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
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
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
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
chmod +x Miniconda3-latest-Linux-x86_64.sh运行安装脚本：
./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
source ~/.bashrc步骤 7：验证安装是否成功
检查 conda 版本：
css
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
conda update conda2.部署 ComfyUI
2.1 克隆代码仓库
bash
git clone https://github.com/comfyanonymous/ComfyUI.git2.2 安装依赖
- 创建 conda 虚拟环境
ini
conda create -n comfyenv python==3.12
conda activate comfyenv- 安装 PyTorch
ini
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia- 安装依赖
bash
cd ComfyUI
pip install -r requirements.txt- 安装 ComfyUI Manager（可选）
bash
#进入插件的文件
cd /ComfyUI/custom_nodes/
#下载ComfyUI Manager
git clone https://github.com/Comfy-Org/ComfyUI-Manager.git3.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
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
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
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
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
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
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
{
"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,
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],
"size": [
271.9126892089844,
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],
"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"
],
[
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0,
3,
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"CONDITIONING"
],
[
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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"
],
[
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0,
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0,
"MODEL"
],
[
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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
{
"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": 9,
"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": 8,
"mode": 0,
"inputs": [
{
"name": "model",
"type": "MODEL",
"link": 95
},
{
"name": "positive",
"type": "CONDITIONING",
"link": 46
},
{
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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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