Instructions to use saik0s/comfy_backup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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="models/text_encoders/Qwen3VL-8B-Uncensored-HauhauCS-Aggressive-Q8_0.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:Q8_0
# Run inference directly in the terminal:
llama cli -hf saik0s/comfy_backup:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf saik0s/comfy_backup:Q8_0
# Run inference directly in the terminal:
llama cli -hf saik0s/comfy_backup:Q8_0

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:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf saik0s/comfy_backup:Q8_0

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:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saik0s/comfy_backup:Q8_0

Use Docker

docker model run hf.co/saik0s/comfy_backup:Q8_0

LM Studio
Jan
Ollama
How to use saik0s/comfy_backup with Ollama:
```
ollama run hf.co/saik0s/comfy_backup:Q8_0
```

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

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:Q8_0

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:Q8_0"
        }
      ]
    }
  }
}

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:Q8_0

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:Q8_0

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:Q8_0

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:Q8_0" \
  --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:Q8_0
```

Lemonade

How to use saik0s/comfy_backup with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saik0s/comfy_backup:Q8_0

Run and chat with the model

lemonade run user.comfy_backup-Q8_0

List all available models

lemonade list

comfy_backup / custom_nodes /rgthree-comfy /py /pyproject.py

saik0s

feat: back up current instance custom_nodes

8e8f673 verified 1 day ago

Raw

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2.28 kB

	import os
	import re
	import json

	from .utils import set_dict_value

	_THIS_DIR = os.path.dirname(os.path.abspath(__file__))
	_FILE_PY_PROJECT = os.path.join(_THIS_DIR, '..', 'pyproject.toml')


	def read_pyproject():
	"""Reads the pyproject.toml file"""
	data = {}
	last_key = ''
	lines = []
	# I'd like to use tomllib/tomli, but I'd much rather not introduce dependencies since I've yet to
	# need to and not everyone may have 3.11. We've got a controlled config file anyway.
	with open(_FILE_PY_PROJECT, "r", encoding='utf-8') as f:
	lines = f.readlines()
	for line in lines:
	line = line.strip()
	if re.match(r'\[([^\]]+)\]$', line):
	last_key = line[1:-1]
	set_dict_value(data, last_key, data[last_key] if last_key in data else {})
	continue
	value_matches = re.match(r'^([^\s\=]+)\s=\s(.*)$', line)
	if value_matches:
	try:
	set_dict_value(data, f'{last_key}.{value_matches[1]}', json.loads(value_matches[2]))
	except json.decoder.JSONDecodeError:
	# We don't handle multiline arrays or curly brackets; that's ok, we know the file.
	pass

	return data


	_DATA = read_pyproject()

	# We would want these to fail if they don't exist, so assume they do.
	VERSION: str = _DATA['project']['version']
	NAME: str = _DATA['project']['name']
	LOGO_URL: str = _DATA['tool']['comfy']['Icon']

	if not LOGO_URL.endswith('.svg'):
	raise ValueError('Bad logo url.')

	LOGO_SVG = None
	async def get_logo_svg():
	import aiohttp
	global LOGO_SVG
	if LOGO_SVG is not None:
	return LOGO_SVG
	# Fetch the logo so we have any updated markup.
	try:
	async with aiohttp.ClientSession(
	trust_env=True, connector=aiohttp.TCPConnector(verify_ssl=True)
	) as session:
	headers = {
	"user-agent": f"rgthree-comfy/{VERSION}",
	'Cache-Control': 'no-cache',
	'Pragma': 'no-cache',
	'Expires': '0'
	}
	async with session.get(LOGO_URL, headers=headers) as resp:
	LOGO_SVG = await resp.text()
	LOGO_SVG = re.sub(r'(id="bg".*fill=)"[^\"]+"', r'\1"{bg}"', LOGO_SVG)
	LOGO_SVG = re.sub(r'(id="fg".*fill=)"[^\"]+"', r'\1"{fg}"', LOGO_SVG)
	except Exception:
	LOGO_SVG = '<svg></svg>'
	return LOGO_SVG