diff --git a/dalle2_pytorch/train_configs.py b/dalle2_pytorch/train_configs.py
index ecdf994..cecd8c7 100644
--- a/dalle2_pytorch/train_configs.py
+++ b/dalle2_pytorch/train_configs.py
@@ -115,7 +115,7 @@ class TrackerConfig(BaseModel):
 class AdapterConfig(BaseModel):
     make: str = "openai"
     model: str = "ViT-L/14"
-    base_model_kwargs: Dict[str, Any] = None
+    base_model_kwargs: Optional[Dict[str, Any]] = None
 
     def create(self):
         if self.make == "openai":
@@ -134,8 +134,8 @@ class AdapterConfig(BaseModel):
 class DiffusionPriorNetworkConfig(BaseModel):
     dim: int
     depth: int
-    max_text_len: int = None
-    num_timesteps: int = None
+    max_text_len: Optional[int] = None
+    num_timesteps: Optional[int] = None
     num_time_embeds: int = 1
     num_image_embeds: int = 1
     num_text_embeds: int = 1
@@ -158,7 +158,7 @@ class DiffusionPriorNetworkConfig(BaseModel):
         return DiffusionPriorNetwork(**kwargs)
 
 class DiffusionPriorConfig(BaseModel):
-    clip: AdapterConfig = None
+    clip: Optional[AdapterConfig] = None
     net: DiffusionPriorNetworkConfig
     image_embed_dim: int
     image_size: int
@@ -195,7 +195,7 @@ class DiffusionPriorTrainConfig(BaseModel):
     use_ema: bool = True
     ema_beta: float = 0.99
     amp: bool = False
-    warmup_steps: int = None             # number of warmup steps
+    warmup_steps: Optional[int] = None   # number of warmup steps
     save_every_seconds: int = 3600       # how often to save
     eval_timesteps: List[int] = [64]     # which sampling timesteps to evaluate with
     best_validation_loss: float = 1e9    # the current best valudation loss observed
@@ -228,10 +228,10 @@ class TrainDiffusionPriorConfig(BaseModel):
 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
+    image_embed_dim: Optional[int] = None
+    text_embed_dim: Optional[int] = None
+    cond_on_text_encodings: Optional[bool] = None
+    cond_dim: Optional[int] = None
     channels: int = 3
     self_attn: ListOrTuple[int]
     attn_dim_head: int = 32
@@ -243,14 +243,14 @@ class UnetConfig(BaseModel):
 
 class DecoderConfig(BaseModel):
     unets: ListOrTuple[UnetConfig]
-    image_size: int = None
+    image_size: Optional[int] = None
     image_sizes: ListOrTuple[int] = None
     clip: Optional[AdapterConfig]   # The clip model to use if embeddings are not provided
     channels: int = 3
     timesteps: int = 1000
     sample_timesteps: Optional[SingularOrIterable[Optional[int]]] = None
     loss_type: str = 'l2'
-    beta_schedule: ListOrTuple[str] = None  # None means all cosine
+    beta_schedule: Optional[ListOrTuple[str]] = None  # None means all cosine
     learned_variance: SingularOrIterable[bool] = True
     image_cond_drop_prob: float = 0.1
     text_cond_drop_prob: float = 0.5
@@ -320,20 +320,20 @@ class DecoderTrainConfig(BaseModel):
     n_sample_images: int = 6                       # The number of example images to produce when sampling the train and test dataset
     cond_scale: Union[float, List[float]] = 1.0
     device: str = 'cuda:0'
-    epoch_samples: int = None                      # Limits the number of samples per epoch. None means no limit. Required if resample_train is true as otherwise the number of samples per epoch is infinite.
-    validation_samples: int = None                 # Same as above but for validation.
+    epoch_samples: Optional[int] = None                      # Limits the number of samples per epoch. None means no limit. Required if resample_train is true as otherwise the number of samples per epoch is infinite.
+    validation_samples: Optional[int] = None                 # Same as above but for validation.
     save_immediately: bool = False
     use_ema: bool = True
     ema_beta: float = 0.999
     amp: bool = False
-    unet_training_mask: ListOrTuple[bool] = None   # If None, use all unets
+    unet_training_mask: Optional[ListOrTuple[bool]] = None   # If None, use all unets
 
 class DecoderEvaluateConfig(BaseModel):
     n_evaluation_samples: int = 1000
-    FID: Dict[str, Any] = None
-    IS: Dict[str, Any] = None
-    KID: Dict[str, Any] = None
-    LPIPS: Dict[str, Any] = None
+    FID: Optional[Dict[str, Any]] = None
+    IS: Optional[Dict[str, Any]] = None
+    KID: Optional[Dict[str, Any]] = None
+    LPIPS: Optional[Dict[str, Any]] = None
 
 class TrainDecoderConfig(BaseModel):
     decoder: DecoderConfig
diff --git a/dalle2_pytorch/version.py b/dalle2_pytorch/version.py
index 18ddad0..5fdff84 100644
--- a/dalle2_pytorch/version.py
+++ b/dalle2_pytorch/version.py
@@ -1 +1 @@
-__version__ = '1.15.2'
+__version__ = '1.15.3'