Initial MeanFlowNFT release

.gitattributes

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+*.pt filter=lfs diff=lfs merge=lfs -text
+assets/algorithm.jpg filter=lfs diff=lfs merge=lfs -text
+assets/meanflownft_logo.jpg filter=lfs diff=lfs merge=lfs -text
+assets/results_sd35.png filter=lfs diff=lfs merge=lfs -text
+assets/results_wan.png filter=lfs diff=lfs merge=lfs -text
+assets/teaser.webp filter=lfs diff=lfs merge=lfs -text

LICENSE

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README.md

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+---
+license: apache-2.0
+base_model: stabilityai/stable-diffusion-3.5-medium
+library_name: diffusers
+pipeline_tag: text-to-image
+tags:
+- text-to-image
+- diffusion
+- flow-matching
+- meanflow
+- reinforcement-learning
+- lora
+---
+
+<!-- markdownlint-disable MD028 MD033 MD041 -->
+<div align="center">
+
+<img src="assets/meanflownft_logo.jpg" width="48%" alt="MeanFlowNFT logo">
+
+<h2>Bringing Forward-Process RL to Average-Velocity Generators</h2>
+
+[Yushi Huang](https://harahan.github.io/)<sup>1,2</sup>\* ·
+[Xiangxin Zhou](https://zhouxiangxin1998.github.io/)<sup>1</sup>\*<sup>✉</sup> ·
+[Jun Zhang](https://eejzhang.people.ust.hk/)<sup>2</sup> ·
+[Liefeng Bo](https://research.cs.washington.edu/istc/lfb/)<sup>1</sup> ·
+[Tianyu Pang](https://p2333.github.io/)<sup>1,✉</sup>
+
+<sup>1</sup>Tencent Hunyuan &nbsp;&nbsp; <sup>2</sup>The Hong Kong University of Science and Technology
+
+\* Equal contribution &nbsp;&nbsp; <sup>✉</sup>Corresponding authors
+
+[![arXiv](https://img.shields.io/badge/arXiv-xxx-A42C25?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/abs/xxx)
+[![Project Page](https://img.shields.io/badge/Project-Page-0F9D8A?style=for-the-badge&logo=googlechrome&logoColor=white)](https://harahan.github.io/meanflownft-project-page/)
+[![Code](https://img.shields.io/badge/GitHub-MeanFlowNFT-181717?style=for-the-badge&logo=github)](https://github.com/Harahan/MeanFlowNFT)
+
+</div>
+
+MeanFlowNFT optimizes reward in induced instantaneous-velocity space while
+retaining the average-velocity parameterization and native any-step MeanFlow
+sampler. Training uses only forward-noised clean samples, without
+reverse-trajectory likelihood estimation.
+
+<p align="center">
+  <a href="https://harahan.github.io/meanflownft-project-page/"><img src="assets/teaser.webp" width="100%" alt="Selected MeanFlowNFT generations"></a>
+</p>
+
+## 🍭 Overview
+
+<table align="center">
+  <tr>
+    <td align="center" width="58%" valign="middle">
+      <img src="assets/overview.webp" width="100%" alt="MeanFlowNFT method overview">
+      <br><sub>Optimize in instantaneous-velocity space while preserving MeanFlow sampling.</sub>
+    </td>
+    <td align="center" width="42%" valign="middle">
+      <img src="assets/algorithm.jpg" width="100%" alt="MeanFlowNFT update algorithm">
+      <br><sub>One practical MeanFlowNFT update.</sub>
+    </td>
+  </tr>
+</table>
+
+## 📦 Contents
+
+This repository contains the three ordered adapter components required to
+reconstruct the final policy:
+
+```text
+.
+├── anyflow_pretrain/
+│   └── generator_ema.pt
+├── anyflow_onpolicy/
+│   └── generator_ema.pt
+└── meanflownft/
+    └── generator_ema.pt
+```
+
+The loading contract is strict:
+
+1. Merge `anyflow_pretrain/generator_ema.pt` into the base transformer.
+2. Merge `anyflow_onpolicy/generator_ema.pt` on top of that result.
+3. Keep `meanflownft/generator_ema.pt` as the active final adapter.
+4. Load the complete `delta_embedder` state from the final checkpoint.
+
+The adapter checkpoints use LoRA rank **32**, alpha **64**, and attention
+projections matching the released code. They also contain the trainable
+flow-map `delta_embedder`, so they are not conventional LoRA-only files.
+`SHA256SUMS` records the checksum of every checkpoint.
+
+## 📊 Results
+
+<p align="center">
+  <a href="assets/results_sd35.png"><img src="assets/results_sd35.png" width="100%" alt="MeanFlowNFT image-generation results"></a>
+</p>
+
+<p align="center">
+  <a href="assets/results_wan.png"><img src="assets/results_wan.png" width="100%" alt="MeanFlowNFT video-generation results"></a>
+</p>
+
+MeanFlowNFT is best on **6 of 8** image metrics among the evaluated few-step
+models, while 4-step Wan2.1 reaches **84.33 VBench**, surpassing 50-step
+LongCat-Video RL. See the
+[project page](https://harahan.github.io/meanflownft-project-page/) for full
+comparisons, scaling curves, and qualitative results.
+
+## 🚀 Usage
+
+Use the [MeanFlowNFT codebase](https://github.com/Harahan/MeanFlowNFT), whose
+inference loader preserves the ordered adapter composition and full
+`delta_embedder` state.
+
+```bash
+git clone https://github.com/Harahan/MeanFlowNFT.git
+cd MeanFlowNFT
+pip install -r requirements.txt
+pip install -e .
+
+export HF_REPO_ID=Harahan/MeanFlowNFT
+hf download "${HF_REPO_ID}" --local-dir ./checkpoints/meanflownft
+
+hf auth login
+hf download stabilityai/stable-diffusion-3.5-medium \
+    --local-dir ./models/stable-diffusion-3.5-medium
+```
+
+Run the final 4-step policy:
+
+```bash
+python inference.py configs/inference/sd35m_meanflow_nft.yaml \
+    --override \
+    pretrained_path=./models/stable-diffusion-3.5-medium \
+    num_stages=3 \
+    stage1_lora_path=./checkpoints/meanflownft/anyflow_pretrain/generator_ema.pt \
+    stage2_lora_path=./checkpoints/meanflownft/anyflow_onpolicy/generator_ema.pt \
+    stage3_lora_path=./checkpoints/meanflownft/meanflownft/generator_ema.pt \
+    num_steps=4 \
+    eval_reward=false \
+    --prompt "a cinematic photo of a red panda"
+```
+
+The first two adapters are merged in order. The final MeanFlowNFT adapter stays
+active and supplies `delta_embedder`, matching training-time evaluation. The
+released inference config generates 1024×1024 images with CFG-free flow-map
+sampling.
+
+> [!IMPORTANT]
+> Do not load only the final checkpoint and do not use a generic LoRA loader
+> that discards non-LoRA tensors. Either mistake drops part of the trained
+> policy.
+
+<!-- -->
+
+> [!WARNING]
+> These `.pt` files use PyTorch serialization. Load checkpoints only from a
+> trusted source.
+
+## 📄 Citation
+
+```bibtex
+@misc{huang2026meanflownft,
+  title  = {MeanFlowNFT: Bringing Forward-Process RL to Average-Velocity Generators},
+  author = {Huang, Yushi and Zhou, Xiangxin and Zhang, Jun and Bo, Liefeng and Pang, Tianyu},
+  year   = {2026},
+  note   = {Preprint}
+}
+```
+
+## 🙌 Acknowledgements
+
+This work builds on
+[MeanFlow](https://github.com/Gsunshine/meanflow),
+[AnyFlow](https://github.com/NVlabs/AnyFlow),
+[DiffusionNFT](https://github.com/NVlabs/DiffusionNFT),
+[Diffusers](https://github.com/huggingface/diffusers),
+[Transformers](https://github.com/huggingface/transformers), and
+[PEFT](https://github.com/huggingface/peft).
+
+## ⚖️ License
+
+The repository metadata and accompanying code are released under Apache 2.0.
+The base model and all derivative weights remain subject to the base model's
+license and usage terms; review them before use or redistribution.

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