Text-to-Image
Diffusers
Safetensors
How to use from the
Use from the
Diffusers library
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("CodeGoat24/FLUX.1-dev-UnifiedReward-Flex", dtype=torch.bfloat16, device_map="cuda")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]

Model Summary

This model is GRPO trained using UnifiedReward-Flex as reward on the training dataset of UniGenBench.

πŸš€ The inference code is available at Github.

For further details, please refer to the following resources:

Qualitative Results

image

image

Quantitative Results

image

Citation

@article{unifiedreward-flex,
  title={Unified Personalized Reward Model for Vision Generation},
  author={Wang, Yibin and Zang, Yuhang and Han, Feng and Bu, Jiazi and Zhou, Yujie and Jin, Cheng and Wang, Jiaqi},
  journal={arXiv preprint arXiv:2602.02380},
  year={2026}
}
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