Instructions to use saik0s/comfy_backup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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="ComfyUI/models/text_encoders/gemma-3-12b-it-q2_k.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:Q4_K_S # Run inference directly in the terminal: llama cli -hf saik0s/comfy_backup:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: llama cli -hf saik0s/comfy_backup:Q4_K_S
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:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf saik0s/comfy_backup:Q4_K_S
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:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf saik0s/comfy_backup:Q4_K_S
Use Docker
docker model run hf.co/saik0s/comfy_backup:Q4_K_S
- LM Studio
- Jan
- Ollama
How to use saik0s/comfy_backup with Ollama:
ollama run hf.co/saik0s/comfy_backup:Q4_K_S
- 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
- Pi
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:Q4_K_S
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:Q4_K_S" } ] } } }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:Q4_K_S
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:Q4_K_S
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use saik0s/comfy_backup with Docker Model Runner:
docker model run hf.co/saik0s/comfy_backup:Q4_K_S
- Lemonade
How to use saik0s/comfy_backup with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull saik0s/comfy_backup:Q4_K_S
Run and chat with the model
lemonade run user.comfy_backup-Q4_K_S
List all available models
lemonade list
File size: 5,294 Bytes
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import folder_paths
import glob
from aiohttp import web
import json
import logging
from functools import lru_cache
from utils.json_util import merge_json_recursive
# Extra locale files to load into main.json
EXTRA_LOCALE_FILES = [
"nodeDefs.json",
"commands.json",
"settings.json",
]
def safe_load_json_file(file_path: str) -> dict:
if not os.path.exists(file_path):
return {}
try:
with open(file_path, "r", encoding="utf-8") as f:
return json.load(f)
except json.JSONDecodeError:
logging.error(f"Error loading {file_path}")
return {}
class CustomNodeManager:
@lru_cache(maxsize=1)
def build_translations(self):
"""Load all custom nodes translations during initialization. Translations are
expected to be loaded from `locales/` folder.
The folder structure is expected to be the following:
- custom_nodes/
- custom_node_1/
- locales/
- en/
- main.json
- commands.json
- settings.json
returned translations are expected to be in the following format:
{
"en": {
"nodeDefs": {...},
"commands": {...},
"settings": {...},
...{other main.json keys}
}
}
"""
translations = {}
for folder in folder_paths.get_folder_paths("custom_nodes"):
# Sort glob results for deterministic ordering
for custom_node_dir in sorted(glob.glob(os.path.join(folder, "*/"))):
locales_dir = os.path.join(custom_node_dir, "locales")
if not os.path.exists(locales_dir):
continue
for lang_dir in glob.glob(os.path.join(locales_dir, "*/")):
lang_code = os.path.basename(os.path.dirname(lang_dir))
if lang_code not in translations:
translations[lang_code] = {}
# Load main.json
main_file = os.path.join(lang_dir, "main.json")
node_translations = safe_load_json_file(main_file)
# Load extra locale files
for extra_file in EXTRA_LOCALE_FILES:
extra_file_path = os.path.join(lang_dir, extra_file)
key = extra_file.split(".")[0]
json_data = safe_load_json_file(extra_file_path)
if json_data:
node_translations[key] = json_data
if node_translations:
translations[lang_code] = merge_json_recursive(
translations[lang_code], node_translations
)
return translations
def add_routes(self, routes, webapp, loadedModules):
example_workflow_folder_names = ["example_workflows", "example", "examples", "workflow", "workflows"]
@routes.get("/workflow_templates")
async def get_workflow_templates(request):
"""Returns a web response that contains the map of custom_nodes names and their associated workflow templates. The ones without templates are omitted."""
files = []
for folder in folder_paths.get_folder_paths("custom_nodes"):
for folder_name in example_workflow_folder_names:
pattern = os.path.join(folder, f"*/{folder_name}/*.json")
matched_files = glob.glob(pattern)
files.extend(matched_files)
workflow_templates_dict = (
{}
) # custom_nodes folder name -> example workflow names
for file in files:
custom_nodes_name = os.path.basename(
os.path.dirname(os.path.dirname(file))
)
workflow_name = os.path.splitext(os.path.basename(file))[0]
workflow_templates_dict.setdefault(custom_nodes_name, []).append(
workflow_name
)
return web.json_response(workflow_templates_dict)
# Serve workflow templates from custom nodes.
for module_name, module_dir in loadedModules:
for folder_name in example_workflow_folder_names:
workflows_dir = os.path.join(module_dir, folder_name)
if os.path.exists(workflows_dir):
if folder_name != "example_workflows":
logging.debug(
"Found example workflow folder '%s' for custom node '%s', consider renaming it to 'example_workflows'",
folder_name, module_name)
webapp.add_routes(
[
web.static(
"/api/workflow_templates/" + module_name, workflows_dir
)
]
)
@routes.get("/i18n")
async def get_i18n(request):
"""Returns translations from all custom nodes' locales folders."""
return web.json_response(self.build_translations())
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