Upload README.md

README.md

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+---
+tags:
+  - relayvsr
+  - lightx2v
+  - video-super-resolution
+  - super-resolution
+  - video-restoration
+  - video-enhancement
+  - 2x-upscaling
+  - safetensors
+---
+
+# RelayVSR X2
+
+We trained this RelayVSR model for **native, fixed 2× video super-resolution**.
+It doubles the input video's width and height; for example, a 768 × 1344 video
+becomes 1536 × 2688.
+
+The [original RelayVSR model](https://huggingface.co/kopper/RelayVSR) uses a fixed
+4× super-resolution scale. This repository provides our separately trained 2×
+variant, with inference supported by [LightX2V](https://github.com/ModelTC/LightX2V).
+
+For inference instructions, see the
+[LightX2V RelayVSR documentation](https://github.com/ModelTC/LightX2V/tree/main/scripts/relayvsr).
+
+| Model | Native scale | Output dimensions |
+| --- | --- | --- |
+| [Original RelayVSR](https://huggingface.co/kopper/RelayVSR) | 4× | 4W × 4H |
+| [RelayVSR X2](https://huggingface.co/lightx2v/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:
+
+```bash
+docker pull lightx2v/lightx2v:26062001-cu130
+```
+
+Run the following steps inside the container. First, clone the latest LightX2V source:
+
+```bash
+git clone https://github.com/ModelTC/LightX2V.git
+cd LightX2V
+```
+
+Download the 2× weights:
+
+```bash
+hf download lightx2v/RelayVSR_X2 \
+    wan_x2.safetensors flash_x2.safetensors \
+    --local-dir ./models/RelayVSR_X2
+```
+
+Edit
+[`scripts/relayvsr/run_relayvsr_x2_sr.sh`](https://github.com/ModelTC/LightX2V/blob/main/scripts/relayvsr/run_relayvsr_x2_sr.sh)
+and set the absolute paths and GPU to use:
+
+```bash
+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
+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](https://github.com/ModelTC/LightX2V/tree/main/scripts/relayvsr).
+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](https://huggingface.co/kopper/RelayVSR)
+- Original GitHub repository: [kopperx/RelayVSR](https://github.com/kopperx/RelayVSR)
+- LightX2V inference scripts: [ModelTC/LightX2V — RelayVSR](https://github.com/ModelTC/LightX2V/tree/main/scripts/relayvsr)
+