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README.md

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----
-license: other
-license_name: license
-license_link: LICENSE
----
+---
+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/2409.17058).
+- **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},
+        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/2409.17058) for details.