Instructions to use LuckyOda/comfyui-carbonara-bundle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use LuckyOda/comfyui-carbonara-bundle 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 LuckyOda/comfyui-carbonara-bundle # Run inference directly in the terminal: llama cli -hf LuckyOda/comfyui-carbonara-bundle
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf LuckyOda/comfyui-carbonara-bundle # Run inference directly in the terminal: llama cli -hf LuckyOda/comfyui-carbonara-bundle
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 LuckyOda/comfyui-carbonara-bundle # Run inference directly in the terminal: ./llama-cli -hf LuckyOda/comfyui-carbonara-bundle
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 LuckyOda/comfyui-carbonara-bundle # Run inference directly in the terminal: ./build/bin/llama-cli -hf LuckyOda/comfyui-carbonara-bundle
Use Docker
docker model run hf.co/LuckyOda/comfyui-carbonara-bundle
- LM Studio
- Jan
- Ollama
How to use LuckyOda/comfyui-carbonara-bundle with Ollama:
ollama run hf.co/LuckyOda/comfyui-carbonara-bundle
- Unsloth Studio
How to use LuckyOda/comfyui-carbonara-bundle 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 LuckyOda/comfyui-carbonara-bundle 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 LuckyOda/comfyui-carbonara-bundle to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for LuckyOda/comfyui-carbonara-bundle to start chatting
- Pi
How to use LuckyOda/comfyui-carbonara-bundle with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LuckyOda/comfyui-carbonara-bundle
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": "LuckyOda/comfyui-carbonara-bundle" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use LuckyOda/comfyui-carbonara-bundle with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LuckyOda/comfyui-carbonara-bundle
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 "LuckyOda/comfyui-carbonara-bundle" \ --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 LuckyOda/comfyui-carbonara-bundle with Docker Model Runner:
docker model run hf.co/LuckyOda/comfyui-carbonara-bundle
- Lemonade
How to use LuckyOda/comfyui-carbonara-bundle with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LuckyOda/comfyui-carbonara-bundle
Run and chat with the model
lemonade run user.comfyui-carbonara-bundle-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use LuckyOda/comfyui-carbonara-bundle with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LuckyOda/comfyui-carbonara-bundle
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 LuckyOda/comfyui-carbonara-bundle
Run Hermes
hermes
- Atomic Chat
File size: 5,626 Bytes
7355abb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | def is_link(obj):
if not isinstance(obj, list):
return False
if len(obj) != 2:
return False
if not isinstance(obj[0], str):
return False
if not isinstance(obj[1], int) and not isinstance(obj[1], float):
return False
return True
# The GraphBuilder is just a utility class that outputs graphs in the form expected by the ComfyUI back-end
class GraphBuilder:
_default_prefix_root = ""
_default_prefix_call_index = 0
_default_prefix_graph_index = 0
def __init__(self, prefix = None):
if prefix is None:
self.prefix = GraphBuilder.alloc_prefix()
else:
self.prefix = prefix
self.nodes = {}
self.id_gen = 1
@classmethod
def set_default_prefix(cls, prefix_root, call_index, graph_index = 0):
cls._default_prefix_root = prefix_root
cls._default_prefix_call_index = call_index
cls._default_prefix_graph_index = graph_index
@classmethod
def alloc_prefix(cls, root=None, call_index=None, graph_index=None):
if root is None:
root = GraphBuilder._default_prefix_root
if call_index is None:
call_index = GraphBuilder._default_prefix_call_index
if graph_index is None:
graph_index = GraphBuilder._default_prefix_graph_index
result = f"{root}.{call_index}.{graph_index}."
GraphBuilder._default_prefix_graph_index += 1
return result
def node(self, class_type, id=None, **kwargs):
if id is None:
id = str(self.id_gen)
self.id_gen += 1
id = self.prefix + id
if id in self.nodes:
return self.nodes[id]
node = Node(id, class_type, kwargs)
self.nodes[id] = node
return node
def lookup_node(self, id):
id = self.prefix + id
return self.nodes.get(id)
def finalize(self):
output = {}
for node_id, node in self.nodes.items():
output[node_id] = node.serialize()
return output
def replace_node_output(self, node_id, index, new_value):
node_id = self.prefix + node_id
to_remove = []
for node in self.nodes.values():
for key, value in node.inputs.items():
if is_link(value) and value[0] == node_id and value[1] == index:
if new_value is None:
to_remove.append((node, key))
else:
node.inputs[key] = new_value
for node, key in to_remove:
del node.inputs[key]
def remove_node(self, id):
id = self.prefix + id
del self.nodes[id]
class Node:
def __init__(self, id, class_type, inputs):
self.id = id
self.class_type = class_type
self.inputs = inputs
self.override_display_id = None
def out(self, index):
return [self.id, index]
def set_input(self, key, value):
if value is None:
if key in self.inputs:
del self.inputs[key]
else:
self.inputs[key] = value
def get_input(self, key):
return self.inputs.get(key)
def set_override_display_id(self, override_display_id):
self.override_display_id = override_display_id
def serialize(self):
serialized = {
"class_type": self.class_type,
"inputs": self.inputs
}
if self.override_display_id is not None:
serialized["override_display_id"] = self.override_display_id
return serialized
def add_graph_prefix(graph, outputs, prefix):
# Change the node IDs and any internal links
new_graph = {}
for node_id, node_info in graph.items():
# Make sure the added nodes have unique IDs
new_node_id = prefix + node_id
new_node = { "class_type": node_info["class_type"], "inputs": {} }
for input_name, input_value in node_info.get("inputs", {}).items():
if is_link(input_value):
new_node["inputs"][input_name] = [prefix + input_value[0], input_value[1]]
else:
new_node["inputs"][input_name] = input_value
new_graph[new_node_id] = new_node
# Change the node IDs in the outputs
new_outputs = []
for n in range(len(outputs)):
output = outputs[n]
if is_link(output):
new_outputs.append([prefix + output[0], output[1]])
else:
new_outputs.append(output)
return new_graph, tuple(new_outputs)
class ExecutionBlocker:
"""
Return this from a node and any users will be blocked with the given error message.
If the message is None, execution will be blocked silently instead.
Generally, you should avoid using this functionality unless absolutely necessary. Whenever it's
possible, a lazy input will be more efficient and have a better user experience.
This functionality is useful in two cases:
1. You want to conditionally prevent an output node from executing. (Particularly a built-in node
like SaveImage. For your own output nodes, I would recommend just adding a BOOL input and using
lazy evaluation to let it conditionally disable itself.)
2. You have a node with multiple possible outputs, some of which are invalid and should not be used.
(I would recommend not making nodes like this in the future -- instead, make multiple nodes with
different outputs. Unfortunately, there are several popular existing nodes using this pattern.)
"""
def __init__(self, message):
self.message = message
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