Python on 🌲Treetopia🌲

Z Image Turbo

Wed, 04 Feb 2026 22:13:42 +0800

Content

This article introduces the usage of Z-Image-Turbo in conjunction with ComfyUI.

Advantages of Z-Image-Turbo:

Strong Chinese prompt-following and Chinese character generation capabilities.
Requires only 8 inference steps for image generation. With a compact 6B parameter count, it can run on consumer-grade hardware (16GB VRAM) using quantization.

Due to network restrictions in certain regions that prevent the use of ComfyUI-Manager for automatic downloads, all file downloads are provided for manual installation.

ComfyUI Guide

Wed, 14 Jan 2026 11:11:51 +0800

Content

This guide details the deployment of ComfyUI on Ubuntu 22.04, including the manual installation of ComfyUI Manager for extended functionalities (custom nodes) and the manual installation of custom nodes, specifically tailored for users within China’s network environment.

Official Documentation: ComfyUI

Installation

First, ensure you have a conda environment on your server and create a new one.

# Clone the ComfyUI git repository
git clone https://github.com/Comfy-Org/ComfyUI.git

# Navigate to the ComfyUI directory, activate the conda environment, and install dependencies
cd ComfyUI
conda activate comfyui_env
pip install -r requirements.txt

# Start ComfyUI, specifying the port number and GPU device
python main.py --listen --port 10020 --cuda-device 0

Install ComfyUI Manager

```Plain
# Change to the custom_nodes subdirectory
cd custom_nodes

# If your network environment has no restrictions
git clone https://github.com/ltdrdata/ComfyUI-Manager.git

# If your network environment has restrictions, manually download the [repository](https://github.com/Comfy-Org/ComfyUI-Manager),
# unzip it, rename the folder to ComfyUI-Manager, and place it in custom_nodes.
# Then, restart ComfyUI.
python main.py --listen --port 10020 --cuda-device 0
```

Install Any Plugins

i.  If the ComfyUI Manager GUI can download nodes:

ii.  If the ComfyUI Manager GUI consistently fails to download nodes:

```Plain
# Clone the corresponding git repository, rename it, and place it in custom_nodes.
git clone https://github.com/some/custom/nodes.git

# Navigate into the node's directory and install its dependencies.
pip install -r requirements.txt

# Restart ComfyUI.

# Some commonly used custom nodes:
# --Control Net: https://github.com/Fannovel16/comfyui_controlnet_aux
# --ComfyUI-Impact-Pack: https://github.com/ltdrdata/ComfyUI-Impact-Pack
# --rgthree-comfy: https://github.com/rgthree/rgthree-comfy


```

Quick Start

The most widely used text-to-image model is Flux. Setting up a workflow using it is an excellent starting point.
Images generated by ComfyUI contain workflow information, which can be directly dragged into the GUI to re-create the workflow.
Example 1: Flux + Lora + ControlNet Workflow
Example 2: MimicMotion Action Simulation Workflow

Conda Guide

Wed, 07 Jan 2026 09:18:13 +0800

Overview

This guide covers installing Conda on Ubuntu 22.04, migrating the Conda path to a data disk, configuring mirror sources (for regions with internet restrictions), and methods for packing environments for offline deployment.

Installation Package

Use wget or click to download the Installation Package directly.

Add Conda to PATH for Persistence

# Locate the conda command
which conda

# Check the conda root installation directory
conda info --base

# Open ~/.bashrc and add the following line at the bottom
export PATH="/home/ubuntu/miniconda3/bin:$PATH"

# Apply changes
source ~/.bashrc
conda init

# Restart your terminal session

Configure Storage Paths for Environments and Packages

# Open ~/.condarc and add the following lines:

envs_dirs:
  - /your/path/to/conda/envs
pkgs_dirs:
  - /your/path/to/conda/pkgs

Configure Conda Mirror Sources (China)

# Using Aliyun mirrors as an example
conda config --add channels https://mirrors.aliyun.com/anaconda/pkgs/main/
conda config --add channels https://mirrors.aliyun.com/anaconda/pkgs/free/
conda config --add channels https://mirrors.aliyun.com/anaconda/cloud/conda-forge/
conda config --add channels https://mirrors.aliyun.com/anaconda/cloud/bioconda/

# Verify the configuration
conda config --show channels

Create Conda Environments

# Create an environment (at a specific path or using the default path)
conda create --prefix /your/path/to/conda/envs/my_env python=3.12
conda create -n my_env python=3.12

# Activate the new environment
conda activate my_env

List Existing Environments

conda info --envs

Examples: Using pip/uv within Conda

# Using pip
pip install tqdm -i https://mirrors.aliyun.com/pypi/simple/

# Using uv for faster installation
uv pip install tqdm -i https://mirrors.aliyun.com/pypi/simple/

Pack Conda Environments

# Pack an existing conda environment:
# (Note: Requires conda-pack to be installed)
conda pack -n my_env

# Transfer the archive to the target server and extract it
tar -xvzf my_env.tar.gz -C /your/conda/envs/my_env

Dockerizing Local Conda Environments

Example Dockerfile: