Instructions to use Jamichsu/Stream-DiffVSR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jamichsu/Stream-DiffVSR with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Jamichsu/Stream-DiffVSR", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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--image_width <YOUR_TARGET_WIDTH>
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```
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## Citation
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If you find this work useful, please cite:
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```bibtex
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@article{
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title={Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion},
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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},
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journal={arXiv preprint arXiv:2512.23709},
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--image_width <YOUR_TARGET_WIDTH>
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```
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## Note
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The provided checkpoint is a **toy / proof-of-concept model** trained on a limited amount of data. As a result, it does not yet cover the full diversity of real-world videos.
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This checkpoint is mainly intended to demonstrate the **overall pipeline and low-latency feasibility**, rather than to deliver production-level upscaling quality.
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Artifacts and inconsistent visual quality are therefore expected at this stage.
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## Citation
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If you find this work useful, please cite:
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```bibtex
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@article{shiu2025stream,
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title={Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion},
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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},
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journal={arXiv preprint arXiv:2512.23709},
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