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ngoctham/
SwiftVR

Name: SwiftVR
Rating: 2.5 (43 reviews)

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SwiftVR: Real-Time One-Step Generative Video Restoration

SwiftVR is the first generative video restoration model to reach real-time 1080p streaming on a consumer-grade GPU (≈26 FPS on a single RTX 5090), sustains 31 FPS at QHD (2560×1440) and 14 FPS at 4K (3840×2160) on a single H100, and streams at resolutions where every compared diffusion-based VR baseline runs out of memory.

GitHub

SwiftVR is a streaming one-step generative video restoration (VR) framework presented in SwiftVR: Real-Time One-Step Generative Video Restoration.

Updates

[2026/06] Release the inference code and pretrained weights 🎉

✨ Highlights

Mask-free shifted-window self-attention (MFSWA). Each spatial window is pre-gathered into a dense tensor, so every attention call reduces to a single standard scaled-dot-product (SDPA) call — no attention mask, cyclic shift, or padding ever enters the graph. This gives a 1.62× throughput gain over its full-attention teacher at essentially identical quality, with no dedicated sparse kernel.
Restoration-aware Autoencoder (ReAE). A lightweight encoder–decoder jointly fine-tuned with the DiT in pixel space removes the heavy-3D-VAE / tiled-decoding bottleneck.
Causal chunk-wise streaming. A minimal causal protocol (no rolling KV cache, no overlapped DiT inference) bounds the temporal axis, confining the residual (\mathcal{O}(N^2)) cost to the spatial axes.

📊 Results

Efficiency at 2560×1440 (single H100, causal streaming, 24 frames)

Metric	DOVE (tile)	SeedVR2-3B (tile)	FlashVSR-Tiny	SwiftVR (Ours)
Avg. Time (s) ↓	27.615	17.320	2.493	0.766
FPS ↑	0.85	1.39	9.61	31.32
Peak Mem. (GB) ↓	59.24	35.35	34.35	38.01

At 3840×2160, every compared diffusion-based VR baseline OOMs on a single H100; SwiftVR sustains 14 FPS.

Qualitative comparison

🛠 Installation

bash

git clone https://github.com/H-oliday/SwiftVR.git
cd SwiftVR

conda create -n swiftvr python=3.10 -y
conda activate swiftvr

# Install PyTorch matching your CUDA toolkit first, e.g. CUDA 12.4:
pip install torch==2.10.0 torchvision==0.25.0 --index-url https://download.pytorch.org/whl/cu124

# Install SwiftVR (editable) and its dependencies:
pip install -e .

Hardware notes

Server: single H100-80G reproduces the QHD/4K numbers above.
Consumer: single RTX 5090 reaches ≈26 FPS at 1080p with the same checkpoint (default PyTorch SDPA path, bfloat16, causal chunk protocol).
No hardware-specific retraining or kernel rewrite is required on any platform.

🗂 Model Zoo

Model Name	Date	Backbone	Link
SwiftVR	2026.06	Wan2.2-TI2V-5B	🤗 HuggingFace

huggingface-cli download H-oliday/SwiftVR --local-dir checkpoints/

Expected checkpoint layout (the directory passed to from_pretrained):

checkpoints/
├── reae.safetensors            # Restoration-aware Autoencoder weights
├── prompt_embedding.safetensors# precomputed empty-prompt text embedding (key: "prompt_emb")
└── transformer/                # diffusers-format DiT
    ├── config.json
    └── diffusion_pytorch_model.safetensors

🚀 Quick Start

Python API

python

from swiftvr import SwiftVRPipeline

pipe = SwiftVRPipeline.from_pretrained("H-oliday/SwiftVR").to("cuda", dtype="bfloat16")

pipe.restore_video("low_quality.mp4", "restored.mp4", upscale=4)

restore_video also accepts an image folder as input and can write a PNG sequence with png_save=True.

Tunable knobs include:

clip_len: middle chunk size, multiple of 4
dit_overlap: overlap for DiT inference
fps: output video frame rate
quality: 0–100, mapped to x265 CRF
queue_size: pipeline queue size

Streaming (causal, chunk by chunk, no future frames)

Causal, chunk-by-chunk restoration without future frames.

python

session = pipe.stream(clip_len=24, resolution=(1920, 1080))

for lq_chunk in read_chunks("low_quality.mp4", n=24):   # lq_chunk: [T, H, W, 3] uint8
    hq = session.step(lq_chunk)        # [1, T', 3, H', W'] in [0, 1], or None if buffered
    if hq is not None:
        write(hq)

tail = session.flush()                 # flush the final buffered frames

Command line

bash

python scripts/inference.py \
  --input low_quality.mp4 \
  --output restored.mp4 \
  --checkpoint checkpoints/ \
  --upscale 4 \
  --clip-len 24 \
  --dtype bfloat16 \

Use --png to write a PNG sequence.

🎬 More Visual Results

Full-length restored clips (low-quality input → SwiftVR, played back to back).

🙏 Acknowledgements

SwiftVR builds on Wan2.2-TI2V-5B, the lightweight autoencoder TAEHV, and the RealBasicVSR degradation pipeline. We thank the authors of DOVE, SeedVR2, and FlashVSR for releasing strong baselines, and the UltraVideo team for the training corpus.

📜 License

SwiftVR is released under the Apache License 2.0.

Licensed under the Apache License, Version 2.0. You may obtain a copy of the License at:

https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, this project is distributed on an "AS IS" BASIS, without warranties or conditions of any kind, either express or implied. See the LICENSE file for the full license text.

📜 Citation

bibtex

@article{yan2026swiftvr,
  title={SwiftVR: Real-Time One-Step Generative Video Restoration},
  author={Yan, Jiaqi and Chen, Xiangyu and Zhong, Xinlin and Huang, Haibin and Zhang, Chi and Liu, Jie and Zhou, Jiantao and Li, Xuelong},
  journal={arXiv preprint arXiv:2606.09516},
  year={2026}
}

Contact

If you have any questions, feel free to reach out:

Email: kakibluee@gmail.com

ngoctham/SwiftVR