ETCHR-FLUX.2-klein-9B
📖Paper | 🏠Homepage | 🤗ETCHR-FLUX.2-klein-9B Model | 🤗ETCHR SFT-400K Dataset | 🤗ETCHR GRPO-10K Dataset | 🤗DL3DV-2K Benchmark
ETCHR-FLUX.2-klein-9B is a novel question-conditioned, reasoning-aware image editor designed to serve as a decoupled visual reasoning assistant for Multimodal Large Language Models. By decoupling the specialized image editor from the downstream understanding model, ETCHR bridges the critical bottleneck where a purely textual chain of thought fails in fine-grained focus or complex spatial transformations.
📢 News
- 🚀 [2026/05/22] We have released the training and evaluation code of ETCHR.
- 🚀 [2026/05/21] We have released the ETCHR-FLUX.2-klein-9B Model, ETCHR-SFT-400K Dataset and ETCHR GRPO-10K Dataset.
🌈 Overview
We are thrilled to introduce ETCHR (Editing To Clarify and Harness Reasoning), a novel question-conditioned, reasoning-aware image editor built on FLUX.2-klein-base-9B designed to serve as a decoupled visual reasoning assistant for Multimodal Large Language Models (MLLMs). By decoupling the specialized image editor from the downstream understanding model, ETCHR bridges the critical bottleneck where a purely textual chain of thought fails in fine-grained focus or complex spatial transformations.
💡 Highlights
- 🔥 Decoupled & Plug-and-Play: ETCHR functions as a separate module, allowing it to assist diverse downstream MLLMs (such as Qwen3-VL-8B, Gemini-3.1-Flash-Lite, or Kimi K2.5) without requiring any task-specific fine-tuning on the understanding models themselves.
- 🔥 Naturally Reflective Pipeline: Introduces an Edit-Verify-Reason inference mechanism where the understanding model filters out noisy or flawed edits, reverting safely to the original image when verification fails.
📊 Results
We evaluate ETCHR across five distinct task families spanning fine-grained perception, chart understanding, logic reasoning, jigsaw restoration, and 3D understanding. Across all evaluated backbones, ETCHR consistently yields major improvements in Pass@1 accuracy:
🛠️ Evaluation
Prepare your environment:
git clone https://github.com/InternLM/ETCHR.git
conda create -n ETCHR python==3.11
conda activate ETCHR
cd RL/Pref-GRPO
bash env_setup.sh fastvideo
pip install "vllm>=0.11.0"
pip install qwen-vl-utils==0.0.14
We Provide an example code running ETCHR on DL3DV-2K Benchmark in Evaluation/inference_dl3dv.py, you can start the evaluation with the following two steps:
Step 1: start a VLLM server for an understanding model (eg. Qwen3-VL-8B, Kimi K2.5, ...).
cd Evaluation
bash launch_vllm.sh
Step 2: Run ETCHR atop any understanding model
python inference_dl3dv.py
Cases
ETCHR can assist with a broad spectrum of understanding tasks, including fine-grained perception, chart reasoning, maze navigation, jigsaw puzzles, and 3D spatial understanding.
📄 License
Our work is based on FLUX.2-klein-base-9B, so please follow FLUX Non-Commercial License.
✒️Citation
If you find this project useful, please kindly cite:
@article{zhang2026etchr,
title={ETCHR: Editing To Clarify and Harness Reasoning},
author={Beichen Zhang, Yuhong Liu, Jinsong Li, Yuhang Zang, Jiaqi Wang, Dahua Lin},
journal={arXiv preprint arXiv:2605.23897},
year={2026}
}
❤️ Acknowledgement
The base model is FLUX.2-klein-base-9B, a powerful image-to-image model.
The work is built upon DiffSynth-Studio and Pref-GRPO, two excellent codebases for Diffusion models training!