diff --git a/extensions-builtin/sd_forge_svd/scripts/forge_svd.py b/extensions-builtin/sd_forge_svd/scripts/forge_svd.py
index 4a6ee121..a675d65d 100644
--- a/extensions-builtin/sd_forge_svd/scripts/forge_svd.py
+++ b/extensions-builtin/sd_forge_svd/scripts/forge_svd.py
@@ -8,7 +8,7 @@ from modules.paths import models_path
 from modules.ui_common import ToolButton, refresh_symbol
 from modules import shared
 
-from modules_forge.forge_util import numpy_to_pytorch, pytorch_to_numpy
+from modules_forge.forge_util import numpy_to_pytorch, pytorch_to_numpy, write_images_to_mp4
 from ldm_patched.modules.sd import load_checkpoint_guess_config
 from ldm_patched.contrib.external_video_model import VideoLinearCFGGuidance, SVD_img2vid_Conditioning
 from ldm_patched.contrib.external import KSampler, VAEDecode
@@ -50,7 +50,10 @@ def predict(filename, width, height, video_frames, motion_bucket_id, fps, augmen
                                       negative, latent_image, sampling_denoise)[0]
     output_pixels = opVAEDecode.decode(vae, output_latent)[0]
     outputs = pytorch_to_numpy(output_pixels)
-    return outputs
+
+    video_filename = write_images_to_mp4(outputs, fps=fps)
+
+    return outputs, video_filename
 
 
 def on_ui_tabs():
@@ -98,10 +101,11 @@ def on_ui_tabs():
                          sampling_denoise, guidance_min_cfg, input_image]
 
             with gr.Column():
+                output_video = gr.Video(autoplay=True)
                 output_gallery = gr.Gallery(label='Gallery', show_label=False, object_fit='contain',
                                             visible=True, height=1024, columns=4)
 
-        generate_button.click(predict, inputs=ctrls, outputs=[output_gallery])
+        generate_button.click(predict, inputs=ctrls, outputs=[output_gallery, output_video])
     return [(svd_block, "SVD", "svd")]
 
 
diff --git a/modules_forge/forge_util.py b/modules_forge/forge_util.py
index 709582dd..30fcc373 100644
--- a/modules_forge/forge_util.py
+++ b/modules_forge/forge_util.py
@@ -1,9 +1,20 @@
 import torch
 import numpy as np
+import os
+import time
+import random
+import string
 
 from ldm_patched.modules.conds import CONDRegular, CONDCrossAttn
 
 
+def generate_random_filename(extension=".txt"):
+    timestamp = time.strftime("%Y%m%d-%H%M%S")
+    random_string = ''.join(random.choices(string.ascii_lowercase + string.digits, k=5))
+    filename = f"{timestamp}-{random_string}{extension}"
+    return filename
+
+
 def cond_from_a1111_to_patched_ldm(cond):
     if isinstance(cond, torch.Tensor):
         result = dict(
@@ -43,3 +54,44 @@ def numpy_to_pytorch(x):
     y = np.ascontiguousarray(y.copy())
     y = torch.from_numpy(y).float()
     return y
+
+
+def write_images_to_mp4(frame_list: list, filename=None, fps=6):
+    from modules.paths_internal import default_output_dir
+
+    video_folder = os.path.join(default_output_dir, 'svd')
+    os.makedirs(video_folder, exist_ok=True)
+
+    if filename is None:
+        filename = generate_random_filename('.mp4')
+
+    full_path = os.path.join(video_folder, filename)
+
+    try:
+        import av
+    except ImportError:
+        from launch import run_pip
+        run_pip(
+            "install imageio[pyav]",
+            "imageio[pyav]",
+        )
+        import av
+
+    options = {
+        "crf": str(23)
+    }
+
+    output = av.open(full_path, "w")
+
+    stream = output.add_stream('libx264', fps, options=options)
+    stream.width = frame_list[0].shape[1]
+    stream.height = frame_list[0].shape[0]
+    for img in frame_list:
+        frame = av.VideoFrame.from_ndarray(img)
+        packet = stream.encode(frame)
+        output.mux(packet)
+    packet = stream.encode(None)
+    output.mux(packet)
+    output.close()
+
+    return full_path