Flow-OPD

Flow-OPD: On-Policy Distillation for Flow Matching Models — Evaluated on SD-3.5-Medium, Flow-OPD achieves +18pt average improvement over vanilla GRPO.

Quick Start

import torch
from diffusers import StableDiffusion3Pipeline
from peft import PeftModel

model_id = "stabilityai/stable-diffusion-3.5-medium"
lora_ckpt_path = "CostaliyA/Flow-OPD"#dev ckpt
device = "cuda"

pipe = StableDiffusion3Pipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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, negative_prompt="").images[0]
image.save("flow_opd.png")

Results

Model	GenEval	OCR	DeQA	PickScore	Average
SD-3.5-M (base)	0.63	0.59	4.07	21.64	0.72
GRPO-Mix	0.73	0.83	4.33	21.84	0.82
Flow-OPD	0.92	0.94	4.35	23.08	0.90

Citation

@article{fang2026flow,
  title={Flow-OPD: On-Policy Distillation for Flow Matching Models},
  author={Fang, Zhen and Huang, Wenxuan and Zeng, Yu and Zhao, Yiming and Chen, Shuang and Feng, Kaituo and Lin, Yunlong and Chen, Lin and Chen, Zehui and Cao, Shaosheng and others},
  journal={arXiv preprint arXiv:2605.08063},
  year={2026}
}