ASASR

--- 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} } ```

Source context: 0 downloads · 0 likes · Pipeline image-to-image · Library diffusers · Repo ngoctham/ASASR