diff --git a/README.md b/README.md
index d9b3714c..8454fc9f 100755
--- a/README.md
+++ b/README.md
@@ -9,22 +9,23 @@
 - We are releasing **[Stable Video 4D (SV4D)](https://huggingface.co/stabilityai/sv4d)**, a video-to-4D diffusion model for novel-view video synthesis. For research purposes:
     - **SV4D** was trained to generate 40 frames (5 video frames x 8 camera views) at 576x576 resolution, given 5 context frames (the input video), and 8 reference views (synthesised from the first frame of the input video, using a multi-view diffusion model like SV3D) of the same size, ideally white-background images with one object.
     - To generate longer novel-view videos (21 frames), we propose a novel sampling method using SV4D, by first sampling 5 anchor frames and then densely sampling the remaining frames while maintaining temporal consistency.
+    - To run the community-build gradio demo locally, run `python -m scripts.demo.gradio_app_sv4d`.
     - Please check our [project page](https://sv4d.github.io), [tech report](https://sv4d.github.io/static/sv4d_technical_report.pdf) and [video summary](https://www.youtube.com/watch?v=RBP8vdAWTgk) for more details.
 
-**QUICKSTART** : `python scripts/sampling/simple_video_sample_4d.py --input_path assets/test_video1.mp4 --output_folder outputs/sv4d` (after downloading [sv4d.safetensors](https://huggingface.co/stabilityai/sv4d) and [sv3d_u.safetensors](https://huggingface.co/stabilityai/sv3d) from HuggingFace into `checkpoints/`)
+**QUICKSTART** : `python scripts/sampling/simple_video_sample_4d.py --input_path assets/sv4d_videos/test_video1.mp4 --output_folder outputs/sv4d` (after downloading [sv4d.safetensors](https://huggingface.co/stabilityai/sv4d) and [sv3d_u.safetensors](https://huggingface.co/stabilityai/sv3d) from HuggingFace into `checkpoints/`)
 
 To run **SV4D** on a single input video of 21 frames:
 - Download SV3D models (`sv3d_u.safetensors` and `sv3d_p.safetensors`) from [here](https://huggingface.co/stabilityai/sv3d) and SV4D model (`sv4d.safetensors`) from [here](https://huggingface.co/stabilityai/sv4d) to `checkpoints/`
 - Run `python scripts/sampling/simple_video_sample_4d.py --input_path <path/to/video>`
     - `input_path` : The input video `<path/to/video>` can be
-      - a single video file in `gif` or `mp4` format, such as `assets/test_video1.mp4`, or
+      - a single video file in `gif` or `mp4` format, such as `assets/sv4d_videos/test_video1.mp4`, or
       - a folder containing images of video frames in `.jpg`, `.jpeg`, or `.png` format, or
       - a file name pattern matching images of video frames.
     - `num_steps` : default is 20, can increase to 50 for better quality but longer sampling time.
     - `sv3d_version` : To specify the SV3D model to generate reference multi-views, set `--sv3d_version=sv3d_u` for SV3D_u or `--sv3d_version=sv3d_p` for SV3D_p.
-    - `elevations_deg` : To generate novel-view videos at a specified elevation (default elevation is 10) using SV3D_p (default is SV3D_u), run `python scripts/sampling/simple_video_sample_4d.py --input_path test_video1.mp4 --sv3d_version sv3d_p --elevations_deg 30.0`
-    - **Background removal** : For input videos with plain background, (optionally) use [rembg](https://github.com/danielgatis/rembg) to remove background and crop video frames by setting `--remove_bg=True`. To obtain higher quality outputs on real-world input videos with noisy background, try segmenting the foreground object using [Cliipdrop](https://clipdrop.co/) before running SV4D.
-    - **Low VRAM environment** : To run on GPUs with low VRAM, try setting `--decoding_t=1` (of frames decoded at a time) or lower video resolution like `--img_size=512`.
+    - `elevations_deg` : To generate novel-view videos at a specified elevation (default elevation is 10) using SV3D_p (default is SV3D_u), run `python scripts/sampling/simple_video_sample_4d.py --input_path assets/sv4d_videos/test_video1.mp4 --sv3d_version sv3d_p --elevations_deg 30.0`
+    - **Background removal** : For input videos with plain background, (optionally) use [rembg](https://github.com/danielgatis/rembg) to remove background and crop video frames by setting `--remove_bg=True`. To obtain higher quality outputs on real-world input videos with noisy background, try segmenting the foreground object using [Clipdrop](https://clipdrop.co/) or [SAM2](https://github.com/facebookresearch/segment-anything-2) before running SV4D.
+    - **Low VRAM environment** : To run on GPUs with low VRAM, try setting `--encoding_t=1` (of frames encoded at a time) and `--decoding_t=1` (of frames decoded at a time) or lower video resolution like `--img_size=512`.
 
   ![tile](assets/sv4d.gif)
 
diff --git a/assets/sv4d_videos/bunnyman.mp4 b/assets/sv4d_videos/bunnyman.mp4
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diff --git a/assets/sv4d_videos/green_robot.mp4 b/assets/sv4d_videos/green_robot.mp4
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diff --git a/assets/sv4d_videos/guppie_v0.mp4 b/assets/sv4d_videos/guppie_v0.mp4
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diff --git a/assets/hiphop_parrot.mp4 b/assets/sv4d_videos/hiphop_parrot.mp4
similarity index 100%
rename from assets/hiphop_parrot.mp4
rename to assets/sv4d_videos/hiphop_parrot.mp4
diff --git a/assets/sv4d_videos/human5.mp4 b/assets/sv4d_videos/human5.mp4
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diff --git a/assets/sv4d_videos/human7.mp4 b/assets/sv4d_videos/human7.mp4
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diff --git a/assets/sv4d_videos/lucia_v000.mp4 b/assets/sv4d_videos/lucia_v000.mp4
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diff --git a/assets/sv4d_videos/monkey.mp4 b/assets/sv4d_videos/monkey.mp4
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diff --git a/assets/sv4d_videos/pistol_v0.mp4 b/assets/sv4d_videos/pistol_v0.mp4
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diff --git a/assets/sv4d_videos/snowboard_v000.mp4 b/assets/sv4d_videos/snowboard_v000.mp4
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diff --git a/assets/sv4d_videos/stroller_v000.mp4 b/assets/sv4d_videos/stroller_v000.mp4
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diff --git a/assets/test_video1.mp4 b/assets/sv4d_videos/test_video1.mp4
similarity index 100%
rename from assets/test_video1.mp4
rename to assets/sv4d_videos/test_video1.mp4
diff --git a/assets/test_video2.mp4 b/assets/sv4d_videos/test_video2.mp4
similarity index 100%
rename from assets/test_video2.mp4
rename to assets/sv4d_videos/test_video2.mp4
diff --git a/assets/sv4d_videos/train_v0.mp4 b/assets/sv4d_videos/train_v0.mp4
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diff --git a/assets/sv4d_videos/wave_hello.mp4 b/assets/sv4d_videos/wave_hello.mp4
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diff --git a/scripts/demo/gradio_app_sv4d.py b/scripts/demo/gradio_app_sv4d.py
new file mode 100644
index 00000000..d4d8b37f
--- /dev/null
+++ b/scripts/demo/gradio_app_sv4d.py
@@ -0,0 +1,496 @@
+# Adding this at the very top of app.py to make 'generative-models' directory discoverable
+import os
+import sys
+
+sys.path.append(os.path.join(os.path.dirname(__file__), "generative-models"))
+
+from glob import glob
+from typing import Optional
+
+import gradio as gr
+import numpy as np
+import torch
+from huggingface_hub import hf_hub_download
+from typing import List, Optional, Union
+import torchvision
+
+from sgm.modules.encoders.modules import VideoPredictionEmbedderWithEncoder
+from scripts.demo.sv4d_helpers import (
+    decode_latents,
+    load_model,
+    initial_model_load,
+    read_video,
+    run_img2vid,
+    prepare_inputs,
+    do_sample_per_step,
+    sample_sv3d,
+    save_video,
+    preprocess_video,
+)
+
+
+# the tmp path, if /tmp/gradio is not writable, change it to a writable path
+# os.environ["GRADIO_TEMP_DIR"] = "gradio_tmp"
+
+version = "sv4d"  # replace with 'sv3d_p' or 'sv3d_u' for other models
+
+# Define the repo, local directory and filename
+repo_id = "stabilityai/sv4d"
+filename = f"{version}.safetensors"  # replace with "sv3d_u.safetensors" or "sv3d_p.safetensors"
+local_dir = "checkpoints"
+local_ckpt_path = os.path.join(local_dir, filename)
+
+# Check if the file already exists
+if not os.path.exists(local_ckpt_path):
+    # If the file doesn't exist, download it
+    hf_hub_download(repo_id=repo_id, filename=filename, local_dir=local_dir)
+    print("File downloaded. (sv4d)")
+else:
+    print("File already exists. No need to download. (sv4d)")
+
+device = "cuda"
+max_64_bit_int = 2**63 - 1
+
+num_frames = 21
+num_steps = 20
+model_config = f"scripts/sampling/configs/{version}.yaml"
+
+# Set model config
+T = 5  # number of frames per sample
+V = 8  # number of views per sample
+F = 8  # vae factor to downsize image->latent
+C = 4
+H, W = 576, 576
+n_frames = 21  # number of input and output video frames
+n_views = V + 1  # number of output video views (1 input view + 8 novel views)
+n_views_sv3d = 21
+subsampled_views = np.array(
+    [0, 2, 5, 7, 9, 12, 14, 16, 19]
+)  # subsample (V+1=)9 (uniform) views from 21 SV3D views
+
+version_dict = {
+    "T": T * V,
+    "H": H,
+    "W": W,
+    "C": C,
+    "f": F,
+    "options": {
+        "discretization": 1,
+        "cfg": 3,
+        "sigma_min": 0.002,
+        "sigma_max": 700.0,
+        "rho": 7.0,
+        "guider": 5,
+        "num_steps": num_steps,
+        "force_uc_zero_embeddings": [
+            "cond_frames",
+            "cond_frames_without_noise",
+            "cond_view",
+            "cond_motion",
+        ],
+        "additional_guider_kwargs": {
+            "additional_cond_keys": ["cond_view", "cond_motion"]
+        },
+    },
+}
+
+# Load SV4D model
+model, filter = load_model(
+    model_config,
+    device,
+    version_dict["T"],
+    num_steps,
+)
+model = initial_model_load(model)
+
+# -----------sv3d config and model loading----------------
+# if version == "sv3d_u":
+sv3d_model_config = "scripts/sampling/configs/sv3d_u.yaml"
+# elif version == "sv3d_p":
+#     sv3d_model_config = "scripts/sampling/configs/sv3d_p.yaml"
+# else:
+#     raise ValueError(f"Version {version} does not exist.")
+
+# Define the repo, local directory and filename
+repo_id = "stabilityai/sv3d"
+filename = f"sv3d_u.safetensors"  # replace with "sv3d_u.safetensors" or "sv3d_p.safetensors"
+local_dir = "checkpoints"
+local_ckpt_path = os.path.join(local_dir, filename)
+
+# Check if the file already exists
+if not os.path.exists(local_ckpt_path):
+    # If the file doesn't exist, download it
+    hf_hub_download(repo_id=repo_id, filename=filename, local_dir=local_dir)
+    print("File downloaded. (sv3d)")
+else:
+    print("File already exists. No need to download. (sv3d)")
+
+# load sv3d model
+sv3d_model, filter = load_model(
+    sv3d_model_config,
+    device,
+    21,
+    num_steps,
+    verbose=False,
+)
+sv3d_model = initial_model_load(sv3d_model)
+# ------------------
+
+def sample_anchor(
+    input_path: str = "assets/test_image.png",  # Can either be image file or folder with image files
+    seed: Optional[int] = None,
+    encoding_t: int = 8,  # Number of frames encoded at a time! This eats most VRAM. Reduce if necessary.
+    decoding_t: int = 4,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
+    num_steps: int = 20,
+    sv3d_version: str = "sv3d_u",  # sv3d_u or sv3d_p
+    fps_id: int = 6,
+    motion_bucket_id: int = 127,
+    cond_aug: float = 1e-5,
+    device: str = "cuda",
+    elevations_deg: Optional[Union[float, List[float]]] = 10.0,
+    azimuths_deg: Optional[List[float]] = None,
+    verbose: Optional[bool] = False,
+):
+    """
+    Simple script to generate multiple novel-view videos conditioned on a video `input_path` or multiple frames, one for each
+    image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`.
+    """
+    output_folder = os.path.dirname(input_path)
+
+    torch.manual_seed(seed)
+    os.makedirs(output_folder, exist_ok=True)
+
+    # Read input video frames i.e. images at view 0
+    print(f"Reading {input_path}")
+    images_v0 = read_video(
+        input_path,
+        n_frames=n_frames,
+        device=device,
+    )
+
+    # Get camera viewpoints
+    if isinstance(elevations_deg, float) or isinstance(elevations_deg, int):
+        elevations_deg = [elevations_deg] * n_views_sv3d
+    assert (
+        len(elevations_deg) == n_views_sv3d
+    ), f"Please provide 1 value, or a list of {n_views_sv3d} values for elevations_deg! Given {len(elevations_deg)}"
+    if azimuths_deg is None:
+        azimuths_deg = np.linspace(0, 360, n_views_sv3d + 1)[1:] % 360
+    assert (
+        len(azimuths_deg) == n_views_sv3d
+    ), f"Please provide a list of {n_views_sv3d} values for azimuths_deg! Given {len(azimuths_deg)}"
+    polars_rad = np.array([np.deg2rad(90 - e) for e in elevations_deg])
+    azimuths_rad = np.array(
+        [np.deg2rad((a - azimuths_deg[-1]) % 360) for a in azimuths_deg]
+    )
+
+    # Sample multi-view images of the first frame using SV3D i.e. images at time 0
+    sv3d_model.sampler.num_steps = num_steps
+    print("sv3d_model.sampler.num_steps", sv3d_model.sampler.num_steps)
+    images_t0 = sample_sv3d(
+        images_v0[0],
+        n_views_sv3d,
+        num_steps,
+        sv3d_version,
+        fps_id,
+        motion_bucket_id,
+        cond_aug,
+        decoding_t,
+        device,
+        polars_rad,
+        azimuths_rad,
+        verbose,
+        sv3d_model,
+    )
+    images_t0 = torch.roll(images_t0, 1, 0)  # move conditioning image to first frame
+
+    sv3d_file = os.path.join(output_folder, "t000.mp4")
+    save_video(sv3d_file, images_t0.unsqueeze(1))
+    
+    for emb in model.conditioner.embedders:
+        if isinstance(emb, VideoPredictionEmbedderWithEncoder):
+            emb.en_and_decode_n_samples_a_time = encoding_t
+    model.en_and_decode_n_samples_a_time = decoding_t
+    # Initialize image matrix
+    img_matrix = [[None] * n_views for _ in range(n_frames)]
+    for i, v in enumerate(subsampled_views):
+        img_matrix[0][i] = images_t0[v].unsqueeze(0)
+    for t in range(n_frames):
+        img_matrix[t][0] = images_v0[t]
+
+    # Interleaved sampling for anchor frames
+    t0, v0 = 0, 0
+    frame_indices = np.arange(T - 1, n_frames, T - 1)  # [4, 8, 12, 16, 20]
+    view_indices = np.arange(V) + 1
+    print(f"Sampling anchor frames {frame_indices}")
+    image = img_matrix[t0][v0]
+    cond_motion = torch.cat([img_matrix[t][v0] for t in frame_indices], 0)
+    cond_view = torch.cat([img_matrix[t0][v] for v in view_indices], 0)
+    polars = polars_rad[subsampled_views[1:]][None].repeat(T, 0).flatten()
+    azims = azimuths_rad[subsampled_views[1:]][None].repeat(T, 0).flatten()
+    azims = (azims - azimuths_rad[v0]) % (torch.pi * 2)
+    model.sampler.num_steps = num_steps
+    version_dict["options"]["num_steps"] = num_steps
+    samples = run_img2vid(
+        version_dict, model, image, seed, polars, azims, cond_motion, cond_view, decoding_t
+    )
+    samples = samples.view(T, V, 3, H, W)
+    for i, t in enumerate(frame_indices):
+        for j, v in enumerate(view_indices):
+            if img_matrix[t][v] is None:
+                img_matrix[t][v] = samples[i, j][None] * 2 - 1
+
+    # concat video
+    grid_list = []
+    for t in frame_indices:
+        imgs_view = torch.cat(img_matrix[t])
+        grid_list.append(torchvision.utils.make_grid(imgs_view, nrow=3).unsqueeze(0))
+    # save output videos
+    anchor_vis_file = os.path.join(output_folder, "anchor_vis.mp4")
+    save_video(anchor_vis_file, grid_list, fps=3)
+    anchor_file = os.path.join(output_folder, "anchor.mp4")
+    image_list = samples.view(T*V, 3, H, W).unsqueeze(1) * 2 - 1
+    save_video(anchor_file, image_list)
+
+    return sv3d_file, anchor_vis_file, anchor_file
+
+
+def sample_all(
+    input_path: str = "inputs/test_video1.mp4",  # Can either be video file or folder with image files
+    sv3d_path: str = "outputs/sv4d/000000_t000.mp4",
+    anchor_path: str = "outputs/sv4d/000000_anchor.mp4",
+    seed: Optional[int] = None,
+    num_steps: int = 20,
+    device: str = "cuda",
+    elevations_deg: Optional[Union[float, List[float]]] = 10.0,
+    azimuths_deg: Optional[List[float]] = None,
+):
+    """
+    Simple script to generate multiple novel-view videos conditioned on a video `input_path` or multiple frames, one for each
+    image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`.
+    """
+    output_folder = os.path.dirname(input_path)
+    torch.manual_seed(seed)
+    os.makedirs(output_folder, exist_ok=True)
+
+    # Read input video frames i.e. images at view 0
+    print(f"Reading {input_path}")
+    images_v0 = read_video(
+        input_path,
+        n_frames=n_frames,
+        device=device,
+    )
+
+    images_t0 = read_video(
+        sv3d_path,
+        n_frames=n_views_sv3d,
+        device=device,
+    )
+
+    # Get camera viewpoints
+    if isinstance(elevations_deg, float) or isinstance(elevations_deg, int):
+        elevations_deg = [elevations_deg] * n_views_sv3d
+    assert (
+        len(elevations_deg) == n_views_sv3d
+    ), f"Please provide 1 value, or a list of {n_views_sv3d} values for elevations_deg! Given {len(elevations_deg)}"
+    if azimuths_deg is None:
+        azimuths_deg = np.linspace(0, 360, n_views_sv3d + 1)[1:] % 360
+    assert (
+        len(azimuths_deg) == n_views_sv3d
+    ), f"Please provide a list of {n_views_sv3d} values for azimuths_deg! Given {len(azimuths_deg)}"
+    polars_rad = np.array([np.deg2rad(90 - e) for e in elevations_deg])
+    azimuths_rad = np.array(
+        [np.deg2rad((a - azimuths_deg[-1]) % 360) for a in azimuths_deg]
+    )
+
+    # Initialize image matrix
+    img_matrix = [[None] * n_views for _ in range(n_frames)]
+    for i, v in enumerate(subsampled_views):
+        img_matrix[0][i] = images_t0[v]
+    for t in range(n_frames):
+        img_matrix[t][0] = images_v0[t]
+
+    # load interleaved sampling for anchor frames
+    t0, v0 = 0, 0
+    frame_indices = np.arange(T - 1, n_frames, T - 1)  # [4, 8, 12, 16, 20]
+    view_indices = np.arange(V) + 1
+
+    anchor_frames = read_video(
+        anchor_path,
+        n_frames=T * V,
+        device=device,
+    )
+    anchor_frames = torch.cat(anchor_frames).view(T, V, 3, H, W)
+    for i, t in enumerate(frame_indices):
+        for j, v in enumerate(view_indices):
+            if img_matrix[t][v] is None:
+                img_matrix[t][v] = anchor_frames[i, j][None]
+
+    # Dense sampling for the rest
+    print(f"Sampling dense frames:")
+    for t0 in np.arange(0, n_frames - 1, T - 1):  # [0, 4, 8, 12, 16]
+        frame_indices = t0 + np.arange(T)
+        print(f"Sampling dense frames {frame_indices}")
+        latent_matrix = torch.randn(n_frames, n_views, C, H // F, W // F).to("cuda")
+
+        polars = polars_rad[subsampled_views[1:]][None].repeat(T, 0).flatten()
+        azims = azimuths_rad[subsampled_views[1:]][None].repeat(T, 0).flatten()
+        azims = (azims - azimuths_rad[v0]) % (torch.pi * 2)
+        
+        # alternate between forward and backward conditioning
+        forward_inputs, forward_frame_indices, backward_inputs, backward_frame_indices = prepare_inputs(
+            frame_indices, 
+            img_matrix, 
+            v0, 
+            view_indices, 
+            model, 
+            version_dict, 
+            seed, 
+            polars, 
+            azims
+        )
+        
+        for step in range(num_steps):
+            if step % 2 == 1:
+                c, uc, additional_model_inputs, sampler = forward_inputs
+                frame_indices = forward_frame_indices
+            else:
+                c, uc, additional_model_inputs, sampler = backward_inputs
+                frame_indices = backward_frame_indices
+            noisy_latents = latent_matrix[frame_indices][:, view_indices].flatten(0, 1)
+                
+            samples = do_sample_per_step(
+                model,
+                sampler,
+                noisy_latents,
+                c,
+                uc,
+                step,
+                additional_model_inputs,
+            )
+            samples = samples.view(T, V, C, H // F, W // F)
+            for i, t in enumerate(frame_indices):
+                for j, v in enumerate(view_indices):
+                    latent_matrix[t, v] = samples[i, j]
+
+        img_matrix = decode_latents(model, latent_matrix, img_matrix, frame_indices, view_indices, T)
+
+
+    # concat video
+    grid_list = []
+    for t in range(n_frames):
+        imgs_view = torch.cat(img_matrix[t])
+        grid_list.append(torchvision.utils.make_grid(imgs_view, nrow=3).unsqueeze(0))
+    # save output videos
+    vid_file = os.path.join(output_folder, "sv4d_final.mp4")
+    save_video(vid_file, grid_list)
+
+    return vid_file, seed
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """# Demo for SV4D from Stability AI ([model](https://huggingface.co/stabilityai/sv4d), [news](https://stability.ai/news/stable-video-4d))
+#### Research release ([_non-commercial_](https://huggingface.co/stabilityai/sv4d/blob/main/LICENSE.md)): generate 8 novel view videos from a single-view video (with white background).
+#### It takes ~45s to generate anchor frames and another ~160s to generate full results (21 frames).  
+#### Hints for improving performance:  
+- Use a white background; 
+- Make the object in the center of the image; 
+- The SV4D process the first 21 frames of the uploaded video. Gradio provides a nice option of trimming the uploaded video if needed.  
+  """
+    )
+    with gr.Row():
+        with gr.Column():
+            input_video = gr.Video(label="Upload your video")
+            generate_btn = gr.Button("Step 1: generate 8 novel view videos (5 anchor frames each)")
+            interpolate_btn = gr.Button("Step 2: Extend novel view videos to 21 frames")
+        with gr.Column():
+            anchor_video = gr.Video(label="SV4D outputs (anchor frames)")
+            sv3d_video = gr.Video(label="SV3D outputs", interactive=False)
+        with gr.Column():
+            sv4d_interpolated_video = gr.Video(label="SV4D outputs (21 frames)")
+
+    with gr.Accordion("Advanced options", open=False):
+        seed = gr.Slider(
+            label="Seed",
+            value=23,
+            # randomize=True,
+            minimum=0,
+            maximum=100,
+            step=1,
+        )
+        encoding_t = gr.Slider(
+            label="Encode n frames at a time",
+            info="Number of frames encoded at a time! This eats most VRAM. Reduce if necessary.",
+            value=8,
+            minimum=1,
+            maximum=40,
+        )
+        decoding_t = gr.Slider(
+            label="Decode n frames at a time",
+            info="Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.",
+            value=4,
+            minimum=1,
+            maximum=14,
+        )
+        denoising_steps = gr.Slider(
+            label="Number of denoising steps",
+            info="Increase will improve the performance but needs more time.",
+            value=20,
+            minimum=10,
+            maximum=50,
+            step=1,
+        )
+        remove_bg = gr.Checkbox(
+            label="Remove background",
+            info="We use rembg. Users can check the alternative way: SAM2 (https://github.com/facebookresearch/segment-anything-2)",
+        )
+
+    input_video.upload(fn=preprocess_video, inputs=[input_video, remove_bg], outputs=input_video, queue=False)
+
+    with gr.Row(visible=False):
+        anchor_frames = gr.Video()
+
+    generate_btn.click(
+        fn=sample_anchor,
+        inputs=[input_video, seed, encoding_t, decoding_t, denoising_steps],
+        outputs=[sv3d_video, anchor_video, anchor_frames],
+        api_name="SV4D output (5 frames)",
+    )
+
+    interpolate_btn.click(
+        fn=sample_all,
+        inputs=[input_video, sv3d_video, anchor_frames, seed, denoising_steps],
+        outputs=[sv4d_interpolated_video, seed],
+        api_name="SV4D interpolation (21 frames)",
+    )
+
+    examples = gr.Examples(
+        fn=preprocess_video,
+        examples=[
+            "./assets/sv4d_videos/test_video1.mp4",
+            "./assets/sv4d_videos/test_video2.mp4",
+            "./assets/sv4d_videos/green_robot.mp4",
+            "./assets/sv4d_videos/dolphin.mp4",
+            "./assets/sv4d_videos/lucia_v000.mp4",
+            "./assets/sv4d_videos/snowboard_v000.mp4",
+            "./assets/sv4d_videos/stroller_v000.mp4",
+            "./assets/sv4d_videos/human5.mp4",
+            "./assets/sv4d_videos/bunnyman.mp4",
+            "./assets/sv4d_videos/hiphop_parrot.mp4",
+            "./assets/sv4d_videos/guppie_v0.mp4",
+            "./assets/sv4d_videos/wave_hello.mp4",
+            "./assets/sv4d_videos/pistol_v0.mp4",
+            "./assets/sv4d_videos/human7.mp4",
+            "./assets/sv4d_videos/monkey.mp4",
+            "./assets/sv4d_videos/train_v0.mp4",
+        ],
+        inputs=[input_video],
+        run_on_click=True,
+        outputs=[input_video],
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=20)
+    demo.launch(share=True)
+ 
\ No newline at end of file
diff --git a/scripts/demo/sv4d_helpers.py b/scripts/demo/sv4d_helpers.py
index d67231df..7296c58c 100755
--- a/scripts/demo/sv4d_helpers.py
+++ b/scripts/demo/sv4d_helpers.py
@@ -121,10 +121,6 @@ def save_video(file_name, imgs, fps=10):
 def read_video(
     input_path: str,
     n_frames: int,
-    W: int,
-    H: int,
-    remove_bg: bool = False,
-    image_frame_ratio: Optional[float] = None,
     device: str = "cuda",
 ):
     path = Path(input_path)
@@ -158,46 +154,120 @@ def read_video(
 
     if len(images) < n_frames:
         images = (images + images[::-1])[:n_frames]
-        
     if len(images) != n_frames:
         raise ValueError(f"Input video contains fewer than {n_frames} frames.")
 
-    # Remove background and crop video frames
     images_v0 = []
-    for t, image in enumerate(images):
+
+    for image in images:
+        image = ToTensor()(image).unsqueeze(0).to(device)
+        images_v0.append(image * 2.0 - 1.0)
+    return images_v0
+
+
+def preprocess_video(input_path, remove_bg=False, n_frames=21, W=576, H=576, output_folder=None, image_frame_ratio = 0.917):
+    print(f"preprocess {input_path}")
+    if output_folder is None:
+        output_folder = os.path.dirname(input_path)
+    path = Path(input_path)
+    is_video_file = False
+    all_img_paths = []
+    if path.is_file():
+        if any([input_path.endswith(x) for x in [".gif", ".mp4"]]):
+            is_video_file = True
+        else:
+            raise ValueError("Path is not a valid video file.")
+    elif path.is_dir():
+        all_img_paths = sorted(
+            [
+                f
+                for f in path.iterdir()
+                if f.is_file() and f.suffix.lower() in [".jpg", ".jpeg", ".png"]
+            ]
+        )[:n_frames]
+    elif "*" in input_path:
+        all_img_paths = sorted(glob(input_path))[:n_frames]
+    else:
+        raise ValueError
+
+    if is_video_file and input_path.endswith(".gif"):
+        images = read_gif(input_path, n_frames)[:n_frames]
+    elif is_video_file and input_path.endswith(".mp4"):
+        images = read_mp4(input_path, n_frames)[:n_frames]
+    else:
+        print(f"Loading {len(all_img_paths)} video frames...")
+        images = [Image.open(img_path) for img_path in all_img_paths]
+
+    if len(images) != n_frames:
+        raise ValueError(f"Input video contains {len(images)} frames, fewer than {n_frames} frames.")
+
+    # Remove background
+    for i, image in enumerate(images):
         if remove_bg:
-            if image.mode != "RGBA":
-                image.thumbnail([W, H], Image.Resampling.LANCZOS)
+            if image.mode == "RGBA":
+                pass
+            else:
+                # image.thumbnail([W, H], Image.Resampling.LANCZOS)
                 image = remove(image.convert("RGBA"), alpha_matting=True)
-            image_arr = np.array(image)
-            in_w, in_h = image_arr.shape[:2]
+            images[i] = image
+
+    # Crop video frames, assume the object is already in the center of the image
+    white_thresh = 250
+    images_v0 = []
+    box_coord = [np.inf, np.inf, 0, 0]
+    for image in images:
+        image_arr = np.array(image)
+        in_w, in_h = image_arr.shape[:2]
+        original_center = (in_w // 2, in_h // 2)
+        if image.mode == "RGBA":
             ret, mask = cv2.threshold(
                 np.array(image.split()[-1]), 0, 255, cv2.THRESH_BINARY
             )
-            x, y, w, h = cv2.boundingRect(mask)
-            max_size = max(w, h)
-            if t == 0:
-                side_len = (
-                    int(max_size / image_frame_ratio)
-                    if image_frame_ratio is not None
-                    else in_w
-                )
-            padded_image = np.zeros((side_len, side_len, 4), dtype=np.uint8)
-            center = side_len // 2
-            padded_image[
-                center - h // 2 : center - h // 2 + h,
-                center - w // 2 : center - w // 2 + w,
-            ] = image_arr[y : y + h, x : x + w]
-            rgba = Image.fromarray(padded_image).resize((W, H), Image.LANCZOS)
-            rgba_arr = np.array(rgba) / 255.0
-            rgb = rgba_arr[..., :3] * rgba_arr[..., -1:] + (1 - rgba_arr[..., -1:])
-            image = Image.fromarray((rgb * 255).astype(np.uint8))
         else:
-            image = image.convert("RGB").resize((W, H), Image.LANCZOS)
-        image = ToTensor()(image).unsqueeze(0).to(device)
-        images_v0.append(image * 2.0 - 1.0)
-    return images_v0
-
+            # assume the input image has white background
+            ret, mask = cv2.threshold(
+                (np.array(image).mean(-1) <= white_thresh).astype(np.uint8) * 255, 0, 255, cv2.THRESH_BINARY
+            )
+            
+        x, y, w, h = cv2.boundingRect(mask)
+        box_coord[0] = min(box_coord[0], x)
+        box_coord[1] = min(box_coord[1], y)
+        box_coord[2] = max(box_coord[2], x + w)
+        box_coord[3] = max(box_coord[3], y + h)
+    box_square = max(original_center[0] - box_coord[0], original_center[1] - box_coord[1])
+    box_square = max(box_square, box_coord[2] - original_center[0])
+    box_square = max(box_square, box_coord[3] - original_center[1])
+    x, y, w, h = original_center[0] - box_square, original_center[1] - box_square, 2 * box_square, 2 * box_square
+    box_size = box_square * 2
+
+    for image in images:
+        if image.mode == "RGB":
+            image = image.convert("RGBA")
+        image_arr = np.array(image)
+        side_len = (
+            int(box_size / image_frame_ratio)
+            if image_frame_ratio is not None
+            else in_w
+        )
+        padded_image = np.zeros((side_len, side_len, 4), dtype=np.uint8)
+        center = side_len // 2
+        padded_image[
+            center - box_size // 2 : center - box_size // 2 + box_size,
+            center - box_size // 2 : center - box_size // 2 + box_size,
+        ] = image_arr[x : x + w, y : y + h]
+
+        rgba = Image.fromarray(padded_image).resize((W, H), Image.LANCZOS)
+        # rgba = image.resize((W, H), Image.LANCZOS)
+        rgba_arr = np.array(rgba) / 255.0
+        rgb = rgba_arr[..., :3] * rgba_arr[..., -1:] + (1 - rgba_arr[..., -1:])
+        image = (rgb * 255).astype(np.uint8)
+        
+        images_v0.append(image)
+    
+    base_count = len(glob(os.path.join(output_folder, "*.mp4"))) // 12
+    processed_file = os.path.join(output_folder, f"{base_count:06d}_process_input.mp4")
+    imageio.mimwrite(processed_file, images_v0, fps=10)
+    return processed_file
 
 def sample_sv3d(
     image,
@@ -212,26 +282,32 @@ def sample_sv3d(
     polar_rad: Optional[Union[float, List[float]]] = None,
     azim_rad: Optional[List[float]] = None,
     verbose: Optional[bool] = False,
+    sv3d_model=None,
 ):
     """
     Simple script to generate a single sample conditioned on an image `input_path` or multiple images, one for each
     image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`.
     """
 
-    if version == "sv3d_u":
-        model_config = "scripts/sampling/configs/sv3d_u.yaml"
-    elif version == "sv3d_p":
-        model_config = "scripts/sampling/configs/sv3d_p.yaml"
+    if sv3d_model is None:
+        if version == "sv3d_u":
+            model_config = "scripts/sampling/configs/sv3d_u.yaml"
+        elif version == "sv3d_p":
+            model_config = "scripts/sampling/configs/sv3d_p.yaml"
+        else:
+            raise ValueError(f"Version {version} does not exist.")
+
+        model, filter = load_model(
+            model_config,
+            device,
+            num_frames,
+            num_steps,
+            verbose,
+        )
     else:
-        raise ValueError(f"Version {version} does not exist.")
-
-    model, filter = load_model(
-        model_config,
-        device,
-        num_frames,
-        num_steps,
-        verbose,
-    )
+        model = sv3d_model
+
+    load_module_gpu(model)
 
     H, W = image.shape[2:]
     F = 8
@@ -286,23 +362,30 @@ def denoiser(input, sigma, c):
                 )
 
             samples_z = model.sampler(denoiser, randn, cond=c, uc=uc)
+            unload_module_gpu(model.model)
+            unload_module_gpu(model.denoiser)
             model.en_and_decode_n_samples_a_time = decoding_t
             samples_x = model.decode_first_stage(samples_z)
             samples_x[-1:] = value_dict["cond_frames_without_noise"]
             samples = torch.clamp(samples_x, min=-1.0, max=1.0)
 
-            return samples
+    unload_module_gpu(model)
+    return samples
 
 
-def decode_latents(model, samples_z, timesteps):
+def decode_latents(model, samples_z, img_matrix, frame_indices, view_indices, timesteps):
     load_module_gpu(model.first_stage_model)
-    if isinstance(model.first_stage_model.decoder, VideoDecoder):
-        samples_x = model.decode_first_stage(samples_z, timesteps=timesteps)
-    else:
-        samples_x = model.decode_first_stage(samples_z)
-    samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+    for t in frame_indices:
+        for v in view_indices:
+            if t != 0 and v != 0:
+                if isinstance(model.first_stage_model.decoder, VideoDecoder):
+                    samples_x = model.decode_first_stage(samples_z[t, v][None], timesteps=timesteps)
+                else:
+                    samples_x = model.decode_first_stage(samples_z[t, v][None])
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+                img_matrix[t][v] = samples * 2 - 1
     unload_module_gpu(model.first_stage_model)
-    return samples
+    return img_matrix
 
 
 def init_embedder_options_no_st(keys, init_dict, prompt=None, negative_prompt=None):
@@ -604,6 +687,7 @@ def run_img2vid(
     azim_rad=np.linspace(0, 360, 21 + 1)[1:],
     cond_motion=None,
     cond_view=None,
+    decoding_t=None,
 ):
     options = version_dict["options"]
     H = version_dict["H"]
@@ -670,12 +754,53 @@ def run_img2vid(
         force_uc_zero_embeddings=options.get("force_uc_zero_embeddings", None),
         force_cond_zero_embeddings=options.get("force_cond_zero_embeddings", None),
         return_latents=False,
-        decoding_t=options.get("decoding_T", T),
+        decoding_t=decoding_t,
     )
 
     return samples
 
 
+def prepare_inputs(frame_indices, img_matrix, v0, view_indices, model, version_dict, seed, polars, azims):
+    load_module_gpu(model.conditioner)
+
+    forward_frame_indices = frame_indices.copy()
+    t0 = forward_frame_indices[0]
+    image = img_matrix[t0][v0]
+    cond_motion = torch.cat([img_matrix[t][v0] for t in forward_frame_indices], 0)
+    cond_view = torch.cat([img_matrix[t0][v] for v in view_indices], 0)
+    forward_inputs = prepare_sampling(
+                version_dict,
+                model,
+                image,
+                seed,
+                polars,
+                azims,
+                cond_motion,
+                cond_view,
+        )
+        
+    # backward sampling
+    backward_frame_indices = frame_indices[
+        ::-1
+    ].copy()
+    t0 = backward_frame_indices[0]
+    image = img_matrix[t0][v0]
+    cond_motion = torch.cat([img_matrix[t][v0] for t in backward_frame_indices], 0)
+    cond_view = torch.cat([img_matrix[t0][v] for v in view_indices], 0)
+    backward_inputs = prepare_sampling(
+            version_dict,
+            model,
+            image,
+            seed,
+            polars,
+            azims,
+            cond_motion,
+            cond_view,
+        )
+
+    unload_module_gpu(model.conditioner)
+    return forward_inputs, forward_frame_indices, backward_inputs, backward_frame_indices
+
 def do_sample(
     model,
     sampler,
@@ -761,13 +886,11 @@ def denoiser(input, sigma, c):
                     return model.denoiser(
                         model.model, input, sigma, c, **additional_model_inputs
                     )
-
                 load_module_gpu(model.model)
                 load_module_gpu(model.denoiser)
                 samples_z = sampler(denoiser, randn, cond=c, uc=uc)
                 unload_module_gpu(model.model)
                 unload_module_gpu(model.denoiser)
-
                 load_module_gpu(model.first_stage_model)
                 if isinstance(model.first_stage_model.decoder, VideoDecoder):
                     samples_x = model.decode_first_stage(
@@ -777,17 +900,15 @@ def denoiser(input, sigma, c):
                     samples_x = model.decode_first_stage(samples_z)
                 samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
                 unload_module_gpu(model.first_stage_model)
-
                 if filter is not None:
                     samples = filter(samples)
 
                 if return_latents:
                     return samples, samples_z
-
                 return samples
 
 
-def do_sample_per_step(
+def prepare_sampling_(
     model,
     sampler,
     value_dict,
@@ -797,8 +918,6 @@ def do_sample_per_step(
     batch2model_input: List = None,
     T=None,
     additional_batch_uc_fields=None,
-    step=None,
-    noisy_latents=None,
 ):
     force_uc_zero_embeddings = default(force_uc_zero_embeddings, [])
     batch2model_input = default(batch2model_input, [])
@@ -812,8 +931,6 @@ def do_sample_per_step(
                     num_samples = [num_samples, T]
                 else:
                     num_samples = [num_samples]
-
-                load_module_gpu(model.conditioner)
                 batch, batch_uc = get_batch(
                     get_unique_embedder_keys_from_conditioner(model.conditioner),
                     value_dict,
@@ -827,8 +944,6 @@ def do_sample_per_step(
                     force_uc_zero_embeddings=force_uc_zero_embeddings,
                     force_cond_zero_embeddings=force_cond_zero_embeddings,
                 )
-                unload_module_gpu(model.conditioner)
-
                 for k in c:
                     if not k == "crossattn":
                         c[k], uc[k] = map(
@@ -859,7 +974,14 @@ def do_sample_per_step(
                             )
                     else:
                         additional_model_inputs[k] = batch[k]
+    return c, uc, additional_model_inputs
+
 
+def do_sample_per_step(model, sampler, noisy_latents, c, uc, step, additional_model_inputs):
+    precision_scope = autocast
+    with torch.no_grad():
+        with precision_scope("cuda"):
+            with model.ema_scope():
                 noisy_latents_scaled, s_in, sigmas, num_sigmas, _, _ = (
                     sampler.prepare_sampling_loop(
                         noisy_latents.clone(), c, uc, sampler.num_steps
@@ -893,13 +1015,10 @@ def denoiser(input, sigma, c):
                     uc,
                     gamma,
                 )
-                unload_module_gpu(model.model)
-                unload_module_gpu(model.denoiser)
-
     return samples_z
 
 
-def run_img2vid_per_step(
+def prepare_sampling(
     version_dict,
     model,
     image,
@@ -908,8 +1027,6 @@ def run_img2vid_per_step(
     azim_rad=np.linspace(0, 360, 21 + 1)[1:],
     cond_motion=None,
     cond_view=None,
-    step=None,
-    noisy_latents=None,
 ):
     options = version_dict["options"]
     H = version_dict["H"]
@@ -962,7 +1079,7 @@ def run_img2vid_per_step(
     sampler, num_rows, num_cols = init_sampling_no_st(options=options)
     num_samples = num_rows * num_cols
 
-    samples = do_sample_per_step(
+    c, uc, additional_model_inputs = prepare_sampling_(
         model,
         sampler,
         value_dict,
@@ -971,11 +1088,9 @@ def run_img2vid_per_step(
         force_cond_zero_embeddings=options.get("force_cond_zero_embeddings", None),
         batch2model_input=["num_video_frames", "image_only_indicator"],
         T=T,
-        step=step,
-        noisy_latents=noisy_latents,
     )
 
-    return samples
+    return c, uc, additional_model_inputs, sampler
 
 
 def get_unique_embedder_keys_from_conditioner(conditioner):
diff --git a/scripts/sampling/simple_video_sample_4d.py b/scripts/sampling/simple_video_sample_4d.py
index c970e74f..704d7a29 100755
--- a/scripts/sampling/simple_video_sample_4d.py
+++ b/scripts/sampling/simple_video_sample_4d.py
@@ -10,15 +10,19 @@
 import torch
 from fire import Fire
 
+from sgm.modules.encoders.modules import VideoPredictionEmbedderWithEncoder
 from scripts.demo.sv4d_helpers import (
     decode_latents,
     load_model,
     initial_model_load,
     read_video,
     run_img2vid,
-    run_img2vid_per_step,
+    prepare_sampling,
+    prepare_inputs,
+    do_sample_per_step,
     sample_sv3d,
     save_video,
+    preprocess_video,
 )
 
 
@@ -32,17 +36,18 @@ def sample(
     motion_bucket_id: int = 127,
     cond_aug: float = 1e-5,
     seed: int = 23,
-    decoding_t: int = 14,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
+    encoding_t: int = 8,  # Number of frames encoded at a time! This eats most VRAM. Reduce if necessary.
+    decoding_t: int = 4,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
     device: str = "cuda",
     elevations_deg: Optional[Union[float, List[float]]] = 10.0,
     azimuths_deg: Optional[List[float]] = None,
-    image_frame_ratio: Optional[float] = None,
+    image_frame_ratio: Optional[float] = 0.917,
     verbose: Optional[bool] = False,
     remove_bg: bool = False,
 ):
     """
     Simple script to generate multiple novel-view videos conditioned on a video `input_path` or multiple frames, one for each
-    image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`.
+    image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t` and `encoding_t`.
     """
     # Set model config
     T = 5  # number of frames per sample
@@ -89,15 +94,16 @@ def sample(
 
     # Read input video frames i.e. images at view 0
     print(f"Reading {input_path}")
-    images_v0 = read_video(
+    processed_input_path = preprocess_video(
         input_path,
+        remove_bg=remove_bg,
         n_frames=n_frames,
         W=W,
         H=H,
-        remove_bg=remove_bg,
+        output_folder=output_folder,
         image_frame_ratio=image_frame_ratio,
-        device=device,
     )
+    images_v0 = read_video(processed_input_path, n_frames=n_frames, device=device)
 
     # Get camera viewpoints
     if isinstance(elevations_deg, float) or isinstance(elevations_deg, int):
@@ -139,7 +145,7 @@ def sample(
     for t in range(n_frames):
         img_matrix[t][0] = images_v0[t]
 
-    base_count = len(glob(os.path.join(output_folder, "*.mp4"))) // 11
+    base_count = len(glob(os.path.join(output_folder, "*.mp4"))) // 12
     save_video(
         os.path.join(output_folder, f"{base_count:06d}_t000.mp4"),
         img_matrix[0],
@@ -158,6 +164,10 @@ def sample(
         verbose,
     )
     model = initial_model_load(model)
+    for emb in model.conditioner.embedders:
+        if isinstance(emb, VideoPredictionEmbedderWithEncoder):
+            emb.en_and_decode_n_samples_a_time = encoding_t
+    model.en_and_decode_n_samples_a_time = decoding_t
 
     # Interleaved sampling for anchor frames
     t0, v0 = 0, 0
@@ -171,7 +181,7 @@ def sample(
     azims = azimuths_rad[subsampled_views[1:]][None].repeat(T, 0).flatten()
     azims = (azims - azimuths_rad[v0]) % (torch.pi * 2)
     samples = run_img2vid(
-        version_dict, model, image, seed, polars, azims, cond_motion, cond_view
+        version_dict, model, image, seed, polars, azims, cond_motion, cond_view, decoding_t
     )
     samples = samples.view(T, V, 3, H, W)
     for i, t in enumerate(frame_indices):
@@ -185,40 +195,48 @@ def sample(
         frame_indices = t0 + np.arange(T)
         print(f"Sampling dense frames {frame_indices}")
         latent_matrix = torch.randn(n_frames, n_views, C, H // F, W // F).to("cuda")
+
+        polars = polars_rad[subsampled_views[1:]][None].repeat(T, 0).flatten()
+        azims = azimuths_rad[subsampled_views[1:]][None].repeat(T, 0).flatten()
+        azims = (azims - azimuths_rad[v0]) % (torch.pi * 2)
+        
+        # alternate between forward and backward conditioning
+        forward_inputs, forward_frame_indices, backward_inputs, backward_frame_indices = prepare_inputs(
+            frame_indices, 
+            img_matrix, 
+            v0, 
+            view_indices, 
+            model, 
+            version_dict, 
+            seed, 
+            polars, 
+            azims
+        )
+        
         for step in tqdm(range(num_steps)):
-            frame_indices = frame_indices[
-                ::-1
-            ].copy()  # alternate between forward and backward conditioning
-            t0 = frame_indices[0]
-            image = img_matrix[t0][v0]
-            cond_motion = torch.cat([img_matrix[t][v0] for t in frame_indices], 0)
-            cond_view = torch.cat([img_matrix[t0][v] for v in view_indices], 0)
-            polars = polars_rad[subsampled_views[1:]][None].repeat(T, 0).flatten()
-            azims = azimuths_rad[subsampled_views[1:]][None].repeat(T, 0).flatten()
-            azims = (azims - azimuths_rad[v0]) % (torch.pi * 2)
+            if step % 2 == 1:
+                c, uc, additional_model_inputs, sampler = forward_inputs
+                frame_indices = forward_frame_indices
+            else:
+                c, uc, additional_model_inputs, sampler = backward_inputs
+                frame_indices = backward_frame_indices
             noisy_latents = latent_matrix[frame_indices][:, view_indices].flatten(0, 1)
-            samples = run_img2vid_per_step(
-                version_dict,
+                
+            samples = do_sample_per_step(
                 model,
-                image,
-                seed,
-                polars,
-                azims,
-                cond_motion,
-                cond_view,
-                step,
+                sampler,
                 noisy_latents,
+                c,
+                uc,
+                step,
+                additional_model_inputs,
             )
             samples = samples.view(T, V, C, H // F, W // F)
             for i, t in enumerate(frame_indices):
                 for j, v in enumerate(view_indices):
                     latent_matrix[t, v] = samples[i, j]
 
-        for t in frame_indices:
-            for v in view_indices:
-                if t != 0 and v != 0:
-                    img = decode_latents(model, latent_matrix[t, v][None], T)
-                    img_matrix[t][v] = img * 2 - 1
+        img_matrix = decode_latents(model, latent_matrix, img_matrix, frame_indices, view_indices, T)
 
     # Save output videos
     for v in view_indices: