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
Configuration Parsing Warning:In adapter_config.json: "peft.base_model_name_or_path" must be a string
Configuration Parsing Warning:In adapter_config.json: "peft.task_type" must be a string
ReCoEdit โ 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 โ 0.480 (21.8ร 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}},
}
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Model tree for Matteoooo46/ReCoEdit-RL
Base model
Qwen/Qwen-Image-Edit-2511