diff --git a/nodes.py b/nodes.py
index 12f4b639..aaa4f87a 100644
--- a/nodes.py
+++ b/nodes.py
@@ -301,17 +301,35 @@ class LatentComposite:
                               "samples_from": ("LATENT",),
                               "x": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}),
                               "y": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}),
+                              "feather": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}),
                               }}
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "composite"
 
     CATEGORY = "latent"
 
-    def composite(self, samples_to, samples_from, x, y, composite_method="normal"):
+    def composite(self, samples_to, samples_from, x, y, composite_method="normal", feather=0):
         x =  x // 8
         y = y // 8
+        feather = feather // 8
         s = samples_to.clone()
-        s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]
+        if feather == 0:
+            s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]
+        else:
+            s_from = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]
+            mask = torch.ones_like(s_from)
+            for t in range(feather):
+                if y != 0:
+                    mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))
+
+                if y + samples_from.shape[2] < samples_to.shape[2]:
+                    mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))
+                if x != 0:
+                    mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))
+                if x + samples_from.shape[3] < samples_to.shape[3]:
+                    mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))
+            rev_mask = torch.ones_like(mask) - mask
+            s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] * mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] * rev_mask
         return (s,)
 
 class LatentCrop: