fix self conditioning shape in diffusion prior

dalle2_pytorch/dalle2_pytorch.py

@@ -1004,9 +1004,9 @@ class DiffusionPriorNetwork(nn.Module):
 
         # setup self conditioning
 
-        self_cond = None
         if self.self_cond:
-            self_cond = default(self_cond, lambda: torch.zeros(batch, 1, self.dim, device = device, dtype = dtype))
+            self_cond = default(self_cond, lambda: torch.zeros(batch, self.dim, device = device, dtype = dtype))
+            self_cond = rearrange(self_cond, 'b d -> b 1 d')
 
         # in section 2.2, last paragraph
         # "... consisting of encoded text, CLIP text embedding, diffusion timestep embedding, noised CLIP image embedding, final embedding for prediction"
@@ -1287,7 +1287,7 @@ class DiffusionPrior(nn.Module):
         image_embed_noisy = self.noise_scheduler.q_sample(x_start = image_embed, t = times, noise = noise)
 
         self_cond = None
-        if self.net.self_cond and random.random() < 0.5:
+        if self.net.self_cond and random.random() < 1.5:
             with torch.no_grad():
                 self_cond = self.net(image_embed_noisy, times, **text_cond).detach()
 

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '1.6.1'
+__version__ = '1.6.2'