accelerate launch --config_file scripts/accelerate_configs/multi_gpu.yaml --num_processes=1 --main_process_port 29503 scripts/train_wan2_1.py --config config/grpo.py:general_ocr_wan2_1

To improve training efficiency, we provide a better set of parameters for Flow-GRPO. We found the following adjustments significantly accelerate training:

• No CFG during training or testing — the RL process effectively performs CFG distillation.
• Use the window mechanism from Flow-GRPO-Fast or MixGRPO — only train on partial steps.
• Adopt Coefficients-Preserving Sampling (CPS) — CPS provides a notable improvement on GenEval, and produces higher-quality samples. A typical setting is noise_level = 0.8, which works well without tuning for different models or step counts.

The figure below shows the test-set performance curves using GenEval and PickScore as rewards, where both training and evaluation are performed without CFG. The experiments are configured with geneval_sd3_fast_nocfg and pickscore_sd3_fast_nocfg, using scripts from scripts/multi_node/sd3_fast.

We propose Flow-GRPO-Fast, an accelerated variant of Flow-GRPO that requires training on only one or two denoising step per trajectory. For each prompt, we first generate a deterministic trajectory using ODE sampling. At a randomly chosen intermediate step, we inject noise and switch to SDE sampling to generate a group. The rest of the process continues with ODE sampling. This confines stochasticity to one or two steps, allowing training to focus solely on that steps. This few-step training idea was primarily proposed by Ziyang Yuan during our discussions in early June.

Flow-GRPO-Fast achieves significant efficiency gains:

• Each trajectory is trained only once or twice, significantly reducing the training cost.

• Sampling before branching requires only a single prompt without group expansion, further speeding up data collection.

Experiments on PickScore show that Flow-GRPO-Fast matches the reward performance of Flow-GRPO while offering faster training speed. The x-axis in the figure represents training epochs. Flow-GRPO-Fast with 2 training steps per iteration performs better than Flow-GRPO, while Flow-GRPO-Fast with only 1 training step per iteration performs slightly worse than Flow-GRPO. In both cases, compared to Flow-GRPO’s 10 training steps per iteration, the training process is significantly faster.

Please use scripts in scripts/multi_node/sd3_fast to run these experiments.

Clone this repository and install packages.

git clone https://github.com/yifan123/flow_grpo.git
cd flow_grpo
conda create -n flow_grpo python=3.10.16
pip install -e .

To avoid redundant downloads and potential storage waste during multi-GPU training, please pre-download the required models in advance.

Models

• SD3.5: stabilityai/stable-diffusion-3.5-medium
• Flux: black-forest-labs/FLUX.1-dev

Reward Models

• PickScore:
  • laion/CLIP-ViT-H-14-laion2B-s32B-b79K
  • yuvalkirstain/PickScore_v1
• CLIPScore: openai/clip-vit-large-patch14
• Aesthetic Score: openai/clip-vit-large-patch14

The steps above only install the current repository. Since each reward model may rely on different versions, combining them in one Conda environment can cause version conflicts. To avoid this, we adopt a remote server setup inspired by ddpo-pytorch. You only need to install the specific reward model you plan to use.

Please create a new Conda virtual environment and install the corresponding dependencies according to the instructions in reward-server.

Please install paddle-ocr:

pip install paddlepaddle-gpu==2.6.2
pip install paddleocr==2.9.1
pip install python-Levenshtein

Then, pre-download the model using the Python command line:

from paddleocr import PaddleOCR
ocr = PaddleOCR(use_angle_cls=False, lang="en", use_gpu=False, show_log=False)

PickScore requires no additional installation. Note that the original pickscore dataset corresponds to dataset/pickscore in this repository, containing some NSFW prompts. We strongly recommend using pickapic_v1_no_images_training_sfw, the SFW version of the Pick-a-Pic dataset, which corresponds to dataset/pickscore_sfw in this repository.

Please create a new Conda virtual environment and install the corresponding dependencies according to the instructions in reward-server.

Since sglang may conflict with other environments, we recommend creating a new conda environment.

conda create -n sglang python=3.10.16
conda activate sglang
pip install "sglang[all]"

We use sglang to deploy the reward service. After installing sglang, please run the following command to launch UnifiedReward:

python -m sglang.launch_server --model-path CodeGoat24/UnifiedReward-7b-v1.5 --api-key flowgrpo --port 17140 --chat-template chatml-llava --enable-p2p-check --mem-fraction-static 0.85

Please install imagereward:

pip install image-reward
pip install git+https://github.com/openai/CLIP.git

• Single-node training:

# sd3
bash scripts/single_node/grpo.sh
# flux
bash scripts/single_node/grpo_flux.sh

• Multi-node training for SD3:

# Master node
bash scripts/multi_node/sd3/main.sh
# Other nodes
bash scripts/multi_node/sd3/main1.sh
bash scripts/multi_node/sd3/main2.sh
bash scripts/multi_node/sd3/main3.sh

• Multi-node training for FLUX.1-dev:

# Master node
bash scripts/multi_node/flux/main.sh
# Other nodes
bash scripts/multi_node/flux/main1.sh
bash scripts/multi_node/flux/main2.sh
bash scripts/multi_node/flux/main3.sh

• Multi-node training for FLUX.1-Kontext-dev:

Please first download generated_images.zip and extract it into the counting_edit directory. You can also use the scripts in the counting_edit directory to generate the data yourself.

Please install diffusers from the main branch to support FLUX.1-Kontext-dev:

pip install git+https://github.com/huggingface/diffusers.git

After upgrading Diffusers, some packages such as PEFT may also need to be upgraded. If you encounter any errors, please upgrade them according to the error messages. Then, run the scripts:

# Master node
bash scripts/multi_node/flux_kontext/main.sh
# Other nodes
bash scripts/multi_node/flux_kontext/main1.sh
bash scripts/multi_node/flux_kontext/main2.sh
bash scripts/multi_node/flux_kontext/main3.sh

• Multi-node training for Qwen-Image:

In the implementation of Qwen-Image, we have unified Flow-GRPO and Flow-GRPO-Fast. You can control the size of the SDE window with config.sample.sde_window_size, and adjust the position of the window with config.sample.sde_window_range.

Please install diffusers from the main branch to support Qwen-Image:

pip install git+https://github.com/huggingface/diffusers.git

Then run the scripts:

# Master node
bash scripts/multi_node/qwenimage/main.sh 0
# Other nodes
bash scripts/multi_node/qwenimage/main.sh 1
bash scripts/multi_node/qwenimage/main.sh 2
bash scripts/multi_node/qwenimage/main.sh 3

Using the provided configuration, the resulting reward curve of Qwen-Image on the test set is shown below.

• Multi-node training for Qwen-Image-Edit:

Same as Flux Kontext, please first download generated_images.zip and extract it into the counting_edit directory. You can also use the scripts in the counting_edit directory to generate the data yourself.

Please install diffusers from the main branch to support Qwen-Image-Edit:

pip install git+https://github.com/huggingface/diffusers.git

Then run the scripts:

# Master node
bash scripts/multi_node/qwenimage_edit/main.sh 0
# Other nodes
bash scripts/multi_node/qwenimage_edit/main.sh 1
bash scripts/multi_node/qwenimage_edit/main.sh 2
bash scripts/multi_node/qwenimage_edit/main.sh 3

Using the provided configuration, the resulting reward curve of Qwen-Image-Edit on the test set is shown below.

Single-node training:

bash scripts/single_node/dpo.sh
bash scripts/single_node/sft.sh

Multi-node training:

Please update the entry Python script and config file names in the scripts/multi_node bash file.

• Please use fp16 for training whenever possible, as it provides higher precision than bf16, resulting in smaller log-probability errors between data collection and training. For Flux and Wan, becauase fp16 inference cannot produce valid images or videos, you will have to use bf16 for training. Note that log-probability errors tend to be smaller at high-noise steps and larger at low-noise steps. Training only on high-noise steps yields better results in this case. Thanks to Jing Wang for these observations.

• When using Flow-GRPO-Fast, set a relatively small clip_range, otherwise training may crash.

• When implementing a new model, please check whether using different batch sizes leads to slight differences in the output. SD3 has this issue, which is why I ensure that the batch size for training is the same as that used for data collection.

To integrate a new model into this framework, please follow the steps below:

1. Add the following files adapted for your model:

• flow_grpo/diffusers_patch/sd3_pipeline_with_logprob.py: This file is adapted from pipeline_stable_diffusion_3.py. You can refer to diffusers for your model.

• scripts/train_sd3.py: This script is based on train_dreambooth_lora_sd3.py from the DreamBooth examples.

• flow_grpo/diffusers_patch/sd3_sde_with_logprob.py: This file handles SDE sampling. In most cases, you don't need to modify it. However, if your definitions of dt or velocity differ in sign or convention, please adjust accordingly.

2. Verify SDE sampling: Set noise_level = 0 in sde_demo.py to check whether the generated images look normal. This helps verify that your SDE implementation is correct.

3. Ensure on-policy consistency: Set config.sample.num_batches_per_epoch = 1 and config.train.gradient_accumulation_steps = 1 to enforce a purely on-policy setup, where the model collecting samples is identical to the one being trained. Under this setting, the ratio should remain exactly 1. If it's not, please check whether the sampling and training code paths differ—for example, through use of torch.compile or other model wrappers—and make sure both share the same logic.

4. Tune reward behavior: Start with config.train.beta = 0 to observe if the reward increases during training. You may also need to adjust the noise level here based on your model. Other hyperparameters are generally model-agnostic and can be kept as default.

For multi-reward settings, you can pass in a dictionary where each key is a reward name and the corresponding value is its weight. For example:

{
    "pickscore": 0.5,
    "ocr": 0.2,
    "aesthetic": 0.3
}

This means the final reward is a weighted sum of the individual rewards.

The following reward models are currently supported:

• Geneval evaluates T2I models on complex compositional prompts.
• OCR provides an OCR-based reward.
• PickScore is a general-purpose T2I reward model trained on human preferences.
• DeQA is a multimodal LLM-based image quality assessment model that measures the impact of distortions and texture damage on perceived quality.
• ImageReward is a general-purpose T2I reward model capturing text-image alignment, visual fidelity, and safety.
• QwenVL is an experimental reward model using prompt engineering.
• Aesthetic is a CLIP-based linear regressor predicting image aesthetic scores.
• JPEG_Compressibility measures image size as a proxy for quality.
• UnifiedReward is a state-of-the-art reward model for multimodal understanding and generation, topping the human preference leaderboard.

You can adjust the parameters in config/grpo.py to tune different hyperparameters. An empirical finding is that config.sample.train_batch_size * num_gpu / config.sample.num_image_per_prompt * config.sample.num_batches_per_epoch = 48, i.e., group_number=48, group_size=24. Additionally, setting config.train.gradient_accumulation_steps = config.sample.num_batches_per_epoch // 2.

This repo is based on ddpo-pytorch and diffusers. We thank the authors for their valuable contributions to the AIGC community. Special thanks to Kevin Black for the excellent ddpo-pytorch repo.

If you find Flow-GRPO useful for your research or projects, we would greatly appreciate it if you could cite the following paper:

@article{liu2025flow,
  title={Flow-grpo: Training flow matching models via online rl},
  author={Liu, Jie and Liu, Gongye and Liang, Jiajun and Li, Yangguang and Liu, Jiaheng and Wang, Xintao and Wan, Pengfei and Zhang, Di and Ouyang, Wanli},
  journal={arXiv preprint arXiv:2505.05470},
  year={2025}
}

If you find Flow-DPO useful for your research or projects, we would greatly appreciate it if you could cite the following paper:

@article{liu2025improving,
  title={Improving video generation with human feedback},
  author={Liu, Jie and Liu, Gongye and Liang, Jiajun and Yuan, Ziyang and Liu, Xiaokun and Zheng, Mingwu and Wu, Xiele and Wang, Qiulin and Qin, Wenyu and Xia, Menghan and others},
  journal={arXiv preprint arXiv:2501.13918},
  year={2025}
}