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: 6,446 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | import os
from pathlib import Path
from typing import Literal
import folder_paths
from app.assets.helpers import normalize_tags
_NON_MODEL_FOLDER_NAMES = frozenset({"custom_nodes"})
def get_comfy_models_folders() -> list[tuple[str, list[str]]]:
"""Build list of (folder_name, base_paths[]) for all model locations.
Includes every category registered in folder_names_and_paths,
regardless of whether its paths are under the main models_dir,
but excludes non-model entries like custom_nodes.
"""
targets: list[tuple[str, list[str]]] = []
for name, values in folder_paths.folder_names_and_paths.items():
if name in _NON_MODEL_FOLDER_NAMES:
continue
paths, _exts = values[0], values[1]
if paths:
targets.append((name, paths))
return targets
def resolve_destination_from_tags(tags: list[str]) -> tuple[str, list[str]]:
"""Validates and maps tags -> (base_dir, subdirs_for_fs)"""
if not tags:
raise ValueError("tags must not be empty")
root = tags[0].lower()
if root == "models":
if len(tags) < 2:
raise ValueError("at least two tags required for model asset")
try:
bases = folder_paths.folder_names_and_paths[tags[1]][0]
except KeyError:
raise ValueError(f"unknown model category '{tags[1]}'")
if not bases:
raise ValueError(f"no base path configured for category '{tags[1]}'")
base_dir = os.path.abspath(bases[0])
raw_subdirs = tags[2:]
elif root == "input":
base_dir = os.path.abspath(folder_paths.get_input_directory())
raw_subdirs = tags[1:]
elif root == "output":
base_dir = os.path.abspath(folder_paths.get_output_directory())
raw_subdirs = tags[1:]
else:
raise ValueError(f"unknown root tag '{tags[0]}'; expected 'models', 'input', or 'output'")
_sep_chars = frozenset(("/", "\\", os.sep))
for i in raw_subdirs:
if i in (".", "..") or _sep_chars & set(i):
raise ValueError("invalid path component in tags")
return base_dir, raw_subdirs if raw_subdirs else []
def validate_path_within_base(candidate: str, base: str) -> None:
cand_abs = Path(os.path.abspath(candidate))
base_abs = Path(os.path.abspath(base))
if not cand_abs.is_relative_to(base_abs):
raise ValueError("destination escapes base directory")
def compute_relative_filename(file_path: str) -> str | None:
"""
Return the model's path relative to the last well-known folder (the model category),
using forward slashes, eg:
/.../models/checkpoints/flux/123/flux.safetensors -> "flux/123/flux.safetensors"
/.../models/text_encoders/clip_g.safetensors -> "clip_g.safetensors"
For non-model paths, returns None.
"""
try:
root_category, rel_path = get_asset_category_and_relative_path(file_path)
except ValueError:
return None
p = Path(rel_path)
parts = [seg for seg in p.parts if seg not in (".", "..", p.anchor)]
if not parts:
return None
if root_category == "models":
# parts[0] is the category ("checkpoints", "vae", etc) – drop it
inside = parts[1:] if len(parts) > 1 else [parts[0]]
return "/".join(inside)
return "/".join(parts) # input/output: keep all parts
def get_asset_category_and_relative_path(
file_path: str,
) -> tuple[Literal["input", "output", "temp", "models"], str]:
"""Determine which root category a file path belongs to.
Categories:
- 'input': under folder_paths.get_input_directory()
- 'output': under folder_paths.get_output_directory()
- 'temp': under folder_paths.get_temp_directory()
- 'models': under any base path from get_comfy_models_folders()
Returns:
(root_category, relative_path_inside_that_root)
Raises:
ValueError: path does not belong to any known root.
"""
fp_abs = os.path.abspath(file_path)
def _check_is_within(child: str, parent: str) -> bool:
return Path(child).is_relative_to(parent)
def _compute_relative(child: str, parent: str) -> str:
# Normalize relative path, stripping any leading ".." components
# by anchoring to root (os.sep) then computing relpath back from it.
return os.path.relpath(
os.path.join(os.sep, os.path.relpath(child, parent)), os.sep
)
# 1) input
input_base = os.path.abspath(folder_paths.get_input_directory())
if _check_is_within(fp_abs, input_base):
return "input", _compute_relative(fp_abs, input_base)
# 2) output
output_base = os.path.abspath(folder_paths.get_output_directory())
if _check_is_within(fp_abs, output_base):
return "output", _compute_relative(fp_abs, output_base)
# 3) temp
temp_base = os.path.abspath(folder_paths.get_temp_directory())
if _check_is_within(fp_abs, temp_base):
return "temp", _compute_relative(fp_abs, temp_base)
# 4) models (check deepest matching base to avoid ambiguity)
best: tuple[int, str, str] | None = None # (base_len, bucket, rel_inside_bucket)
for bucket, bases in get_comfy_models_folders():
for b in bases:
base_abs = os.path.abspath(b)
if not _check_is_within(fp_abs, base_abs):
continue
cand = (len(base_abs), bucket, _compute_relative(fp_abs, base_abs))
if best is None or cand[0] > best[0]:
best = cand
if best is not None:
_, bucket, rel_inside = best
combined = os.path.join(bucket, rel_inside)
return "models", os.path.relpath(os.path.join(os.sep, combined), os.sep)
raise ValueError(
f"Path is not within input, output, temp, or configured model bases: {file_path}"
)
def get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list[str]]:
"""Return (name, tags) derived from a filesystem path.
- name: base filename with extension
- tags: [root_category] + parent folder names in order
Raises:
ValueError: path does not belong to any known root.
"""
root_category, some_path = get_asset_category_and_relative_path(file_path)
p = Path(some_path)
parent_parts = [
part for part in p.parent.parts if part not in (".", "..", p.anchor)
]
return p.name, list(dict.fromkeys(normalize_tags([root_category, *parent_parts])))
|