Instructions to use windecay/SimpleSDXL2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use windecay/SimpleSDXL2 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf windecay/SimpleSDXL2:Q2_0
# Run inference directly in the terminal:
llama cli -hf windecay/SimpleSDXL2:Q2_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf windecay/SimpleSDXL2:Q2_0
# Run inference directly in the terminal:
llama cli -hf windecay/SimpleSDXL2:Q2_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf windecay/SimpleSDXL2:Q2_0
# Run inference directly in the terminal:
./llama-cli -hf windecay/SimpleSDXL2:Q2_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf windecay/SimpleSDXL2:Q2_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf windecay/SimpleSDXL2:Q2_0

Use Docker

docker model run hf.co/windecay/SimpleSDXL2:Q2_0

LM Studio
Jan
Ollama
How to use windecay/SimpleSDXL2 with Ollama:
```
ollama run hf.co/windecay/SimpleSDXL2:Q2_0
```
Unsloth Desktop

How to use windecay/SimpleSDXL2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf windecay/SimpleSDXL2:Q2_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "windecay/SimpleSDXL2:Q2_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use windecay/SimpleSDXL2 with Docker Model Runner:
```
docker model run hf.co/windecay/SimpleSDXL2:Q2_0
```

Lemonade

How to use windecay/SimpleSDXL2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull windecay/SimpleSDXL2:Q2_0

Run and chat with the model

lemonade run user.SimpleSDXL2-Q2_0

List all available models

lemonade list

Hermes Agent

How to use windecay/SimpleSDXL2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf windecay/SimpleSDXL2:Q2_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default windecay/SimpleSDXL2:Q2_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use windecay/SimpleSDXL2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf windecay/SimpleSDXL2:Q2_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "windecay/SimpleSDXL2:Q2_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

windecay commited on May 5, 2025

Commit

06ee8fd

verified ·

1 Parent(s): dd335a8

Update update_history.txt

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update_history.txt +4 -2

update_history.txt CHANGED Viewed

@@ -1,10 +1,11 @@
 启动器更新：
-一键部署更新日期【2025.04.23主线】，50系目前不支持一键部署需要手动下程序包。
 2025-0408 v1.35 增加了一键部署程序+基础模型的功能。
 2025-0331 v1.34 添加了程序图标
 2025-0325 v1.33 新增了日志加载刷新，防止更新日志加载失败。切换可视化分支也执行强制同步，防止一些更新失败的情况。
 2025-0319 v1.32 资源分享网盘添加了50系显卡专用临时版本。优化了打开用户和模型目录、修改模型文件夹路径的操作确认逻辑。
 0406测试主分支更新：
 - [2025.04.22]更新内容：
   (1)新增：在日志记录页面里增加视频生成的历史并可播放视频；
@@ -25,5 +26,6 @@
 0406测试可视化分支更新：
 2025-0422同步了主分支0422版本
 2025-0417添加了悬浮窗主动躲避鼠标

 启动器更新：
+一键部署更新日期【2025.05.05主线】支持RTX全系。
 2025-0408 v1.35 增加了一键部署程序+基础模型的功能。
 2025-0331 v1.34 添加了程序图标
 2025-0325 v1.33 新增了日志加载刷新，防止更新日志加载失败。切换可视化分支也执行强制同步，防止一些更新失败的情况。
 2025-0319 v1.32 资源分享网盘添加了50系显卡专用临时版本。优化了打开用户和模型目录、修改模型文件夹路径的操作确认逻辑。
+0505测试主分支更新
+- [新环境Pytorch2.7+cu128,comfyd后端升级到5月最新版，framepack升级到最新版]
 0406测试主分支更新：
 - [2025.04.22]更新内容：
   (1)新增：在日志记录页面里增加视频生成的历史并可播放视频；
 0406测试可视化分支更新：
+2025-0506同步主分支更新。
 2025-0422同步了主分支0422版本
 2025-0417添加了悬浮窗主动躲避鼠标