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.
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.
Resources for more information: GitHub Repository.
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 + 20K samples from FFHQ datasets.
Training Procedure InvSR achieves the goal of image super-resolution via diffusion inversion technique on SD-Turbo, detailed training pipelines can be found in our GitHub repo.
We currently provide the following checkpoints:
- noise_predictor_sd_turbo_v5.pth: Noise estimation network trained for SD-Turbo.
Evaluation Results
See Paper for details.