yifan123/flow_grpo

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2498 stars · 167 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Flow-GRPO is the official PyTorch implementation of a NeurIPS 2025 paper that trains flow matching models (e.g., SD3.5, FLUX.1, Qwen-Image, Wan2.1) via online reinforcement learning with GRPO. It provides training scripts, reward model integrations (CLIPScore, Geneval, OCR), and a faster Flow-GRPO-Fast variant.

Use cases

• fine-tune a flow matching diffusion model with online RL
• train Stable Diffusion 3.5 with GRPO rewards
• improve text-to-image prompt adherence using CLIPScore reward
• train FLUX.1-dev with reinforcement learning
• apply RL post-training to Qwen-Image or Wan2.1 video models
• train image editing models with Geneval counting rewards
• speed up RL fine-tuning with few-step Flow-GRPO-Fast

When to choose

• you want to RL fine-tune a flow matching generative model with custom rewards
• you need a research-grade reference implementation of GRPO for diffusion models
• you work with supported models like SD3.5, FLUX, Qwen-Image, or Wan2.1 and have GPU resources

When to avoid

• you need a production training framework with broad model support out of the box
• your models are not flow matching based (e.g., classic DDPM diffusion)
• you lack multi-GPU hardware or don't want to write custom reward functions

Facets

library · maturity active

machine-learning llm-training reinforcement-learning stable-diffusion gpu-computing machine-learning deep-learning image-processing artificial-intelligence python flow-matching grpo online-rl diffusion-models text-to-image research-code neurips-2025 gpu linux

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