NVlabs/DiffusionNFT

Health v2 · maintenance only

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1034 stars · 45 forks observed · 2026-08-28

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

DiffusionNFT is a research library implementing an online reinforcement learning paradigm for diffusion models that optimizes policy directly on the forward diffusion process. Built on the Flow-GRPO codebase, it supports multiple reward models (GenEval, OCR, PickScore, CLIP, HPSv2, Aesthetic) for training diffusion models like SD3.5M.

Use cases

• fine-tune diffusion models with reinforcement learning
• train text-to-image models with reward-based optimization
• apply online RL to diffusion policy without sampling trajectories
• optimize image generation with reward models like PickScore or HPSv2
• research forward-process policy optimization for diffusion models
• integrate RL training into existing flow-matching codebases

When to choose

• you need solver-agnostic RL fine-tuning of diffusion models compatible with any black-box sampler
• you want memory-efficient training that only requires clean images rather than full sampling trajectories
• you want to reward-optimize diffusion models using standard flow-matching objectives
• you are reproducing ICLR 2026 research on diffusion reinforcement learning

When to avoid

• you need general-purpose diffusion model inference or image generation without RL training
• you are training non-diffusion models such as LLMs or standard supervised classifiers
• you need a production-ready, stable training framework with extensive documentation and support
• you lack GPU resources, as diffusion RL training is compute-intensive

Facets

library · maturity active

machine-learning llm-training image-processing machine-learning deep-learning image-processing artificial-intelligence python diffusion-models reinforcement-learning flow-matching policy-optimization reward-models research-code iclr-2026 gpu linux

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