diff --git a/nerfstudio/pipelines/base_pipeline.py b/nerfstudio/pipelines/base_pipeline.py
index a746a81ab..651a2c300 100644
--- a/nerfstudio/pipelines/base_pipeline.py
+++ b/nerfstudio/pipelines/base_pipeline.py
@@ -26,6 +26,7 @@ from typing import Any, Dict, List, Literal, Mapping, Optional, Tuple, Type, Uni
 
 import torch
 import torch.distributed as dist
+import torchvision.utils as vutils
 from rich.progress import BarColumn, MofNCompleteColumn, Progress, TextColumn, TimeElapsedColumn
 from torch import nn
 from torch.cuda.amp.grad_scaler import GradScaler
@@ -361,6 +362,8 @@ class VanillaPipeline(Pipeline):
         metrics_dict_list = []
         assert isinstance(self.datamanager, (VanillaDataManager, ParallelDataManager, FullImageDatamanager))
         num_images = len(self.datamanager.fixed_indices_eval_dataloader)
+        if output_path is not None:
+            output_path.mkdir(exist_ok=True, parents=True)
         with Progress(
             TextColumn("[progress.description]{task.description}"),
             BarColumn(),
@@ -369,15 +372,18 @@ class VanillaPipeline(Pipeline):
             transient=True,
         ) as progress:
             task = progress.add_task("[green]Evaluating all eval images...", total=num_images)
+            idx = 0
             for camera, batch in self.datamanager.fixed_indices_eval_dataloader:
                 # time this the following line
                 inner_start = time()
                 outputs = self.model.get_outputs_for_camera(camera=camera)
                 height, width = camera.height, camera.width
                 num_rays = height * width
-                metrics_dict, _ = self.model.get_image_metrics_and_images(outputs, batch)
+                metrics_dict, image_dict = self.model.get_image_metrics_and_images(outputs, batch)
                 if output_path is not None:
-                    raise NotImplementedError("Saving images is not implemented yet")
+                    for key in image_dict.keys():
+                        image = image_dict[key]  # [H, W, C] order
+                        vutils.save_image(image.permute(2, 0, 1).cpu(), output_path / f"eval_{key}_{idx:04d}.png")
 
                 assert "num_rays_per_sec" not in metrics_dict
                 metrics_dict["num_rays_per_sec"] = (num_rays / (time() - inner_start)).item()
@@ -386,6 +392,7 @@ class VanillaPipeline(Pipeline):
                 metrics_dict[fps_str] = (metrics_dict["num_rays_per_sec"] / (height * width)).item()
                 metrics_dict_list.append(metrics_dict)
                 progress.advance(task)
+                idx = idx + 1
         # average the metrics list
         metrics_dict = {}
         for key in metrics_dict_list[0].keys():