README.md

metadata

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 (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

huggingface-cli download wafer-bob/ASASR --local-dir ./checkpoints

Then follow the GitHub README for inference (bash scripts/infer.sh) and training.

License

This project is released under CC-BY-NC-4.0 for non-commercial research use only.

Copyright (c) 2026 The Authors and Kuaishou Technology.

Citation

@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}
}