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
- 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:Q4_K_S
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:Q4_K_S" \ --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: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: 7,851 Bytes
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import base64
import json
import time
import logging
import folder_paths
import glob
import comfy.utils
from aiohttp import web
from PIL import Image
from io import BytesIO
from folder_paths import map_legacy, filter_files_extensions, filter_files_content_types
class ModelFileManager:
def __init__(self) -> None:
self.cache: dict[str, tuple[list[dict], dict[str, float], float]] = {}
def get_cache(self, key: str, default=None) -> tuple[list[dict], dict[str, float], float] | None:
return self.cache.get(key, default)
def set_cache(self, key: str, value: tuple[list[dict], dict[str, float], float]):
self.cache[key] = value
def clear_cache(self):
self.cache.clear()
def add_routes(self, routes):
# NOTE: This is an experiment to replace `/models`
@routes.get("/experiment/models")
async def get_model_folders(request):
model_types = list(folder_paths.folder_names_and_paths.keys())
folder_black_list = ["configs", "custom_nodes"]
output_folders: list[dict] = []
for folder in model_types:
if folder in folder_black_list:
continue
output_folders.append({"name": folder, "folders": folder_paths.get_folder_paths(folder)})
return web.json_response(output_folders)
# NOTE: This is an experiment to replace `/models/{folder}`
@routes.get("/experiment/models/{folder}")
async def get_all_models(request):
folder = request.match_info.get("folder", None)
if folder not in folder_paths.folder_names_and_paths:
return web.Response(status=404)
files = self.get_model_file_list(folder)
return web.json_response(files)
@routes.get("/experiment/models/preview/{folder}/{path_index}/{filename:.*}")
async def get_model_preview(request):
folder_name = request.match_info.get("folder", None)
path_index = int(request.match_info.get("path_index", None))
filename = request.match_info.get("filename", None)
if folder_name not in folder_paths.folder_names_and_paths:
return web.Response(status=404)
folders = folder_paths.folder_names_and_paths[folder_name]
folder = folders[0][path_index]
full_filename = os.path.join(folder, filename)
previews = self.get_model_previews(full_filename)
default_preview = previews[0] if len(previews) > 0 else None
if default_preview is None or (isinstance(default_preview, str) and not os.path.isfile(default_preview)):
return web.Response(status=404)
try:
with Image.open(default_preview) as img:
img_bytes = BytesIO()
img.save(img_bytes, format="WEBP")
img_bytes.seek(0)
return web.Response(body=img_bytes.getvalue(), content_type="image/webp")
except:
return web.Response(status=404)
def get_model_file_list(self, folder_name: str):
folder_name = map_legacy(folder_name)
folders = folder_paths.folder_names_and_paths[folder_name]
output_list: list[dict] = []
for index, folder in enumerate(folders[0]):
if not os.path.isdir(folder):
continue
out = self.cache_model_file_list_(folder)
if out is None:
out = self.recursive_search_models_(folder, index)
self.set_cache(folder, out)
output_list.extend(out[0])
return output_list
def cache_model_file_list_(self, folder: str):
model_file_list_cache = self.get_cache(folder)
if model_file_list_cache is None:
return None
if not os.path.isdir(folder):
return None
if os.path.getmtime(folder) != model_file_list_cache[1]:
return None
for x in model_file_list_cache[1]:
time_modified = model_file_list_cache[1][x]
folder = x
if os.path.getmtime(folder) != time_modified:
return None
return model_file_list_cache
def recursive_search_models_(self, directory: str, pathIndex: int) -> tuple[list[str], dict[str, float], float]:
if not os.path.isdir(directory):
return [], {}, time.perf_counter()
excluded_dir_names = [".git"]
# TODO use settings
include_hidden_files = False
result: list[str] = []
dirs: dict[str, float] = {}
for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
if not include_hidden_files:
subdirs[:] = [d for d in subdirs if not d.startswith(".")]
filenames = [f for f in filenames if not f.startswith(".")]
filenames = filter_files_extensions(filenames, folder_paths.supported_pt_extensions)
for file_name in filenames:
try:
full_path = os.path.join(dirpath, file_name)
relative_path = os.path.relpath(full_path, directory)
# Get file metadata
file_info = {
"name": relative_path,
"pathIndex": pathIndex,
"modified": os.path.getmtime(full_path), # Add modification time
"created": os.path.getctime(full_path), # Add creation time
"size": os.path.getsize(full_path) # Add file size
}
result.append(file_info)
except Exception as e:
logging.warning(f"Warning: Unable to access {file_name}. Error: {e}. Skipping this file.")
continue
for d in subdirs:
path: str = os.path.join(dirpath, d)
try:
dirs[path] = os.path.getmtime(path)
except FileNotFoundError:
logging.warning(f"Warning: Unable to access {path}. Skipping this path.")
continue
return result, dirs, time.perf_counter()
def get_model_previews(self, filepath: str) -> list[str | BytesIO]:
dirname = os.path.dirname(filepath)
if not os.path.exists(dirname):
return []
basename = os.path.splitext(filepath)[0]
match_files = glob.glob(f"{basename}.*", recursive=False)
image_files = filter_files_content_types(match_files, "image")
safetensors_file = next(filter(lambda x: x.endswith(".safetensors"), match_files), None)
safetensors_metadata = {}
result: list[str | BytesIO] = []
for filename in image_files:
_basename = os.path.splitext(filename)[0]
if _basename == basename:
result.append(filename)
if _basename == f"{basename}.preview":
result.append(filename)
if safetensors_file:
safetensors_filepath = os.path.join(dirname, safetensors_file)
header = comfy.utils.safetensors_header(safetensors_filepath, max_size=8*1024*1024)
if header:
safetensors_metadata = json.loads(header)
safetensors_images = safetensors_metadata.get("__metadata__", {}).get("ssmd_cover_images", None)
if safetensors_images:
safetensors_images = json.loads(safetensors_images)
for image in safetensors_images:
result.append(BytesIO(base64.b64decode(image)))
return result
def __exit__(self, exc_type, exc_value, traceback):
self.clear_cache()
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