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
| """ | |
| Feature flags module for ComfyUI WebSocket protocol negotiation. | |
| This module handles capability negotiation between frontend and backend, | |
| allowing graceful protocol evolution while maintaining backward compatibility. | |
| """ | |
| import logging | |
| from typing import Any, TypedDict | |
| from comfy.cli_args import args | |
| class FeatureFlagInfo(TypedDict): | |
| type: str | |
| default: Any | |
| description: str | |
| # Registry of known CLI-settable feature flags. | |
| # Launchers can query this via --list-feature-flags to discover valid flags. | |
| CLI_FEATURE_FLAG_REGISTRY: dict[str, FeatureFlagInfo] = { | |
| "show_signin_button": { | |
| "type": "bool", | |
| "default": False, | |
| "description": "Show the sign-in button in the frontend even when not signed in", | |
| }, | |
| } | |
| def _coerce_bool(v: str) -> bool: | |
| """Strict bool coercion: only 'true'/'false' (case-insensitive). | |
| Anything else raises ValueError so the caller can warn and drop the flag, | |
| rather than silently treating typos like 'ture' or 'yes' as False. | |
| """ | |
| lower = v.lower() | |
| if lower == "true": | |
| return True | |
| if lower == "false": | |
| return False | |
| raise ValueError(f"expected 'true' or 'false', got {v!r}") | |
| _COERCE_FNS: dict[str, Any] = { | |
| "bool": _coerce_bool, | |
| "int": lambda v: int(v), | |
| "float": lambda v: float(v), | |
| } | |
| def _coerce_flag_value(key: str, raw_value: str) -> Any: | |
| """Coerce a raw string value using the registry type, or keep as string. | |
| Returns the raw string if the key is unregistered or the type is unknown. | |
| Raises ValueError/TypeError if the key is registered with a known type but | |
| the value cannot be coerced; callers are expected to warn and drop the flag. | |
| """ | |
| info = CLI_FEATURE_FLAG_REGISTRY.get(key) | |
| if info is None: | |
| return raw_value | |
| coerce = _COERCE_FNS.get(info["type"]) | |
| if coerce is None: | |
| return raw_value | |
| return coerce(raw_value) | |
| def _parse_cli_feature_flags() -> dict[str, Any]: | |
| """Parse --feature-flag key=value pairs from CLI args into a dict. | |
| Items without '=' default to the value 'true' (bare flag form). | |
| Flags whose value cannot be coerced to the registered type are dropped | |
| with a warning, so a typo like '--feature-flag some_bool=ture' does not | |
| silently take effect as the wrong value. | |
| """ | |
| result: dict[str, Any] = {} | |
| for item in getattr(args, "feature_flag", []): | |
| key, sep, raw_value = item.partition("=") | |
| key = key.strip() | |
| if not key: | |
| continue | |
| if not sep: | |
| raw_value = "true" | |
| try: | |
| result[key] = _coerce_flag_value(key, raw_value.strip()) | |
| except (ValueError, TypeError) as e: | |
| info = CLI_FEATURE_FLAG_REGISTRY.get(key, {}) | |
| logging.warning( | |
| "Could not coerce --feature-flag %s=%r to %s (%s); dropping flag.", | |
| key, raw_value.strip(), info.get("type", "?"), e, | |
| ) | |
| return result | |
| # Default server capabilities | |
| _CORE_FEATURE_FLAGS: dict[str, Any] = { | |
| "supports_preview_metadata": True, | |
| "max_upload_size": args.max_upload_size * 1024 * 1024, # Convert MB to bytes | |
| "extension": {"manager": {"supports_v4": True}}, | |
| "node_replacements": True, | |
| "assets": args.enable_assets, | |
| } | |
| # CLI-provided flags cannot overwrite core flags | |
| _cli_flags = {k: v for k, v in _parse_cli_feature_flags().items() if k not in _CORE_FEATURE_FLAGS} | |
| SERVER_FEATURE_FLAGS: dict[str, Any] = {**_CORE_FEATURE_FLAGS, **_cli_flags} | |
| def get_connection_feature( | |
| sockets_metadata: dict[str, dict[str, Any]], | |
| sid: str, | |
| feature_name: str, | |
| default: Any = False | |
| ) -> Any: | |
| """ | |
| Get a feature flag value for a specific connection. | |
| Args: | |
| sockets_metadata: Dictionary of socket metadata | |
| sid: Session ID of the connection | |
| feature_name: Name of the feature to check | |
| default: Default value if feature not found | |
| Returns: | |
| Feature value or default if not found | |
| """ | |
| if sid not in sockets_metadata: | |
| return default | |
| return sockets_metadata[sid].get("feature_flags", {}).get(feature_name, default) | |
| def supports_feature( | |
| sockets_metadata: dict[str, dict[str, Any]], | |
| sid: str, | |
| feature_name: str | |
| ) -> bool: | |
| """ | |
| Check if a connection supports a specific feature. | |
| Args: | |
| sockets_metadata: Dictionary of socket metadata | |
| sid: Session ID of the connection | |
| feature_name: Name of the feature to check | |
| Returns: | |
| Boolean indicating if feature is supported | |
| """ | |
| return get_connection_feature(sockets_metadata, sid, feature_name, False) is True | |
| def get_server_features() -> dict[str, Any]: | |
| """ | |
| Get the server's feature flags. | |
| Returns: | |
| Dictionary of server feature flags | |
| """ | |
| return SERVER_FEATURE_FLAGS.copy() | |