# absl-py==2.3.1 accelerate # addict==2.4.0 # aiofiles==23.2.1 # aiohappyeyeballs==2.6.1 # aiohttp==3.12.15 # aiosignal==1.4.0 # altair==5.5.0 # annotated-types==0.7.0 # antlr4-python3-runtime==4.9.3 # anyio==4.10.0 # async-timeout==5.0.1 # attrs==25.3.0 # beautifulsoup4==4.13.4 # bs4==0.0.2 # certifi==2025.8.3 # cycler==0.12.1 # diffusers==0.32.2 # dill==0.3.8 einops # ffmpy==0.6.1 # filelock==3.19.1 # flatbuffers==25.2.10 # fonttools==4.59.1 # frozenlist==1.7.0 # fsspec==2025.3.0 # ftfy==6.3.1 # git-lfs==1.6 # grpcio==1.74.0 # h11==0.16.0 # hf-xet==1.1.7 # hjson==3.1.0 # httpcore==1.0.9 # httpx==0.28.1 # humanfriendly==10.0 # idna==3.10 # importlib_metadata==8.7.0 # importlib_resources==6.5.2 # Jinja2==3.1.6 # jsonschema==4.25.0 # jsonschema-specifications==2025.4.1 # kiwisolver==1.4.7 # MarkupSafe==2.1.5 # matplotlib==3.9.4 # mdurl==0.1.2 # mpmath==1.3.0 # multidict==6.6.4 # multiprocess==0.70.16 # narwhals==2.1.2 # networkx==3.2.1 numpy # nvidia-cublas-cu12==12.4.5.8 # nvidia-cuda-cupti-cu12==12.4.127 # nvidia-cuda-nvrtc-cu12==12.4.127 # nvidia-cuda-runtime-cu12==12.4.127 # nvidia-cudnn-cu12==9.1.0.70 # nvidia-cufft-cu12==11.2.1.3 # nvidia-cufile-cu12==1.13.1.3 # nvidia-curand-cu12==10.3.5.147 # nvidia-cusolver-cu12==11.6.1.9 # nvidia-cusparse-cu12==12.3.1.170 # nvidia-cusparselt-cu12==0.6.2 # nvidia-ml-py==13.580.65 # nvidia-nccl-cu12==2.21.5 # nvidia-nvjitlink-cu12==12.4.127 # nvidia-nvtx-cu12==12.4.127 # omegaconf==2.3.0 # opencv-python==4.11.0.86 # orjson==3.11.2 # packaging==25.0 # pillow==10.4.0 # platformdirs==4.3.8 # propcache==0.3.2 # protobuf==3.20.2 # psutil==7.0.0 # py-cpuinfo==9.0.0 # pydantic==2.11.7 # pydantic_core==2.33.2 # pydub==0.25.1 # Pygments==2.19.2 # pyparsing==3.2.3 # python-dateutil==2.9.0.post0 # pytz==2025.2 # PyYAML==6.0.2 # referencing==0.36.2 # regex==2025.7.34 # requests==2.32.4 # rich==14.1.0 # rpds-py==0.27.0 # safetensors==0.6.2 # scipy==1.13.1 # semantic-version==2.10.0 # sentencepiece==0.1.99 # shellingham==1.5.4 # six==1.17.0 # sniffio==1.3.1 # timm==0.6.12 # tokenizers==0.19.1 # tomli==2.2.1 # tomlkit==0.12.0 torch torchaudio torchvision tqdm transformers
LucidFlux: Caption-Free Universal Image Restoration with a Large-Scale Diffusion Transformer，you can use it in ComfyUI
1.Installation
In the ./ComfyUI/custom_nodes directory, run the following:
git clone https://github.com/smthemex/ComfyUI_LucidFlux2.requirements
pip install -r requirements.txt3.checkpoints
├── ComfyUI/models/
| ├── diffusion_models/any flux dit # 任意flux dit模型 ，就用kj的或者x flux的，名字要带dev 否则跑schnell
| ├── vae/ae.safetensors #comfy
| ├── clip/
| ├──clip-l.safetensors #comfy optional 可选，不推荐，如果使用prompt_embeddings.pt
| ├──t5xxl_fp8_e4m3fn.safetensors #comfy optional 可选，不推荐，，如果使用prompt_embeddings.pt
| ├── clip_vision/siglip2-so400m-patch16-512.safetensors #rename from model.safetensors 最好重命名个，不然都是siglip 的model.safetensors
| ├── LucidFlux/
| ├──general_swinir_v1.ckpt
| ├──lucidflux.pth
| ├──prompt_embeddings.pt # 已适配，使用时不要连clip
| ├── lucid_connector.pthLucidFlux
@article{fei2025lucidflux,
title={LucidFlux: Caption-Free Universal Image Restoration via a Large-Scale Diffusion Transformer},
author={Fei, Song and Ye, Tian and Wang, Lujia and Zhu, Lei},
journal={arXiv preprint arXiv:2509.22414},
year={2025}
}