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--- |
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license: apache-2.0 |
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pipeline_tag: image-to-image |
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--- |
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# S3Diff Model Card |
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This model card focuses on the models associated with the S3Diff, available [here](https://github.com/ArcticHare105/S3Diff). |
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## Model Details |
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- **Developed by:** Aiping Zhang |
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- **Model type:** Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors |
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- **Model Description:** This is the model used in [Paper](https://arxiv.org/abs/2409.17058). |
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- **Resources for more information:** [GitHub Repository](https://github.com/ArcticHare105/S3Diff). |
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- **Cite as:** |
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@article{2024s3diff, |
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author = {Aiping Zhang, Zongsheng Yue, Renjing Pei, Wenqi Ren, Xiaochun Cao}, |
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title = {Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors}, |
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journal = {arxiv}, |
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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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- S3Diff requires a tiled operation for generating a high-resolution image, which would largely increase the inference time. |
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- S3Diff sometimes cannot keep 100% fidelity due to its generative nature. |
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- S3Diff 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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We conjecture the main reason is that our model does not rely on text prompts but on low-resolution images. |
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Such strong conditions make our model less likely to be affected. |
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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) + 10K samples from FFHQ datasets. |
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**Training Procedure** |
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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. |
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- The LR images are fed to the degradation estimation network, trained by [mm-realsr](https://github.com/TencentARC/MM-RealSR), to predict degradation scores. |
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- We only inject LoRA layers into the VAE encoder and UNet. |
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- 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. |
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- [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 |
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See [Paper](https://arxiv.org/abs/2409.17058) for details. |
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