self._input_size[0] * min(self.tile, self._input_size[1]) * min(self.tile, self._input_size[2]) * self._input_channels * dtype_size
-        output_size = self.output_size[0] * self.output_size[1] * self.output_size[2] * self._output_channels * dtype_size
-        extra_memory = (input_size + output_size) * 2  # This is an estimate, adjust as needed
+        batch_size = self._input_size[0]
+        input_size = batch_size * min(self.tile, self._input_size[1]) * min(self.tile, self._input_size[2]) * self._input_channels * dtype_size
+        output_size = batch_size * min(self.tile * self.scale, self.output_size[1]) * min(self.tile * self.scale, self.output_size[2]) * self._output_channels * dtype_size
 
-        return model_params_size + input_size + output_size + extra_memory
+        return model_params_size + input_size + output_size
 
     def model_patches_to(self, arg: torch.device | torch.dtype):
         if isinstance(arg, torch.device):
@@ -160,17 +160,20 @@ class ImageUpscaleWithModel:
                 pbar = utils.ProgressBar(steps)
                 s = utils.tiled_scale(in_img, lambda a: upscale_model.model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)
                 oom = False
-            except RuntimeError as exc_info:
-                if "have 1 channels, but got 3 channels instead" in str(exc_info):
-                    # convert RGB to luminance (assuming sRGB)
-                    rgb_weights = torch.tensor([0.2126, 0.7152, 0.0722], device=in_img.device, dtype=in_img.dtype)
-                    in_img = (in_img * rgb_weights.view(1, 3, 1, 1)).sum(dim=1, keepdim=True)
-                    continue
             except model_management.OOM_EXCEPTION as e:
                 tile //= 2
                 overlap //= 2
                 if tile < 64 or overlap < 4:
                     raise e
+            except RuntimeError as exc_info:
+                if "have 1 channels, but got 3 channels instead" in str(exc_info):
+                    # convert RGB to luminance (assuming sRGB)
+
+                    rgb_weights = torch.tensor([0.2126, 0.7152, 0.0722], device=in_img.device, dtype=in_img.dtype)
+                    in_img = (in_img * rgb_weights.view(1, 3, 1, 1)).sum(dim=1, keepdim=True)
+                    continue
+                else:
+                    raise exc_info
 
         # upscale_model.to("cpu")
         s = torch.clamp(s.movedim(-3, -1), min=0, max=1.0)
@@ -178,6 +181,7 @@ class ImageUpscaleWithModel:
         if s.shape[-1] == 1:
             s = s.expand(-1, -1, -1, 3)
 
+        del in_img
         return (s,)