X-GenGroup/PaCo-FLUX.1-Kontext-Lora

PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling

       

The model presented in PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling.

🌟 Overview

PaCo-RL is a comprehensive framework for consistent image generation through reinforcement learning, addressing challenges in preserving identities, styles, and logical coherence across multiple images for storytelling and character design applications.

Key Components

• PaCo-Reward: A pairwise consistency evaluator with task-aware instruction and CoT reasoning.
• PaCo-GRPO: Efficient RL optimization with resolution-decoupled training and log-tamed multi-reward aggregation

Example Usage

import torch
from diffusers import FluxKontextPipeline
from peft import PeftModel
from diffusers.utils import load_image

pipe = FluxKontextPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Kontext-dev",
    torch_dtype=torch.bfloat16,
    device_map="cuda"
)

pipe.transformer = PeftModel.from_pretrained(
    pipe.transformer,
    'X-GenGroup/PaCo-FLUX.1-Kontext-Lora'
)
input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")

image = pipe(
  image=input_image,
  prompt="Add a blue hat to the cat",
  guidance_scale=2.5
).images[0]

🎁 Model Zoo

Model	Type	HuggingFace
PaCo-Reward-7B	Reward Model	🤗 Link
PaCo-Reward-7B-Lora	Reward Model (LoRA)	🤗 Link
PaCo-FLUX.1-dev	T2I Model (LoRA)	🤗 Link
PaCo-FLUX.1-Kontext-dev	Image Editing Model (LoRA)	🤗 Link
PaCo-QwenImage-Edit	Image Editing Model (LoRA)	🤗 Link

⭐ Citation

@misc{ping2025pacorladvancingreinforcementlearning,
      title={PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling}, 
      author={Bowen Ping and Chengyou Jia and Minnan Luo and Changliang Xia and Xin Shen and Zhuohang Dang and Hangwei Qian},
      year={2025},
      eprint={2512.04784},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2512.04784}, 
}

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