【本地AI部署】comfyUI + zImageTurbo

news/2025/12/4 0:12:47/文章来源:https://www.cnblogs.com/zwj/p/19304354/ai_local_zimg

首先声明，这是我第一次折腾这些，不是很懂，一些说法表达可能不准确，因此仅供参考。

硬件准备

电脑，我的是windows11， RTX3060 6G，配置不高，因此一些大模型就没法考虑了
关键是我电脑存储空间也不太够……

环境准备

CUDA：https://developer.nvidia.com/cuda-toolkit

下载安装。https://www.cnblogs.com/zwj/p/20220214cnda.html
（安装了cuda就行）

Anconda。不用anconda本地就装个python也行（3.10），如果想用anconda但是没安装的话，可以看：https://www.cnblogs.com/zwj/p/Anaconda-install.html
可以百度下怎么安装。

nvidia-smi

ComfyUI

参考资料：https://comfyui-wiki.com/zh/install/install-comfyui/install-comfyui-on-windows

（官方能直接下载windows exe：https://www.comfy.org/zh-cn/download）

我是看的这个博客：https://comfyui.org/zh/comfyui-windows-conda-venv

conda创建环境，下载comfyUI，安装依赖

conda create -n comfyui_env python=3.10
conda activate comfyui_env
git clone https://github.com/comfyanonymous/ComfyUI.git C:\ComfyUI
cd C:\ComfyUI
pip install -r requirements.txt

注意，不想安装C记得改，安装C请确保至少有20GB空间

如果遇到PyTorch不兼容问题，删除了重新安装

pip uninstall torch torchvision torchaudio

https://pytorch.org/get-started/locally/
这个浏览器里面，选择适合你的情况，然后复杂最后的命令

安装完可以验证一下：

python -c "import torch; print(torch.cuda.is_available())"

模型

models\text_encoders：qwen_3_4b.safetensors
下载地址：https://huggingface.co/Comfy-Org/z_image_turbo/blob/main/split_files/text_encoders/qwen_3_4b.safetensors

models\vae：ae.safetensors
下载地址：https://huggingface.co/Comfy-Org/z_image_turbo/blob/main/split_files/vae/ae.safetensors

models\checkpoints：zImageTurboQuantized_fp8ScaledE4m3fnKJ.safetensors
下载地址：
https://civitai.com/models/2169712/z-image-turbo-quantized-for-low-vram?modelVersionId=2443345

放到对应文件夹，然后运行

python main.py

打开 http://127.0.0.1:8188

将工作流拖进去

{"2": {"inputs": {"text": "a beautiful landscape, high quality, 8k","speak_and_recognation": {"__value__": [false,true]},"clip": ["16",0]},"class_type": "CLIPTextEncode","_meta": {"title": "正向"}},"4": {"inputs": {"seed": 1065951732236213,"steps": 8,"cfg": 1,"sampler_name": "euler","scheduler": "simple","denoise": 1,"model": ["15",0],"positive": ["2",0],"negative": ["9",0],"latent_image": ["5",0]},"class_type": "KSampler","_meta": {"title": "K采样器"}},"5": {"inputs": {"width": 768,"height": 768,"batch_size": 1},"class_type": "EmptyLatentImage","_meta": {"title": "空Latent图像"}},"6": {"inputs": {"vae_name": "ae.safetensors"},"class_type": "VAELoader","_meta": {"title": "加载VAE"}},"7": {"inputs": {"samples": ["4",0],"vae": ["6",0]},"class_type": "VAEDecode","_meta": {"title": "VAE解码"}},"8": {"inputs": {"filename_prefix": "ComfyUI","images": ["7",0]},"class_type": "SaveImage","_meta": {"title": "保存图像"}},"9": {"inputs": {"text": "blurry, ugly, bad, lowres, jpeg artifacts, watermark, distorted, noisy, artifact, glitch, oversaturation, neon tones, harsh contrast or glow, color cast, pixelated, blocky","speak_and_recognation": {"__value__": [false,true]},"clip": ["16",0]},"class_type": "CLIPTextEncode","_meta": {"title": "反向"}},"15": {"inputs": {"ckpt_name": "zImageTurboQuantized_fp8ScaledE4m3fnKJ.safetensors"},"class_type": "CheckpointLoaderSimple","_meta": {"title": "Checkpoint加载器（简易）"}},"16": {"inputs": {"clip_name": "qwen_3_4b.safetensors","type": "stable_diffusion","device": "default"},"class_type": "CLIPLoader","_meta": {"title": "加载CLIP"}}
}