Instructions to use saik0s/comfy_backup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use saik0s/comfy_backup with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="saik0s/comfy_backup", filename="ComfyUI/models/text_encoders/gemma-3-12b-it-q2_k.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use saik0s/comfy_backup 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 saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: llama cli -hf saik0s/comfy_backup:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: llama cli -hf saik0s/comfy_backup:Q4_K_S
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 saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf saik0s/comfy_backup:Q4_K_S
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 saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf saik0s/comfy_backup:Q4_K_S
Use Docker
docker model run hf.co/saik0s/comfy_backup:Q4_K_S
- LM Studio
- Jan
- Ollama
How to use saik0s/comfy_backup with Ollama:
ollama run hf.co/saik0s/comfy_backup:Q4_K_S
- Unsloth Studio
How to use saik0s/comfy_backup with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for saik0s/comfy_backup to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for saik0s/comfy_backup to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for saik0s/comfy_backup to start chatting
- Pi
How to use saik0s/comfy_backup with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf saik0s/comfy_backup:Q4_K_S
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "saik0s/comfy_backup:Q4_K_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use saik0s/comfy_backup with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf saik0s/comfy_backup:Q4_K_S
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 saik0s/comfy_backup:Q4_K_S
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use saik0s/comfy_backup with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf saik0s/comfy_backup:Q4_K_S
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 "saik0s/comfy_backup:Q4_K_S" \ --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"
- Docker Model Runner
How to use saik0s/comfy_backup with Docker Model Runner:
docker model run hf.co/saik0s/comfy_backup:Q4_K_S
- Lemonade
How to use saik0s/comfy_backup with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull saik0s/comfy_backup:Q4_K_S
Run and chat with the model
lemonade run user.comfy_backup-Q4_K_S
List all available models
lemonade list
| from comfy.cldm.control_types import UNION_CONTROLNET_TYPES | |
| import nodes | |
| import comfy.utils | |
| from typing_extensions import override | |
| from comfy_api.latest import ComfyExtension, io | |
| class SetUnionControlNetType(io.ComfyNode): | |
| def define_schema(cls): | |
| return io.Schema( | |
| node_id="SetUnionControlNetType", | |
| category="model/conditioning/controlnet", | |
| inputs=[ | |
| io.ControlNet.Input("control_net"), | |
| io.Combo.Input("type", options=["auto"] + list(UNION_CONTROLNET_TYPES.keys())), | |
| ], | |
| outputs=[ | |
| io.ControlNet.Output(), | |
| ], | |
| ) | |
| def execute(cls, control_net, type) -> io.NodeOutput: | |
| control_net = control_net.copy() | |
| type_number = UNION_CONTROLNET_TYPES.get(type, -1) | |
| if type_number >= 0: | |
| control_net.set_extra_arg("control_type", [type_number]) | |
| else: | |
| control_net.set_extra_arg("control_type", []) | |
| return io.NodeOutput(control_net) | |
| set_controlnet_type = execute # TODO: remove | |
| class ControlNetInpaintingAliMamaApply(io.ComfyNode): | |
| def define_schema(cls): | |
| return io.Schema( | |
| node_id="ControlNetInpaintingAliMamaApply", | |
| search_aliases=["masked controlnet"], | |
| category="model/conditioning/controlnet", | |
| inputs=[ | |
| io.Conditioning.Input("positive"), | |
| io.Conditioning.Input("negative"), | |
| io.ControlNet.Input("control_net"), | |
| io.Vae.Input("vae"), | |
| io.Image.Input("image"), | |
| io.Mask.Input("mask"), | |
| io.Float.Input("strength", default=1.0, min=0.0, max=10.0, step=0.01), | |
| io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True), | |
| io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001, advanced=True), | |
| ], | |
| outputs=[ | |
| io.Conditioning.Output(display_name="positive"), | |
| io.Conditioning.Output(display_name="negative"), | |
| ], | |
| ) | |
| def execute(cls, positive, negative, control_net, vae, image, mask, strength, start_percent, end_percent) -> io.NodeOutput: | |
| extra_concat = [] | |
| if control_net.concat_mask: | |
| mask = 1.0 - mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) | |
| mask_apply = comfy.utils.common_upscale(mask, image.shape[2], image.shape[1], "bilinear", "center").round() | |
| image = image * mask_apply.movedim(1, -1).repeat(1, 1, 1, image.shape[3]) | |
| extra_concat = [mask] | |
| result = nodes.ControlNetApplyAdvanced().apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent, vae=vae, extra_concat=extra_concat) | |
| return io.NodeOutput(result[0], result[1]) | |
| apply_inpaint_controlnet = execute # TODO: remove | |
| class ControlNetExtension(ComfyExtension): | |
| async def get_node_list(self) -> list[type[io.ComfyNode]]: | |
| return [ | |
| SetUnionControlNetType, | |
| ControlNetInpaintingAliMamaApply, | |
| ] | |
| async def comfy_entrypoint() -> ControlNetExtension: | |
| return ControlNetExtension() | |