X-GenGroup/PaCo-Reward-7B-Lora

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

       

PaCo-RL is a comprehensive framework designed for consistent image generation using reinforcement learning. It tackles the challenges of preserving identities, styles, and logical coherence across multiple images, which is crucial for applications such as storytelling and character design.

This model is presented in the paper PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling.

• Project Page: https://x-gengroup.github.io/HomePage_PaCo-RL/
• Code Repository: https://github.com/X-GenGroup/PaCo-RL
• Data & Models Collection: https://huggingface.co/collections/X-GenGroup/paco-rl

🌟 Overview

PaCo-RL argues that reinforcement learning offers a promising alternative for learning complex and subjective visual criteria in a data-free manner. The framework combines a specialized consistency reward model with an efficient RL algorithm.

🔑 Key Components

• PaCo-Reward: A pairwise consistency evaluator trained on a large-scale dataset constructed via automated sub-figure pairing. It evaluates consistency through a generative, autoregressive scoring mechanism enhanced by task-aware instructions and Chain-of-Thought (CoT) reasons.
• PaCo-GRPO: An efficient RL algorithm leveraging a novel resolution-decoupled optimization strategy to substantially reduce RL cost, alongside a log-tamed multi-reward aggregation mechanism that ensures balanced and stable reward optimization.

Extensive experiments show that PaCo-Reward significantly improves alignment with human perceptions of visual consistency, and PaCo-GRPO achieves state-of-the-art consistency performance with improved training efficiency and stability.

💻 Example Usage

For detailed installation, training of the reward model (PaCo-Reward), and running the full RL training (PaCo-GRPO), please refer to the official GitHub repository. The repository provides comprehensive documentation for each component.

import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from peft import PeftModel
from qwen_vl_utils import process_vision_info

# Load base model
base_model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2.5-VL-7B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")

# Load LoRA adapter
model = PeftModel.from_pretrained(
    base_model,
    "X-GenGroup/PaCo-Reward-7B-Lora"
)

image1 = 'https://huggingface.co/X-GenGroup/PaCo-Reward-7B/resolve/main/images/image_1.jpg'
image2 = 'https://huggingface.co/X-GenGroup/PaCo-Reward-7B/resolve/main/images/image_2.jpg'

main_prompt = 'Generate multiple images portraying a medical scene of a dentist in scrubs. The images should include activities such as explaining oral hygiene to a patient, taking X-rays of teeth, cleaning teeth in a dental office, and filling a cavity during an appointment. The setting should depict a realistic dental clinic.'
text_prompt = (
    f"Given two subfigures generated based on the theme: \"{main_prompt}\", "
    f"do the two images maintain consistency in terms of style, logic and identity? "
    f"Answer \"Yes\" and \"No\" first, and then provide detailed reasons."
)

# Example: Compare whether two images are visually consistent
messages_1 = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image1},
            {"type": "image", "image": image2},
            {"type": "text", "text": text_prompt},
        ],
    }
]

# Preparation for inference
text = processor.apply_chat_template(
    messages_1, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages_1)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")

# Inference: Calculate consistency score
# Get logits for first token
with torch.no_grad():
    outputs = model(**inputs)
    first_token_logits = outputs.logits[0, -1, :]  # Last position of prompt

# Get token IDs for "Yes" and "No"
yes_id = processor.tokenizer.encode("Yes", add_special_tokens=False)[0]
no_id = processor.tokenizer.encode("No", add_special_tokens=False)[0]

# Calculate probability
yes_logit = first_token_logits[yes_id]
no_logit = first_token_logits[no_id]
yes_prob = torch.exp(yes_logit) / (torch.exp(yes_logit) + torch.exp(no_logit))

# PaCo-Reward-7B and this model may differ in scores due to numerical precision
print(f"Consistency Score (Yes Conditional Probability): {yes_prob.item():.4f}")

# Inference: Generate detailed reasons
generated_ids = model.generate(**inputs, max_new_tokens=512)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text[0])

🎁 Model Zoo

The PaCo-RL framework includes several models available on Hugging Face:

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

🤗 Acknowledgement

Our work is built upon Flow-GRPO, LLaMA-Factory, vLLM, and Qwen2.5-VL. We sincerely thank the authors for their valuable contributions to the community.

⭐ 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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