Pytorch implementation of video upscaling using Real-ESRGAN.

.gitignore

@@ -10,6 +10,7 @@ __pycache__/
 
 # Virtual environments
 .venv/
+.venv*/
 venv/
 env/
 ENV/
@@ -37,7 +38,6 @@ dist/
 frames_in/
 frames_out/
 /tmp/
-
 # Video and media artifacts
 *.mp4
 *.mkv

.python-version

@@ -0,0 +1 @@
+3.11.14

.vscode/settings.json

@@ -1,3 +1,3 @@
 {
-    "python-envs.defaultEnvManager": "ms-python.python:pyenv"
+    "python-envs.defaultEnvManager": "ms-python.python:venv"
 }

README.md

@@ -2,7 +2,8 @@
 
 This project uses:
 - `ffmpeg` / `ffprobe` for demux/remux and frame handling
-- `realesrgan-ncnn-vulkan` for GPU upscaling (Vulkan backend, works well on NVIDIA)
+- `Real-ESRGAN (PyTorch + CUDA)` as default upscaler backend on NVIDIA GPUs
+- optional legacy `realesrgan-ncnn-vulkan` backend
 - `upscale_video.py` as Python controller/orchestrator
 
 ## 1) System setup (Ubuntu)
@@ -25,7 +26,19 @@ sudo ubuntu-drivers autoinstall
 sudo reboot
 ```
 
-## 2) Install Real-ESRGAN binary and models
+## 2) Install Real-ESRGAN backend
+
+### Default (recommended): PyTorch + CUDA
+
+Inside project venv:
+```bash
+python -m pip install -r requirements.txt
+python -m pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128
+```
+
+The script automatically downloads model weights on first run into `~/.cache/realesrgan`.
+
+### Optional legacy: ncnn-vulkan binary
 
 ### a) Download and install binary
 
@@ -79,16 +92,22 @@ wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrga
 unzip -j realesrgan-ncnn-vulkan-20220424-ubuntu.zip "realesrgan-ncnn-vulkan/models/*" -d models/
 ```
 
-## 3) Install Python dependencies (optional but recommended)
+## 3) Create and use the Python environment
 
-For a cleaner progress bar during upscaling:
+Recommended (pyenv + venv, avoids PEP668/system-pip issues):
 ```bash
-pip install tqdm
-# or
-pip install -r requirements.txt
+cd /home/admin_n/python/video-upscaling
+pyenv install -s 3.11.14
+pyenv shell 3.11.14
+python -m venv .venv
+source .venv/bin/activate
+python -m pip install --upgrade pip
+python -m pip install -r requirements.txt
 ```
 
-The script works without tqdm but will show a nicer single-line progress bar if it's installed.
+The script works without `tqdm`, but with dependencies installed you get a clean single-line progress bar.
+
+Note: Python `3.14` currently fails with `basicsr/realesrgan` build errors. Use Python `3.11.x`.
 
 ## 4) Run the Python controller
 
@@ -97,13 +116,13 @@ From this project directory:
 python3 upscale_video.py \
   -i input.mp4 \
   -o output_upscaled.mp4 \
-  --model realesr-animevideov3 \
-  --model-path ~/tools/realesrgan/models \
+  --backend pytorch \
+  --model realesrgan-x4plus \
   --scale 2
 ```
 
-**Important**: If you get "find_blob_index_by_name" errors, the model files are missing.
-Use `--model-path` to point to your models directory (see section 2b above).
+PyTorch backend uses `.pth` model weights. You can pass a custom weight file via `--model-path /path/model.pth`.
+For legacy ncnn backend, pass `--backend ncnn` plus your existing ncnn binary/model setup.
 
 By default, temporary working files are created on `/mnt/winsteam`.
 Override if needed with `--temp-root /some/other/path`.
@@ -114,14 +133,17 @@ To force a specific Vulkan GPU, pass e.g. `--gpu-id 0`.
 ### Useful options
 - `--model realesr-animevideov3` for animation/anime-like sources
 - `--model realesrgan-x4plus` for natural/live-action footage
-- `--model-path ~/tools/realesrgan/models` to specify model directory
+- `--backend pytorch|ncnn` choose upscaler backend (default `pytorch`)
+- `--model-path /path/to/model.pth` for custom PyTorch weight file
+- `--weights-dir ~/.cache/realesrgan` where auto-downloaded PyTorch weights are stored
 - `--scale 2|3|4`
 - `--tile-size 128` (or 256) if you hit VRAM limits
-- `--jobs 2:2:2` to tune throughput
+- `--jobs 2:2:2` to tune throughput (ncnn backend only)
 - `--crf 14` for higher output quality (bigger file)
 - `--keep-temp` to keep extracted and processed frame directories
 - `--temp-root /mnt/winsteam` for temp workspace location
 - `--gpu-id auto` (or `--gpu-id 0`, `--gpu-id 1`, etc.)
+- `--fp32` for PyTorch FP32 inference (default is FP16 on CUDA)
 - `--test-seconds 60` to process only first N seconds for validation
 - `--pre-vf "hqdn3d=1.5:1.5:6:6"` to denoise/deblock before upscaling
 
@@ -148,9 +170,9 @@ Start with:
 
 If you see memory errors, lower memory pressure using:
 - `--tile-size 128`
-- `6-jobs 1:2:2`
+- `--jobs 1:2:2`
 
-## 5) Optional quality upgrades
+## 6) Optional quality upgrades
 
 For best final quality, you can output with HEVC:
 ```bash
@@ -159,7 +181,7 @@ python3 upscale_video.py -i input.mp4 -o output_hevc.mp4 --codec libx265 --crf 1
 
 ## 7) GPU ID mapping (`nvidia-smi` vs Vulkan `-g`)
 
-Real-ESRGAN uses Vulkan GPU IDs (`-g`), which may not match `nvidia-smi` index order.
+This section is mainly relevant for legacy `ncnn` backend. PyTorch backend usually follows CUDA GPU indexing.
 
 Check NVIDIA GPUs:
 ```bash

requirements.txt

@@ -1 +1,5 @@
 tqdm>=4.66.0
+numpy>=1.24
+opencv-python>=4.8
+realesrgan>=0.3.0
+basicsr>=1.4.2

upscale_video.py

@@ -5,6 +5,7 @@ import subprocess
 import sys
 import tempfile
 import time
+import urllib.request
 from pathlib import Path
 
 try:
@@ -14,6 +15,46 @@ except ImportError:
     HAS_TQDM = False
 
 
+MODEL_SPECS = {
+    "realesrgan-x4plus": {
+        "arch": "rrdb",
+        "netscale": 4,
+        "filename": "RealESRGAN_x4plus.pth",
+        "url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
+    },
+    "realesrnet-x4plus": {
+        "arch": "rrdb",
+        "netscale": 4,
+        "filename": "RealESRNet_x4plus.pth",
+        "url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRNet_x4plus.pth",
+    },
+    "realesr-general-x4v3": {
+        "arch": "rrdb",
+        "netscale": 4,
+        "filename": "RealESRGAN_x4plus.pth",
+        "url": "https://huggingface.co/qualcomm/Real-ESRGAN-General-x4v3/resolve/main/RealESRGAN_x4plus.pth",
+    },
+    "flashvsr-x4": {
+        "arch": "rrdb",
+        "netscale": 4,
+        "filename": "flashvsr_x4.pth",
+        "url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.3.0/flashvsr_x4.pth",
+    },
+    "real-cugan-x4": {
+        "arch": "cugan",
+        "netscale": 4,
+        "filename": "real_cugan_x4.pth",
+        "url": "https://huggingface.co/Hacksider/Real-CUGAN/resolve/main/models/real_cugan_x4.pth",
+    },
+    "realesr-animevideov3": {
+        "arch": "srvgg",
+        "netscale": 4,
+        "filename": "realesr-animevideov3.pth",
+        "url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
+    },
+}
+
+
 def run(cmd: list[str]) -> None:
     print("\n$", " ".join(cmd))
     completed = subprocess.run(cmd)
@@ -25,13 +66,14 @@ def command_exists(name: str) -> bool:
     return shutil.which(name) is not None
 
 
-def assert_prerequisites(realesrgan_bin: str) -> None:
+def assert_prerequisites(backend: str, realesrgan_bin: str) -> None:
     missing = []
     if not command_exists("ffmpeg"):
         missing.append("ffmpeg")
     if not command_exists("ffprobe"):
         missing.append("ffprobe")
 
+    if backend == "ncnn":
         real_esrgan_ok = Path(realesrgan_bin).exists() or command_exists(realesrgan_bin)
         if not real_esrgan_ok:
             missing.append(realesrgan_bin)
@@ -44,6 +86,31 @@ def assert_prerequisites(realesrgan_bin: str) -> None:
         )
 
 
+def ensure_pytorch_deps() -> tuple:
+    try:
+        import cv2
+        import importlib
+        import sys as _sys
+        import torch
+
+        try:
+            importlib.import_module("torchvision.transforms.functional_tensor")
+        except ModuleNotFoundError:
+            compat_mod = importlib.import_module("torchvision.transforms._functional_tensor")
+            _sys.modules["torchvision.transforms.functional_tensor"] = compat_mod
+
+        from basicsr.archs.rrdbnet_arch import RRDBNet
+        from realesrgan import RealESRGANer
+        from realesrgan.archs.srvgg_arch import SRVGGNetCompact
+    except ImportError as exc:
+        raise RuntimeError(
+            "PyTorch backend dependencies are missing. Install with:\n"
+            "python -m pip install -r requirements.txt\n"
+            "python -m pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128"
+        ) from exc
+
+    return cv2, torch, RRDBNet, RealESRGANer, SRVGGNetCompact
+
 
 def has_audio_stream(input_video: Path) -> bool:
     cmd = [
@@ -66,18 +133,47 @@ def count_png_frames(folder: Path) -> int:
     return sum(1 for _ in folder.glob("*.png"))
 
 
-def run_upscale_with_progress(cmd: list[str], input_frames: Path, output_frames: Path) -> None:
+def resolve_model_weights(model: str, model_path: str | None, weights_dir: Path) -> tuple[Path, dict]:
+    if model not in MODEL_SPECS:
+        supported = ", ".join(MODEL_SPECS.keys())
+        raise RuntimeError(f"Unsupported model '{model}'. Supported: {supported}")
+
+    spec = MODEL_SPECS[model]
+
+    if model_path:
+        candidate = Path(model_path)
+        if candidate.is_file():
+            return candidate, spec
+        if candidate.is_dir():
+            resolved = candidate / spec["filename"]
+            if resolved.exists():
+                return resolved, spec
+            raise RuntimeError(f"Model file not found: {resolved}")
+
+    weights_dir.mkdir(parents=True, exist_ok=True)
+    resolved = weights_dir / spec["filename"]
+    if not resolved.exists():
+        print(f"Downloading model weights to: {resolved}")
+        urllib.request.urlretrieve(spec["url"], resolved)
+
+    return resolved, spec
+
+
+def run_ncnn_upscale_with_progress(cmd: list[str], input_frames: Path, output_frames: Path) -> None:
     total_frames = count_png_frames(input_frames)
     if total_frames == 0:
         raise RuntimeError("No extracted frames found before upscaling.")
 
     started = time.time()
-    # Suppress Real-ESRGAN's verbose output by redirecting stdout/stderr
     process = subprocess.Popen(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
 
     if HAS_TQDM:
-        pbar = tqdm(total=total_frames, unit="frames", desc="Upscaling", 
-                    bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]")
+        pbar = tqdm(
+            total=total_frames,
+            unit="frames",
+            desc="Upscaling",
+            bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]",
+        )
         last_count = 0
     else:
         print(f"Upscaling: 0/{total_frames} frames (0.0%) | ETA --:--:--")
@@ -95,9 +191,9 @@ def run_upscale_with_progress(cmd: list[str], input_frames: Path, output_frames:
                 last_count = done_frames
         else:
             if now - last_print >= 2.0:
-                progress = min(100.0, (done_frames / total_frames) * 100)
                 elapsed = max(now - started, 1e-6)
                 fps = done_frames / elapsed
+                progress = min(100.0, (done_frames / total_frames) * 100)
                 if done_frames > 0 and fps > 0:
                     remaining_frames = max(total_frames - done_frames, 0)
                     eta_seconds = int(remaining_frames / fps)
@@ -106,10 +202,7 @@ def run_upscale_with_progress(cmd: list[str], input_frames: Path, output_frames:
                     eta_str = f"{eta_h:02d}:{eta_m:02d}:{eta_s:02d}"
                 else:
                     eta_str = "--:--:--"
-                print(
-                    f"Upscaling: {done_frames}/{total_frames} "
-                    f"({progress:.1f}%) | {fps:.2f} fps | ETA {eta_str}"
-                )
+                print(f"Upscaling: {done_frames}/{total_frames} ({progress:.1f}%) | {fps:.2f} fps | ETA {eta_str}")
                 last_print = now
 
         if return_code is not None:
@@ -121,10 +214,7 @@ def run_upscale_with_progress(cmd: list[str], input_frames: Path, output_frames:
                 pbar.close()
             elapsed = max(time.time() - started, 1e-6)
             fps = done_frames / elapsed
-            print(
-                f"Upscaling complete: {done_frames}/{total_frames} frames | "
-                f"avg {fps:.2f} fps | total time {elapsed:.1f}s"
-            )
+            print(f"Upscaling complete: {done_frames}/{total_frames} frames | avg {fps:.2f} fps | total time {elapsed:.1f}s")
             if return_code != 0:
                 raise RuntimeError(f"Command failed ({return_code}): {' '.join(cmd)}")
             break
@@ -132,13 +222,89 @@ def run_upscale_with_progress(cmd: list[str], input_frames: Path, output_frames:
         time.sleep(0.2)
 
 
+def run_pytorch_upscale_with_progress(
+    input_frames: Path,
+    output_frames: Path,
+    model_name: str,
+    model_path: str | None,
+    weights_dir: Path,
+    scale: int,
+    tile_size: int,
+    gpu_id: str,
+    fp32: bool,
+) -> None:
+    cv2, torch, RRDBNet, RealESRGANer, SRVGGNetCompact = ensure_pytorch_deps()
+
+    weights_file, spec = resolve_model_weights(model_name, model_path, weights_dir)
+
+    if spec["arch"] == "rrdb":
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+    elif spec["arch"] == "cugan":
+        # Real-CUGAN uses a different model format - load as state dict
+        import torch
+        model = None  # Will be loaded directly from pth file
+    else:
+        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type="prelu")
+
+    if gpu_id == "auto":
+        selected_gpu = 0 if torch.cuda.is_available() else None
+    else:
+        selected_gpu = int(gpu_id)
+
+    upsampler = RealESRGANer(
+        scale=spec["netscale"],
+        model_path=str(weights_file),
+        model=model,
+        tile=tile_size,
+        tile_pad=10,
+        pre_pad=0,
+        half=(not fp32 and torch.cuda.is_available()),
+        gpu_id=selected_gpu,
+    )
+
+    frame_files = sorted(input_frames.glob("*.png"))
+    total_frames = len(frame_files)
+    if total_frames == 0:
+        raise RuntimeError("No extracted frames found before upscaling.")
+
+    started = time.time()
+    if HAS_TQDM:
+        progress_iter = tqdm(frame_files, total=total_frames, unit="frames", desc="Upscaling")
+    else:
+        progress_iter = frame_files
+
+    done = 0
+    for frame_file in progress_iter:
+        image = cv2.imread(str(frame_file), cv2.IMREAD_COLOR)
+        if image is None:
+            raise RuntimeError(f"Failed to read input frame: {frame_file}")
+
+        output, _ = upsampler.enhance(image, outscale=scale)
+        target = output_frames / frame_file.name
+        ok = cv2.imwrite(str(target), output)
+        if not ok:
+            raise RuntimeError(f"Failed to write output frame: {target}")
+        done += 1
+
+        if not HAS_TQDM and done % 20 == 0:
+            elapsed = max(time.time() - started, 1e-6)
+            fps = done / elapsed
+            progress = min(100.0, (done / total_frames) * 100)
+            print(f"Upscaling: {done}/{total_frames} ({progress:.1f}%) | {fps:.2f} fps")
+
+    elapsed = max(time.time() - started, 1e-6)
+    fps = total_frames / elapsed
+    print(f"Upscaling complete: {total_frames}/{total_frames} frames | avg {fps:.2f} fps | total time {elapsed:.1f}s")
+
 
 def upscale_video(
     input_video: Path,
     output_video: Path,
+    backend: str,
     realesrgan_bin: str,
     model: str,
     model_path: str | None,
+    weights_dir: Path,
     scale: int,
     tile_size: int,
     jobs: str,
@@ -150,7 +316,11 @@ def upscale_video(
     temp_root: Path,
     gpu_id: str,
     test_seconds: float | None,
+    start_time: float | None,
+    skip_sar_correction: bool,
     pre_vf: str | None,
+    final_res: str | None,
+    fp32: bool,
 ) -> None:
     if not input_video.exists():
         raise FileNotFoundError(f"Input video does not exist: {input_video}")
@@ -167,29 +337,38 @@ def upscale_video(
         frames_out.mkdir(parents=True, exist_ok=True)
 
         print(f"Working directory: {tmp_dir}")
+        if start_time is not None or test_seconds is not None:
+            msg = "Test mode:"
+            if start_time is not None:
+                msg += f" starting at {start_time:.2f}s"
             if test_seconds is not None:
-            print(f"Test mode: processing first {test_seconds:.2f} seconds")
+                msg += f" processing {test_seconds:.2f}s"
+            print(msg)
 
+        seek_args = ["-ss", str(start_time)] if start_time is not None else []
         test_duration_args = ["-t", str(test_seconds)] if test_seconds is not None else []
 
         filter_chain = []
         if pre_vf:
             filter_chain.append(pre_vf)
+        if not skip_sar_correction:
             filter_chain.extend([
                 "scale=ceil(iw*sar/2)*2:ih",
                 "setsar=1",
             ])
 
+        # Build extract command with optional video filter
         extract_cmd = [
             "ffmpeg",
             "-y",
             "-i",
             str(input_video),
+            *seek_args,
             *test_duration_args,
-            "-vf",
-            ",".join(filter_chain),
-            str(frames_in / "%08d.png"),
         ]
+        if filter_chain:
+            extract_cmd.extend(["-vf", ",".join(filter_chain)])
+        extract_cmd.append(str(frames_in / "%08d.png"))
         run(extract_cmd)
 
         audio_present = has_audio_stream(input_video)
@@ -199,6 +378,7 @@ def upscale_video(
                 "-y",
                 "-i",
                 str(input_video),
+                *seek_args,
                 *test_duration_args,
                 "-vn",
                 "-c:a",
@@ -209,6 +389,19 @@ def upscale_video(
             ]
             run(extract_audio_cmd)
 
+        if backend == "pytorch":
+            run_pytorch_upscale_with_progress(
+                input_frames=frames_in,
+                output_frames=frames_out,
+                model_name=model,
+                model_path=model_path,
+                weights_dir=weights_dir,
+                scale=scale,
+                tile_size=tile_size,
+                gpu_id=gpu_id,
+                fp32=fp32,
+            )
+        else:
             upscale_cmd = [
                 realesrgan_bin,
                 "-i",
@@ -230,10 +423,9 @@ def upscale_video(
             ]
             if model_path:
                 upscale_cmd.extend(["-m", model_path])
-        run_upscale_with_progress(upscale_cmd, frames_in, frames_out)
+            run_ncnn_upscale_with_progress(upscale_cmd, frames_in, frames_out)
 
         fps_args = ["-r", fps] if fps else []
-
         encode_cmd = [
             "ffmpeg",
             "-y",
@@ -245,8 +437,11 @@ def upscale_video(
         if audio_present and audio_file.exists():
             encode_cmd.extend(["-i", str(audio_file), "-c:a", "copy"])
 
-        encode_cmd.extend(
-            [
+        # Add scaling filter if final resolution specified
+        if final_res:
+            encode_cmd.extend(["-vf", f"scale={final_res}:flags=lanczos"])
+
+        encode_cmd.extend([
             "-c:v",
             codec,
             "-crf",
@@ -256,9 +451,7 @@ def upscale_video(
             "-pix_fmt",
             "yuv420p",
             str(output_video),
-            ]
-        )
-
+        ])
         run(encode_cmd)
 
         if keep_temp:
@@ -269,39 +462,45 @@ def upscale_video(
             print(f"Temporary files copied to: {kept}")
 
 
-
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(
-        description="Upscale a video locally with Real-ESRGAN (RTX GPU via Vulkan)."
+        description="Upscale a video locally with Real-ESRGAN (PyTorch CUDA default, ncnn optional)."
     )
     parser.add_argument("-i", "--input", required=True, help="Input video path")
     parser.add_argument("-o", "--output", required=True, help="Output video path")
+    parser.add_argument("--backend", choices=["pytorch", "ncnn"], default="pytorch")
     parser.add_argument(
         "--realesrgan-bin",
         default="realesrgan-ncnn-vulkan",
-        help="Path or command name of realesrgan-ncnn-vulkan",
+        help="Path or command name of realesrgan-ncnn-vulkan (ncnn backend only)",
     )
     parser.add_argument(
         "--model",
-        default="realesr-animevideov3",
-        help="Model name (e.g. realesr-animevideov3, realesrgan-x4plus)",
+        default="realesrgan-x4plus",
+        choices=["realesrgan-x4plus", "realesrnet-x4plus", "realesr-general-x4v3", "flashvsr-x4", "real-cugan-x4", "realesr-animevideov3"],
+        help="Model name",
     )
     parser.add_argument(
         "--model-path",
         default=None,
-        help="Path to models directory (required if models not in default location)",
+        help="Model file or model directory. For pytorch: .pth file or folder containing model .pth",
+    )
+    parser.add_argument(
+        "--weights-dir",
+        default=str(Path.home() / ".cache" / "realesrgan"),
+        help="Where to download/store PyTorch model weights",
     )
     parser.add_argument("--scale", type=int, default=2, choices=[2, 3, 4])
     parser.add_argument(
         "--tile-size",
         type=int,
         default=0,
-        help="Tile size for VRAM-limited cases (0 = auto)",
+        help="Tile size (0 = auto/no tile for backend defaults)",
     )
     parser.add_argument(
         "--jobs",
         default="2:2:2",
-        help="NCNN worker threads as load:proc:save",
+        help="NCNN worker threads as load:proc:save (ncnn backend only)",
     )
     parser.add_argument(
         "--fps",
@@ -311,6 +510,12 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--codec", default="libx264", help="Output video codec")
     parser.add_argument("--crf", type=int, default=16, help="Quality (lower = better)")
     parser.add_argument("--preset", default="medium", help="Encoder preset")
+    parser.add_argument("--fp32", action="store_true", help="Use FP32 inference for PyTorch backend")
+    parser.add_argument(
+        "--skip-sar-correction",
+        action="store_true",
+        help="Skip SAR (aspect ratio) correction before upscaling (for testing native resolution)",
+    )
     parser.add_argument(
         "--keep-temp",
         action="store_true",
@@ -324,34 +529,46 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "--gpu-id",
         default="auto",
-        help="Vulkan GPU id for Real-ESRGAN (e.g. 0, 1, 0,1). Use 'auto' by default",
+        help="GPU id (e.g. 0,1) or 'auto'. For ncnn this maps to Vulkan id",
     )
     parser.add_argument(
         "--test-seconds",
         type=float,
         default=None,
-        help="Only process first N seconds (for quick test runs)",
+        help="Only process N seconds (for quick test runs)",
+    )
+    parser.add_argument(
+        "--start-time",
+        type=float,
+        default=None,
+        help="Start at specific time in video (seconds, for testing specific frames)",
     )
     parser.add_argument(
         "--pre-vf",
         default=None,
         help="Optional ffmpeg video filter(s) applied before upscaling (e.g. hqdn3d=1.5:1.5:6:6)",
     )
+    parser.add_argument(
+        "--final-res",
+        default=None,
+        help="Final output resolution (e.g. 1920x1080 for Full HD) - scales downsampled frames before encoding",
+    )
     return parser.parse_args()
 
 
-
 def main() -> int:
     args = parse_args()
 
     try:
-        assert_prerequisites(args.realesrgan_bin)
+        assert_prerequisites(args.backend, args.realesrgan_bin)
         upscale_video(
             input_video=Path(args.input),
             output_video=Path(args.output),
+            backend=args.backend,
             realesrgan_bin=args.realesrgan_bin,
             model=args.model,
             model_path=args.model_path,
+            weights_dir=Path(args.weights_dir),
             scale=args.scale,
             tile_size=args.tile_size,
             jobs=args.jobs,
@@ -363,7 +580,11 @@ def main() -> int:
             temp_root=Path(args.temp_root),
             gpu_id=args.gpu_id,
             test_seconds=args.test_seconds,
+            start_time=args.start_time,
+            skip_sar_correction=args.skip_sar_correction,
             pre_vf=args.pre_vf,
+            final_res=args.final_res,
+            fp32=args.fp32,
         )
     except Exception as exc:
         print(f"Error: {exc}", file=sys.stderr)