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 / script_examples /basic_api_example.py

LuckyOda

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7d17286 verified 3 months ago

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	import json
	from urllib import request

	#This is the ComfyUI api prompt format.

	#If you want it for a specific workflow you can "File -> Export (API)" in the interface.

	#this is the one for the default workflow
	prompt_text = """
	{
	"3": {
	"class_type": "KSampler",
	"inputs": {
	"cfg": 8,
	"denoise": 1,
	"latent_image": [
	"5",
	0
	],
	"model": [
	"4",
	0
	],
	"negative": [
	"7",
	0
	],
	"positive": [
	"6",
	0
	],
	"sampler_name": "euler",
	"scheduler": "normal",
	"seed": 8566257,
	"steps": 20
	}
	},
	"4": {
	"class_type": "CheckpointLoaderSimple",
	"inputs": {
	"ckpt_name": "v1-5-pruned-emaonly.safetensors"
	}
	},
	"5": {
	"class_type": "EmptyLatentImage",
	"inputs": {
	"batch_size": 1,
	"height": 512,
	"width": 512
	}
	},
	"6": {
	"class_type": "CLIPTextEncode",
	"inputs": {
	"clip": [
	"4",
	1
	],
	"text": "masterpiece best quality girl"
	}
	},
	"7": {
	"class_type": "CLIPTextEncode",
	"inputs": {
	"clip": [
	"4",
	1
	],
	"text": "bad hands"
	}
	},
	"8": {
	"class_type": "VAEDecode",
	"inputs": {
	"samples": [
	"3",
	0
	],
	"vae": [
	"4",
	2
	]
	}
	},
	"9": {
	"class_type": "SaveImage",
	"inputs": {
	"filename_prefix": "ComfyUI",
	"images": [
	"8",
	0
	]
	}
	}
	}
	"""

	def queue_prompt(prompt):
	p = {"prompt": prompt}

	# If the workflow contains API nodes, you can add a Comfy API key to the `extra_data`` field of the payload.
	# p["extra_data"] = {
	# "api_key_comfy_org": "comfyui-87d01e28d*******************************************************" # replace with real key
	# }
	# See: https://docs.comfy.org/tutorials/api-nodes/overview
	# Generate a key here: https://platform.comfy.org/login

	data = json.dumps(p).encode('utf-8')
	req = request.Request("http://127.0.0.1:8188/prompt", data=data)
	request.urlopen(req)


	prompt = json.loads(prompt_text)
	#set the text prompt for our positive CLIPTextEncode
	prompt["6"]["inputs"]["text"] = "masterpiece best quality man"

	#set the seed for our KSampler node
	prompt["3"]["inputs"]["seed"] = 5


	queue_prompt(prompt)