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 abc import ABC, abstractmethod | |
| from typing import Any, Mapping, Sequence, Tuple | |
| SDXL_SUPPORTED_RESOLUTIONS = [ | |
| (1024, 1024, 1.0), | |
| (1152, 896, 1.2857142857142858), | |
| (896, 1152, 0.7777777777777778), | |
| (1216, 832, 1.4615384615384615), | |
| (832, 1216, 0.6842105263157895), | |
| (1344, 768, 1.75), | |
| (768, 1344, 0.5714285714285714), | |
| (1536, 640, 2.4), | |
| (640, 1536, 0.4166666666666667), | |
| ] | |
| SDXL_EXTENDED_RESOLUTIONS = [ | |
| (512, 2048, 0.25), | |
| (512, 1984, 0.26), | |
| (512, 1920, 0.27), | |
| (512, 1856, 0.28), | |
| (576, 1792, 0.32), | |
| (576, 1728, 0.33), | |
| (576, 1664, 0.35), | |
| (640, 1600, 0.4), | |
| (640, 1536, 0.42), | |
| (704, 1472, 0.48), | |
| (704, 1408, 0.5), | |
| (704, 1344, 0.52), | |
| (768, 1344, 0.57), | |
| (768, 1280, 0.6), | |
| (832, 1216, 0.68), | |
| (832, 1152, 0.72), | |
| (896, 1152, 0.78), | |
| (896, 1088, 0.82), | |
| (960, 1088, 0.88), | |
| (960, 1024, 0.94), | |
| (1024, 1024, 1.0), | |
| (1024, 960, 1.8), | |
| (1088, 960, 1.14), | |
| (1088, 896, 1.22), | |
| (1152, 896, 1.30), | |
| (1152, 832, 1.39), | |
| (1216, 832, 1.47), | |
| (1280, 768, 1.68), | |
| (1344, 768, 1.76), | |
| (1408, 704, 2.0), | |
| (1472, 704, 2.10), | |
| (1536, 640, 2.4), | |
| (1600, 640, 2.5), | |
| (1664, 576, 2.90), | |
| (1728, 576, 3.0), | |
| (1792, 576, 3.12), | |
| (1856, 512, 3.63), | |
| (1920, 512, 3.76), | |
| (1984, 512, 3.89), | |
| (2048, 512, 4.0), | |
| ] | |
| class Resolution(ABC): | |
| def resolutions(cls) -> Sequence[Tuple[int, int, float]]: ... | |
| def INPUT_TYPES(cls) -> Mapping[str, Any]: | |
| return { | |
| "required": { | |
| "resolution": ([f"{res[0]}x{res[1]}" for res in cls.resolutions()],) | |
| } | |
| } | |
| RETURN_TYPES = ("INT", "INT") | |
| RETURN_NAMES = ("width", "height") | |
| FUNCTION = "op" | |
| CATEGORY = "math/graphics" | |
| def op(self, resolution: str) -> tuple[int, int]: | |
| width, height = resolution.split("x") | |
| return (int(width), int(height)) | |
| class NearestResolution(ABC): | |
| def resolutions(cls) -> Sequence[Tuple[int, int, float]]: ... | |
| def INPUT_TYPES(cls) -> Mapping[str, Any]: | |
| return {"required": {"image": ("IMAGE",)}} | |
| RETURN_TYPES = ("INT", "INT") | |
| RETURN_NAMES = ("width", "height") | |
| FUNCTION = "op" | |
| CATEGORY = "math/graphics" | |
| def op(self, image) -> tuple[int, int]: | |
| image_width = image.size()[2] | |
| image_height = image.size()[1] | |
| print(f"Input image resolution: {image_width}x{image_height}") | |
| image_ratio = image_width / image_height | |
| differences = [ | |
| (abs(image_ratio - resolution[2]), resolution) | |
| for resolution in self.resolutions() | |
| ] | |
| smallest = None | |
| for difference in differences: | |
| if smallest is None: | |
| smallest = difference | |
| else: | |
| if difference[0] < smallest[0]: | |
| smallest = difference | |
| if smallest is not None: | |
| width = smallest[1][0] | |
| height = smallest[1][1] | |
| else: | |
| width = 1024 | |
| height = 1024 | |
| print(f"Selected resolution: {width}x{height}") | |
| return (width, height) | |
| class SDXLResolution(Resolution): | |
| def resolutions(cls): | |
| return SDXL_SUPPORTED_RESOLUTIONS | |
| class SDXLExtendedResolution(Resolution): | |
| def resolutions(cls): | |
| return SDXL_EXTENDED_RESOLUTIONS | |
| class NearestSDXLResolution(NearestResolution): | |
| def resolutions(cls): | |
| return SDXL_SUPPORTED_RESOLUTIONS | |
| class NearestSDXLExtendedResolution(NearestResolution): | |
| def resolutions(cls): | |
| return SDXL_EXTENDED_RESOLUTIONS | |
| NODE_CLASS_MAPPINGS = { | |
| "CM_SDXLResolution": SDXLResolution, | |
| "CM_NearestSDXLResolution": NearestSDXLResolution, | |
| "CM_SDXLExtendedResolution": SDXLExtendedResolution, | |
| "CM_NearestSDXLExtendedResolution": NearestSDXLExtendedResolution, | |
| } | |