Instructions to use LuckyOda/comfyui-carbonara-bundle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use LuckyOda/comfyui-carbonara-bundle with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="LuckyOda/comfyui-carbonara-bundle", filename="models/text_encoders/qwen-4b-zimage-heretic-q8.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 LuckyOda/comfyui-carbonara-bundle 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 LuckyOda/comfyui-carbonara-bundle # Run inference directly in the terminal: llama cli -hf LuckyOda/comfyui-carbonara-bundle
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf LuckyOda/comfyui-carbonara-bundle # Run inference directly in the terminal: llama cli -hf LuckyOda/comfyui-carbonara-bundle
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 LuckyOda/comfyui-carbonara-bundle # Run inference directly in the terminal: ./llama-cli -hf LuckyOda/comfyui-carbonara-bundle
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 LuckyOda/comfyui-carbonara-bundle # Run inference directly in the terminal: ./build/bin/llama-cli -hf LuckyOda/comfyui-carbonara-bundle
Use Docker
docker model run hf.co/LuckyOda/comfyui-carbonara-bundle
- LM Studio
- Jan
- Ollama
How to use LuckyOda/comfyui-carbonara-bundle with Ollama:
ollama run hf.co/LuckyOda/comfyui-carbonara-bundle
- Unsloth Studio
How to use LuckyOda/comfyui-carbonara-bundle 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 LuckyOda/comfyui-carbonara-bundle 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 LuckyOda/comfyui-carbonara-bundle to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for LuckyOda/comfyui-carbonara-bundle to start chatting
- Pi
How to use LuckyOda/comfyui-carbonara-bundle with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LuckyOda/comfyui-carbonara-bundle
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": "LuckyOda/comfyui-carbonara-bundle" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use LuckyOda/comfyui-carbonara-bundle with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LuckyOda/comfyui-carbonara-bundle
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 LuckyOda/comfyui-carbonara-bundle
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use LuckyOda/comfyui-carbonara-bundle with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LuckyOda/comfyui-carbonara-bundle
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 "LuckyOda/comfyui-carbonara-bundle" \ --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 LuckyOda/comfyui-carbonara-bundle with Docker Model Runner:
docker model run hf.co/LuckyOda/comfyui-carbonara-bundle
- Lemonade
How to use LuckyOda/comfyui-carbonara-bundle with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LuckyOda/comfyui-carbonara-bundle
Run and chat with the model
lemonade run user.comfyui-carbonara-bundle-{{QUANT_TAG}}List all available models
lemonade list
| # SPDX-License-Identifier: MIT | |
| # Copyright (C) 2025 ComfyUI-Multiband Contributors | |
| """Save Multiband Image node.""" | |
| import os | |
| from ..multiband_types import MULTIBAND_IMAGE, multiband_to_numpy, get_channel_names | |
| from ..utils.io_numpy import save_numpy, save_npz | |
| from ..utils.io_tiff import save_tiff | |
| from ..utils.io_exr import save_exr, is_available as exr_available | |
| class SaveMultibandImage: | |
| """ | |
| Save a multi-band image to disk. | |
| Formats: | |
| - npy: Simple numpy array (no metadata) | |
| - npz: Numpy with channel names and metadata | |
| - tiff: Multi-page TIFF with metadata in description | |
| - exr: OpenEXR with named channels | |
| """ | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": { | |
| "multiband": (MULTIBAND_IMAGE,), | |
| "file_path": ("STRING", { | |
| "default": "output/multiband", | |
| "tooltip": "Output file path (extension determines format if not specified)" | |
| }), | |
| "format": (["npz", "npy", "tiff", "exr"], { | |
| "default": "npz", | |
| "tooltip": "Output file format" | |
| }), | |
| }, | |
| } | |
| RETURN_TYPES = ("STRING",) | |
| RETURN_NAMES = ("saved_path",) | |
| FUNCTION = "save" | |
| CATEGORY = "multiband/io" | |
| OUTPUT_NODE = True | |
| def save(self, multiband: dict, file_path: str, format: str = "npz"): | |
| # Get numpy array | |
| arr = multiband_to_numpy(multiband) | |
| channel_names = get_channel_names(multiband) | |
| metadata = multiband.get('metadata', {}) | |
| # Ensure output directory exists | |
| os.makedirs(os.path.dirname(file_path) or '.', exist_ok=True) | |
| # Save based on format | |
| if format == 'npy': | |
| saved_path = save_numpy(file_path, arr) | |
| elif format == 'npz': | |
| saved_path = save_npz(file_path, arr, channel_names, metadata) | |
| elif format == 'tiff': | |
| saved_path = save_tiff(file_path, arr, channel_names, metadata) | |
| elif format == 'exr': | |
| if not exr_available(): | |
| raise ImportError("OpenEXR not installed. Install with: pip install OpenEXR") | |
| saved_path = save_exr(file_path, arr, channel_names, metadata) | |
| else: | |
| raise ValueError(f"Unsupported format: {format}") | |
| print(f"SaveMultibandImage: Saved to {saved_path}") | |
| print(f" Shape: {arr.shape}") | |
| print(f" Format: {format}") | |
| return (saved_path,) | |