diff --git a/comfy_extras/nodes_train.py b/comfy_extras/nodes_train.py
index 1c0e03b43..17a03a1e0 100644
--- a/comfy_extras/nodes_train.py
+++ b/comfy_extras/nodes_train.py
@@ -64,7 +64,8 @@ class BiasDiff(torch.nn.Module):
         self.bias = bias
 
     def __call__(self, b):
-        return b + self.bias
+        org_dtype = b.dtype
+        return (b.to(self.bias) + self.bias).to(org_dtype)
 
     def passive_memory_usage(self):
         return self.bias.nelement() * self.bias.element_size()
@@ -285,19 +286,19 @@ class TrainLoraNode:
                 "steps": (
                     IO.INT,
                     {
-                        "default": 50,
+                        "default": 16,
                         "min": 1,
-                        "max": 1000,
+                        "max": 100000,
                         "tooltip": "The number of steps to train the LoRA for.",
                     },
                 ),
                 "learning_rate": (
                     IO.FLOAT,
                     {
-                        "default": 0.0003,
+                        "default": 0.0005,
                         "min": 0.0000001,
                         "max": 1.0,
-                        "step": 0.00001,
+                        "step": 0.000001,
                         "tooltip": "The learning rate to use for training.",
                     },
                 ),
@@ -311,9 +312,9 @@ class TrainLoraNode:
                     },
                 ),
                 "optimizer": (
-                    ["Adam", "AdamW", "SGD", "RMSprop"],
+                    ["AdamW", "Adam", "SGD", "RMSprop"],
                     {
-                        "default": "Adam",
+                        "default": "AdamW",
                         "tooltip": "The optimizer to use for training.",
                     },
                 ),
@@ -434,7 +435,7 @@ class TrainLoraNode:
                         else:
                             diff = torch.nn.Parameter(
                                 torch.zeros(
-                                    m.weight.shape, dtype=dtype, requires_grad=True
+                                    m.weight.shape, dtype=lora_dtype, requires_grad=True
                                 )
                             )
                             diff_module = BiasDiff(diff)
@@ -444,7 +445,7 @@ class TrainLoraNode:
                     if hasattr(m, "bias") and m.bias is not None:
                         key = "{}.bias".format(n)
                         bias = torch.nn.Parameter(
-                            torch.zeros(m.bias.shape, dtype=dtype, requires_grad=True)
+                            torch.zeros(m.bias.shape, dtype=lora_dtype, requires_grad=True)
                         )
                         bias_module = BiasDiff(bias)
                         lora_sd["{}.diff_b".format(n)] = bias