| --- |
| 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](https://huggingface.co/papers/2604.16114). 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/](https://dengyuhai.github.io/ICTone_Project/) |
| - Code: [https://github.com/dengyuhai/ICTone](https://github.com/dengyuhai/ICTone) |
| - Dataset: [TST100K](https://huggingface.co/datasets/ToneStyle/TST100K) |
| - Benchmark: [TST2K](https://huggingface.co/datasets/ToneStyle/TST2K) |
| - Online demo: [https://huggingface.co/spaces/ToneStyle/ICTone-Fill](https://huggingface.co/spaces/ToneStyle/ICTone-Fill) |
|
|
| ## Usage |
|
|
| The following inference example is taken from the GitHub README: |
|
|
| ```bash |
| 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](https://github.com/dengyuhai/ICTone). |
|
|
| ## License |
|
|
| Non-commercial research use only. See the repository and paper for details. |
