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: 5,151 Bytes
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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.
"""
@classmethod
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(),
],
)
@classmethod
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 []
@classmethod
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
@PromptServer.instance.routes.get("/hello")
async def get_hello(request):
return web.json_response("hello")
class ExampleExtension(ComfyExtension):
@override
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()
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