fix remaining issues with deriving cond_on_text_encodings from child unet settings

README.md

@@ -368,7 +368,8 @@ unet1 = Unet(
     image_embed_dim = 512,
     cond_dim = 128,
     channels = 3,
-    dim_mults=(1, 2, 4, 8)
+    dim_mults=(1, 2, 4, 8),
+    cond_on_text_encodings = True    # set to True for any unets that need to be conditioned on text encodings
 ).cuda()
 
 unet2 = Unet(
@@ -385,8 +386,7 @@ decoder = Decoder(
     clip = clip,
     timesteps = 100,
     image_cond_drop_prob = 0.1,
-    text_cond_drop_prob = 0.5,
-    condition_on_text_encodings = False  # set this to True if you wish to condition on text during training and sampling
+    text_cond_drop_prob = 0.5
 ).cuda()
 
 for unet_number in (1, 2):

dalle2_pytorch/dalle2_pytorch.py

@@ -1781,13 +1781,6 @@ class Decoder(nn.Module):
     ):
         super().__init__()
 
-        self.unconditional = unconditional
-
-        # text conditioning
-
-        assert not (condition_on_text_encodings and unconditional), 'unconditional decoder image generation cannot be set to True if conditioning on text is present'
-        self.condition_on_text_encodings = condition_on_text_encodings
-
         # clip
 
         self.clip = None
@@ -1819,12 +1812,16 @@ class Decoder(nn.Module):
 
         self.channels = channels
 
-        # automatically take care of ensuring that first unet is unconditional
-        # while the rest of the unets are conditioned on the low resolution image produced by previous unet
+        # verify conditioning method
 
         unets = cast_tuple(unet)
         num_unets = len(unets)
 
+        self.unconditional = unconditional
+
+        # automatically take care of ensuring that first unet is unconditional
+        # while the rest of the unets are conditioned on the low resolution image produced by previous unet
+
         vaes = pad_tuple_to_length(cast_tuple(vae), len(unets), fillvalue = NullVQGanVAE(channels = self.channels))
 
         # whether to use learned variance, defaults to True for the first unet in the cascade, as in paper
@@ -1860,6 +1857,10 @@ class Decoder(nn.Module):
             self.unets.append(one_unet)
             self.vaes.append(one_vae.copy_for_eval())
 
+        # determine from unets whether conditioning on text encoding is needed
+
+        self.condition_on_text_encodings = any([unet.cond_on_text_encodings for unet in self.unets])
+
         # create noise schedulers per unet
 
         if not exists(beta_schedule):

dalle2_pytorch/train_configs.py

@@ -158,6 +158,8 @@ class UnetConfig(BaseModel):
     dim: int
     dim_mults: ListOrTuple(int)
     image_embed_dim: int = None
+    text_embed_dim: int = None
+    cond_on_text_encodings: bool = None
     cond_dim: int = None
     channels: int = 3
     attn_dim_head: int = 32
@@ -170,7 +172,6 @@ class DecoderConfig(BaseModel):
     unets: ListOrTuple(UnetConfig)
     image_size: int = None
     image_sizes: ListOrTuple(int) = None
-    condition_on_text_encodings: bool = False
     clip: Optional[AdapterConfig]   # The clip model to use if embeddings are not provided
     channels: int = 3
     timesteps: int = 1000
@@ -283,21 +284,27 @@ class TrainDecoderConfig(BaseModel):
     def check_has_embeddings(cls, values):
         # Makes sure that enough information is provided to get the embeddings specified for training
         data_config, decoder_config = values.get('data'), values.get('decoder')
-        if data_config is None or decoder_config is None:
+
+        if not exists(data_config) or not exists(decoder_config):
             # Then something else errored and we should just pass through
             return values
-        using_text_embeddings = decoder_config.condition_on_text_encodings
+
+        using_text_encodings = any([unet.cond_on_text_encodings for unet in decoder_config.unets])
         using_clip = exists(decoder_config.clip)
         img_emb_url = data_config.img_embeddings_url
         text_emb_url = data_config.text_embeddings_url
+
         if using_text_embeddings:
             # Then we need some way to get the embeddings
-            assert using_clip or text_emb_url is not None, 'If condition_on_text_encodings is true, either clip or text_embeddings_url must be provided'
+            assert using_clip or exists(text_emb_url), 'If text conditioning, either clip or text_embeddings_url must be provided'
+
         if using_clip:
             if using_text_embeddings:
-                assert text_emb_url is None or img_emb_url is None, 'Loaded clip, but also provided text_embeddings_url and img_embeddings_url. This is redundant. Remove the clip model or the embeddings'
+                assert not exists(text_emb_url) or not exists(img_emb_url), 'Loaded clip, but also provided text_embeddings_url and img_embeddings_url. This is redundant. Remove the clip model or the text embeddings'
             else:
-                assert img_emb_url is None, 'Loaded clip, but also provided img_embeddings_url. This is redundant. Remove the clip model or the embeddings'
+                assert not exists(img_emb_url), 'Loaded clip, but also provided img_embeddings_url. This is redundant. Remove the clip model or the embeddings'
+
         if text_emb_url:
             assert using_text_embeddings, "Text embeddings are being loaded, but text embeddings are not being conditioned on. This will slow down the dataloader for no reason."
+
         return values

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '0.12.1'
+__version__ = '0.12.2'

train_decoder.py

@@ -596,9 +596,11 @@ def initialize_training(config, config_path):
 
     has_img_embeddings = config.data.img_embeddings_url is not None
     has_text_embeddings = config.data.text_embeddings_url is not None
-    conditioning_on_text = config.decoder.condition_on_text_encodings
+    conditioning_on_text = any([unet.cond_on_text_encodings for unet in config.decoder.unets])
+
     has_clip_model = config.decoder.clip is not None
     data_source_string = ""
+
     if has_img_embeddings:
         data_source_string += "precomputed image embeddings"
     elif has_clip_model:
@@ -622,7 +624,7 @@ def initialize_training(config, config_path):
         inference_device=accelerator.device,
         load_config=config.load,
         evaluate_config=config.evaluate,
-        condition_on_text_encodings=config.decoder.condition_on_text_encodings,
+        condition_on_text_encodings=conditioning_on_text,
         **config.train.dict(),
     )