T2I_GRPO训练代码与数据
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<h1 align="center"> Flow-GRPO:<br>Training Flow Matching Models via Online RL </h1> <div align="center"> <a href='https://arxiv.org/abs/2505.05470'><img src='https://img.shields.io/badge/ArXiv-red?logo=arxiv'></a> <a href='https://gongyeliu.github.io/Flow-GRPO/'><img src='https://img.shields.io/badge/Visualization-green?logo=github'></a> <a href="https://github.com/yifan123/flow_grpo"><img src="https://img.shields.io/badge/Code-9E95B7?logo=github"></a> <a href='https://huggingface.co/collections/jieliu/sd35m-flowgrpo-68298ec27a27af64b0654120'><img src='https://img.shields.io/badge/Model-blue?logo=huggingface'></a> <a href='https://huggingface.co/spaces/jieliu/SD3.5-M-Flow-GRPO'><img src='https://img.shields.io/badge/Demo-blue?logo=huggingface'></a> </div> ## Changelog **2025-08-15** * Thanks [Jing Wang](https://scholar.google.com.hk/citations?user=Q9Np_KQAAAAJ&hl=zh-CN) for adding **Wan2.1**. Training command ```bash 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 ``` **2025-08-14** * Adding reward curve of Flow-GRPO-Fast vs. Flow-GRPO. In Pickscore reward, Flow-GRPO-Fast is comparable to Flow-GRPO with only 2 steps training. **2025-08-04** * Adding support for **FLUX.1-Kontext-dev**. For the counting task, we use Geneval reward to detect object counts and CLIP feature similarity to ensure consistency between the original and edited images. This implementation offers a runnable pipeline, but the training set contains only 800 samples. Making Flow-GRPO truly effective for editing tasks still requires further exploration by the community. **2025-07-31** - Adding Flow-GRPO-Fast. **2025-07-28** - Adding support for **FLUX.1-dev**. - Adding support for CLIPScore as reward model. - Introducing `config.sample.same_latent` to control whether the same noise is reused for identical prompts, addressing [Issue #7](https://github.com/yifan123/flow_grpo/issues/7). **2025-05-15** - 🔥We showcase image examples from three tasks and their training evolution at https://gongyeliu.github.io/Flow-GRPO. Check them out! - 🔥We now provide an online demo for all three tasks at https://huggingface.co/spaces/jieliu/SD3.5-M-Flow-GRPO. You're welcome to try it out! ## FAQ * 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](https://scholar.google.com.hk/citations?user=Q9Np_KQAAAAJ&hl=zh-CN) 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. ## Flow-GRPO-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](https://scholar.google.com/citations?user=fWxWEzsAAAAJ&hl=en) 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. <p align="center"> <img src="flow_grpo/assets/flow_grpo_fast.png" alt="Flow-GRPO-Fast Illustration" width=450"/> </p> Please use scripts in `scripts/multi_node/sd3_fast` to run these experiments. ## 🤗 Model | Task | Model | | -------- | -------- | | GenEval | [🤗GenEval](https://huggingface.co/jieliu/SD3.5M-FlowGRPO-GenEval) | | Text Rendering | [🤗Text](https://huggingface.co/jieliu/SD3.5M-FlowGRPO-Text) | | Human Preference Alignment | [🤗PickScore](https://huggingface.co/jieliu/SD3.5M-FlowGRPO-PickScore) | ## 🚀 Quick Started ### 1. Environment Set Up Clone this repository and install packages. ```bash git clone https://github.com/yifan123/flow_grpo.git cd flow_grpo conda create -n flow_grpo python=3.10.16 pip install -e . ``` ### 2. Model Download 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` ### 3. Reward Preparation 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. #### GenEval Please create a new Conda virtual environment and install the corresponding dependencies according to the instructions in [reward-server](https://github.com/yifan123/reward-server). #### OCR Please install paddle-ocr: ```bash 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: ```python from paddleocr import PaddleOCR ocr = PaddleOCR(use_angle_cls=False, lang="en", use_gpu=False, show_log=False) ``` #### Pickscore PickScore requires no additional installation. #### DeQA Please create a new Conda virtual environment and install the corresponding dependencies according to the instructions in [reward-server](https://github.com/yifan123/reward-server). #### UnifiedReward Since `sglang` may conflict with other environments, we recommend creating a new conda environment. ```bash 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: ```bash 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 ``` #### ImageReward Please install imagereward: ```bash pip install image-reward pip install git+https://github.com/openai/CLIP.git ``` ### 4. Start Training #### GRPO Single-node training: ```bash # sd3 bash scripts/single_node/grpo.sh # flux bash scripts/single_node/grpo_flux.sh ``` Multi-node training for SD3: ```bash # 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: ```bash # 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](https://huggingface.co/datasets/jieliu/counting_edit/blob/main/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`: ```bash 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: ```bash # 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 ``` #### DPO / OnlineDPO / SFT / OnlineSFT Single-node training: ```bash 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. ## How to Support Other Models 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](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion_3/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](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/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](https://github.com/yifan123/flow_grpo/tree/main/scripts/demo/sd3_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`](https://github.com/yifan123/flow_grpo/blob/main/config/grpo.py#L120) and [`config.train.gradient_accumulation_steps = 1`](https://github.com/yifan123/flow_grpo/blob/main/config/grpo.py#L125C5-L125C47) to enforce a purely on-policy setup, where the model collecting samples is identical to the one being trained. Under this setting, the [ratio](https://github.com/yifan123/flow_grpo/blob/main/scripts/train_sd3.py#L886) 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](https://github.com/yifan123/flow_grpo/blob/main/flow_grpo/diffusers_patch/sd3_sde_with_logprob.py#L47) based on your model. Other hyperparameters are generally model-agnostic and can be kept as default. ## 🏁 Multi Reward Training 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: ```python { "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](https://github.com/zhiyuanyou/DeQA-Score)** 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. ## ✨ Important Hyperparameters 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`. ## 🤗 Acknowledgement This repo is based on [ddpo-pytorch](https://github.com/kvablack/ddpo-pytorch) and [diffusers](https://github.com/huggingface/diffusers). We thank the authors for their valuable contributions to the AIGC community. Special thanks to Kevin Black for the excellent *ddpo-pytorch* repo. ## ⭐Citation 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} } ```
