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ComfyUI本地部署无限制Z-Image和wan2.2图片视频生成工作流教程，实现文生图，图生图，文生视频，图生视频
一、前言
1. 什么是 ComfyUI 本地部署
ComfyUI 是一款基于节点工作流的 AI 绘图、视频生成工具。和传统 WebUI 不同，ComfyUI 可以把模型加载、提示词、采样器、VAE 解码、图片输入、视频输出等步骤拆成一个个节点，方便自由组合。
本文主要部署两个方向：
Wan2.2 5B：适合图像转视频、文生视频、短视频生成
Z-Image Turbo：适合本地文生图、提示词绘图、ControlNet 控图
这里说的“无限制”，主要指本地运行不受在线平台次数、队列和接口额度限制，但仍建议合理使用，避免违法、侵权或恶意用途。
2. 关于 Z-Image 和 Wan2.2
Z-Image Turbo 是一个高效图像生成模型，官方介绍为 6B 参数的蒸馏版本，主打速度快、效果好、支持中英文提示词。它适合做头像、插画、写实图、商品图、海报图，也可以通过 ControlNet 保持构图和姿势。
Wan2.2 是视频生成模型。本文推荐使用 Wan2.2 TI2V 5B，因为它比 14B 版本更适合普通玩家，8GB 显存也可以通过 ComfyUI 的卸载机制尝试运行。
文生图：
图生图：
文生视频：
图生视频：
二、部署过程
Z-Image 模型整合包下载： 【点击前往】
1. 电脑配置建议
最低建议：
- 系统：Windows 10 / Windows 11
- 显卡：NVIDIA 8GB 显存起步
- 内存：32GB 推荐
- 硬盘：至少预留 80GB
- 驱动：建议更新 NVIDIA 显卡驱动
如果只有 8GB 显存，建议不要同时运行大语言模型、ComfyUI、游戏和剪辑软件。
2. 安装 ComfyUI
推荐新手使用 ComfyUI Desktop。
官方下载页：
https://www.comfy.org/download
安装完成后，默认访问地址一般是：
http://127.0.0.1:8188
如果使用 ComfyUI Desktop，也可能是：
http://127.0.0.1:8190
3. 准备模型目录
建议单独建两个目录：
D:\AppData\AI\Z-Image D:\AppData\AI\Wan2.2
也可以直接放进 ComfyUI 自带的 models 目录。单独放的好处是后期更容易管理和备份。
4. 下载 Z-Image Turbo 模型（有整合包这步可省略）
Z-Image Turbo 官方 ComfyUI 模型页：
https://huggingface.co/Comfy-Org/z_image_turbo
需要下载三个文件：
放置路径：
D:\AppData\AI\Z-Image\diffusion_models\z_image_turbo_bf16.safetensors
D:\AppData\AI\Z-Image\text_encoders\qwen_3_4b.safetensors
D:\AppData\AI\Z-Image\vae\ae.safetensors
5. 下载 Z-Image ControlNet 模型
如果要做图生图、控图、边缘线稿控制，可以下载 ControlNet Union。
模型页：
https://huggingface.co/alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union
放置路径：
D:\AppData\AI\Z-Image\model_patches\Z-Image-Turbo-Fun-Controlnet-Union.safetensors
6. 下载 Wan2.2 5B 模型
5B模型效果不好，有能力推荐用14B，个人实测14B模型加上优化补丁我的电脑配置是可以跑的，可以自己找其他教程安装。
ComfyUI 官方 Wan2.2 教程：
https://docs.comfy.org/tutorials/video/wan/wan2_2
模型仓库：
https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged
需要下载三个文件：
- 视频主模型：
wan2.2_ti2v_5B_fp16.safetensors - 文本编码器：
umt5_xxl_fp8_e4m3fn_scaled.safetensors - VAE：
wan2.2_vae.safetensors
放置路径：
D:\AppData\AI\Wan2.2\wan2.2_ti2v_5B_fp16.safetensors D:\AppData\AI\Wan2.2\umt5_xxl_fp8_e4m3fn_scaled.safetensors D:\AppData\AI\Wan2.2\wan2.2_vae.safetensors
7. 挂载外部模型目录
如果模型没有放在 ComfyUI 默认目录，需要编辑：
ComfyUI\extra_model_paths.yaml
示例配置：
z_image:
base_path: D:/AppData/AI/Z-Image
diffusion_models: diffusion_models
text_encoders: text_encoders
vae: vae
model_patches: model_patches
loras: loras
wan2_2:
base_path: D:/AppData/AI/Wan2.2
diffusion_models: .
text_encoders: .
vae: .保存后重启 ComfyUI。
8. 下载工作流
Z-Image Turbo 文生图工作流：
image_z_image_turbo.json
Z-Image Turbo ControlNet 工作流：
image_z_image_turbo_fun_union_controlnet.json
Wan2.2 5B 视频工作流：
video_wan2_2_5B_ti2v.json
下载后可以直接拖进 ComfyUI 页面加载。
自己制作的Z-Image和Wan2.2 14B 整合视频工作流（点我）
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9. Z-Image Turbo 推荐参数
低配建议先用：
分辨率：1024x1024 或 832x1216 Steps：8 CFG：0 Sampler：默认即可 Negative Prompt：一般不需要
如果爆显存，先降到：
768x768
Z-Image Turbo 的重点是提示词要写清楚，比如人物、场景、光线、镜头、材质、风格，不要只写几个关键词。
10. Wan2.2 推荐参数
8GB 显存不要直接跑官方默认高参数。建议先用：
宽度：640 高度：352 长度：33 Steps：16 FPS：16 Sampler：uni_pc Scheduler：simple
这里的“长度”是视频帧数，不是秒数。
计算方式：
视频时长 = 长度 ÷ FPS
例如：
33帧 ÷ 16FPS ≈ 2秒 61帧 ÷ 16FPS ≈ 3.8秒
跑通以后再逐步提高到：
长度：49 或 61 分辨率：640x360 或 832x480 Steps：20
11. 图像转视频怎么用
Wan2.2 图生视频需要注意两点：
- 启用 Load Image 节点。
- 确保图片连接到 Wan22ImageToVideoLatent 的 start_image。
如果 Load Image 是灰色禁用状态，可以选中节点后按：
Ctrl + B
启用之后再上传图片。
12. 常见问题
为什么一直卡在 VAE 解码？
通常不是卡死，而是分辨率和帧数太高。比如 1280×704 + 121帧 对 8GB 显存压力很大，VAE 解码会非常慢。
解决办法：
- 降到 640×352
- 帧数降到 33
- 步数降到 16
- 关闭其他占显存软件
- 必要时使用 VAEDecodeTiled
为什么工作流打开后缺节点？
一般是 ComfyUI 版本太旧。先更新 ComfyUI，再更新依赖和前端。
Z-Image 需要负面提示词吗？
Turbo 版本一般不需要。提示词写清楚比堆负面词更重要。
8GB 显存能不能跑？
Z-Image Turbo 可以尝试低分辨率或量化版本。Wan2.2 建议只跑 5B 版本，也可以尝试14B版本并使用低分辨率、短帧数。
