diff --git a/donkey.lua b/donkey.lua
index 9b4cf83..14eb609 100755
--- a/donkey.lua
+++ b/donkey.lua
@@ -243,7 +243,7 @@ local function makeData(images, flows)
 
   elseif opt.level == 3 then
     local coarseImages = image.scale(images, opt.fineWidth/2, opt.fineHeight/2)
-    initFlow = computeInitFlowL1(coarseImages:resize(1,coarseImages:size(1), 
+    initFlow = computeInitFlowL2(coarseImages:resize(1,coarseImages:size(1), 
               coarseImages:size(2), coarseImages:size(3)):cuda())
     initFlow = scaleFlow(initFlow:squeeze():float(), opt.fineHeight, opt.fineWidth)
 
@@ -252,7 +252,7 @@ local function makeData(images, flows)
 
   elseif opt.level == 4 then
     local coarseImages = image.scale(images, opt.fineWidth/2, opt.fineHeight/2)
-    initFlow = computeInitFlowL1(coarseImages:resize(1,coarseImages:size(1), 
+    initFlow = computeInitFlowL3(coarseImages:resize(1,coarseImages:size(1), 
               coarseImages:size(2), coarseImages:size(3)):cuda())
     initFlow = scaleFlow(initFlow:squeeze():float(), opt.fineHeight, opt.fineWidth)
 
@@ -261,7 +261,7 @@ local function makeData(images, flows)
 
   elseif opt.level == 5 then
     local coarseImages = image.scale(images, opt.fineWidth/2, opt.fineHeight/2)
-    initFlow = computeInitFlowL1(coarseImages:resize(1,coarseImages:size(1), 
+    initFlow = computeInitFlowL4(coarseImages:resize(1,coarseImages:size(1), 
               coarseImages:size(2), coarseImages:size(3)):cuda())
     initFlow = scaleFlow(initFlow:squeeze():float(), opt.fineHeight, opt.fineWidth)
 
diff --git a/spynet.lua b/spynet.lua
index 8793eea..f020031 100644
--- a/spynet.lua
+++ b/spynet.lua
@@ -389,14 +389,54 @@ local easyComputeFlow = function(im1, im2)
 
 end
 
-local function easy_setup()
-  modelL1path = paths.concat('models', 'modelL1_F.t7')
-  modelL2path = paths.concat('models', 'modelL2_F.t7')
-  modelL3path = paths.concat('models', 'modelL3_F.t7')
-  modelL4path = paths.concat('models', 'modelL4_F.t7')
-  modelL5path = paths.concat('models', 'modelL5_F.t7')
-  modelL6path = paths.concat('models', 'modelL6_F.t7')
+local function easy_setup(opt)
+  opt = opt or 'sintelFinal'
 
+  if opt=="sintelFinal" then
+    modelL1path = paths.concat('models', 'modelL1_F.t7')
+    modelL2path = paths.concat('models', 'modelL2_F.t7')
+    modelL3path = paths.concat('models', 'modelL3_F.t7')
+    modelL4path = paths.concat('models', 'modelL4_F.t7')
+    modelL5path = paths.concat('models', 'modelL5_F.t7')
+    modelL6path = paths.concat('models', 'modelL6_F.t7')
+  end
+
+  if opt=="sintelClean" then
+    modelL1path = paths.concat('models', 'modelL1_C.t7')
+    modelL2path = paths.concat('models', 'modelL2_C.t7')
+    modelL3path = paths.concat('models', 'modelL3_C.t7')
+    modelL4path = paths.concat('models', 'modelL4_C.t7')
+    modelL5path = paths.concat('models', 'modelL5_C.t7')
+    modelL6path = paths.concat('models', 'modelL6_C.t7')
+  end
+
+  if opt=="chairsClean" then
+    modelL1path = paths.concat('models', 'modelL1_4.t7')
+    modelL2path = paths.concat('models', 'modelL2_4.t7')
+    modelL3path = paths.concat('models', 'modelL3_4.t7')
+    modelL4path = paths.concat('models', 'modelL4_4.t7')
+    modelL5path = paths.concat('models', 'modelL5_4.t7')
+    modelL6path = paths.concat('models', 'modelL5_4.t7')
+  end
+
+  if opt=="chairsFinal" then
+    modelL1path = paths.concat('models', 'modelL1_3.t7')
+    modelL2path = paths.concat('models', 'modelL2_3.t7')
+    modelL3path = paths.concat('models', 'modelL3_3.t7')
+    modelL4path = paths.concat('models', 'modelL4_3.t7')
+    modelL5path = paths.concat('models', 'modelL5_3.t7')
+    modelL6path = paths.concat('models', 'modelL5_3.t7')
+  end
+
+  if opt=="kittiFinal" then
+    modelL1path = paths.concat('models', 'modelL1_K.t7')
+    modelL2path = paths.concat('models', 'modelL2_K.t7')
+    modelL3path = paths.concat('models', 'modelL3_K.t7')
+    modelL4path = paths.concat('models', 'modelL4_K.t7')
+    modelL5path = paths.concat('models', 'modelL5_K.t7')
+    modelL6path = paths.concat('models', 'modelL6_K.t7')
+  end
+  
   modelL1 = torch.load(modelL1path)
   if torch.type(modelL1) == 'nn.DataParallelTable' then
      modelL1 = modelL1:get(1)