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="models/text_encoders/Qwen3VL-8B-Uncensored-HauhauCS-Aggressive-Q8_0.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:Q8_0 # Run inference directly in the terminal: llama cli -hf saik0s/comfy_backup:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf saik0s/comfy_backup:Q8_0 # Run inference directly in the terminal: llama cli -hf saik0s/comfy_backup:Q8_0
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:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf saik0s/comfy_backup:Q8_0
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:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf saik0s/comfy_backup:Q8_0
Use Docker
docker model run hf.co/saik0s/comfy_backup:Q8_0
- LM Studio
- Jan
- Ollama
How to use saik0s/comfy_backup with Ollama:
ollama run hf.co/saik0s/comfy_backup:Q8_0
- 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:Q8_0
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:Q8_0" } ] } } }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:Q8_0
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:Q8_0
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:Q8_0
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:Q8_0" \ --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:Q8_0
- Lemonade
How to use saik0s/comfy_backup with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull saik0s/comfy_backup:Q8_0
Run and chat with the model
lemonade run user.comfy_backup-Q8_0
List all available models
lemonade list
| import random | |
| import server | |
| from enum import Enum | |
| class SGmode(Enum): | |
| FIX = 1 | |
| INCR = 2 | |
| DECR = 3 | |
| RAND = 4 | |
| class SeedGenerator: | |
| def __init__(self, base_value, action): | |
| self.base_value = base_value | |
| if action == "fixed" or action == "increment" or action == "decrement" or action == "randomize": | |
| self.action = SGmode.FIX | |
| elif action == 'increment for each node': | |
| self.action = SGmode.INCR | |
| elif action == 'decrement for each node': | |
| self.action = SGmode.DECR | |
| elif action == 'randomize for each node': | |
| self.action = SGmode.RAND | |
| def next(self): | |
| seed = self.base_value | |
| if self.action == SGmode.INCR: | |
| self.base_value += 1 | |
| if self.base_value > 1125899906842624: | |
| self.base_value = 0 | |
| elif self.action == SGmode.DECR: | |
| self.base_value -= 1 | |
| if self.base_value < 0: | |
| self.base_value = 1125899906842624 | |
| elif self.action == SGmode.RAND: | |
| self.base_value = random.randint(0, 1125899906842624) | |
| return seed | |
| def control_seed(v, action, seed_is_global): | |
| action = v['inputs']['action'] if seed_is_global else action | |
| value = v['inputs']['value'] if seed_is_global else v['inputs']['seed_num'] | |
| if action == 'increment' or action == 'increment for each node': | |
| value = value + 1 | |
| if value > 1125899906842624: | |
| value = 0 | |
| elif action == 'decrement' or action == 'decrement for each node': | |
| value = value - 1 | |
| if value < 0: | |
| value = 1125899906842624 | |
| elif action == 'randomize' or action == 'randomize for each node': | |
| value = random.randint(0, 1125899906842624) | |
| if seed_is_global: | |
| v['inputs']['value'] = value | |
| return value | |
| def prompt_seed_update(json_data): | |
| try: | |
| seed_widget_map = json_data['extra_data']['extra_pnginfo']['workflow']['seed_widgets'] | |
| except: | |
| return None | |
| workflow = json_data['extra_data']['extra_pnginfo']['workflow'] | |
| seed_widget_map = workflow['seed_widgets'] | |
| value = None | |
| mode = None | |
| node = None | |
| action = None | |
| seed_is_global = False | |
| for k, v in json_data['prompt'].items(): | |
| if 'class_type' not in v: | |
| continue | |
| cls = v['class_type'] | |
| if cls == 'easy globalSeed': | |
| mode = v['inputs']['mode'] | |
| action = v['inputs']['action'] | |
| value = v['inputs']['value'] | |
| node = k, v | |
| seed_is_global = True | |
| # control before generated | |
| if mode is not None and mode and seed_is_global: | |
| value = control_seed(node[1], action, seed_is_global) | |
| if seed_is_global: | |
| if value is not None: | |
| seed_generator = SeedGenerator(value, action) | |
| for k, v in json_data['prompt'].items(): | |
| for k2, v2 in v['inputs'].items(): | |
| if isinstance(v2, str) and '$GlobalSeed.value$' in v2: | |
| v['inputs'][k2] = v2.replace('$GlobalSeed.value$', str(value)) | |
| if k not in seed_widget_map: | |
| continue | |
| if 'seed_num' in v['inputs']: | |
| if isinstance(v['inputs']['seed_num'], int): | |
| v['inputs']['seed_num'] = seed_generator.next() | |
| if 'seed' in v['inputs']: | |
| if isinstance(v['inputs']['seed'], int): | |
| v['inputs']['seed'] = seed_generator.next() | |
| if 'noise_seed' in v['inputs']: | |
| if isinstance(v['inputs']['noise_seed'], int): | |
| v['inputs']['noise_seed'] = seed_generator.next() | |
| for k2, v2 in v['inputs'].items(): | |
| if isinstance(v2, str) and '$GlobalSeed.value$' in v2: | |
| v['inputs'][k2] = v2.replace('$GlobalSeed.value$', str(value)) | |
| # control after generated | |
| if mode is not None and not mode: | |
| control_seed(node[1], action, seed_is_global) | |
| return value is not None | |
| def workflow_seed_update(json_data): | |
| nodes = json_data['extra_data']['extra_pnginfo']['workflow']['nodes'] | |
| seed_widget_map = json_data['extra_data']['extra_pnginfo']['workflow']['seed_widgets'] | |
| prompt = json_data['prompt'] | |
| updated_seed_map = {} | |
| value = None | |
| for node in nodes: | |
| node_id = str(node['id']) | |
| if node_id in prompt: | |
| if node['type'] == 'easy globalSeed': | |
| value = prompt[node_id]['inputs']['value'] | |
| length = len(node['widgets_values']) | |
| node['widgets_values'][length-1] = node['widgets_values'][0] | |
| node['widgets_values'][0] = value | |
| elif node_id in seed_widget_map: | |
| widget_idx = seed_widget_map[node_id] | |
| if 'seed_num' in prompt[node_id]['inputs']: | |
| seed = prompt[node_id]['inputs']['seed_num'] | |
| elif 'noise_seed' in prompt[node_id]['inputs']: | |
| seed = prompt[node_id]['inputs']['noise_seed'] | |
| else: | |
| seed = prompt[node_id]['inputs']['seed'] | |
| node['widgets_values'][widget_idx] = seed | |
| updated_seed_map[node_id] = seed | |
| server.PromptServer.instance.send_sync("easyuse-global-seed", {"id": node_id, "value": value, "seed_map": updated_seed_map}) | |
| def onprompt(json_data): | |
| is_changed = prompt_seed_update(json_data) | |
| if is_changed: | |
| workflow_seed_update(json_data) | |
| return json_data | |
| server.PromptServer.instance.add_on_prompt_handler(onprompt) |