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
| import io | |
| from comfy_api.input_impl.video_types import ( | |
| container_to_output_format, | |
| get_open_write_kwargs, | |
| ) | |
| from comfy_api.util import VideoContainer | |
| def test_container_to_output_format_empty_string(): | |
| """Test that an empty string input returns None. `None` arg allows default auto-detection.""" | |
| assert container_to_output_format("") is None | |
| def test_container_to_output_format_none(): | |
| """Test that None input returns None.""" | |
| assert container_to_output_format(None) is None | |
| def test_container_to_output_format_comma_separated(): | |
| """Test that a comma-separated list returns a valid singular format from the list.""" | |
| comma_separated_format = "mp4,mov,m4a" | |
| output_format = container_to_output_format(comma_separated_format) | |
| assert output_format in comma_separated_format | |
| def test_container_to_output_format_single(): | |
| """Test that a single format string (not comma-separated list) is returned as is.""" | |
| assert container_to_output_format("mp4") == "mp4" | |
| def test_get_open_write_kwargs_filepath_no_format(): | |
| """Test that 'format' kwarg is NOT set when dest is a file path.""" | |
| kwargs_auto = get_open_write_kwargs("output.mp4", "mp4", VideoContainer.AUTO) | |
| assert "format" not in kwargs_auto, "Format should not be set for file paths (AUTO)" | |
| kwargs_specific = get_open_write_kwargs("output.avi", "mp4", "avi") | |
| fail_msg = "Format should not be set for file paths (Specific)" | |
| assert "format" not in kwargs_specific, fail_msg | |
| def test_get_open_write_kwargs_base_options_mode(): | |
| """Test basic kwargs for file path: mode and movflags.""" | |
| kwargs = get_open_write_kwargs("output.mp4", "mp4", VideoContainer.AUTO) | |
| assert kwargs["mode"] == "w", "mode should be set to write" | |
| fail_msg = "movflags should be set to preserve custom metadata tags" | |
| assert "movflags" in kwargs["options"], fail_msg | |
| assert kwargs["options"]["movflags"] == "use_metadata_tags", fail_msg | |
| def test_get_open_write_kwargs_bytesio_auto_format(): | |
| """Test kwargs for BytesIO dest with AUTO format.""" | |
| dest = io.BytesIO() | |
| container_fmt = "mov,mp4,m4a" | |
| kwargs = get_open_write_kwargs(dest, container_fmt, VideoContainer.AUTO) | |
| assert kwargs["mode"] == "w" | |
| assert kwargs["options"]["movflags"] == "use_metadata_tags" | |
| fail_msg = ( | |
| "Format should be a valid format from the container's format list when AUTO" | |
| ) | |
| assert kwargs["format"] in container_fmt, fail_msg | |
| def test_get_open_write_kwargs_bytesio_specific_format(): | |
| """Test kwargs for BytesIO dest with a specific single format.""" | |
| dest = io.BytesIO() | |
| container_fmt = "avi" | |
| to_fmt = VideoContainer.MP4 | |
| kwargs = get_open_write_kwargs(dest, container_fmt, to_fmt) | |
| assert kwargs["mode"] == "w" | |
| assert kwargs["options"]["movflags"] == "use_metadata_tags" | |
| fail_msg = "Format should be the specified format (lowercased) when output format is not AUTO" | |
| assert kwargs["format"] == "mp4", fail_msg | |
| def test_get_open_write_kwargs_bytesio_specific_format_list(): | |
| """Test kwargs for BytesIO dest with a specific comma-separated format.""" | |
| dest = io.BytesIO() | |
| container_fmt = "avi" | |
| to_fmt = "mov,mp4,m4a" # A format string that is a list | |
| kwargs = get_open_write_kwargs(dest, container_fmt, to_fmt) | |
| assert kwargs["mode"] == "w" | |
| assert kwargs["options"]["movflags"] == "use_metadata_tags" | |
| fail_msg = "Format should be a valid format from the specified format list when output format is not AUTO" | |
| assert kwargs["format"] in to_fmt, fail_msg | |