Duplicate from OAOA/InvSR

Co-authored-by: Zongsheng Yue <OAOA@users.noreply.huggingface.co>

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LICENSE

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+S-Lab License 1.0
+
+Copyright 2024 S-Lab
+
+Redistribution and use for non-commercial purpose in source and 
+binary forms, with or without modification, are permitted provided 
+that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright 
+   notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright 
+   notice, this list of conditions and the following disclaimer in 
+   the documentation and/or other materials provided with the 
+   distribution.
+
+3. Neither the name of the copyright holder nor the names of its 
+   contributors may be used to endorse or promote products derived 
+   from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT 
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT 
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, 
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY 
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+In the event that redistribution and/or use for commercial purpose in 
+source or binary forms, with or without modification is required, 
+please contact the contributor(s) of the work.

README.md

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+---
+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/2412.09013).
+- **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 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](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/2412.09013) for details.

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