bump to newer package of clip-anytorch that allows for text encodings < maximum context length

dalle2_pytorch/dalle2_pytorch.py

@@ -1124,7 +1124,7 @@ class DiffusionPriorNetwork(nn.Module):
         learned_queries = repeat(self.learned_query, 'd -> b 1 d', b = batch)
 
         if self.self_cond:
-            learned_queries = torch.cat((image_embed, self_cond), dim = -2)
+            learned_queries = torch.cat((self_cond, learned_queries), dim = -2)
 
         tokens = torch.cat((
             text_encodings,
@@ -2496,7 +2496,7 @@ class Decoder(nn.Module):
         dynamic_thres_percentile = 0.95,
         p2_loss_weight_gamma = 0.,                  # p2 loss weight, from https://arxiv.org/abs/2204.00227 - 0 is equivalent to weight of 1 across time - 1. is recommended
         p2_loss_weight_k = 1,
-        ddim_sampling_eta = 1.                      # can be set to 0. for deterministic sampling afaict
+        ddim_sampling_eta = 0.                      # can be set to 0. for deterministic sampling afaict
     ):
         super().__init__()
 

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '1.11.4'
+__version__ = '1.12.2'

setup.py

@@ -27,7 +27,7 @@ setup(
     'accelerate',
     'click',
     'open-clip-torch>=2.0.0,<3.0.0',
-    'clip-anytorch>=2.4.0',
+    'clip-anytorch>=2.5.2',
     'coca-pytorch>=0.0.5',
     'ema-pytorch>=0.0.7',
     'einops>=0.4',