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README.md

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-license: apache-2.0
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+---
+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).
+
+## 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
+
+### Limitations
+
+- 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.
+
+### 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.
+
+## 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) + 100K samples from FFHQ datasets.
+
+**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.
+
+- 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.
+
+We currently provide the following checkpoints:
+
+- [s3diff.pkl](https://huggingface.co/Iceclear/StableSR/resolve/main/stablesr_000117.ckpt): S3Diff finetuned on [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo) for 30k iterations.
+- [de_net.pth](https://huggingface.co/Iceclear/StableSR/resolve/main/stablesr_000117.ckpt): The degradation estimation network, extracted from [mm-realsr](https://github.com/TencentARC/MM-RealSR).
+
+## Evaluation Results 
+See [Paper](https://arxiv.org/abs/2409.17058) for details.