HZLの閉鎖空間

[Basic Information]
– Theme: {Creator Economy Monetization Blueprint}
– Content Outline: {Help Creators Make Decisions}
– Target Audience: {Creators Who Want to Monetize}

[Design Style]
– Reserve about 25% white space to avoid visual clutter
– Clear information hierarchy, with key data prominently displayed
– All text clear and readable, with coordinated proportions between icons and text
– Use visual guide lines or color blocks to distinguish different information modules

[Output Requirements]
– One core concept per page, avoid text piling
– Present data as visually as possible
– Ample white space, clear hierarchy

[Basic Information]
– Font Style: {Modern Minimalist/Business Formal/Creative Playful}
– Chart Type: Automatically determine based on data characteristics (flowchart/comparison chart/timeline/data dashboard/hierarchy chart, etc.)
– Color Scheme: Automatically select the optimal color palette based on the content theme, ensuring color contrast suitable for reading
– Visual Style: Design based on the audience and content nature

[Layout Specifications]
– Reserve about 25% white space to avoid visual clutter
– Clear information hierarchy, with key data prominently displayed
– All text clear and readable, with coordinated proportions between icons and text
– Use visual guide lines or color blocks to distinguish different information modules

# Role: Rigorous Digital Business Analyst and Fact-Checker

## Background
I am seeking real and reliable online monetization opportunities.
I need a market analysis report based on **real data** and **verifiable sources**.
Please discard vague theories and focus on “proven successful paths.”

## Core Task
Analyze 10 mainstream online monetization models and provide 3-5 **real cases** of ordinary people using AI tools to monetize.

## Critical Constraints (Data Accuracy Ironclad Rules)
1. **No Fabrication**: All cases must be real; strictly prohibit fictional characters or data.
2. **Mandatory Sourcing**: Every mentioned success case **must** include the original URL of the source material.
3. **URL Compilation**: At the end of the article, must create a “Source Bibliography” for easy one-click copying of all reference URLs.
4. **Format Requirements**: Use Markdown syntax, output in English.

## Workflows

### First Part: 10 Major Mainstream Monetization Model Assessment Matrix
Present in table form with the following columns:
* **Monetization Model**
* **Core Skill Threshold**
* **Startup Cost** (specific to USD or TWD range)
* **Difficulty Level** (1-5 stars)
* **Income Potential** (1-5 stars)
* **Empowering AI Tools** (list the 1-2 strongest tools in the field)
* **Risk and Opportunity Cost Brief Review**

### Second Part: Real Cases of Ordinary People Using AI for Monetization (with Evidence)
Search and compile 3-5 specific cases, focusing on “ordinary people” starting stories. Each case must strictly follow this format:
* **[Case Protagonist]**: (Name/Channel Name/Account Name)
* **[Monetization Model]**:
* **[AI Tools Used]**:
* **[Operation Method Summary]**: (What did they specifically do? How did they solve the problem?)
* **[Verification Data]**: (Income amount, traffic data, follower growth numbers; specific figures required)
* **[Source of Information]**:
* *Publisher*: (e.g., Business Insider, The Verge, the creator’s own YouTube video title)
* *Content Summary*: (One sentence summarizing the main point of the source article)

### Third Part: Source URL Compilation Library (Reference List)
**(Important: Organize all “source URLs” cited in the text here; do not include tool official websites, only content links that prove data authenticity)**
Use code block format for easy one-click copying:
“`
https://…
https://…
https://…
“`

0、安裝cuda_11.8，安裝完成後，下載cudnn archive並解壓縮到cuda的安裝目錄。使用visual studio installer安裝C++ build tool和windows SDK（按照安裝的默認選項即可）。

1、在conda新建一個虛擬環境並進入
conda create -n facefusion python=3.10
conda activate facefusion

2、安裝pytorch，讓pytorch使用gpu運作
conda install conda-forge::cuda-runtime=12.4.1 conda-forge::cudnn=9.2.1.18 conda-forge::gputil=1.4.0

3、新建一個文件夾，在文件夾內獲取facefusion代碼
git clone https://github.com/facefusion/facefusion.git

4、安裝環境依賴
pip install -r requirements.txt

5、安裝ffmpeg
conda install -c conda-forge ffmpeg

6、應該可以正常使用gpu運行了
python run.py –execution-providers cuda

yolo是一個object detect model，顧名思義就是拿來檢測物件的。

使用yolo官網的安裝步驟

git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install

在全新的環境中去執行的話，可能最終安裝完成之後會變成只能夠使用CPU來運算的方式。

我解決的方式是，先將本地安裝好的pytorch刪除，然後去pytorch官網https://pytorch.org/get-started/locally/選擇適用於自己的選項來安裝。

然後再根據yolo官網的安裝步驟安裝環境，完成後，即會使用GPU來運算。速度會比CPU快得多。