Duplicate from ruchangyao/FlashVSR_plus

Co-authored-by: yaoruchang <ruchangyao@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+datasets:
+- ruchangyao/FlashVSR_Plus_Dataset
+base_model:
+- Wan-AI/Wan2.1-T2V-1.3B
+pipeline_tag: video-to-video
+tags:
+- video-super-resolution
+- video-restoration
+- flashvsr
+---
+
+# FlashVSR+: Efficient Video Super-Resolution via Hybrid Attention and Reward-Yield Distribution Matching
+
+<div align="center">
+
+[**Project Page**](https://flashvsr.tech) | [**Paper**](https://arxiv.org/abs/2610.17552) | [**GitHub**](https://github.com/OpenImagingLab/FlashVSR_Plus) | [**Dataset**](https://huggingface.co/datasets/ruchangyao/FlashVSR_Plus_Dataset)
+
+</div>
+
+## Overview
+
+**FlashVSR+** is an efficient one-step diffusion framework for high-quality video super-resolution, enabling **spatially untiled 4K inference on a single 24GB RTX 4090 GPU**.
+
+FlashVSR+ introduces two key innovations:
+
+- **Hybrid Attention:** Exploits spatial and temporal attention locality while retaining sparse global interactions, achieving up to **2× DiT acceleration** and **45% lower peak memory**.
+- **Reward-Yield Distribution Matching Distillation (RY-DMD):** Enables preference alignment with black-box rewards without differentiating through the reward model, improving perceptual quality and generalizing to text-to-image generation.
+
+For visual comparisons and additional results, please visit our [project page](https://flashvsr.tech).
+
+## Released Models
+
+We provide two FlashVSR+ variants:
+
+| Model | Description |
+|---|---|
+| **FlashVSR+ Turbo** | Faster inference, recommended for efficient high-resolution video super-resolution. |
+| **FlashVSR+ Base** | Higher-quality restoration, recommended when reconstruction quality is the priority. |
+
+Both models support one-step inference and share the same decoder.
+
+## Model Files
+
+The released checkpoints are organized as follows:
+
+```text
+ckpts/
+├── turbo/
+│   ├── dit.safetensors
+│   └── lq_proj_in.safetensors
+├── base/
+│   ├── dit.safetensors
+│   └── lq_proj_in.safetensors
+├── TCDecoder.ckpt
+└── Wan2.1_VAE.pth
+```
+
+- `dit.safetensors`: Main diffusion transformer weights.
+- `lq_proj_in.safetensors`: Low-quality video conditioning weights.
+- `TCDecoder.ckpt`: Shared decoder used by the default inference pipeline.
+- `Wan2.1_VAE.pth`: Optional VAE decoder weights.
+
+## Citation
+
+If you find FlashVSR+ useful, please cite:
+
+```bibtex
+@misc{yao2026flashvsrefficientvideosuperresolution,
+  title={FlashVSR+: Efficient Video Super-Resolution via Hybrid Attention and Reward-Yield Distribution Matching},
+  author={Ruchang Yao and Shi Guo and Junhao Zhuang and Mingde Yao and Xin Cai and Jiahao Zhan and Bin Fu and Xinyuan Chen and Yu Qiao and Tianfan Xue},
+  year={2026},
+  eprint={2610.17552},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  url={https://arxiv.org/abs/2610.17552}
+}
+```
+
+## Acknowledgements
+
+FlashVSR+ builds upon the original [FlashVSR](https://arxiv.org/abs/2510.12747). We thank the authors of the open-source projects and pretrained models that support this work.

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config.json

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+{
+  "model_name": "FlashVSR+",
+  "task": "video-super-resolution",
+  "framework": "pytorch",
+  "variants": {
+    "base": {
+      "path": "base"
+    },
+    "turbo": {
+      "path": "turbo"
+    }
+  },
+  "components": {
+    "vae": "Wan2.1_VAE.pth",
+    "decoder": "TCDecoder.ckpt"
+  }
+}

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