diff --git a/examples/text_to_image/train_text_to_image.py b/examples/text_to_image/train_text_to_image.py
index 82370fc4e2dd..bbf7bf9b85bb 100644
--- a/examples/text_to_image/train_text_to_image.py
+++ b/examples/text_to_image/train_text_to_image.py
@@ -115,7 +115,7 @@ def log_validation(vae, text_encoder, tokenizer, unet, args, accelerator, weight
 def parse_args():
     parser = argparse.ArgumentParser(description="Simple example of a training script.")
     parser.add_argument(
-        "--input_pertubation", type=float, default=0, help="The scale of input pretubation. Recommended 0.1."
+        "--input_perturbation", type=float, default=0, help="The scale of input perturbation. Recommended 0.1."
     )
     parser.add_argument(
         "--pretrained_model_name_or_path",
@@ -830,8 +830,8 @@ def collate_fn(examples):
                     noise += args.noise_offset * torch.randn(
                         (latents.shape[0], latents.shape[1], 1, 1), device=latents.device
                     )
-                if args.input_pertubation:
-                    new_noise = noise + args.input_pertubation * torch.randn_like(noise)
+                if args.input_perturbation:
+                    new_noise = noise + args.input_perturbation * torch.randn_like(noise)
                 bsz = latents.shape[0]
                 # Sample a random timestep for each image
                 timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
@@ -839,7 +839,7 @@ def collate_fn(examples):
 
                 # Add noise to the latents according to the noise magnitude at each timestep
                 # (this is the forward diffusion process)
-                if args.input_pertubation:
+                if args.input_perturbation:
                     noisy_latents = noise_scheduler.add_noise(latents, new_noise, timesteps)
                 else:
                     noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)