RelayVSR_X2

We trained this RelayVSR model for native, fixed 2× video super-resolution. It doubles the input video's width and height.

We found that RelayVSR's model design is effective at suppressing temporal flicker in upscaled videos. This is particularly useful for videos produced by generative video models: the generated video may occasionally contain subtle flicker, which super-resolution algorithms can amplify.

The original RelayVSR model uses a fixed 4× super-resolution scale, which incurs substantial inference time. To reduce this cost, we modified the model architecture and continued training to obtain a fixed 2× RelayVSR model, with inference supported by LightX2V.

We recommend RelayVSR as a super-resolution post-processing model for video generators such as MiniMax-H3. For example, a MiniMax-H3 video generated at 768 × 1344 can be upscaled by 2× to 1536 × 2688.

For inference instructions, see the LightX2V RelayVSR documentation.

Model	Native scale	Output dimensions
Original RelayVSR	4×	4W × 4H
RelayVSR X2	2×	2W × 2H

Model Files

Download both checkpoints into the same model directory:

File	Description
wan_x2.safetensors	Wan checkpoint for the 2× model
flash_x2.safetensors	Flash checkpoint for the 2× model

Usage with LightX2V

You can use the LightX2V Docker image:

docker pull lightx2v/lightx2v:26062001-cu130

Run the following steps inside the container. First, clone the latest LightX2V source:

git clone https://github.com/ModelTC/LightX2V.git
cd LightX2V

Download the 2× weights:

hf download lightx2v/RelayVSR_X2 \
    wan_x2.safetensors flash_x2.safetensors \
    --local-dir ./models/RelayVSR_X2

Edit scripts/relayvsr/run_relayvsr_x2_sr.sh and set the absolute paths and GPU to use:

lightx2v_path="/absolute/path/to/LightX2V"
model_path="/absolute/path/to/LightX2V/models/RelayVSR_X2"
video_path="/absolute/path/to/input.mp4"
export CUDA_VISIBLE_DEVICES=0

Keep the script's X2 settings: --model_cls relayvsr_x2, --config_json "${lightx2v_path}/configs/relayvsr/relayvsr_x2.json", and --sr_ratio 2. Use both X2 checkpoints together.

Run from the LightX2V repository root:

bash scripts/relayvsr/run_relayvsr_x2_sr.sh

The default output is save_results/relayvsr_x2.mp4 inside the LightX2V repository. Change --save_result_path in the script to choose another output location.

For block offloading or multi-GPU execution, see the LightX2V RelayVSR usage guide. Use the corresponding relayvsr_x2_block_offload.json or relayvsr_x2_sp.json configuration. The default configuration enables compilation and warmup.

Upstream Sources

This model builds on the original RelayVSR project. Credit goes to the RelayVSR authors for their method and implementation.

• Original Hugging Face model: kopper/RelayVSR
• Original GitHub repository: kopperx/RelayVSR