Instructions to use LuckyOda/comfyui-carbonara-bundle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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

comfyui-carbonara-bundle / custom_nodes /ComfyUI-Multiband /__init__.py

LuckyOda

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	# SPDX-License-Identifier: MIT
	# Copyright (C) 2025 ComfyUI-Multiband Contributors

	"""
	ComfyUI-Multiband - Multi-channel image handling for ComfyUI.

	This package provides a MULTIBAND_IMAGE type for handling images with arbitrary
	number of channels (segmentation masks, feature maps, spectral bands, etc.)
	along with nodes for loading, saving, previewing, and manipulating them.
	"""

	import os

	from .multiband_types import MULTIBAND_IMAGE
	from .nodes import (
	LoadMultibandImage,
	LoadMultibandFromPath,
	SaveMultibandImage,
	PreviewMultibandImage,
	ImageToMultiband,
	MultibandToImage,
	MaskToMultiband,
	MultibandToMasks,
	BatchToMultiband,
	ComposeMultiband,
	DecomposeMultiband,
	SelectMultibandChannels,
	ConcatMultiband,
	RenameMultibandChannels,
	ResizeMultiband,
	)


	NODE_CLASS_MAPPINGS = {
	# I/O
	"MultibandLoad": LoadMultibandImage,
	"MultibandLoadFromPath": LoadMultibandFromPath,
	"MultibandSave": SaveMultibandImage,
	"MultibandPreview": PreviewMultibandImage,

	# Conversion
	"MultibandFromImage": ImageToMultiband,
	"MultibandToImage": MultibandToImage,
	"MultibandFromMask": MaskToMultiband,
	"MultibandToMasks": MultibandToMasks,
	"MultibandFromBatch": BatchToMultiband,

	# Compose/Decompose
	"MultibandCompose": ComposeMultiband,
	"MultibandDecompose": DecomposeMultiband,

	# Operations
	"MultibandSelectChannels": SelectMultibandChannels,
	"MultibandConcat": ConcatMultiband,
	"MultibandRenameChannels": RenameMultibandChannels,
	"MultibandResize": ResizeMultiband,
	}

	NODE_DISPLAY_NAME_MAPPINGS = {
	# I/O
	"MultibandLoad": "Load Multiband Image",
	"MultibandLoadFromPath": "Load Multiband Image from Path",
	"MultibandSave": "Save Multiband Image",
	"MultibandPreview": "Preview Multiband Image",

	# Conversion
	"MultibandFromImage": "Image to Multiband",
	"MultibandToImage": "Multiband to Image",
	"MultibandFromMask": "Mask to Multiband",
	"MultibandToMasks": "Multiband to Masks",
	"MultibandFromBatch": "Batch to Multiband",

	# Compose/Decompose
	"MultibandCompose": "Compose Multiband",
	"MultibandDecompose": "Decompose Multiband",

	# Operations
	"MultibandSelectChannels": "Select Multiband Channels",
	"MultibandConcat": "Concat Multiband",
	"MultibandRenameChannels": "Rename Multiband Channels",
	"MultibandResize": "Resize Multiband",
	}

	__all__ = [
	'NODE_CLASS_MAPPINGS',
	'NODE_DISPLAY_NAME_MAPPINGS',
	'MULTIBAND_IMAGE',
	]

	WEB_DIRECTORY = os.path.join(os.path.dirname(__file__), "web")

	print("ComfyUI-Multiband loaded: 15 nodes registered")