diff --git a/app/stable_diffusion_xl.py b/app/stable_diffusion_xl.py
index 1eec066..c1fd09f 100644
--- a/app/stable_diffusion_xl.py
+++ b/app/stable_diffusion_xl.py
@@ -32,15 +32,14 @@ class SDXLTxt2Img:
         else:
             print(f"The directory '{self.cache_path}' does not exist.")
 
-        self.pipe = diffusers.DiffusionPipeline.from_pretrained(
+        self.pipe = diffusers.StableDiffusionXLPipeline.from_pretrained(
             self.cache_path,
             torch_dtype=torch.float16,
             use_safetensors=True,
         )
 
-        self.upscaler_cache_path = self.cache_path
-        self.upscaler = diffusers.StableDiffusionXLImg2ImgPipeline.from_pretrained(
-            self.upscaler_cache_path,
+        self.refiner = diffusers.StableDiffusionXLImg2ImgPipeline.from_pretrained(
+            self.cache_path,
             torch_dtype=torch.float16,
             use_safetensors=True,
         )
@@ -92,7 +91,7 @@ class SDXLTxt2Img:
         self.pipe.to("cuda")
         self.pipe.enable_vae_tiling()
         self.pipe.enable_xformers_memory_efficient_attention()
-        generated_images = self.pipe(
+        generated_image = self.pipe(
             prompt=prompt,
             negative_prompt=n_prompt,
             guidance_scale=7,
@@ -100,25 +99,25 @@ class SDXLTxt2Img:
             width=width,
             generator=generator,
             num_inference_steps=steps,
-        ).images
+        ).images[0]
+
+        generated_images = [generated_image]
 
         if use_upscaler:
-            base_images = generated_images
-            for image in base_images:
-                image = self._resize_image(image=image, scale_factor=2)
-                self.upscaler.to("cuda")
-                self.upscaler.enable_vae_tiling()
-                self.upscaler.enable_xformers_memory_efficient_attention()
-                upscaled_images = self.upscaler(
-                    prompt=prompt,
-                    negative_prompt=n_prompt,
-                    num_inference_steps=steps,
-                    strength=0.3,
-                    guidance_scale=7,
-                    generator=generator,
-                    image=image,
-                ).images
-            generated_images.extend(upscaled_images)
+            self.refiner.to("cuda")
+            self.refiner.enable_vae_tiling()
+            self.refiner.enable_xformers_memory_efficient_attention()
+            base_image = self._double_image_size(generated_image)
+            image = self.refiner(
+                prompt=prompt,
+                negative_prompt=n_prompt,
+                num_inference_steps=50,
+                strength=0.3,
+                guidance_scale=7.5,
+                generator=generator,
+                image=base_image,
+            ).images[0]
+            generated_images.append(image)
 
         image_output = []
         for image in generated_images:
@@ -128,8 +127,7 @@ class SDXLTxt2Img:
 
         return image_output
 
-    def _resize_image(self, image: PIL.Image.Image, scale_factor: int) -> PIL.Image.Image:
+    def _double_image_size(self, image: PIL.Image.Image) -> PIL.Image.Image:
         image = image.convert("RGB")
         width, height = image.size
-        img = image.resize((width * scale_factor, height * scale_factor), resample=PIL.Image.LANCZOS)
-        return img
+        return image.resize((width * 2, height * 2), resample=PIL.Image.LANCZOS)