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
| from typing_extensions import override | |
| from comfy_api.latest import ComfyExtension, io | |
| class Example(io.ComfyNode): | |
| """ | |
| An example node | |
| Class methods | |
| ------------- | |
| define_schema (io.Schema): | |
| Tell the main program the metadata, input, output parameters of nodes. | |
| fingerprint_inputs: | |
| optional method to control when the node is re executed. | |
| check_lazy_status: | |
| optional method to control list of input names that need to be evaluated. | |
| """ | |
| def define_schema(cls) -> io.Schema: | |
| """ | |
| Return a schema which contains all information about the node. | |
| Some types: "Model", "Vae", "Clip", "Conditioning", "Latent", "Image", "Int", "String", "Float", "Combo". | |
| For outputs the "io.Model.Output" should be used, for inputs the "io.Model.Input" can be used. | |
| The type can be a "Combo" - this will be a list for selection. | |
| """ | |
| return io.Schema( | |
| node_id="Example", | |
| display_name="Example Node", | |
| category="Example", | |
| inputs=[ | |
| io.Image.Input("image"), | |
| io.Int.Input( | |
| "int_field", | |
| min=0, | |
| max=4096, | |
| step=64, # Slider's step | |
| display_mode=io.NumberDisplay.number, # Cosmetic only: display as "number" or "slider" | |
| lazy=True, # Will only be evaluated if check_lazy_status requires it | |
| ), | |
| io.Float.Input( | |
| "float_field", | |
| default=1.0, | |
| min=0.0, | |
| max=10.0, | |
| step=0.01, | |
| round=0.001, #The value representing the precision to round to, will be set to the step value by default. Can be set to False to disable rounding. | |
| display_mode=io.NumberDisplay.number, | |
| lazy=True, | |
| ), | |
| io.Combo.Input("print_to_screen", options=["enable", "disable"]), | |
| io.String.Input( | |
| "string_field", | |
| multiline=False, # True if you want the field to look like the one on the ClipTextEncode node | |
| default="Hello world!", | |
| lazy=True, | |
| ) | |
| ], | |
| outputs=[ | |
| io.Image.Output(), | |
| ], | |
| ) | |
| def check_lazy_status(cls, image, string_field, int_field, float_field, print_to_screen): | |
| """ | |
| Return a list of input names that need to be evaluated. | |
| This function will be called if there are any lazy inputs which have not yet been | |
| evaluated. As long as you return at least one field which has not yet been evaluated | |
| (and more exist), this function will be called again once the value of the requested | |
| field is available. | |
| Any evaluated inputs will be passed as arguments to this function. Any unevaluated | |
| inputs will have the value None. | |
| """ | |
| if print_to_screen == "enable": | |
| return ["int_field", "float_field", "string_field"] | |
| else: | |
| return [] | |
| def execute(cls, image, string_field, int_field, float_field, print_to_screen) -> io.NodeOutput: | |
| if print_to_screen == "enable": | |
| print(f"""Your input contains: | |
| string_field aka input text: {string_field} | |
| int_field: {int_field} | |
| float_field: {float_field} | |
| """) | |
| #do some processing on the image, in this example I just invert it | |
| image = 1.0 - image | |
| return io.NodeOutput(image) | |
| """ | |
| The node will always be re executed if any of the inputs change but | |
| this method can be used to force the node to execute again even when the inputs don't change. | |
| You can make this node return a number or a string. This value will be compared to the one returned the last time the node was | |
| executed, if it is different the node will be executed again. | |
| This method is used in the core repo for the LoadImage node where they return the image hash as a string, if the image hash | |
| changes between executions the LoadImage node is executed again. | |
| """ | |
| #@classmethod | |
| #def fingerprint_inputs(s, image, string_field, int_field, float_field, print_to_screen): | |
| # return "" | |
| # Set the web directory, any .js file in that directory will be loaded by the frontend as a frontend extension | |
| # WEB_DIRECTORY = "./somejs" | |
| # Add custom API routes, using router | |
| from aiohttp import web | |
| from server import PromptServer | |
| async def get_hello(request): | |
| return web.json_response("hello") | |
| class ExampleExtension(ComfyExtension): | |
| async def get_node_list(self) -> list[type[io.ComfyNode]]: | |
| return [ | |
| Example, | |
| ] | |
| async def comfy_entrypoint() -> ExampleExtension: # ComfyUI calls this to load your extension and its nodes. | |
| return ExampleExtension() | |