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--- |
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license: other |
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license_name: license |
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license_link: LICENSE |
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pipeline_tag: image-to-image |
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tags: |
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- Image Super-resolution |
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- Diffusion Inversion |
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--- |
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# InvSR Model Card |
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This model card focuses on the models associated with the InvSR project, which is available [here](https://github.com/zsyOAOA/InvSR). |
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## Model Details |
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- **Developed by:** Zongsheng Yue |
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- **Model type:** Arbitrary-steps Image Super-resolution via Diffusion Inversion |
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- **Model Description:** This is the model used in [Paper](https://arxiv.org/abs/2412.09013). |
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- **Resources for more information:** [GitHub Repository](https://github.com/zsyOAOA/InvSR). |
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- **Cite as:** |
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@article{yue2024invSR, |
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author = {Zongsheng Yue, Kang Liao, Chen Change Loy}, |
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title = {Arbitrary-steps Image Super-resolution via Diffusion Inversion}, |
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journal = {arXiv preprint arXiv:2412.09013}, |
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year = {2024}, |
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} |
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## Limitations and Bias |
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### Limitations |
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- InvSR requires a tiled operation for generating a high-resolution image, which would largely increase the inference time. |
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- InvSR sometimes cannot keep 100% fidelity due to its generative nature. |
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- InvSR sometimes cannot generate perfect details under complex real-world scenarios. |
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### Bias |
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While our model is based on a pre-trained SD-Turbo model, currently we do not observe obvious bias in generated results. |
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## Training |
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**Training Data** |
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The model developer used the following dataset for training the model: |
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- Our model is finetuned on [LSDIR](https://data.vision.ee.ethz.ch/yawli/index.html) + 20K samples from FFHQ datasets. |
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**Training Procedure** |
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InvSR achieves the goal of image super-resolution via diffusion inversion technique on [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo), detailed training pipelines can be found in our GitHub [repo](https://github.com/zsyOAOA/InvSR). |
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We currently provide the following checkpoints: |
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- [noise_predictor_sd_turbo_v5.pth](https://huggingface.co/OAOA/InvSR/blob/main/noise_predictor_sd_turbo_v5.pth): Noise estimation network trained for [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo). |
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## Evaluation Results |
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See [Paper](https://arxiv.org/abs/2412.09013) for details. |
