diff --git a/examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py b/examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py
index 1e67f05abe7a..f1f6b3215201 100644
--- a/examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py
+++ b/examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py
@@ -527,9 +527,17 @@ def deepspeed_zero_init_disabled_context_manager():
 
     # lora attn processor
     prior_lora_config = LoraConfig(
-        r=args.rank, target_modules=["to_k", "to_q", "to_v", "to_out.0", "add_k_proj", "add_v_proj"]
+        r=args.rank,
+        lora_alpha=args.rank,
+        target_modules=["to_k", "to_q", "to_v", "to_out.0", "add_k_proj", "add_v_proj"],
     )
+    # Add adapter and make sure the trainable params are in float32.
     prior.add_adapter(prior_lora_config)
+    if args.mixed_precision == "fp16":
+        for param in prior.parameters():
+            # only upcast trainable parameters (LoRA) into fp32
+            if param.requires_grad:
+                param.data = param.to(torch.float32)
 
     # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
     def save_model_hook(models, weights, output_dir):