Model reference · open weights
ETCHR-FLUX.2-klein is an open-weight image model from internlm, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
ETCHR-FLUX.2-klein-9B 📖<a href="https://arxiv.org/abs/2605.23897"Paper</a 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: We Provide an example code running ETCHR on DL3DV-2K Benchmark in Evaluation/inferencedl3dv.py, you can start the evaluation with the following two steps: Step 1: start a VLLM server for an understa
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | internlm |
|---|---|
| Type | Image models |
| Parameters (lead) | 9.1B |
| Variants | 1 |
| Runs with | diffusers |
| Based on | FLUX.2-klein-base-9B |
| Released | 2026-05-21 |
| Popularity | 8 downloads / month |
| Likes | 8 |
| Licence | Commercial licence needed |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| ETCHR-FLUX.2-klein-9B | 9.1B | BF16 | ~20.9 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys etchr-flux-2-klein for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (etchr-flux-2-klein below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/images/generations \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"etchr-flux-2-klein","prompt":"a red bicycle","size":"1024x1024"}'
Licence
The weights are open but its licence needs a commercial agreement for business use. AxForge can arrange that licence and host the model for you — you pay AxForge, we settle with the model’s maker. Ask us for a quote. Read the licence ↗