wrap up cross embed layer feature

README.md

@@ -1002,12 +1002,13 @@ Once built, images will be saved to the same directory the command is invoked
 - [x] make sure resnet hyperparameters can be configurable across unet depth (groups and expansion factor)
 - [x] pull logic for training diffusion prior into a class DiffusionPriorTrainer, for eventual script based + CLI based training
 - [x] make sure the cascading ddpm in the repository can be trained unconditionally, offer a one-line CLI tool for training on a folder of images
+- [x] bring in cross-scale embedding from iclr paper https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/crossformer.py#L14
+- [x] cross embed layers for downsampling, as an option
 - [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet (test out unet² in ddpm repo) - consider https://github.com/lucidrains/uformer-pytorch attention-based unet
 - [ ] transcribe code to Jax, which lowers the activation energy for distributed training, given access to TPUs
 - [ ] train on a toy task, offer in colab
 - [ ] think about how best to design a declarative training config that handles preencoding for prior and training of multiple networks in decoder
 - [ ] extend diffusion head to use diffusion-gan (potentially using lightweight-gan) to speed up inference
-- [ ] bring in cross-scale embedding from iclr paper https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/crossformer.py#L14
 - [ ] figure out if possible to augment with external memory, as described in https://arxiv.org/abs/2204.11824
 - [ ] test out grid attention in cascading ddpm locally, decide whether to keep or remove
 - [ ] use an experimental tracker agnostic setup, as done <a href="https://github.com/lucidrains/tf-bind-transformer#simple-trainer-class-for-fine-tuning">here</a>
@@ -1093,4 +1094,15 @@ Once built, images will be saved to the same directory the command is invoked
 }
 ```
 
+```bibtex
+@misc{wang2021crossformer,
+    title   = {CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention},
+    author  = {Wenxiao Wang and Lu Yao and Long Chen and Binbin Lin and Deng Cai and Xiaofei He and Wei Liu},
+    year    = {2021},
+    eprint  = {2108.00154},
+    archivePrefix = {arXiv},
+    primaryClass = {cs.CV}
+}
+```
+
 *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

@@ -1228,6 +1228,33 @@ class LinearAttention(nn.Module):
         out = self.nonlin(out)
         return self.to_out(out)
 
+class CrossEmbedLayer(nn.Module):
+    def __init__(
+        self,
+        dim_in,
+        kernel_sizes,
+        dim_out = None,
+        stride = 2
+    ):
+        super().__init__()
+        assert all([*map(lambda t: (t % 2) == (stride % 2), kernel_sizes)])
+        dim_out = default(dim_out, dim_in)
+
+        kernel_sizes = sorted(kernel_sizes)
+        num_scales = len(kernel_sizes)
+
+        # calculate the dimension at each scale
+        dim_scales = [int(dim_out / (2 ** i)) for i in range(1, num_scales)]
+        dim_scales = [*dim_scales, dim_out - sum(dim_scales)]
+
+        self.convs = nn.ModuleList([])
+        for kernel, dim_scale in zip(kernel_sizes, dim_scales):
+            self.convs.append(nn.Conv2d(dim_in, dim_scale, kernel, stride = stride, padding = (kernel - stride) // 2))
+
+    def forward(self, x):
+        fmaps = tuple(map(lambda conv: conv(x), self.convs))
+        return torch.cat(fmaps, dim = 1)
+
 class Unet(nn.Module):
     def __init__(
         self,
@@ -1252,6 +1279,9 @@ class Unet(nn.Module):
         init_dim = None,
         init_conv_kernel_size = 7,
         resnet_groups = 8,
+        init_cross_embed_kernel_sizes = (3, 7, 15),
+        cross_embed_downsample = False,
+        cross_embed_downsample_kernel_sizes = (2, 4),
         **kwargs
     ):
         super().__init__()
@@ -1270,10 +1300,9 @@ class Unet(nn.Module):
         self.channels = channels
 
         init_channels = channels if not lowres_cond else channels * 2 # in cascading diffusion, one concats the low resolution image, blurred, for conditioning the higher resolution synthesis
-        init_dim = default(init_dim, dim // 2)
+        init_dim = default(init_dim, dim // 3 * 2)
 
-        assert (init_conv_kernel_size % 2) == 1
+        self.init_conv = CrossEmbedLayer(init_channels, dim_out = init_dim, kernel_sizes = init_cross_embed_kernel_sizes, stride = 1)
-        self.init_conv = nn.Conv2d(init_channels, init_dim, init_conv_kernel_size, padding = init_conv_kernel_size // 2)
 
         dims = [init_dim, *map(lambda m: dim * m, dim_mults)]
         in_out = list(zip(dims[:-1], dims[1:]))
@@ -1333,6 +1362,12 @@ class Unet(nn.Module):
 
         assert len(resnet_groups) == len(in_out)
 
+        # downsample klass
+
+        downsample_klass = Downsample
+        if cross_embed_downsample:
+            downsample_klass = partial(CrossEmbedLayer, kernel_sizes = cross_embed_downsample_kernel_sizes)
+
         # layers
 
         self.downs = nn.ModuleList([])
@@ -1348,7 +1383,7 @@ class Unet(nn.Module):
                 ResnetBlock(dim_in, dim_out, time_cond_dim = time_cond_dim, groups = groups),
                 Residual(LinearAttention(dim_out, **attn_kwargs)) if sparse_attn else nn.Identity(),
                 ResnetBlock(dim_out, dim_out, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim, groups = groups),
-                Downsample(dim_out) if not is_last else nn.Identity()
+                downsample_klass(dim_out) if not is_last else nn.Identity()
             ]))
 
         mid_dim = dims[-1]

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.2.6',
+  version = '0.2.8',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',