diff --git a/flux_train_network.py b/flux_train_network.py
index 7c762c86..e4be97ad 100644
--- a/flux_train_network.py
+++ b/flux_train_network.py
@@ -250,7 +250,7 @@ class FluxNetworkTrainer(train_network.NetworkTrainer):
         # )
 
         with accelerator.autocast():
-            # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
+            # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
             model_pred = unet(
                 img=packed_noisy_model_input,
                 img_ids=img_ids,
diff --git a/library/flux_models.py b/library/flux_models.py
index d0955e37..92c79bcc 100644
--- a/library/flux_models.py
+++ b/library/flux_models.py
@@ -685,11 +685,11 @@ class DoubleStreamBlock(nn.Module):
         attn = attention(q, k, v, pe=pe)
         txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
 
-        # calculate the img bloks
+        # calculate the img blocks
         img = img + img_mod1.gate * self.img_attn.proj(img_attn)
         img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
 
-        # calculate the txt bloks
+        # calculate the txt blocks
         txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
         txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
         return img, txt
diff --git a/library/flux_utils.py b/library/flux_utils.py
index ba828d50..166cd833 100644
--- a/library/flux_utils.py
+++ b/library/flux_utils.py
@@ -20,7 +20,7 @@ MODEL_VERSION_FLUX_V1 = "flux1"
 
 
 def load_flow_model(name: str, ckpt_path: str, dtype: torch.dtype, device: Union[str, torch.device]) -> flux_models.Flux:
-    logger.info(f"Bulding Flux model {name}")
+    logger.info(f"Building Flux model {name}")
     with torch.device("meta"):
         model = flux_models.Flux(flux_models.configs[name].params).to(dtype)