diff --git a/denoising_diffusion_pytorch/karras_unet.py b/denoising_diffusion_pytorch/karras_unet.py
index 7520404c1..f25cba636 100644
--- a/denoising_diffusion_pytorch/karras_unet.py
+++ b/denoising_diffusion_pytorch/karras_unet.py
@@ -624,6 +624,62 @@ def forward(
 
         return self.output_block(x)
 
+# improvised MP Transformer
+
+class MPFeedForward(Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        mult = 4,
+        mp_add_t = 0.3
+    ):
+        super().__init__()
+        dim_inner = int(dim * mult)
+        self.net = nn.Sequential(
+            PixelNorm(dim = 1),
+            Conv2d(dim, dim_inner, 1),
+            MPSiLU(),
+            Conv2d(dim_inner, dim, 1)
+        )
+
+        self.mp_add = MPAdd(t = mp_add_t)
+
+    def forward(self, x):
+        res = x
+        out = self.net(x)
+        return self.mp_add(out, res)
+
+class MPImageTransformer(Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth,
+        dim_head = 64,
+        heads = 8,
+        num_mem_kv = 4,
+        ff_mult = 4,
+        attn_flash = False,
+        residual_mp_add_t = 0.3
+    ):
+        super().__init__()
+        self.layers = ModuleList([])
+
+        for _ in range(depth):
+            self.layers.append(ModuleList([
+                Attention(dim = dim, heads = heads, dim_head = dim_head, num_mem_kv = num_mem_kv, flash = attn_flash, mp_add_t = residual_mp_add_t),
+                MPFeedForward(dim = dim, mult = ff_mult, mp_add_t = residual_mp_add_t)
+            ]))
+
+    def forward(self, x):
+
+        for attn, ff in self.layers:
+            x = attn(x)
+            x = ff(x)
+
+        return x
+
 # example
 
 if __name__ == '__main__':
diff --git a/denoising_diffusion_pytorch/version.py b/denoising_diffusion_pytorch/version.py
index 78a6e51e6..420b4d0ed 100644
--- a/denoising_diffusion_pytorch/version.py
+++ b/denoising_diffusion_pytorch/version.py
@@ -1 +1 @@
-__version__ = '1.10.3'
+__version__ = '1.10.5'