Instructions to use vidfom/Ltx-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use vidfom/Ltx-3 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="vidfom/Ltx-3", filename="ComfyUI/models/text_encoders/gemma-3-12b-it-qat-UD-Q4_K_XL.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use vidfom/Ltx-3 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf vidfom/Ltx-3:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf vidfom/Ltx-3:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf vidfom/Ltx-3:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf vidfom/Ltx-3:UD-Q4_K_XL
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 vidfom/Ltx-3:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf vidfom/Ltx-3:UD-Q4_K_XL
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 vidfom/Ltx-3:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf vidfom/Ltx-3:UD-Q4_K_XL
Use Docker
docker model run hf.co/vidfom/Ltx-3:UD-Q4_K_XL
- LM Studio
- Jan
- Ollama
How to use vidfom/Ltx-3 with Ollama:
ollama run hf.co/vidfom/Ltx-3:UD-Q4_K_XL
- Unsloth Studio new
How to use vidfom/Ltx-3 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 vidfom/Ltx-3 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 vidfom/Ltx-3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for vidfom/Ltx-3 to start chatting
- Docker Model Runner
How to use vidfom/Ltx-3 with Docker Model Runner:
docker model run hf.co/vidfom/Ltx-3:UD-Q4_K_XL
- Lemonade
How to use vidfom/Ltx-3 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull vidfom/Ltx-3:UD-Q4_K_XL
Run and chat with the model
lemonade run user.Ltx-3-UD-Q4_K_XL
List all available models
lemonade list
File size: 4,913 Bytes
e00eceb | 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 | # Internal infrastructure for ComfyAPI
from .api_registry import (
ComfyAPIBase as ComfyAPIBase,
ComfyAPIWithVersion as ComfyAPIWithVersion,
register_versions as register_versions,
get_all_versions as get_all_versions,
)
import asyncio
from dataclasses import asdict
from typing import Callable, Optional
def first_real_override(cls: type, name: str, *, base: type=None) -> Optional[Callable]:
"""Return the *callable* override of `name` visible on `cls`, or None if every
implementation up to (and including) `base` is the placeholder defined on `base`.
If base is not provided, it will assume cls has a GET_BASE_CLASS
"""
if base is None:
if not hasattr(cls, "GET_BASE_CLASS"):
raise ValueError("base is required if cls does not have a GET_BASE_CLASS; is this a valid ComfyNode subclass?")
base = cls.GET_BASE_CLASS()
base_attr = getattr(base, name, None)
if base_attr is None:
return None
base_func = base_attr.__func__
for c in cls.mro(): # NodeB, NodeA, ComfyNode, object …
if c is base: # reached the placeholder – we're done
break
if name in c.__dict__: # first class that *defines* the attr
func = getattr(c, name).__func__
if func is not base_func: # real override
return getattr(cls, name) # bound to *cls*
return None
class _ComfyNodeInternal:
"""Class that all V3-based APIs inherit from for ComfyNode.
This is intended to only be referenced within execution.py, as it has to handle all V3 APIs going forward."""
@classmethod
def GET_NODE_INFO_V1(cls):
...
class _NodeOutputInternal:
"""Class that all V3-based APIs inherit from for NodeOutput.
This is intended to only be referenced within execution.py, as it has to handle all V3 APIs going forward."""
...
def as_pruned_dict(dataclass_obj):
'''Return dict of dataclass object with pruned None values.'''
return prune_dict(asdict(dataclass_obj))
def prune_dict(d: dict):
return {k: v for k,v in d.items() if v is not None}
def is_class(obj):
'''
Returns True if is a class type.
Returns False if is a class instance.
'''
return isinstance(obj, type)
def copy_class(cls: type) -> type:
'''
Copy a class and its attributes.
'''
if cls is None:
return None
cls_dict = {
k: v for k, v in cls.__dict__.items()
if k not in ('__dict__', '__weakref__', '__module__', '__doc__')
}
# new class
new_cls = type(
cls.__name__,
(cls,),
cls_dict
)
# metadata preservation
new_cls.__module__ = cls.__module__
new_cls.__doc__ = cls.__doc__
return new_cls
class classproperty(object):
def __init__(self, f):
self.f = f
def __get__(self, obj, owner):
return self.f(owner)
# NOTE: this was ai generated and validated by hand
def shallow_clone_class(cls, new_name=None):
'''
Shallow clone a class while preserving super() functionality.
'''
new_name = new_name or f"{cls.__name__}Clone"
# Include the original class in the bases to maintain proper inheritance
new_bases = (cls,) + cls.__bases__
return type(new_name, new_bases, dict(cls.__dict__))
# NOTE: this was ai generated and validated by hand
def lock_class(cls):
'''
Lock a class so that its top-levelattributes cannot be modified.
'''
# Locked instance __setattr__
def locked_instance_setattr(self, name, value):
raise AttributeError(
f"Cannot set attribute '{name}' on immutable instance of {type(self).__name__}"
)
# Locked metaclass
class LockedMeta(type(cls)):
def __setattr__(cls_, name, value):
raise AttributeError(
f"Cannot modify class attribute '{name}' on locked class '{cls_.__name__}'"
)
# Rebuild class with locked behavior
locked_dict = dict(cls.__dict__)
locked_dict['__setattr__'] = locked_instance_setattr
return LockedMeta(cls.__name__, cls.__bases__, locked_dict)
def make_locked_method_func(type_obj, func, class_clone):
"""
Returns a function that, when called with **inputs, will execute:
getattr(type_obj, func).__func__(lock_class(class_clone), **inputs)
Supports both synchronous and asynchronous methods.
"""
locked_class = lock_class(class_clone)
method = getattr(type_obj, func).__func__
# Check if the original method is async
if asyncio.iscoroutinefunction(method):
async def wrapped_async_func(**inputs):
return await method(locked_class, **inputs)
return wrapped_async_func
else:
def wrapped_func(**inputs):
return method(locked_class, **inputs)
return wrapped_func
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