bring in blur, as it will be used somewhere in the cascading DDPM in the decoder eventually, once i figure it out

dalle2_pytorch/dalle2_pytorch.py

@@ -11,6 +11,8 @@ from einops.layers.torch import Rearrange
 from einops_exts import rearrange_many, repeat_many, check_shape
 from einops_exts.torch import EinopsToAndFrom
 
+from kornia.filters import filter2d
+
 from dalle2_pytorch.tokenizer import tokenizer
 
 # use x-clip
@@ -731,6 +733,17 @@ class Unet(nn.Module):
 
         return self.final_conv(x)
 
+class Blur(nn.Module):
+    def __init__(self):
+        super().__init__()
+        filt = torch.Tensor([1, 2, 1])
+        self.register_buffer('filt', filt)
+
+    def forward(self, x):
+        filt = self.filt
+        filt = rearrange(filt, '... j -> ... 1 j') * rearrange(flit, '... i -> ... i 1')
+        return filter2d(x, filt, normalized = True)
+
 class Decoder(nn.Module):
     def __init__(
         self,

setup.py

@@ -25,6 +25,7 @@ setup(
     'click',
     'einops>=0.4',
     'einops-exts>=0.0.3',
+    'kornia>=0.5.4',
     'pillow',
     'torch>=1.10',
     'torchvision',