5B 参数，消费级显卡可部署：Wan2.2-TI2V-5B 本地部署教程，9分钟跑出电影级大片！

算家计算2025-08-11 18:01

一、简介

Wan2.2-TI2V-5B 是阿里巴巴通义万相（Tongyi Wanxiang）团队于 2025 年 7 月开源的一款轻量级统一视频生成模型，属于 Wan2.2 系列中的核心成员。它以高效部署和多功能生成为特点，显著降低了电影级视频制作的技术门槛。

1. 核心定位与功能亮点

统一生成能力

  • 多模态输入支持：同时支持文本生成视频（Text-to-Video）和图文结合生成视频（Image+Text-to-Video），实现 "二合一" 功能，满足多样化的创作需求。
  • 电影级美学控制：用户可通过调整光影、色彩、构图等超 60 个参数，精细化控制画面风格，生成具有电影质感的动态内容。

轻量化设计

参数规模仅 5B（50 亿参数），远小于同系列 MoE 架构模型（如 T2V-A14B 的 27B），但通过高压缩技术实现高性能输出。

2. 技术架构创新

高压缩 3D VAE 架构

  • 采用 时空压缩比 4×16×16 的 3D 变分自编码器，信息压缩率提升至 64 倍（远超前代 Wan2.1 的 4×8×8），显著减少显存占用。
  • 技术优势：在保持 720P 高清画质（24fps）的同时，大幅提升生成效率。

消费级硬件适配

  • 仅需 22GB 显存（如单张 RTX 4090）即可运行，支持共享显存模式（最低 8GB 显存可生成，但速度较慢）。
  • 生成耗时：单次生成 5 秒视频仅需数分钟，是目前开源模型中速度最快的 720P 视频生成方案之一。

3. 性能与效率表现

指标 参数 / 性能 对比优势
分辨率与帧率 720P @ 24fps 支持高清流畅输出
单次生成时长 5 秒 满足短视频、分镜需求
显存占用 22GB（推荐） / 8GB（最低） 消费级显卡可部署
生成速度 ≈2.5 分钟 / 20 步（RTX 4090） 较同系列 A14B 模型快 50% 以上

4.Wan2.2 系列核心型号及功能对比

型号 Wan2.2-T2V-A14B Wan2.2-I2V-A14B Wan2.2-TI2V-5B
类型 文生视频（Text-to-Video） 图生视频（Image-to-Video） 统一视频生成（Text/Image-to-Video）
参数量 总参数 27B，激活参数 14B 总参数 27B，激活参数 14B 5B（密集架构）
核心架构 MoE（混合专家）架构 MoE 架构 高压缩 3D VAE 架构
生成质量 电影级（光影 / 构图 / 微表情） 电影级（细节优化突出） 高清 720P（流畅性优先）
美学控制 ✅ 支持 60 + 参数精细调节 ✅ 支持同等美学控制 ⚠️ 部分支持（依赖提示词）
硬件要求 高性能 GPU（显存≥80GB） 高性能 GPU（显存≥80GB） 消费级 GPU（22GB 显存）
生成速度 5 秒视频约 15 分钟（高性能卡） 类似 T2V-A14B 5 秒视频≤9 分钟（RTX 4090）
开源平台 GitHub/HuggingFace/ 魔搭社区 同左 同左

二、本地部署

本次部署采用 ComfyUI 作为本地部署框架。

环境 版本号
Python 3.12
PyTorch 2.5.1
cuda 12.1
Ubtuntu 22.4.0

1.安装 Miniconda

步骤 1：更新系统

首先，更新系统软件包：

sql 复制代码 
sudo apt update
sudo apt upgrade -y

步骤 2：下载 Miniconda 安装脚本

访问 Miniconda 的官方网站或使用以下命令直接下载最新版本的安装脚本（以 Python 3 为例）：

arduino 复制代码 
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

步骤 3：验证安装脚本的完整性（可选）

下载 SHA256 校验和文件并验证安装包的完整性：

bash 复制代码 
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh.sha256
sha256sum Miniconda3-latest-Linux-x86_64.sh

比较输出的校验和与.sha256 文件中的值是否一致，确保文件未被篡改。

步骤 4：运行安装脚本

为安装脚本添加执行权限：

bash 复制代码 
chmod +x Miniconda3-latest-Linux-x86_64.sh

运行安装脚本：

复制代码 
./Miniconda3-latest-Linux-x86_64.sh

步骤 5：按照提示完成安装

安装过程中需要：

阅读许可协议 ：按 Enter 键逐页阅读，或者按 Q 退出阅读。
接受许可协议 ：输入 yes 并按 Enter。
选择安装路径 ：默认路径为/home/用户名/miniconda3，直接按 Enter 即可，或输入自定义路径。
是否初始化 Miniconda ：输入 yes 将 Miniconda 添加到您的 PATH 环境变量中。
步骤 6：激活 Miniconda 环境

安装完成后，使环境变量生效：

bash 复制代码 
source ~/.bashrc

步骤 7：验证安装是否成功

检查 conda 版本：

css 复制代码 
conda --version

步骤 8：更新 conda（推荐）

为了获得最新功能和修复，更新 conda：

sql 复制代码 
conda update conda

2.部署 ComfyUI

2.1 克隆代码仓库

bash 复制代码 
git clone https://github.com/comfyanonymous/ComfyUI.git

2.2 安装依赖

  • 创建 conda 虚拟环境
ini 复制代码 
conda create -n comfyenv python==3.12
conda activate comfyenv
  • 安装 PyTorch
ini 复制代码 
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
  • 安装依赖
bash 复制代码 
cd ComfyUI
pip install -r requirements.txt
  • 安装 ComfyUI Manager（可选）
bash 复制代码 
#进入插件的文件
cd /ComfyUI/custom_nodes/
#下载ComfyUI Manager
git clone https://github.com/Comfy-Org/ComfyUI-Manager.git

3.下载 Wan2.2-TI2V-5B 模型相关文件

使用 modelscope 下载 Wan2.2-TI2V-5B 模型：以下是必要的模型文件

Diffusion Model wan2.2_ti2v_5B_fp16.safetensors

Text Encoder umt5_xxl_fp8_e4m3fn_scaled.safetensors

VAE wan2.2_vae.safetensors

需要将以上三个文件放入之前下载的 comfyUI 的对应目录下：

ComfyUI/

├───📂 models/

│ ├───📂 diffusion_models/

│ │ └───wan2.2_ti2v_5B_fp16.safetensors

│ ├───📂 text_encoders/

│ │ └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors

│ └───📂 vae/

│ └── wan2.2_vae.safetensors

对应下载命令：（local_dir 部分的路径可以自定义）

wan2.2_ti2v_5B_fp16.safetensors：

css 复制代码 
modelscope download --model Comfy-Org/Wan_2.2_ComfyUI_Repackaged --include '*/wan2.2_ti2v_5B_fp16.safetensors' --local_dir /models/diffusion_models/

umt5_xxl_fp8_e4m3fn_scaled.safetensors：

css 复制代码 
modelscope download --model Comfy-Org/Wan_2.2_ComfyUI_Repackaged --include '*/umt5_xxl_fp8_e4m3fn_scaled.safetensors' --local_dir /models/diffusion_models/

wan2.2_vae.safetensors：

css 复制代码 
modelscope download --model Comfy-Org/Wan_2.2_ComfyUI_Repackaged --include '*/wan2.2_vae.safetensors' --local_dir /models/diffusion_models/

4.启动 ComfyUI

之前下载的 comfyUI 根目录应该有 main.py 文件。

css 复制代码 
python main.py

运行 main.py 文件 之后根据控制台显示的 url 在浏览器输入该url网址进入 ComfyUI 的 GUI 界面：

css 复制代码 
To see the GUI go to: http://127.0.0.1:8188

如图：

5.使用 Wan2.2-TI2V-5B 工作流

依次点击 工作流 -> 打开，即可在电脑本地导入工作流 josn 文件。

选择导入：

完成，工作流文件在以下红色框住的位置点击后出现。然后选取想使用的 Wan2.2-TI2V-5B 工作流

注： 当然也可以自主定制 工作流，后面放出三个工作流 json 文件内容作为参考。

Wan2.2-TI2V-5B 工作流文件内容

wan2.2_image_to_video.json
css 复制代码 
{
  "id": "91f6bbe2-ed41-4fd6-bac7-71d5b5864ecb",
  "revision": 0,
  "last_node_id": 57,
  "last_link_id": 106,
  "nodes": [
    {
      "id": 8,
      "type": "VAEDecode",
      "pos": [
        1210,
        190
      ],
      "size": [
        210,
        46
      ],
      "flags": {},
      "order": 10,
      "mode": 0,
      "inputs": [
        {
          "name": "samples",
          "type": "LATENT",
          "link": 35
        },
        {
          "name": "vae",
          "type": "VAE",
          "link": 76
        }
      ],
      "outputs": [
        {
          "name": "IMAGE",
          "type": "IMAGE",
          "slot_index": 0,
          "links": [
            56,
            93
          ]
        }
      ],
      "properties": {
        "Node name for S&R": "VAEDecode"
      },
      "widgets_values": []
    },
    {
      "id": 7,
      "type": "CLIPTextEncode",
      "pos": [
        413,
        389
      ],
      "size": [
        425.27801513671875,
        180.6060791015625
      ],
      "flags": {},
      "order": 6,
      "mode": 0,
      "inputs": [
        {
          "name": "clip",
          "type": "CLIP",
          "link": 75
        }
      ],
      "outputs": [
        {
          "name": "CONDITIONING",
          "type": "CONDITIONING",
          "slot_index": 0,
          "links": [
            52
          ]
        }
      ],
      "title": "CLIP Text Encode (Negative Prompt)",
      "properties": {
        "Node name for S&R": "CLIPTextEncode"
      },
      "widgets_values": [
        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
      ],
      "color": "#322",
      "bgcolor": "#533"
    },
    {
      "id": 3,
      "type": "KSampler",
      "pos": [
        863,
        187
      ],
      "size": [
        315,
        262
      ],
      "flags": {},
      "order": 9,
      "mode": 0,
      "inputs": [
        {
          "name": "model",
          "type": "MODEL",
          "link": 95
        },
        {
          "name": "positive",
          "type": "CONDITIONING",
          "link": 46
        },
        {
          "name": "negative",
          "type": "CONDITIONING",
          "link": 52
        },
        {
          "name": "latent_image",
          "type": "LATENT",
          "link": 104
        }
      ],
      "outputs": [
        {
          "name": "LATENT",
          "type": "LATENT",
          "slot_index": 0,
          "links": [
            35
          ]
        }
      ],
      "properties": {
        "Node name for S&R": "KSampler"
      },
      "widgets_values": [
        869177064731501,
        "randomize",
        30,
        5,
        "uni_pc",
        "simple",
        1
      ]
    },
    {
      "id": 28,
      "type": "SaveAnimatedWEBP",
      "pos": [
        1460,
        190
      ],
      "size": [
        870.8511352539062,
        648.4141235351562
      ],
      "flags": {},
      "order": 11,
      "mode": 0,
      "inputs": [
        {
          "name": "images",
          "type": "IMAGE",
          "link": 56
        }
      ],
      "outputs": [],
      "properties": {},
      "widgets_values": [
        "ComfyUI",
        24.000000000000004,
        false,
        90,
        "default"
      ]
    },
    {
      "id": 39,
      "type": "VAELoader",
      "pos": [
        20,
        340
      ],
      "size": [
        330,
        60
      ],
      "flags": {},
      "order": 0,
      "mode": 0,
      "inputs": [],
      "outputs": [
        {
          "name": "VAE",
          "type": "VAE",
          "slot_index": 0,
          "links": [
            76,
            105
          ]
        }
      ],
      "properties": {
        "Node name for S&R": "VAELoader"
      },
      "widgets_values": [
        "wan2.2_vae.safetensors"
      ],
      "color": "#223",
      "bgcolor": "#335"
    },
    {
      "id": 38,
      "type": "CLIPLoader",
      "pos": [
        20,
        190
      ],
      "size": [
        380,
        106
      ],
      "flags": {},
      "order": 1,
      "mode": 0,
      "inputs": [],
      "outputs": [
        {
          "name": "CLIP",
          "type": "CLIP",
          "slot_index": 0,
          "links": [
            74,
            75
          ]
        }
      ],
      "properties": {
        "Node name for S&R": "CLIPLoader"
      },
      "widgets_values": [
        "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
        "wan",
        "default"
      ],
      "color": "#223",
      "bgcolor": "#335"
    },
    {
      "id": 48,
      "type": "ModelSamplingSD3",
      "pos": [
        440,
        60
      ],
      "size": [
        210,
        58
      ],
      "flags": {},
      "order": 7,
      "mode": 0,
      "inputs": [
        {
          "name": "model",
          "type": "MODEL",
          "link": 94
        }
      ],
      "outputs": [
        {
          "name": "MODEL",
          "type": "MODEL",
          "slot_index": 0,
          "links": [
            95
          ]
        }
      ],
      "properties": {
        "Node name for S&R": "ModelSamplingSD3"
      },
      "widgets_values": [
        8.000000000000002
      ]
    },
    {
      "id": 37,
      "type": "UNETLoader",
      "pos": [
        20,
        60
      ],
      "size": [
        346.7470703125,
        82
      ],
      "flags": {},
      "order": 2,
      "mode": 0,
      "inputs": [],
      "outputs": [
        {
          "name": "MODEL",
          "type": "MODEL",
          "slot_index": 0,
          "links": [
            94
          ]
        }
      ],
      "properties": {
        "Node name for S&R": "UNETLoader"
      },
      "widgets_values": [
        "wan2.2_ti2v_5B_fp16.safetensors",
        "default"
      ],
      "color": "#223",
      "bgcolor": "#335"
    },
    {
      "id": 47,
      "type": "SaveWEBM",
      "pos": [
        2367.213134765625,
        193.6114959716797
      ],
      "size": [
        670,
        650
      ],
      "flags": {},
      "order": 12,
      "mode": 0,
      "inputs": [
        {
          "name": "images",
          "type": "IMAGE",
          "link": 93
        }
      ],
      "outputs": [],
      "properties": {
        "Node name for S&R": "SaveWEBM"
      },
      "widgets_values": [
        "ComfyUI",
        "vp9",
        24,
        16.111083984375
      ]
    },
    {
      "id": 57,
      "type": "LoadImage",
      "pos": [
        87.407958984375,
        620.4816284179688
      ],
      "size": [
        274.080078125,
        314
      ],
      "flags": {},
      "order": 3,
      "mode": 0,
      "inputs": [],
      "outputs": [
        {
          "name": "IMAGE",
          "type": "IMAGE",
          "links": [
            106
          ]
        },
        {
          "name": "MASK",
          "type": "MASK",
          "links": null
        }
      ],
      "properties": {
        "Node name for S&R": "LoadImage"
      },
      "widgets_values": [
        "fennec_girl_hug.png",
        "image"
      ]
    },
    {
      "id": 56,
      "type": "Note",
      "pos": [
        710.781005859375,
        608.9545288085938
      ],
      "size": [
        320.9936218261719,
        182.6057586669922
      ],
      "flags": {},
      "order": 4,
      "mode": 0,
      "inputs": [],
      "outputs": [],
      "properties": {},
      "widgets_values": [
        "Optimal resolution is: 1280x704 length 121\n\nThe reason it's lower in this workflow is just because I didn't want you to wait too long to get an initial video.\n\nTo get image to video just plug in a start image. For text to video just don't give it a start image."
      ],
      "color": "#432",
      "bgcolor": "#653"
    },
    {
      "id": 55,
      "type": "Wan22ImageToVideoLatent",
      "pos": [
        420,
        610
      ],
      "size": [
        271.9126892089844,
        150
      ],
      "flags": {},
      "order": 8,
      "mode": 0,
      "inputs": [
        {
          "name": "vae",
          "type": "VAE",
          "link": 105
        },
        {
          "name": "start_image",
          "shape": 7,
          "type": "IMAGE",
          "link": 106
        }
      ],
      "outputs": [
        {
          "name": "LATENT",
          "type": "LATENT",
          "links": [
            104
          ]
        }
      ],
      "properties": {
        "Node name for S&R": "Wan22ImageToVideoLatent"
      },
      "widgets_values": [
        1280,
        704,
        41,
        1
      ]
    },
    {
      "id": 6,
      "type": "CLIPTextEncode",
      "pos": [
        415,
        186
      ],
      "size": [
        422.84503173828125,
        164.31304931640625
      ],
      "flags": {},
      "order": 5,
      "mode": 0,
      "inputs": [
        {
          "name": "clip",
          "type": "CLIP",
          "link": 74
        }
      ],
      "outputs": [
        {
          "name": "CONDITIONING",
          "type": "CONDITIONING",
          "slot_index": 0,
          "links": [
            46
          ]
        }
      ],
      "title": "CLIP Text Encode (Positive Prompt)",
      "properties": {
        "Node name for S&R": "CLIPTextEncode"
      },
      "widgets_values": [
        "a cute anime girl with fennec ears and a fluffy tail walking in a beautiful field"
      ],
      "color": "#232",
      "bgcolor": "#353"
    }
  ],
  "links": [
    [
      35,
      3,
      0,
      8,
      0,
      "LATENT"
    ],
    [
      46,
      6,
      0,
      3,
      1,
      "CONDITIONING"
    ],
    [
      52,
      7,
      0,
      3,
      2,
      "CONDITIONING"
    ],
    [
      56,
      8,
      0,
      28,
      0,
      "IMAGE"
    ],
    [
      74,
      38,
      0,
      6,
      0,
      "CLIP"
    ],
    [
      75,
      38,
      0,
      7,
      0,
      "CLIP"
    ],
    [
      76,
      39,
      0,
      8,
      1,
      "VAE"
    ],
    [
      93,
      8,
      0,
      47,
      0,
      "IMAGE"
    ],
    [
      94,
      37,
      0,
      48,
      0,
      "MODEL"
    ],
    [
      95,
      48,
      0,
      3,
      0,
      "MODEL"
    ],
    [
      104,
      55,
      0,
      3,
      3,
      "LATENT"
    ],
    [
      105,
      39,
      0,
      55,
      0,
      "VAE"
    ],
    [
      106,
      57,
      0,
      55,
      1,
      "IMAGE"
    ]
  ],
  "groups": [],
  "config": {},
  "extra": {
    "ds": {
      "scale": 1.1167815779425287,
      "offset": [
        3.5210927484772534,
        -9.231468990407302
      ]
    },
    "frontendVersion": "1.23.4"
  },
  "version": 0.4
}
wan2.2_text_to_video.json
css 复制代码 
{
  "id": "91f6bbe2-ed41-4fd6-bac7-71d5b5864ecb",
  "revision": 0,
  "last_node_id": 57,
  "last_link_id": 106,
  "nodes": [
    {
      "id": 8,
      "type": "VAEDecode",
      "pos": [
        1210,
        190
      ],
      "size": [
        210,
        46
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wan2.2_workflow.json
swift 复制代码 
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        "[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2\n) | [教程](https://docs.comfy.org/zh-CN/tutorials/video/wan/wan2_2\n)\n\n**Diffusion Model**\n- [wan2.2_ti2v_5B_fp16.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors)\n\n**VAE**\n- [wan2.2_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan2.2_vae.safetensors)\n\n**Text Encoder**   \n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\n\n\nFile save location\n\n```\nComfyUI/\n├───📂 models/\n│   ├───📂 diffusion_models/\n│   │   └───wan2.2_ti2v_5B_fp16.safetensors\n│   ├───📂 text_encoders/\n│   │   └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \n│   └───📂 vae/\n│       └── wan2.2_vae.safetensors\n```\n"
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