use null container pattern to cleanup some conditionals, save more cleanup for next week

README.md

@@ -383,9 +383,11 @@ You can also train the decoder on images of greater than the size (say 512x512)
 
 For the layperson, no worries, training will all be automated into a CLI tool, at least for small scale training.
 
-## Experimental - DALL-E2 with Latent Diffusion
+## Experimental
 
-This repository decides to take the next step and offer DALL-E2 combined with latent diffusion, from Rombach et al.
+### DALL-E2 with Latent Diffusion
+
+This repository decides to take the next step and offer DALL-E2 combined with <a href="https://huggingface.co/spaces/multimodalart/latentdiffusion">latent diffusion</a>, from Rombach et al.
 
 You can use it as follows. Latent diffusion can be limited to just the first U-Net in the cascade, or to any number you wish.
 
@@ -409,9 +411,10 @@ clip = CLIP(
     visual_heads = 8
 )
 
-# 2 unets for the decoder (a la cascading DDPM)
+# 3 unets for the decoder (a la cascading DDPM)
 
-# 1st unet is doing latent diffusion
+# first two unets are doing latent diffusion
+# vqgan-vae must be trained before hand
 
 vae1 = VQGanVAE(
     dim = 32,
@@ -469,7 +472,7 @@ decoder = Decoder(
 
 # mock images (get a lot of this)
 
-images = torch.randn(1, 3, 512, 512).cuda()
+images = torch.randn(1, 3, 1024, 1024).cuda()
 
 # feed images into decoder, specifying which unet you want to train
 # each unet can be trained separately, which is one of the benefits of the cascading DDPM scheme
@@ -482,6 +485,10 @@ with decoder.one_unet_in_gpu(2):
     loss = decoder(images, unet_number = 2)
     loss.backward()
 
+with decoder.one_unet_in_gpu(3):
+    loss = decoder(images, unet_number = 3)
+    loss.backward()
+
 # do the above for many steps for both unets
 
 # then it will learn to generate images based on the CLIP image embeddings
@@ -492,7 +499,11 @@ mock_image_embed = torch.randn(1, 512).cuda()
 images = decoder.sample(mock_image_embed) # (1, 3, 1024, 1024)
 ```
 
-## CLI Usage (work in progress)
+## Training wrapper (wip)
+
+Offer training wrappers
+
+## CLI (wip)
 
 ```bash
 $ dream 'sharing a sunset at the summit of mount everest with my dog'
@@ -500,9 +511,7 @@ $ dream 'sharing a sunset at the summit of mount everest with my dog'
 
 Once built, images will be saved to the same directory the command is invoked
 
-## Training wrapper (wip)
+<a href="https://github.com/lucidrains/big-sleep">template</a>
-
-Offer training wrappers
 
 ## Training CLI (wip)
 
@@ -525,6 +534,8 @@ Offer training wrappers
 - [ ] copy the cascading ddpm code to a separate repo (perhaps https://github.com/lucidrains/denoising-diffusion-pytorch) as the main contribution of dalle2 really is just the prior network
 - [ ] train on a toy task, offer in colab
 - [ ] extend diffusion head to use diffusion-gan (potentially using lightweight-gan) to speed up inference
+- [ ] bring in tools to train vqgan-vae
+- [ ] for decoder, allow ability to customize objective (predict epsilon vs x0), in case latent diffusion does better with prediction of x0
 
 ## Citations
 

dalle2_pytorch/dalle2_pytorch.py

@@ -16,6 +16,7 @@ from einops_exts.torch import EinopsToAndFrom
 from kornia.filters import gaussian_blur2d
 
 from dalle2_pytorch.tokenizer import tokenizer
+from dalle2_pytorch.vqgan_vae import NullVQGanVAE, VQGanVAE
 
 # use x-clip
 
@@ -48,11 +49,11 @@ def is_list_str(x):
         return False
     return all([type(el) == str for el in x])
 
-def pad_tuple_to_length(t, length):
+def pad_tuple_to_length(t, length, fillvalue = None):
     remain_length = length - len(t)
     if remain_length <= 0:
         return t
-    return (*t, *((None,) * remain_length))
+    return (*t, *((fillvalue,) * remain_length))
 
 # for controlling freezing of CLIP
 
@@ -1135,12 +1136,15 @@ class Decoder(nn.Module):
         # while the rest of the unets are conditioned on the low resolution image produced by previous unet
 
         unets = cast_tuple(unet)
-        vaes = pad_tuple_to_length(cast_tuple(vae), len(unets))
+        vaes = pad_tuple_to_length(cast_tuple(vae), len(unets), fillvalue = NullVQGanVAE(channels = self.channels))
 
         self.unets = nn.ModuleList([])
         self.vaes = nn.ModuleList([])
 
         for ind, (one_unet, one_vae) in enumerate(zip(unets, vaes)):
+            assert isinstance(one_unet, Unet)
+            assert isinstance(one_vae, (VQGanVAE, NullVQGanVAE))
+
             is_first = ind == 0
             latent_dim = one_vae.encoded_dim if exists(one_vae) else None
 
@@ -1152,7 +1156,7 @@ class Decoder(nn.Module):
             )
 
             self.unets.append(one_unet)
-            self.vaes.append(one_vae.copy_for_eval() if exists(one_vae) else None)
+            self.vaes.append(one_vae.copy_for_eval())
 
         # unet image sizes
 
@@ -1362,12 +1366,11 @@ class Decoder(nn.Module):
                 if unet.lowres_cond:
                     lowres_cond_img = self.to_lowres_cond(img, target_image_size = image_size)
 
-                if exists(vae):
+                image_size = vae.get_encoded_fmap_size(image_size)
-                    image_size //= (2 ** vae.layers)
+                shape = (batch_size, vae.encoded_dim, image_size, image_size)
-                    shape = (batch_size, vae.encoded_dim, image_size, image_size)
 
-                    if exists(lowres_cond_img):
+                if exists(lowres_cond_img):
-                        lowres_cond_img = vae.encode(lowres_cond_img)
+                    lowres_cond_img = vae.encode(lowres_cond_img)
 
                 img = self.p_sample_loop(
                     unet,
@@ -1378,8 +1381,7 @@ class Decoder(nn.Module):
                     lowres_cond_img = lowres_cond_img
                 )
 
-                if exists(vae):
+                img = vae.decode(img)
-                    img = vae.decode(img)
 
         return img
 
@@ -1415,13 +1417,12 @@ class Decoder(nn.Module):
         lowres_cond_img = self.to_lowres_cond(image, target_image_size = target_image_size, downsample_image_size = self.image_sizes[unet_index - 1]) if unet_number > 1 else None
         image = resize_image_to(image, target_image_size)
 
-        if exists(vae):
+        vae.eval()
-            vae.eval()
+        with torch.no_grad():
-            with torch.no_grad():
+            image = vae.encode(image)
-                image = vae.encode(image)
 
-                if exists(lowres_cond_img):
+            if exists(lowres_cond_img):
-                    lowres_cond_img = vae.encode(lowres_cond_img)
+                lowres_cond_img = vae.encode(lowres_cond_img)
 
         return self.p_losses(unet, image, times, image_embed = image_embed, text_encodings = text_encodings, lowres_cond_img = lowres_cond_img)
 

dalle2_pytorch/vqgan_vae.py

@@ -287,6 +287,28 @@ class VQGanAttention(nn.Module):
 
         return out + residual
 
+class NullVQGanVAE(nn.Module):
+    def __init__(
+        self,
+        *,
+        channels
+    ):
+        super().__init__()
+        self.encoded_dim = channels
+        self.layers = 0
+
+    def get_encoded_fmap_size(self, size):
+        return size
+
+    def copy_for_eval(self):
+        return self
+
+    def encode(self, x):
+        return x
+
+    def decode(self, x):
+        return x
+
 class VQGanVAE(nn.Module):
     def __init__(
         self,
@@ -407,6 +429,9 @@ class VQGanVAE(nn.Module):
         self.discr_loss = hinge_discr_loss if use_hinge_loss else bce_discr_loss
         self.gen_loss = hinge_gen_loss if use_hinge_loss else bce_gen_loss
 
+    def get_encoded_fmap_size(self, image_size):
+        return image_size // (2 ** self.layers)
+
     def copy_for_eval(self):
         device = next(self.parameters()).device
         vae_copy = copy.deepcopy(self.cpu())

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.0.37',
+  version = '0.0.38',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',