diff --git a/replicate_predictor.py b/replicate_predictor.py
index c7143a0..66e7d2f 100644
--- a/replicate_predictor.py
+++ b/replicate_predictor.py
@@ -9,39 +9,38 @@ torch.backends.cudnn.deterministic = False
 
 class ReplicatePredictor(BasePredictor):
     def setup(self):
-        self.model = MinDalle(is_mega=True, is_reusable=True)
+        self.model = MinDalle(
+            is_mega=True, 
+            is_reusable=True, 
+            dtype=torch.float32
+        )
 
     def predict(
         self,
         text: str = Input(default='Dali painting of WALL·E'),
         intermediate_outputs: bool = Input(default=True),
         grid_size: int = Input(ge=1, le=9, default=5),
-        log2_temperature: float = Input(ge=-3, le=3, default=1),
+        log2_temperature: float = Input(ge=-3, le=3, default=0.0),
         log2_top_k: int = Input(ge=0, le=14, default=7),
-        log2_supercondition_factor: int = Input(ge=2, le=6, default=4)
+        log2_supercondition_factor: float = Input(ge=2, le=6, default=4)
     ) -> Iterator[Path]:
-        try:
-            image_stream = self.model.generate_image_stream(
-                text = text,
-                seed = -1,
-                grid_size = grid_size,
-                log2_mid_count = 3 if intermediate_outputs else 0,
-                temperature = 2 ** log2_temperature,
-                supercondition_factor = 2 ** log2_supercondition_factor,
-                top_k = 2 ** log2_top_k,
-                is_verbose = True
-            )
+        log2_mid_count = 3 if intermediate_outputs else 0
+        image_stream = self.model.generate_image_stream(
+            text = text,
+            seed = -1,
+            grid_size = grid_size,
+            log2_mid_count = log2_mid_count,
+            temperature = 2 ** log2_temperature,
+            supercondition_factor = 2 ** log2_supercondition_factor,
+            top_k = 2 ** log2_top_k,
+            is_verbose = True
+        )
 
-            iter = 0
-            path = Path(tempfile.mkdtemp())
-            for image in image_stream:
-                iter += 1
-                image_path = path / 'min-dalle-iter-{}.jpg'.format(iter)
-                image.save(str(image_path))
-                yield image_path
-        except:
-            print("An error occured, deleting model")
-            del self.model
-            torch.cuda.empty_cache()
-            self.setup()
-            raise Exception("There was an error, please try again")
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+        i = 0
+        path = Path(tempfile.mkdtemp())
+        for image in image_stream:
+            i += 1
+            ext = 'png' if i == 2 ** log2_mid_count else 'jpg'
+            image_path = path / 'min-dalle-iter-{}.{}'.format(i, ext)
+            image.save(str(image_path))
+            yield image_path
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