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README.md

@@ -1,130 +1,126 @@
----
-license: apache-2.0
+<h1 align="center">SwiftVR: Real-Time One-Step Generative Video Restoration</h1>
 
----
+<p align="center"><img src="assets/teaser.avif" width="100%" alt="SwiftVR teaser"></p>
 
-<h1 align="center">RVR: One-step Generative Streaming Real-time Video Restoration</h1>
-
-
-
-<p align="center"><img src="assets/teaser.avif" width="100%" alt="RVR teaser"></p>
-
-
-
-> **RVR** 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.
+> **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.
 
 <p>
   <a href="https://arxiv.org/abs/XXXX.XXXXX"><img src="https://img.shields.io/badge/arXiv-XXXX.XXXXX-b31b1b.svg?style=flat-square" alt="arXiv"></a>
-  <a href="https://github.com/H-oliday/RVR/"><img src="https://img.shields.io/badge/Project-Page-1f8acb.svg?style=flat-square" alt="Project Page"></a>
-  <a href="https://huggingface.co/H-oliday/RVR"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Model-ffce00.svg?style=flat-square" alt="HuggingFace"></a>
-  <a href="https://github.com/H-oliday/RVR/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-Apache%202.0-green.svg?style=flat-square" alt="License"></a>
-
+  <a href="https://github.com/H-oliday/SwiftVR/"><img src="https://img.shields.io/badge/Project-Page-1f8acb.svg?style=flat-square" alt="Project Page"></a>
+  <a href="https://huggingface.co/H-oliday/SwiftVR"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Model-ffce00.svg?style=flat-square" alt="HuggingFace"></a>
+  <a href="https://github.com/H-oliday/SwiftVR/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-Apache%202.0-green.svg?style=flat-square" alt="License"></a>
 </p>
 
-
----
 ## Updates
-- [2026/06] Release the inference code and pretrained weights 🎉
 
+* [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.
 
----
-## ✨ 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 | SeedVR2-3B (tile)| DOVE (tile)| FlashVSR-Tiny | **RVR (Ours)** |
+### Efficiency at 2560×1440
+
+Single H100, causal streaming, 24 frames.
+
+| Metric | DOVE (tile) | SeedVR2-3B (tile)| FlashVSR-Tiny | **SwiftVR (Ours)** |
 |---|:---:|:---:|:---:|:---:|
-| Avg. Time (s) ↓ | 17.320 | 27.615 | 2.493 | 0.766 |
-| FPS ↑ | 1.39 | 0.85 | 9.61 | 31.32 |
-| Peak Mem. (GB) ↓ | 35.35 | 59.24 | 34.35 | 38.01 |
+| 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; RVR sustains **14 FPS**.
+> At **3840×2160**, every compared diffusion-based VR baseline **OOMs** on a single H100; SwiftVR sustains **14 FPS**.
 
 ### Qualitative comparison
 
-<img src="assets/qualitative.png" width="100%" alt="RVR teaser">
-
+<img src="assets/qualitative.png" width="100%" alt="SwiftVR qualitative comparison">
 
----
 ## 🛠 Installation
+
 ```bash
-git clone https://github.com/Holiday/RVR.git
-cd RVR
+git clone https://github.com/H-oliday/SwiftVR.git
+cd SwiftVR
 
-conda create -n rvr python=3.10 -y
-conda activate rvr
+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 RVR (editable) and its dependencies:
+# Install SwiftVR (editable) and its dependencies:
 pip install -e .
 ```
 
 <details>
 <summary><b>Hardware notes</b></summary>
 
-- **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.
-</details>
+* **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.
 
+</details>
 
----
 ## 🗂 Model Zoo
-| Model Name | Date | Backbone | Link |
-|---|---|---|---|
-| RVR | 2026.06 | Wan2.2-TI2V-5B | [🤗 HuggingFace](https://huggingface.co/H-oliday/RVR) |
+
+| Model Name | Date    | Backbone       | Link                                                  |
+| ---------- | ------- | -------------- | ----------------------------------------------------- |
+| SwiftVR        | 2026.06 | Wan2.2-TI2V-5B | [🤗 HuggingFace](https://huggingface.co/H-oliday/SwiftVR) |
 
 ```bash
-huggingface-cli download H-oliday/RVR --local-dir checkpoints/
+huggingface-cli download H-oliday/SwiftVR --local-dir checkpoints/
 ```
 
-Expected checkpoint layout (the directory passed to `from_pretrained`):
+Expected checkpoint layout, where `checkpoints/` is the directory passed to `from_pretrained`:
 
-```
+```text
 checkpoints/
-├── reae.safetensors            # Restoration-aware Autoencoder weights
-├── prompt_embedding.safetensors# precomputed empty-prompt text embedding (key: "prompt_emb")
-└── transformer/                # diffusers-format DiT
+├── 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 rvr import RVRPipeline
+from swiftvr import SwiftVRPipeline
 
-pipe = RVRPipeline.from_pretrained("checkpoints/").to("cuda", dtype="bfloat16")
+pipe = SwiftVRPipeline.from_pretrained("checkpoints/").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
-(`png_save=True`). Tunable knobs: `clip_len` (MIDDLE chunk size, multiple of 4),
-`dit_overlap`, `fps`, `quality` (0–100, mapped to x265 CRF), `queue_size`.
+`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)
+### Streaming
+
+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
+    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
+tail = session.flush()                                  # flush the final buffered frames
 ```
 
 ### Command line
@@ -136,56 +132,61 @@ python scripts/inference.py \
   --checkpoint checkpoints/ \
   --upscale 4 \
   --clip-len 24 \
-  --dtype bfloat16 \
+  --dtype bfloat16
 ```
 
 Use `--png` to write a PNG sequence.
 
-
-
----
 ## 📁 Repository Structure
-```
-RVR/
+
+```text
+SwiftVR/
 ├── README.md
+├── LICENSE
 ├── requirements.txt
 ├── setup.py
 ├── scripts/
-│   └── inference.py              # CLI entry point (thin wrapper over RVRPipeline)
-└── rvr/
-    ├── __init__.py               # exports RVRPipeline
-    ├── pipeline.py               # RVRPipeline: from_pretrained / to / restore_video / stream
-    ├── runner.py                 # four-stage pipelined runner (reader → H2D → GPU → writer)
+│   └── inference.py              # CLI entry point, thin wrapper over SwiftVRPipeline
+└── swiftvr/
+    ├── __init__.py               # exports SwiftVRPipeline
+    ├── pipeline.py               # SwiftVRPipeline: from_pretrained / to / restore_video / stream
+    ├── runner.py                 # four-stage pipelined runner: reader → H2D → GPU → writer
     ├── io.py                     # frame reading, GPU preprocessing, mp4 / PNG writing
     ├── models/
-    │   ├── reae.py               # ★ Restoration-aware Autoencoder
-    │   └── transformer.py        # ★ DiT + mask-free shifted-window self-attention
+    │   ├── reae.py               # Restoration-aware Autoencoder
+    │   └── transformer.py        # DiT + mask-free shifted-window self-attention
     └── streaming/
         ├── chunk.py              # fixed-size causal chunk protocol
-        ├── tae.py                # streaming autoencoder (causal boundary state)
-        └── dit.py                # one-step streaming DiT (fixed timestep, RoPE offsets)
+        ├── tae.py                # streaming autoencoder with causal boundary state
+        └── dit.py                # one-step streaming DiT with fixed timestep and RoPE offsets
 ```
 
+## 🙏 Acknowledgements
 
----
-## 🎬 More Visual Results
-> Full-length restored clips (low-quality input → RVR, played back to back).
+SwiftVR builds on [Wan2.2-TI2V-5B](https://github.com/Wan-Video), the lightweight autoencoder [TAEHV](https://github.com/madebyollin/taehv), and the [RealBasicVSR](https://github.com/ckkelvinchan/RealBasicVSR) degradation pipeline.
 
+We thank the authors of [DOVE](https://github.com/zhengchen1999/DOVE), [SeedVR2](https://github.com/ByteDance-Seed/SeedVR), and [FlashVSR](https://github.com/OpenImagingLab/FlashVSR) for releasing strong baselines, and the [UltraVideo](https://huggingface.co/datasets/APRIL-AIGC/UltraVideo) team for the training corpus.
 
-<video src="https://huggingface.co/H-oliday/RVR/resolve/main/assets/demo_1.mp4" controls width="100%"></video>
+## 📜 License
 
-<video src="https://huggingface.co/H-oliday/RVR/resolve/main/assets/demo_2.mp4" controls width="100%"></video>
+SwiftVR is released under the **Apache License 2.0**.
 
-<video src="https://huggingface.co/H-oliday/RVR/resolve/main/assets/demo_3.mp4" controls width="100%"></video>
+Copyright 2026 SwiftVR Authors.
 
+Licensed under the Apache License, Version 2.0. You may obtain a copy of the License at:
 
----
-## 🙏 Acknowledgements
-RVR builds on [Wan2.2-TI2V-5B](https://github.com/Wan-Video), the lightweight autoencoder [TAEHV](https://github.com/madebyollin/taehv), and the [RealBasicVSR](https://github.com/ckkelvinchan/RealBasicVSR) degradation pipeline. We thank the authors of [SeedVR2](https://github.com/ByteDance-Seed/SeedVR), [DOVE](https://github.com/zhengchen1999/DOVE), and [FlashVSR](https://github.com/OpenImagingLab/FlashVSR) for releasing strong baselines, and the [UltraVideo](https://github.com/Tele-AI/UltraVideo) team for the training corpus.
+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](./LICENSE) file for the full license text.
 
----
-## 📜 License
-Released under the [Apache 2.0 License](LICENSE). The Wan2.2 backbone and any third-party weights remain subject to their original licenses.
 
 
+## Contact
+
+If you have any questions, feel free to reach out:
+
+* Email: [kakibluee@gmail.com](mailto:kakibluee@gmail.com)
+
+<div align="center">
+<sub>If SwiftVR is useful to your research or product, please consider giving it a ⭐.</sub>
+</div>