VideoProc Converter AI — Model Zoo (VideoProc 8.x)

Model weights extracted from the local model stack of VideoProc Converter AI (Digiarty, 8.x series). This repository is a preservation / interoperability archive of the neural networks the application downloads and runs locally: image/video super-resolution, frame interpolation, and a small set of bundled offline NCNN / DeepFilterNet models.

The TensorRT engines are GPU-locked; the ONNX weights are the hardware-agnostic target.

Path	What	Files	Size
`models_unpacked/`	Native weights: 31 portable ONNX + 1 508 TensorRT engines	1 539	~53 GiB
`app/models/`	Bundled offline models shipped in the installer (NCNN bin+param, DeepFilterNet)	12 weight files	~63 MiB
`MODEL_ZOO_MANIFEST.json` / `.csv`	Full per-file catalogue (category, backend, size, model key)	—	~1 MiB
`MODELS.md`	Technical detail: backends, layout, feature→model mapping	—	—
`tools/`	ONNX inference example (`test_sr_onnx.py`)	—	—

Layout

models_unpacked/
├── image/super_resolution/onnx/                6 × ONNX image-SR (×1/×2/×4, MoDetail V2)
├── image/super_resolution/tensorrt861-rt6xx/   TRT engines (RTX 3060 class, TRT 8.6.1)
├── video/super_resolution/onnx/                6 × ONNX video-SR (×1/×2/×4, Video4K V9/V10)
├── v3/image/super_resolution/onnx/            11 × AIGC / MoDetail V3 image-SR
├── v3/image/super_resolution/tensorrt*/        per-GPU/per-resolution engines
├── v3/video/super_resolution/onnx/             7 × video-SR (superVIDEO / MoDetail / smooth)
├── v3/video/super_resolution/tensorrt*/        per-GPU/per-resolution engines
└── v3/video/frame_interpolation/onnx/          2 × flownet (fp16 / fp32)
app/models/
├── df/dfn30.m, dfn31.m                         DeepFilterNet3 audio denoise (gzipped ONNX tar)
├── film/flownet.m, flownet.p                   NCNN frame interpolation
├── sr/x4.m, x4.p                               NCNN ×4 image SR
└── sr_ani/{pro,se}/*.m, *.p                    NCNN anime image SR

Inventory

Category	Portable ONNX	TensorRT engines
Image super-resolution (v1)	6	50
Image super-resolution (v3, AIGC)	11	119
Video super-resolution (v1 + v3)	13	~1 410
Frame interpolation	2	226
Bundled NCNN / DeepFilterNet	—	12 files
Total	31 ONNX	1 508 engines

TensorRT engines are serialized per GPU and precision — they load only on matching hardware + matching TRT/CUDA build:

Tag	Target	Notes
`tensorrt861-rt601/705/806/809`	NVIDIA RTX 30/40 series	TensorRT 8.6.1 class
`tensorrt1090-rt1200{,-compatible}`	NVIDIA RTX 50 series	TensorRT 10.9, fp32/fp16

For generic hardware, use the ONNX set through onnxruntime.

Usage

ONNX (portable, any CPU/GPU)

import onnxruntime as ort, numpy as np

sess = ort.InferenceSession(
    "models_unpacked/v3/video/super_resolution/onnx/"
    "Aiarty_superVIDEO_MoDetail_x2_V11_dim40_327000_768.onnx",
    providers=["CPUExecutionProvider"],  # or CUDAExecutionProvider
)
inp, out = sess.get_inputs()[0], sess.get_outputs()[0]
x = np.random.rand(1, 3, 128, 128).astype(np.float32)
y = sess.run([out.name], {inp.name: x})[0]     # dynamic shapes resolved at load time

Input/output are NCHW float32 in [0,1]. Scale factors are fixed by the model (×1 / ×2 / ×4), and dynamic input dimensions are resolved at load time — verified: 128×128 → 256×256 (×2), 256×256 → 1024×1024 (×4).

TensorRT engines

Require the same NVIDIA GPU the engine was serialized for, plus a compatible TensorRT/CUDA runtime (8.6.1 or 10.9 families). Deserialize with nvinfer1::IRuntime::deserializeCudaEngine (C++) or tensorrt.Runtime (Python).

Bundled NCNN / DeepFilterNet

app/models/**/*.p + *.m — NCNN param + bin (param magic 7767517), run with Tencent ncnn (Vulkan) or CPU fallback.
app/models/df/dfn30.m, dfn31.m — gzipped ONNX tar for DeepFilterNet3 audio denoise, consumed by Rust tract or ONNX Runtime.
app/models/film/flownet.* — frame interpolation (NCNN).

Notes

The 11 v3/contrib/* entries (CodeFormer, GFPGAN, DDColor, htdemucs, YOLO, ResNet-34, AdaAttn) are 808-byte stubs in the source distribution — they carry no payload, so there is nothing to mirror.
Windows runtime glue (GUI, worker, model-manager DLL, D3D11 capture): not required to run the weights.
Classic OpenCV video stabilization: not a neural network.

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