diff --git a/finetrainers/args.py b/finetrainers/args.py
index cb4bc82..2948963 100644
--- a/finetrainers/args.py
+++ b/finetrainers/args.py
@@ -892,6 +892,12 @@ def _add_validation_arguments(parser: argparse.ArgumentParser) -> None:
         default=None,
         help="Run validation every X training steps. Validation consists of running the validation prompt `args.num_validation_videos` times.",
     )
+    parser.add_argument(
+        "--validation_frame_rate",
+        type=int,
+        default=None,
+        help="Run inference validation at a specified frame rate every X training steps with the same output frame rate. `args.validation_frame_rate`",
+    )
     parser.add_argument(
         "--enable_model_cpu_offload",
         action="store_true",
diff --git a/finetrainers/ltx_video/ltx_video_lora.py b/finetrainers/ltx_video/ltx_video_lora.py
index 0e1af9b..c5c1df2 100644
--- a/finetrainers/ltx_video/ltx_video_lora.py
+++ b/finetrainers/ltx_video/ltx_video_lora.py
@@ -225,7 +225,7 @@ def validation(
     height: Optional[int] = None,
     width: Optional[int] = None,
     num_frames: Optional[int] = None,
-    frame_rate: int = 25,
+    frame_rate: int = 24,
     num_videos_per_prompt: int = 1,
     generator: Optional[torch.Generator] = None,
     **kwargs,
diff --git a/finetrainers/trainer.py b/finetrainers/trainer.py
index d37eb81..67c1b1e 100644
--- a/finetrainers/trainer.py
+++ b/finetrainers/trainer.py
@@ -11,7 +11,6 @@ import diffusers
 import torch
 import torch.backends
 import transformers
-import wandb
 from accelerate import Accelerator, DistributedType
 from accelerate.logging import get_logger
 from accelerate.utils import (
@@ -31,6 +30,8 @@ from huggingface_hub import create_repo, upload_folder
 from peft import LoraConfig, get_peft_model_state_dict, set_peft_model_state_dict
 from tqdm import tqdm
 
+import wandb
+
 from .args import _INVERSE_DTYPE_MAP, Args, validate_args
 from .constants import (
     FINETRAINERS_LOG_LEVEL,
@@ -926,7 +927,7 @@ class Trainer:
             height = self.args.validation_heights[i]
             width = self.args.validation_widths[i]
             num_frames = self.args.validation_num_frames[i]
-
+            frame_rate = self.args.validation_frame_rate
             if image is not None:
                 image = load_image(image)
             if video is not None:
@@ -944,6 +945,7 @@ class Trainer:
                 height=height,
                 width=width,
                 num_frames=num_frames,
+                frame_rate=frame_rate,
                 num_videos_per_prompt=self.args.num_validation_videos_per_prompt,
                 generator=torch.Generator(device=accelerator.device).manual_seed(
                     self.args.seed if self.args.seed is not None else 0
@@ -983,7 +985,7 @@ class Trainer:
                 elif artifact_type == "video":
                     logger.debug(f"Saving video to {filename}")
                     # TODO: this should be configurable here as well as in validation runs where we call the pipeline that has `fps`.
-                    export_to_video(artifact_value, filename, fps=15)
+                    export_to_video(artifact_value, filename, fps=frame_rate)
                     artifact_value = wandb.Video(filename, caption=prompt)
 
                 all_processes_artifacts.append(artifact_value)