| --- |
| license: apache-2.0 |
| pipeline_tag: image-to-image |
| --- |
| |
| # S3Diff Model Card |
| This model card focuses on the models associated with the S3Diff, available [here](https://github.com/ArcticHare105/S3Diff). |
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|
| ## Model Details |
| - **Developed by:** Aiping Zhang |
| - **Model type:** Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors |
| - **Model Description:** This is the model used in [Paper](https://arxiv.org/abs/2409.17058). |
| - **Resources for more information:** [GitHub Repository](https://github.com/ArcticHare105/S3Diff). |
| - **Cite as:** |
|
|
| @article{2024s3diff, |
| author = {Aiping Zhang, Zongsheng Yue, Renjing Pei, Wenqi Ren, Xiaochun Cao}, |
| title = {Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors}, |
| journal = {arxiv}, |
| year = {2024}, |
| } |
| |
| ## Limitations and Bias |
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| ### Limitations |
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| - S3Diff requires a tiled operation for generating a high-resolution image, which would largely increase the inference time. |
| - S3Diff sometimes cannot keep 100% fidelity due to its generative nature. |
| - S3Diff sometimes cannot generate perfect details under complex real-world scenarios. |
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| ### Bias |
| While our model is based on a pre-trained SD-Turbo model, currently we do not observe obvious bias in generated results. |
| We conjecture the main reason is that our model does not rely on text prompts but on low-resolution images. |
| Such strong conditions make our model less likely to be affected. |
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| ## Training |
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| **Training Data** |
| 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) + 10K samples from FFHQ datasets. |
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| **Training Procedure** |
| S3Diff is an image super-resolution model finetuned on [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo), further equipped with a degradation-guided LoRA and online negative prompting. |
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| - Following SD-Turbo, images are encoded through the fixed autoencoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4. |
| - The LR images are fed to the degradation estimation network, trained by [mm-realsr](https://github.com/TencentARC/MM-RealSR), to predict degradation scores. |
| - We only inject LoRA layers into the VAE encoder and UNet. |
| - The total loss includes an L2 Loss, an LPIPS loss, and a GAN loss. |
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| We currently provide the following checkpoints: |
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| - [s3diff.pkl](https://huggingface.co/zhangap/S3Diff/blob/main/s3diff.pkl): S3Diff finetuned on [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo) for 30k iterations. |
| - [de_net.pth](https://huggingface.co/zhangap/S3Diff/blob/main/de_net.pth): The degradation estimation network, extracted from [mm-realsr](https://github.com/TencentARC/MM-RealSR). |
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| ## Evaluation Results |
| See [Paper](https://arxiv.org/abs/2409.17058) for details. |
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