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README.md

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+---
+library_name: diffusers
+pipeline_tag: image-to-image
+---
+
+# Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion
+
+Stream-DiffVSR is a causally conditioned diffusion framework designed for efficient online Video Super-Resolution (VSR). It operates strictly on past frames to maintain low latency, making it suitable for real-time deployment.
+
+[[Paper](https://huggingface.co/papers/2512.23709)] [[Project Page](https://jamichss.github.io/stream-diffvsr-project-page/)] [[GitHub](https://github.com/jamichss/Stream-DiffVSR)]
+
+## Description
+Diffusion-based VSR methods often struggle with latency due to multi-step denoising and reliance on future frames. Stream-DiffVSR addresses this with:
+- **Causal Conditioning:** Operates only on past frames for online processing.
+- **Four-step Distilled Denoiser:** Enables fast inference without sacrificing quality.
+- **Auto-regressive Temporal Guidance (ARTG):** Injects motion-aligned cues during denoising.
+- **Lightweight Temporal Decoder:** Enhances temporal coherence and fine details.
+
+Stream-DiffVSR can process 720p frames in 0.328 seconds on an RTX 4090, achieving significant latency reductions compared to prior diffusion-based VSR methods.
+
+## Usage
+
+### Installation
+```bash
+git clone https://github.com/jamichss/Stream-DiffVSR.git
+cd Stream-DiffVSR
+conda env create -f requirements.yml
+conda activate stream-diffvsr
+```
+
+### Inference
+You can run inference using the following command. The script will automatically fetch the necessary weights from this repository.
+
+```bash
+python inference.py \
+    --model_id 'Jamichsu/Stream-DiffVSR' \
+    --out_path 'YOUR_OUTPUT_PATH' \
+    --in_path 'YOUR_INPUT_PATH' \
+    --num_inference_steps 4
+```
+
+The expected file structure for the inference input data is as follows:
+```
+YOUR_INPUT_PATH/
+├── seq1/
+│   ├── frame_0001.png
+│   ├── frame_0002.png
+│   └── ...
+├── seq2/
+│   ├── frame_0001.png
+│   ├── frame_0002.png
+│   └── ...
+```
+
+For NVIDIA TensorRT acceleration:
+```bash
+python inference.py \
+    --model_id 'Jamichsu/Stream-DiffVSR' \
+    --out_path 'YOUR_OUTPUT_PATH' \
+    --in_path 'YOUR_INPUT_PATH' \
+    --num_inference_steps 4 \
+    --enable_tensorrt \
+    --image_height <YOUR_TARGET_HEIGHT> \
+    --image_width <YOUR_TARGET_WIDTH>
+```
+
+## Citation
+If you find this work useful, please cite:
+```bibtex
+@article{shiu2025streamdiffvsr,
+  title={Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion},
+  author={Shiu, Hau-Shiang and Lin, Chin-Yang and Wang, Zhixiang and Hsiao, Chi-Wei and Yu, Po-Fan and Chen, Yu-Chih and Liu, Yu-Lun},
+  journal={arXiv preprint arXiv:2512.23709},
+  year={2025}
+}
+```