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