metadata
language: en
license: apache-2.0
library_name: diffusers
base_model: black-forest-labs/FLUX.1-dev
tags:
- flux
- diffusers
- lora
- cmo
- text-to-image
pipeline_tag: text-to-image
FLUX.1-dev-CMO
🤗 Hugging Face | 📄 arXiv
🌟 Official LoRA Adapter for Correlation-Weighted Multi-Reward Optimization for Compositional Generation
This repository contains the official LoRA adapter for black-forest-labs/FLUX.1-dev fine-tuned using CMO (Correlation-Weighted Multi-Reward Optimization) to enhance compositional generation capabilities.
🚀 Usage
Below is the code to load and merge the LoRA adapter with the base FLUX.1-dev model.
import torch
from diffusers import FluxPipeline
from peft import PeftModel
model_id = "black-forest-labs/FLUX.1-dev"
lora_ckpt_path = "Bruece/FLUX.1-dev-CMO"
device = "cuda"
pipe = FluxPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipe.transformer = PeftModel.from_pretrained(pipe.transformer, lora_ckpt_path)
pipe.transformer = pipe.transformer.merge_and_unload()
pipe = pipe.to(device)
prompt = 'a photo of a black kite and a green bear'
image = pipe(prompt, height=512, width=512, num_inference_steps=40, guidance_scale=4.5).images[0]
image.save("flux_cmo_lora.png")
🖼️ Qualitative Results
🛠️ Training Details
- Base Model: FLUX.1-dev
- Algorithm: Correlation-Weighted Multi-Reward Optimization (CMO)
- Precision: bfloat16
📜 Citation
If you find this model useful for your research, please cite:
@article{wi2026correlation,
title={Correlation-Weighted Multi-Reward Optimization for Compositional Generation},
author={Wi, Jungmyung and Kim, Hyunsoo and Kim, Donghyun},
journal={arXiv preprint arXiv:2603.18528},
year={2026}
}