no CLIP altogether for training DiffusionPrior

README.md

@@ -446,6 +446,55 @@ loss.backward()
 # now the diffusion prior can generate image embeddings from the text embeddings
 ```
 
+You can also completely go `CLIP`-less, in which case you will need to pass in the `image_embed_dim` into the `DiffusionPrior` on initialization
+
+```python
+import torch
+from dalle2_pytorch import DiffusionPriorNetwork, DiffusionPrior
+
+# setup prior network, which contains an autoregressive transformer
+
+prior_network = DiffusionPriorNetwork(
+    dim = 512,
+    depth = 6,
+    dim_head = 64,
+    heads = 8
+).cuda()
+
+# diffusion prior network, which contains the CLIP and network (with transformer) above
+
+diffusion_prior = DiffusionPrior(
+    net = prior_network,
+    image_embed_dim = 512,               # this needs to be set
+    timesteps = 100,
+    cond_drop_prob = 0.2,
+    condition_on_text_encodings = False  # this probably should be true, but just to get Laion started
+).cuda()
+
+# mock data
+
+text = torch.randint(0, 49408, (4, 256)).cuda()
+images = torch.randn(4, 3, 256, 256).cuda()
+
+# precompute the text and image embeddings
+# here using the diffusion prior class, but could be done with CLIP alone
+
+clip_image_embeds = torch.randn(4, 512).cuda()
+clip_text_embeds = torch.randn(4, 512).cuda()
+
+# feed text and images into diffusion prior network
+
+loss = diffusion_prior(
+    text_embed = clip_text_embeds,
+    image_embed = clip_image_embeds
+)
+
+loss.backward()
+
+# do the above for many many many steps
+# now the diffusion prior can generate image embeddings from the text embeddings
+```
+
 ## Experimental
 
 ### DALL-E2 with Latent Diffusion
@@ -594,7 +643,7 @@ Once built, images will be saved to the same directory the command is invoked
 - [x] build out latent diffusion architecture, with the vq-reg variant (vqgan-vae), make it completely optional and compatible with cascading ddpms
 - [x] for decoder, allow ability to customize objective (predict epsilon vs x0), in case latent diffusion does better with prediction of x0
 - [x] use attention-based upsampling https://arxiv.org/abs/2112.11435
-- [ ] spend one day cleaning up tech debt in decoder
+- [ ] abstract interface for CLIP adapter class, so other CLIPs can be brought in - use inheritance just this once for sharing logic between decoder and prior network ddpms
 - [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet
 - [ ] 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
 - [ ] transcribe code to Jax, which lowers the activation energy for distributed training, given access to TPUs

dalle2_pytorch/dalle2_pytorch.py

@@ -486,7 +486,10 @@ class DiffusionPrior(nn.Module):
         self,
         net,
         *,
-        clip,
+        clip = None,
+        image_embed_dim = None,
+        image_size = None,
+        image_channels = 3,
         timesteps = 1000,
         cond_drop_prob = 0.2,
         loss_type = "l1",
@@ -495,14 +498,18 @@ class DiffusionPrior(nn.Module):
         condition_on_text_encodings = True, # the paper suggests this is needed, but you can turn it off for your CLIP preprocessed text embed -> image embed training
     ):
         super().__init__()
-        assert isinstance(clip, CLIP)
+
-        freeze_model_and_make_eval_(clip)
+        if exists(clip):
-        self.clip = clip
+            assert isinstance(clip, CLIP)
+            freeze_model_and_make_eval_(clip)
+            self.clip = clip
+        else:
+            assert exists(image_embed_dim), 'latent dimension must be given, if training prior network without CLIP given'
+            self.clip = None
 
         self.net = net
-        self.image_embed_dim = clip.dim_latent
+        self.image_embed_dim = default(image_embed_dim, lambda: clip.dim_latent)
-        self.channels = clip.image_channels
+        self.channels = default(image_channels, lambda: clip.image_channels)
-        self.image_size = clip.image_size
 
         self.cond_drop_prob = cond_drop_prob
         self.condition_on_text_encodings = condition_on_text_encodings
@@ -559,6 +566,8 @@ class DiffusionPrior(nn.Module):
 
     @torch.no_grad()
     def get_image_embed(self, image):
+        assert exists(self.clip)
+
         image_encoding = self.clip.visual_transformer(image)
         image_cls = image_encoding[:, 0]
         image_embed = self.clip.to_visual_latent(image_cls)
@@ -566,6 +575,8 @@ class DiffusionPrior(nn.Module):
 
     @torch.no_grad()
     def get_text_cond(self, text):
+        assert exists(self.clip)
+
         text_encodings = self.clip.text_transformer(text)
         text_cls, text_encodings = text_encodings[:, 0], text_encodings[:, 1:]
         text_embed = self.clip.to_text_latent(text_cls)

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.0.49',
+  version = '0.0.50',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',