metadata
license: other
library_name: diffusers
pipeline_tag: image-to-image
ICTone-Fill-LoRA
This repository contains the LoRA weights for ICTone (ECCV 2026), described in the paper Towards In-Context Tone Style Transfer with a Large-Scale Triplet Dataset. ICTone performs reference-based tone style transfer by jointly conditioning on the content and reference images with a diffusion transformer (FLUX.1-Fill).
- Project page: https://dengyuhai.github.io/ICTone_Project/
- Code: https://github.com/dengyuhai/ICTone
- Dataset: TST100K
- Benchmark: TST2K
- Online demo: https://huggingface.co/spaces/ToneStyle/ICTone-Fill
Usage
The following inference example is taken from the GitHub README:
CUDA_VISIBLE_DEVICES=0 python inference.py \
--content assets/example_content.png \
--reference assets/example_reference.png \
--flux-path black-forest-labs/FLUX.1-Fill-dev \
--lora-path ToneStyle/ICTone-Fill-LoRA \
--output-file ./output/example_output.png \
--num-inference-steps 4 \
--guidance-scale 50.0 \
--seed 666
For full training and evaluation instructions, please refer to the GitHub repository.
License
Non-commercial research use only. See the repository and paper for details.