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---
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. |