diff --git a/SimpleSampler.py b/SimpleSampler.py
index 65e46c7..f9647d3 100644
--- a/SimpleSampler.py
+++ b/SimpleSampler.py
@@ -17,7 +17,9 @@ import hashlib
 import copy
 import traceback
 
+
 from PIL import Image
+from nodes import common_ksampler
 from PIL.PngImagePlugin import PngInfo
 import numpy as np
 
@@ -25,10 +27,12 @@ import numpy as np
 #print(f"SimpleSampler __file__ {os.path.splitext(os.path.basename(__file__))[0]}")
 
 import os
-if __name__ == os.path.splitext(os.path.basename(__file__))[0] :
+if __name__ == os.path.splitext(os.path.basename(__file__))[0] or __name__ =='__main__':
     from ConsoleColor import print, console
+    from wildcards import wildcards
 else:
     from .ConsoleColor import print, console
+    from .wildcards import wildcards
 #print(__file__)
 #print(os.path.basename(__file__))
 
@@ -46,6 +50,46 @@ except:
     err_msg = traceback.format_exc()
     print(err_msg)
     
+    
+def encode(clip, text):    
+    if wildcardsOn:
+        text=wildcards.run(text)
+    return [[clip.encode(text), {}]]
+    
+def generate(width, height, batch_size=1):
+    latent = torch.zeros([batch_size, 4, height // 8, width // 8])
+    return {"samples":latent}
+    # RETURN_TYPES = ("LATENT",)
+    
+def decode(vae, samples):
+    return vae.decode(samples["samples"])
+    # RETURN_TYPES = ("IMAGE",)
+    
+def sample(
+    model, seed, steps, cfg, sampler_name, scheduler, 
+    clip,
+    vae,
+    positive, negative, 
+    #latent_image, 
+    width, height, denoise=1.0, batch_size=1
+    ):
+
+    samples=common_ksampler(
+        model, seed, steps, cfg, sampler_name, scheduler, 
+        #positive, 
+        encode(clip, positive),
+        #negative, 
+        encode(clip, negative),
+        #latent_image, 
+        generate( width, height, batch_size=1), 
+        denoise=denoise)[0]
+
+    return (decode(vae,samples),)
+    
+def load_vae(vae_name):
+    vae_path = folder_paths.get_full_path("vae", vae_name)
+    vae = comfy.sd.VAE(ckpt_path=vae_path)
+    return vae
 #----------------------------
 class SimpleSampler:
     @classmethod
@@ -54,39 +98,88 @@ class SimpleSampler:
                     {
                     "model": ("MODEL",),
                     #"positive": ("CONDITIONING", ),
-                    "positive": ("STRING", {"multiline": True}), "clip": ("CLIP", ),
+                    "clip": ("CLIP", ),
+                    "vae": ("VAE", ),
+                    "positive": ("STRING", {"multiline": True}), 
                     #"negative": ("CONDITIONING", ),
-                    "negative": ("STRING", {"multiline": True}), "clip": ("CLIP", ),
+                    "negative": ("STRING", {"multiline": True}), 
                     "width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
                     "height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
                     "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
+                    "cfg": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0}),
                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
                     "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
-                    "latent_image": ("LATENT", ),
+                    #"latent_image": ("LATENT", ),
                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                     }}
-
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "sample"
+    RETURN_TYPES = ("IMAGE",)
+    #RETURN_TYPES = ("LATENT",)
+    FUNCTION = "simple"
 
     CATEGORY = "sampling"
     
-    def encode(self, clip, text):    
-        if wildcardsOn:
-            text=wildcards.run(text)
-        return ([[clip.encode(text), {}]], )
-
-    def generate(self, width, height, batch_size=1):
-        latent = torch.zeros([batch_size, 4, height // 8, width // 8])
-        return ({"samples":latent}, )
-
-    def sample(self, 
-        model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, 
+    def simple(self, 
+        model, seed, steps, cfg, sampler_name, scheduler, 
+        clip,
+        vae,
+        positive, negative, 
         width, height, denoise=1.0, batch_size=1
         ):
 
-        return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, self.encode(clip, positive), elf.encode(clip, negative), self.generate( width, height, batch_size=1), denoise=denoise)
-        
+        return sample(
+            model, seed, steps, cfg, sampler_name, scheduler, 
+            clip,
+            vae,
+            positive, negative, 
+            width, height, denoise, batch_size
+            )
+
+#----------------------------
+class SimpleSamplerVAE:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {
+                    "model": ("MODEL",),
+                    #"positive": ("CONDITIONING", ),
+                    "clip": ("CLIP", ),
+                    #"vae": ("VAE", ),
+                    "vae_name": (folder_paths.get_filename_list("vae"), ),
+                    "positive": ("STRING", {"multiline": True}), 
+                    #"negative": ("CONDITIONING", ),
+                    "negative": ("STRING", {"multiline": True}), 
+                    "width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
+                    "height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
+                    "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
+                    "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                    "cfg": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0}),
+                    "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
+                    "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
+                    #"latent_image": ("LATENT", ),
+                    "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                    }}
+    RETURN_TYPES = ("IMAGE",)
+    #RETURN_TYPES = ("LATENT",)
+    FUNCTION = "simple"
+
+    CATEGORY = "sampling"
+    
+    def simple(self, 
+        model, seed, steps, cfg, sampler_name, scheduler, 
+        clip,
+        vae_name,
+        positive, negative, 
+        width, height, denoise=1.0, batch_size=1
+        ):
+
+        return sample(
+            model, seed, steps, cfg, sampler_name, scheduler, 
+            clip,
+            load_vae(vae_name),
+            positive, negative, 
+            width, height, denoise, batch_size
+            )
+
diff --git a/__init__.py b/__init__.py
index 195f730..f292f5e 100644
--- a/__init__.py
+++ b/__init__.py
@@ -68,7 +68,7 @@ if not md.startswith("custom_nodes."):
     add("LoraLoaderTextRandom")
     add("Random_Sampler")
     add("VAELoaderDecode")
-    add("SimpleSampler")
+    add("SimpleSampler",["SimpleSampler","SimpleSamplerVAE"])
     add("SaveImageSimple")
     #add("test")