diff --git a/.gitignore b/.gitignore
index a8f7742..74a8187 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,4 @@
 engines
 /__pycache__
 /utils/__pycache__
+/models
diff --git a/img2img.py b/img2img.py
index f850fe0..229bcf6 100644
--- a/img2img.py
+++ b/img2img.py
@@ -106,8 +106,8 @@ class Pipeline:
         )
 
     def predict(self, image: Image.Image, params: "Pipeline.InputParams") -> Image.Image:
-        # image_tensor = self.stream.preprocess_image(image)
+        image_tensor = self.stream.preprocess_image(image)
         # output_image = self.stream(image=image_tensor, prompt=params.prompt)
-        output_image = self.stream(image=image, prompt=params.prompt)
+        output_image = self.stream(image=image_tensor, prompt=params.prompt)
 
         return output_image
\ No newline at end of file
diff --git a/main.py b/main.py
index 2db4b63..fb7e7fc 100644
--- a/main.py
+++ b/main.py
@@ -5,15 +5,15 @@ import torch
 from PIL import Image
 import numpy as np
 import SpoutGL
-from OpenGL.GL import GL_RGBA
+from OpenGL.GL import GL_RGBA, GL_BGRA
 import time
 import img2img
 from multiprocessing import Queue
 
 def main():
     TARGET_FPS = 60
-    SPOUT_RECEIVER_NAME = "Spout DX11 Sender"
-    SPOUT_SENDER_NAME = "Output - StreamDiffusion"
+    SPOUT_RECEIVER_NAME = "NoiseSender"
+    SPOUT_SENDER_NAME = "StreamDiffusionSender"
     WIDTH = 512
     HEIGHT = 512
     PROMPT = "a beautiful landscape painting, trending on artstation, 8k, hyperrealistic"
@@ -82,7 +82,7 @@ def main():
                     continue
 
                 image_rgb_array = image_bgra[:, :, [2,1,0]]                
-                # image_rgb_array = image_rgb_array.astype(np.float32) / 255.0 
+                image_rgb_array = (image_rgb_array+ 1.0 )/2.0
                 input_image = Image.fromarray(image_rgb_array, 'RGB')                
                 # input_image.save("debug_input.png")
                 
@@ -100,7 +100,9 @@ def main():
                 # output_rgba_array = np.array(output_image.convert("RGBA"))
                 # output_bgra_array = output_rgba_array[:, :, [2, 1, 0, 3]]
                 # buffer = np.ascontiguousarray(output_bgra_array)
-                output_bgr_array = np.array(output_image, dtype=np.uint8)[:, :, ::-1]
+                # output_bgr_array = np.array(output_image, dtype=np.uint8)[:, :, ::-1]
+                output_bgr_array=np.array(output_image)
+                                
                 output_bgra_array = np.zeros((HEIGHT, WIDTH, 4), dtype=np.uint8)
                 output_bgra_array[:, :, :3] = output_bgr_array
                 output_bgra_array[:, :, 3] = 255