diff --git a/README.md b/README.md
index c530fe3..8dfb7b2 100644
--- a/README.md
+++ b/README.md
@@ -26,6 +26,6 @@ c. Create a write token/Copy an existing Token key and enter in the cmd window.
 ## Requirements
 * 11GB of free space.
 * Nvidia card with 8GB+ video memory.
-
+	* Currently min dalle can run in 6gb. Testing purpose
 ## Additional info
-Tested on RTX3070. One picture was making 14 seconds.
\ No newline at end of file
+Tested on RTX3070. One picture was making 12 - 14 seconds.
\ No newline at end of file
diff --git a/scripts/minDalle.py b/scripts/minDalle.py
index 9f377b3..4e9513b 100644
--- a/scripts/minDalle.py
+++ b/scripts/minDalle.py
@@ -3,13 +3,14 @@ from distutils.log import error
 from msilib.schema import Directory
 import os
 import time
-from IPython.display import display, update_display
 import torch
 from tqdm.auto import tqdm
 from min_dalle import MinDalle
 
 t = torch.cuda.get_device_properties(0).total_memory
-if t <=8500000000:
+
+is_mega = True
+if t <=6400000000:
     print("Not enough GPU memory to generate pictures")
 else:
     amount = int(input("Amount: "))
@@ -26,11 +27,18 @@ else:
     dtype = "float32" 
     if t <= 10500000000:
         dtype = "float16"
-    
+        print("Running float16 type")
+    else:
+        print("Running float32 type")
+    if t<= 8500000000:
+        print("Not enough memory to run mega dalle. Running smaller variant")
+        is_mega=False
+    else:
+        print("Running in mega mode")
     model = MinDalle(
         dtype=getattr(torch, dtype),
         device='cuda',
-        is_mega=True, 
+        is_mega=False, 
         is_reusable=True
     )
 
diff --git a/scripts/stable_diff.py b/scripts/stable_diff.py
index 87d13da..2b6c9cf 100644
--- a/scripts/stable_diff.py
+++ b/scripts/stable_diff.py
@@ -45,6 +45,10 @@ else:
     ).to(device)
     if t <= 10500000000:
         pipe.enable_attention_slicing()
+        
+    def dummy_checker(images, **kwargs):
+        return images, False
+    pipe.safety_checker = dummy_checker
 
     def preprocess(image):
         w, h = image.size
@@ -76,7 +80,7 @@ else:
             if filename.endswith(".png"):
                 path_img.append(os.path.join(root, filename))
 
-    print("Found %i pictures", len(path_img))
+    print("Found " +str(len(path_img)) +" pictures")
     start_time = time.time()
 
     counterr=0
@@ -99,10 +103,10 @@ else:
 
         for seed in range(endSeed-startSeed+1):
             generator = torch.Generator(device=device).manual_seed(startSeed+seed)
-            strenght = startStrength
-            while strenght <= endStrength:
-                guidance_scale = startScale
-                while guidance_scale <= endScale:
+            guidance_scale = startScale
+            while guidance_scale <= endScale:
+                strenght = startStrength
+                while strenght <= endStrength:
                     with autocast("cuda"):
                         image = pipe(prompt=prompt, init_image=init_image, strength=strenght, guidance_scale=guidance_scale, generator=generator)["sample"][0]
                     image.save(directory+str(counterr)+"/" + str(allwork) +".jpg")
@@ -118,9 +122,10 @@ else:
                     piexif.insert(exif_bytes, directory+str(counterr)+"/" + str(allwork) +".jpg")
 
                     allwork+=1
-                    
-                    guidance_scale+=deltaScale
-                strenght+=deltaStrength
+
+                    strenght+=deltaStrength
+                guidance_scale+=deltaScale
+                
         
         counterr+=1