AnyFlow-FAR-Wan2.1-14B-Diffusers实战教程：3分钟实现文本到视频（T2V）生成

AnyFlow-FAR-Wan2.1-14B-Diffusers实战教程3分钟实现文本到视频T2V生成【免费下载链接】AnyFlow-FAR-Wan2.1-14B-Diffusers项目地址: https://ai.gitcode.com/hf_mirrors/nvidia/AnyFlow-FAR-Wan2.1-14B-DiffusersAnyFlow-FAR-Wan2.1-14B-Diffusers是一款强大的14B参数因果视频扩散模型基于Hugging Face Diffusers格式构建支持文本到视频T2V、图像到视频I2V和视频到视频V2V多种生成任务。本文将带你快速上手这款AI视频生成工具仅需3分钟即可完成从文本描述到视频输出的全过程。 核心优势解析AnyFlow框架凭借创新的流图蒸馏技术带来三大突破性特性任意步数生成⚡无需绑定固定推理步数可根据需求灵活调整采样步骤兼顾生成速度与质量多任务支持一个模型即可实现文本转视频、图像转视频和视频风格迁移三大功能高性能扩展性从1.3B到14B参数规模均通过验证480P分辨率下保持流畅生成体验 环境准备2分钟极速配置1️⃣ 创建专属环境conda create -n far python3.10 conda activate far2️⃣ 安装依赖包pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128 pip install -r requirements.txt --no-build-isolation 模型获取方法克隆仓库git clone https://gitcode.com/hf_mirrors/nvidia/AnyFlow-FAR-Wan2.1-14B-Diffusers cd AnyFlow-FAR-Wan2.1-14B-Diffusers下载模型权重pip install huggingface_hub[cli] hf download nvidia/AnyFlow-FAR-Wan2.1-14B-Diffusers --repo-type model --local-dir ./✨ 文本到视频生成实战1分钟出结果创建Python脚本输入以下代码import torch from diffusers.utils import export_to_video from far.pipelines.pipeline_far_wan_anyflow import FARWanAnyFlowPipeline # 加载模型 pipeline FARWanAnyFlowPipeline.from_pretrained(./).to(cuda, dtypetorch.bfloat16) # 输入文本描述 prompt CG game concept digital art, a majestic elephant with a vibrant tusk and sleek fur running swiftly towards a herd of its kind. # 生成视频 video pipeline( promptprompt, height480, width832, num_frames81, num_inference_steps4, # 快速生成仅需4步 generatortorch.Generator(cuda).manual_seed(0) ).frames[0] # 导出为MP4 export_to_video(video, output.mp4, fps16)运行脚本后当前目录将生成output.mp4文件包含81帧、16fps的480P视频。 进阶应用场景图像到视频I2V通过assets/example_image.jpg作为初始帧扩展生成动态视频from PIL import Image from torchvision import transforms image Image.open(assets/example_image.jpg).convert(RGB) image transforms.ToTensor()(transforms.Resize([480, 832])(image)).unsqueeze(0).unsqueeze(0) video pipeline( promptA towering, battle-scarred humanoid robot walking through city ruins, context_sequence{raw: image}, # 传入初始图像 num_frames81, num_inference_steps4 ).frames[0]视频到视频V2V基于assets/example_video.mp4进行风格转换import decord decord.bridge.set_bridge(torch) video_reader decord.VideoReader(assets/example_video.mp4) frames video_reader.get_batch(range(10)) # 提取10帧作为参考 frames (frames / 255.0).float().permute(0, 3, 1, 2).contiguous() video pipeline( promptA focused trail runner moving through a sun-dappled forest, context_sequence{raw: frames}, # 传入参考视频帧 num_frames81 ).frames[0] 许可证信息本模型采用NVIDIA非商业许可证NSCLv1发布禁止用于商业用途。根据许可证条款NVIDIA不对使用模型生成的内容主张所有权。 致谢项目基于Diffusers框架构建同时参考了FAR、Self-Forcing和TiM的实现方案感谢相关项目的开源贡献。如果觉得本教程有帮助欢迎引用相关研究论文article{gu2026anyflow, title{AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation}, author{Gu, Yuchao and Fang, Guian and Jiang, Yuxin and Mao, Weijia and Han, Song and Cai, Han and Shou, Mike Zheng}, journal{arXiv preprint arXiv:2605.13724}, year{2026} }【免费下载链接】AnyFlow-FAR-Wan2.1-14B-Diffusers项目地址: https://ai.gitcode.com/hf_mirrors/nvidia/AnyFlow-FAR-Wan2.1-14B-Diffusers创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考