AnyFlow Wan 2.1 T2V 14B Diffusers

--- license: other language: - en base_model: - Wan-AI/Wan2.1-T2V-14B-Diffusers pipeline_tag: text-to-video tags: - Any-Step - Text-to-Video --- # AnyFlow <p align="center"> 🖥️ <a href="https://github.com/NVlabs/AnyFlow">GitHub</a> &nbsp;&nbsp; ｜ &nbsp;&nbsp; 🤗 <a href="https://huggingface.co/collections/nvidia/anyflow">Hugging Face</a> &nbsp;&nbsp; ｜ &nbsp;&nbsp; 📑 <a href="https://arxiv.org/">Paper</a> &nbsp;&nbsp; ｜ &nbsp;&nbsp; 🌐 <a href="https://nvlabs.github.io/AnyFlow">Website</a> <br> </p> ----- **AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation** In this repository, we present **AnyFlow**, the first any-step video diffusion framework built on flow maps. **AnyFlow** offers these key features: - ⚡ **Any-Step Generation**: Unlike traditional distilled models tied to fixed step budgets, **AnyFlow** enables a single model to adapt to arbitrary inference budgets. It achieves high-quality few-step generation while providing stable improvements as more sampling steps are added. - 🔀 **Multiple Architectures**: **AnyFlow** supports any-step distillation for both **causal** and **bidirectional** video diffusion models. - 🎬 **Multiple Tasks**: **AnyFlow** supports Text-to-Video, Image-to-Video, and Video-to-Video generation within one causal video diffusion model. - 📈 **Scalable Performance**: **AnyFlow** is validated from **1.3B** up to **14B** parameters. This directory contains **AnyFlow-Wan2.1-T2V-14B-Diffusers** (a 14B bidirectional video diffusion model) in Hugging Face Diffusers format, derived from the [**Wan2.1-T2V-14B-Diffusers**](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B-Diffusers) text-to-video backbone. ## Video Demos <div align="center"> <video width="80%" autoplay loop muted playsinline controls> <source src="https://nvlabs.github.io/AnyFlow/assets/videos/demo_video.mp4" type="video/mp4"> Your browser does not support the video tag. </video> </div> ## 🔥 Latest News!! * May 4, 2026: 👋 We've released the codebase and weights of AnyFlow. ## Quickstart ### Setup Environment **1️⃣ Create Conda Environment** ```bash conda create -n far python=3.10 conda activate far ``` **2️⃣ Install PyTorch and Dependencies** ```bash pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128 pip install -r requirements.txt --no-build-isolation ``` ### Model Download...