diff --git a/models/ModelBase.py b/models/ModelBase.py
index e4136b2..5403a48 100644
--- a/models/ModelBase.py
+++ b/models/ModelBase.py
@@ -92,7 +92,7 @@ class ModelBase(object):
                     if not isinstance(generator, SampleGeneratorBase):
                         raise Exception('training data generator is not subclass of SampleGeneratorBase')
                         
-            if self.sample_for_preview is None:
+            if (self.sample_for_preview is None) or (self.epoch == 0):
                 self.sample_for_preview = self.generate_next_sample()
 
         print ("===== Model summary =====")
diff --git a/models/Model_RecycleGAN/Model.py b/models/Model_RecycleGAN/Model.py
index 2073531..8fcb9ef 100644
--- a/models/Model_RecycleGAN/Model.py
+++ b/models/Model_RecycleGAN/Model.py
@@ -20,10 +20,7 @@ class Model(ModelBase):
     #override
     def onInitialize(self, batch_size=-1, **in_options):
         exec(nnlib.code_import_all, locals(), globals())
-        self.set_vram_batch_requirements( {6:6} )
- 
-        
-        
+
         created_batch_size = self.get_batch_size()
         if self.epoch == 0: 
             #first run
@@ -31,13 +28,22 @@ class Model(ModelBase):
             print ("\nModel first run. Enter options.")
             
             try:
-                input_created_batch_size = int ( input ("Batch_size (default - based on VRAM) : ") )
+                created_resolution = int ( input ("Resolution (default:64, valid: 64,128,256) : ") )
             except:
-                input_created_batch_size = 0
+                created_resolution = 64
                 
-            if input_created_batch_size != 0:
-                created_batch_size = input_created_batch_size
+            if created_resolution not in [64,128,256]:
+                created_resolution = 64
+
+            try:
+                created_batch_size = int ( input ("Batch_size (minimum/default - 16) : ") )
+            except:
+                created_batch_size = 16
+            created_batch_size = max(created_batch_size,1)
             
+            print ("Done. If training won't start, decrease resolution")
+     
+            self.options['created_resolution'] = created_resolution
             self.options['created_batch_size'] = created_batch_size
             self.created_vram_gb = self.device_config.gpu_total_vram_gb
         else: 
@@ -45,9 +51,14 @@ class Model(ModelBase):
             if 'created_batch_size' in self.options.keys():
                 created_batch_size = self.options['created_batch_size']
             else:
-                raise Exception("Continue traning, but created_batch_size not found.")
+                raise Exception("Continue training, but created_batch_size not found.")
+                
+            if 'created_resolution' in self.options.keys():
+                created_resolution = self.options['created_resolution']
+            else:
+                raise Exception("Continue training, but created_resolution not found.")
         
-        resolution = 128
+        resolution = created_resolution
         bgr_shape = (resolution, resolution, 3)
         ngf = 64
         npf = 64
@@ -181,10 +192,6 @@ class Model(ModelBase):
                         output_sample_types=[ [f.SOURCE | f.MODE_BGR, resolution] ] ),
                    ])
       
-        #import code        
-        #code.interact(local=dict(globals(), **locals()))
-        self.supress_std_once = False
-        
     #override
     def onSave(self):
         self.save_weights_safe( [[self.GA,    self.get_strpath_storage_for_file(self.GAH5)],
@@ -237,7 +244,5 @@ class Model(ModelBase):
     def get_converter(self, **in_options):
         from models import ConverterImage
                    
-        return ConverterImage(self.predictor_func, predictor_input_size=128, output_size=128, **in_options)
+        return ConverterImage(self.predictor_func, predictor_input_size=self.options['created_resolution'], output_size=self.options['created_resolution'], **in_options)
 
-
-            
\ No newline at end of file
diff --git a/nnlib/nnlib.py b/nnlib/nnlib.py
index 6843457..90a9038 100644
--- a/nnlib/nnlib.py
+++ b/nnlib/nnlib.py
@@ -551,8 +551,8 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
                 for i in range(n_blocks):
                     x = ResnetBlock(ngf*4)(x)
                     
-                x = ReLU()(XNormalization(Conv2DTranspose(ngf*2, 3, 2, 'same')(x)))
-                x = ReLU()(XNormalization(Conv2DTranspose(ngf  , 3, 2, 'same')(x)))
+                x = ReLU()(XNormalization(PixelShuffler()(Conv2D(ngf*2 *4, 3, 1, 'same')(x))))
+                x = ReLU()(XNormalization(PixelShuffler()(Conv2D(ngf   *4, 3, 1, 'same')(x))))
                 
                 x = ReflectionPadding2D((3,3))(x)
                 x = Conv2D(output_nc, 7, 1, 'valid')(x)