Duplicate from anphunl/ASASR

Co-authored-by: Phu Nguyen <anphunl@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-nc-4.0
+tags:
+  - super-resolution
+  - image-super-resolution
+  - flux
+  - lora
+  - dpo
+  - diffusion
+library_name: diffusers
+pipeline_tag: image-to-image
+---
+
+# ASASR — Coloring the Noise: Adversarial Sobolev Alignment for Faithful Image Super-Resolution
+
+Pretrained weights for the ICML 2026 paper
+**[Coloring the Noise: Adversarial Sobolev Alignment for Faithful Image Super-Resolution](https://arxiv.org/abs/2605.23264)**
+(Hongbo Wang, Huaibo Huang, Pin Wang, Jinhua Hao, Chao Zhou, Ran He).
+
+➡️ **Code & full instructions: https://github.com/wafer-bob/ASASR**
+
+ASASR performs ×4 image super-resolution with a **FLUX.1-dev** backbone and dual-LoRA
+inference: a base **SR LoRA** (upscaling prior, OminiControl) plus our **DPO LoRA** trained
+with a Sobolev frequency-weighted, adversarially-guided DPO objective (AS-DPO).
+
+## Files
+
+| File | Size | Use |
+|---|---|---|
+| `sr_lora/pytorch_lora_weights_v2.safetensors` | ~885 MB | base SR LoRA — **inference** |
+| `dpo_lora/adapter_model.safetensors` | ~111 MB | ASASR AS-DPO LoRA — **inference** |
+| `adv_lora/adapter_model.safetensors` | ~111 MB | rank-16 AMG adversary — **training only** |
+
+## Download
+
+```bash
+huggingface-cli download wafer-bob/ASASR --local-dir ./checkpoints
+```
+
+Then follow the [GitHub README](https://github.com/wafer-bob/ASASR) for inference
+(`bash scripts/infer.sh`) and training.
+
+## License
+
+This project is released under [CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/) for **non-commercial research use only**.
+
+Copyright (c) 2026 The Authors and Kuaishou Technology.
+
+## Citation
+
+```bibtex
+@inproceedings{wang2026asasr,
+  title     = {Coloring the Noise: Adversarial Sobolev Alignment for Faithful Image Super-Resolution},
+  author    = {Wang, Hongbo and Huang, Huaibo and Wang, Pin and Hao, Jinhua and Zhou, Chao and He, Ran},
+  booktitle = {International Conference on Machine Learning (ICML)},
+  year      = {2026}
+}
+```

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