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 -1
update_history.txt
CHANGED
|
@@ -8,7 +8,7 @@
|
|
| 8 |
1,高级选项菜单继续优化调整,去掉高级tab,将关联出图的高级设置合并到“设置”栏里,进行分栏处理。
|
| 9 |
2,将原高级tab应用级的设置项合并到“我的”“应用设置”里。新增一些记忆最后设置的项。
|
| 10 |
3,恢复联系tab到顶部,添加simpai logo图片。
|
| 11 |
-
4,ipadpter目录可见
|
| 12 |
5,重绘加载图片自动反推
|
| 13 |
6,队列生图有步骤就不受总超时时长限制。
|
| 14 |
7,p2p远程生图流程已打通,将开启内部测试。
|
|
|
|
| 8 |
1,高级选项菜单继续优化调整,去掉高级tab,将关联出图的高级设置合并到“设置”栏里,进行分栏处理。
|
| 9 |
2,将原高级tab应用级的设置项合并到“我的”“应用设置”里。新增一些记忆最后设置的项。
|
| 10 |
3,恢复联系tab到顶部,添加simpai logo图片。
|
| 11 |
+
4,ipadpter目录可见controlnet目录模型
|
| 12 |
5,重绘加载图片自动反推
|
| 13 |
6,队列生图有步骤就不受总超时时长限制。
|
| 14 |
7,p2p远程生图流程已打通,将开启内部测试。
|