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
- llama.cpp
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
- Pi
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"
Update update_history.txt
Browse files- update_history.txt +1 -11
update_history.txt
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启动器更新:
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一键部署更新日期【2025.05.05主线】支持RTX全系。
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2025-0408 v1.35 增加了一键部署程序+基础模型的功能。
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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。
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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。
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0505测试主分支更新
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- [新环境Pytorch2.7+cu128,comfyd后端升级到5月最新版,framepack升级到最新版]
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0406测试主分支更新:
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- [2025.04.22]更新内容:
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(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;
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(2)修复:之前规格化生成参数时导致增强修图参数异常。
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(4)增强:系统密钥自恢复机制,避免被环境干扰;
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(5)修复:p2p远程调用并发丢包和指令乱序;
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(6)修复:reserved_vram和wavespeed参数默认值丢失。
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1,优化:base包从hf迁到国内地址,检测有更新再下载本地实施本地包升级,规避网络故障导致启动失败;
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2,优化:代码仓更新改为强制同步,直接覆盖本地文件,规避本地代码仓故障导致更新失败;
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3,新增:p2p模块新增固定端口配置项;
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4,优化:一键转绘预置包升级。
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2025-0409-瘦身了新的程序包,新程序包编号0406,由于改动大,若启动时出现Update Fail可以点击启动器选择分支按钮强制同步,仍然建议使用最新的0406版程序。
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0505测试可视化分支更新:
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2025-0509新增隐藏轮播图勾选项
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启动器更新:
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一键部署更新日期【2025.05.05主线】支持RTX全系。
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2025-0408 v1.35 增加了一键部署程序+基础模型的功能。
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0505测试主分支更新
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- [新环境Pytorch2.7+cu128,comfyd后端升级到5月最新版,framepack升级到最新版]
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- [2025.04.22]更新内容:
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(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;
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(2)修复:之前规格化生成参数时导致增强修图参数异常。
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(4)增强:系统密钥自恢复机制,避免被环境干扰;
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(5)修复:p2p远程调用并发丢包和指令乱序;
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(6)修复:reserved_vram和wavespeed参数默认值丢失。
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0505测试可视化分支更新:
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2025-0509新增隐藏轮播图勾选项
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