additional conditioning on image embedding by summing to time embeddings (for FiLM like conditioning in subsequent layers), from passage found in paper by @mhh0318

dalle2_pytorch/dalle2_pytorch.py

@@ -1343,6 +1343,7 @@ class Unet(nn.Module):
         cond_on_text_encodings = False,
         max_text_len = 256,
         cond_on_image_embeds = False,
+        add_image_embeds_to_time = True, # alerted by @mhh0318 to a phrase in the paper - "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and adding CLIP embeddings to the existing timestep embedding"
         init_dim = None,
         init_conv_kernel_size = 7,
         resnet_groups = 8,
@@ -1396,11 +1397,16 @@ class Unet(nn.Module):
             nn.Linear(time_cond_dim, time_cond_dim)
         )
 
-        self.image_to_cond = nn.Sequential(
+        self.image_to_tokens = nn.Sequential(
             nn.Linear(image_embed_dim, cond_dim * num_image_tokens),
             Rearrange('b (n d) -> b n d', n = num_image_tokens)
         ) if cond_on_image_embeds and image_embed_dim != cond_dim else nn.Identity()
 
+        self.to_image_hiddens = nn.Sequential(
+            nn.Linear(image_embed_dim, time_cond_dim),
+            nn.GELU()
+        ) if cond_on_image_embeds and add_image_embeds_to_time else None
+
         self.norm_cond = nn.LayerNorm(cond_dim)
         self.norm_mid_cond = nn.LayerNorm(cond_dim)
 
@@ -1558,6 +1564,13 @@ class Unet(nn.Module):
         time_tokens = self.to_time_tokens(time_hiddens)
         t = self.to_time_cond(time_hiddens)
 
+        # image embedding to be summed to time embedding
+        # discovered by @mhh0318 in the paper
+
+        if exists(image_embed) and exists(self.to_image_hiddens):
+            image_hiddens = self.to_image_hiddens(image_embed)
+            t = t + image_hiddens
+
         # conditional dropout
 
         image_keep_mask = prob_mask_like((batch_size,), 1 - image_cond_drop_prob, device = device)
@@ -1571,7 +1584,7 @@ class Unet(nn.Module):
         image_tokens = None
 
         if self.cond_on_image_embeds:
-            image_tokens = self.image_to_cond(image_embed)
+            image_tokens = self.image_to_tokens(image_embed)
             null_image_embed = self.null_image_embed.to(image_tokens.dtype) # for some reason pytorch AMP not working
 
             image_tokens = torch.where(