upgrade to best downsample

README.md

@@ -1285,4 +1285,14 @@ For detailed information on training the diffusion prior, please refer to the [d
 }
 ```
 
+```bibtex
+@article{Sunkara2022NoMS,
+    title   = {No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects},
+    author  = {Raja Sunkara and Tie Luo},
+    journal = {ArXiv},
+    year    = {2022},
+    volume  = {abs/2208.03641}
+}
+```
+
 *Creating noise from data is easy; creating data from noise is generative modeling.* - <a href="https://arxiv.org/abs/2011.13456">Yang Song's paper</a>

dalle2_pytorch/dalle2_pytorch.py

@@ -1479,9 +1479,14 @@ class PixelShuffleUpsample(nn.Module):
     def forward(self, x):
         return self.net(x)
 
-def Downsample(dim, *, dim_out = None):
+def Downsample(dim, dim_out = None):
+    # https://arxiv.org/abs/2208.03641 shows this is the most optimal way to downsample
+    # named SP-conv in the paper, but basically a pixel unshuffle
     dim_out = default(dim_out, dim)
-    return nn.Conv2d(dim, dim_out, 4, 2, 1)
+    return nn.Sequential(
+        Rearrange('b c (h s1) (w s2) -> b (c s1 s2) h w', s1 = 2, s2 = 2),
+        nn.Conv2d(dim * 4, dim_out, 1)
+    )
 
 class WeightStandardizedConv2d(nn.Conv2d):
     """

dalle2_pytorch/trainer.py

@@ -519,7 +519,7 @@ class DecoderTrainer(nn.Module):
             clip = decoder.clip
             clip.to(precision_type)
 
-        decoder, train_dataloader, *optimizers = list(self.accelerator.prepare(decoder, dataloaders['train'], *optimizers))
+        decoder, *optimizers = list(self.accelerator.prepare(decoder, *optimizers))
 
         self.decoder = decoder
 

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '1.9.0'
+__version__ = '1.10.0'