Instructions to use Matteoooo46/ReCoEdit-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Matteoooo46/ReCoEdit-RL with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
metadata
base_model: Qwen/Qwen-Image-Edit-2511
library_name: peft
license: apache-2.0
tags:
- lora
- diffusion
- image-editing
- product-consistency
- grpo
- reinforcement-learning
ReCoEdit — RL LoRA Adapter (Epoch 24)
LoRA adapter for Qwen-Image-Edit-2511, trained with Flow-GRPO and a product consistency reward.
Plug this into Qwen/Qwen-Image-Edit-2511 to get a model that better preserves product identity (color, shape, logo) during image editing.
- Base model:
Qwen/Qwen-Image-Edit-2511 - Training: GRPO (Group Relative Policy Optimization) on e-commerce product editing data
- Reward: Qwen3-VL-30B-A3B-Instruct as VLM judge, 1-5 product consistency score
- Result: Reward improved from 0.022 → 0.480 (21.8× over SFT baseline)
- LoRA rank / alpha: 64 / 128
- Checkpoint: Epoch 24 (peak performance)
Usage
from peft import PeftModel
from huggingface_hub import snapshot_download
# Load base model (via DiffSynth-Studio)
# See https://github.com/Matteoooo46/ReCoEdit for full inference script
lora_path = snapshot_download("Matteoooo46/ReCoEdit-RL")
pipe.load_lora(pipe.dit, lora_path + "/adapter_model.safetensors")
See the ReCoEdit repository for the complete inference pipeline including APG guidance and prompt rewriter.
Citation
@misc{recoedit2025,
title={ReCoEdit: Rewriter-Guided Consistency Alignment for Product Image Editing},
author={},
year={2025},
howpublished={\url{https://github.com/Matteoooo46/ReCoEdit}},
}