diff --git a/modules/ldsr_model.py b/modules/ldsr_model.py
index 4d8687c2f01e474ef0d884ff4346f1cf49bb907a..7dff0a9c27e627df52d6aa4b90b210cf86ffe354 100644
--- a/modules/ldsr_model.py
+++ b/modules/ldsr_model.py
@@ -24,13 +24,18 @@ class UpscalerLDSR(Upscaler):
     def load_model(self, path: str):
         # Remove incorrect project.yaml file if too big
         yaml_path = os.path.join(self.model_path, "project.yaml")
+        old_model_path = os.path.join(self.model_path, "model.pth")
+        new_model_path = os.path.join(self.model_path, "model.ckpt")
         if os.path.exists(yaml_path):
             statinfo = os.stat(yaml_path)
-            if statinfo.st_size <= 10485760:
+            if statinfo.st_size >= 10485760:
                 print("Removing invalid LDSR YAML file.")
                 os.remove(yaml_path)
+        if os.path.exists(old_model_path):
+            print("Renaming model from model.pth to model.ckpt")
+            os.rename(old_model_path, new_model_path)
         model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
-                                   file_name="model.pth", progress=True)
+                                   file_name="model.ckpt", progress=True)
         yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,
                                   file_name="project.yaml", progress=True)
 
diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py
index 7faac6e18387e27df1038a82b624e5b2dddfd65d..093a321063229b4ebf796684255a63d072b4d5f3 100644
--- a/modules/ldsr_model_arch.py
+++ b/modules/ldsr_model_arch.py
@@ -100,7 +100,6 @@ class LDSR:
         # If we can adjust the max upscale size, then the 4 below should be our variable
         print("Foo")
         down_sample_rate = target_scale / 4
-        print(f"Downsample rate is {down_sample_rate}")
         wd = width_og * down_sample_rate
         hd = height_og * down_sample_rate
         width_downsampled_pre = int(wd)
@@ -111,7 +110,7 @@ class LDSR:
                 f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
             im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
         else:
-            print(f"Down sample rate is 1 from {target_scale} / 4")
+            print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
         logs = self.run(model["model"], im_og, diffusion_steps, eta)
 
         sample = logs["sample"]