use some magic just this once to remove the need for researchers to think

README.md

@@ -109,7 +109,7 @@ unet = Unet(
 # decoder, which contains the unet and clip
 
 decoder = Decoder(
-    net = unet,
+    unet = unet,
     clip = clip,
     timesteps = 100,
     cond_drop_prob = 0.2
@@ -182,9 +182,9 @@ loss.backward()
 # now the diffusion prior can generate image embeddings from the text embeddings
 ```
 
-In the paper, they actually used a <a href="https://cascaded-diffusion.github.io/">recently discovered technique</a>, from <a href="http://www.jonathanho.me/">Jonathan Ho</a> himself (original author of DDPMs, from which DALL-E2 is based).
+In the paper, they actually used a <a href="https://cascaded-diffusion.github.io/">recently discovered technique</a>, from <a href="http://www.jonathanho.me/">Jonathan Ho</a> himself (original author of DDPMs, the core technique used in DALL-E v2) for high resolution image synthesis.
 
-This can easily be used within the framework offered in this repository as so
+This can easily be used within this framework as so
 
 ```python
 import torch
@@ -218,13 +218,12 @@ unet1 = Unet(
 unet2 = Unet(
     dim = 16,
     image_embed_dim = 512,
-    lowres_cond = True,         # subsequence unets must have this turned on (and first unet must have this turned off)
     cond_dim = 128,
     channels = 3,
     dim_mults = (1, 2, 4, 8, 16)
 ).cuda()
 
-# decoder, which contains the unet and clip
+# decoder, which contains the unet(s) and clip
 
 decoder = Decoder(
     clip = clip,
@@ -349,8 +348,7 @@ unet2 = Unet(
     image_embed_dim = 512,
     cond_dim = 128,
     channels = 3,
-    dim_mults = (1, 2, 4, 8, 16),
-    lowres_cond = True
+    dim_mults = (1, 2, 4, 8, 16)
 ).cuda()
 
 decoder = Decoder(
@@ -412,6 +410,9 @@ Offer training wrappers
 - [x] build the cascading ddpm by having Decoder class manage multiple unets at different resolutions
 - [ ] use an image resolution cutoff and do cross attention conditioning only if resources allow, and MLP + sum conditioning on rest
 - [ ] make unet more configurable
+- [ ] figure out some factory methods to make cascading unet instantiations less error-prone
+- [ ] offload unets not being trained on to CPU for memory efficiency (for training each resolution unets separately)
+- [ ] become an expert with unets, port learnings over to https://github.com/lucidrains/x-unet
 - [ ] train on a toy task, offer in colab
 - [ ] add attention to unet - apply some personal tricks with efficient attention - use the sparse attention mechanism from https://github.com/lucidrains/vit-pytorch#maxvit
 - [ ] build out latent diffusion architecture in separate file, as it is not faithful to dalle-2 (but offer it as as setting)

dalle2_pytorch/dalle2_pytorch.py

@@ -1,6 +1,7 @@
 import math
 from tqdm import tqdm
 from inspect import isfunction
+from functools import partial
 
 import torch
 import torch.nn.functional as F
@@ -11,7 +12,7 @@ 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.gaussian import GaussianBlur2d
+from kornia.filters import gaussian_blur2d
 
 from dalle2_pytorch.tokenizer import tokenizer
 
@@ -811,15 +812,22 @@ class Unet(nn.Module):
         lowres_cond = False, # for cascading diffusion - https://cascaded-diffusion.github.io/
         lowres_cond_upsample_mode = 'bilinear',
         blur_sigma = 0.1,
+        blur_kernel_size = 3,
         attend_at_middle = True, # whether to have a layer of attention at the bottleneck (can turn off for higher resolution in cascading DDPM, before bringing in efficient attention)
     ):
         super().__init__()
+        # save locals to take care of some hyperparameters for cascading DDPM
+
+        self._locals = locals()
+        del self._locals['self']
+        del self._locals['__class__']
 
         # for eventual cascading diffusion
 
         self.lowres_cond = lowres_cond
         self.lowres_cond_upsample_mode = lowres_cond_upsample_mode
-        self.lowres_cond_blur = GaussianBlur2d((3, 3), (blur_sigma, blur_sigma))
+        self.lowres_blur_kernel_size = blur_kernel_size
+        self.lowres_blur_sigma = blur_sigma
 
         # determine dimensions
 
@@ -893,6 +901,15 @@ class Unet(nn.Module):
             nn.Conv2d(dim, out_dim, 1)
         )
 
+    # if the current settings for the unet are not correct
+    # for cascading DDPM, then reinit the unet with the right settings
+    def force_lowres_cond(self, lowres_cond):
+        if lowres_cond == self.lowres_cond:
+            return self
+
+        updated_kwargs = {**self._locals, 'lowres_cond': lowres_cond}
+        return self.__class__(**updated_kwargs)
+
     def forward_with_cond_scale(
         self,
         *args,
@@ -915,7 +932,9 @@ class Unet(nn.Module):
         image_embed,
         lowres_cond_img = None,
         text_encodings = None,
-        cond_drop_prob = 0.
+        cond_drop_prob = 0.,
+        blur_sigma = None,
+        blur_kernel_size = None
     ):
         batch_size, device = x.shape[0], x.device
 
@@ -926,7 +945,9 @@ class Unet(nn.Module):
         if exists(lowres_cond_img):
             if self.training:
                 # when training, blur the low resolution conditional image
-                lowres_cond_img = self.lowres_cond_blur(lowres_cond_img)
+                blur_sigma = default(blur_sigma, self.lowres_blur_sigma)
+                blur_kernel_size = default(blur_kernel_size, self.lowres_blur_kernel_size)
+                lowres_cond_img = gaussian_blur2d(lowres_cond_img, cast_tuple(blur_kernel_size, 2), cast_tuple(blur_sigma, 2))
 
             lowres_cond_img = resize_image_to(lowres_cond_img, x.shape[-2:], mode = self.lowres_cond_upsample_mode)
             x = torch.cat((x, lowres_cond_img), dim = 1)
@@ -1014,7 +1035,17 @@ class Decoder(nn.Module):
         self.clip_image_size = clip.image_size
         self.channels = clip.image_channels
 
-        self.unets = cast_tuple(unet)
+        # automatically take care of ensuring that first unet is unconditional
+        # while the rest of the unets are conditioned on the low resolution image produced by previous unet
+
+        self.unets = nn.ModuleList([])
+        for ind, one_unet in enumerate(cast_tuple(unet)):
+            is_first = ind == 0
+            one_unet = one_unet.force_lowres_cond(not is_first)
+            self.unets.append(one_unet)
+
+        # unet image sizes
+
         image_sizes = default(image_sizes, (clip.image_size,))
         image_sizes = tuple(sorted(set(image_sizes)))
 

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.0.20',
+  version = '0.0.22',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',