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
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: 6,318 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | from __future__ import annotations
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING
from comfy_api.internal import ComfyAPIBase
from comfy_api.internal.singleton import ProxiedSingleton
from comfy_api.internal.async_to_sync import create_sync_class
from ._input import ImageInput, AudioInput, MaskInput, LatentInput, VideoInput
from ._input_impl import VideoFromFile, VideoFromComponents
from ._util import VideoCodec, VideoContainer, VideoComponents, MESH, VOXEL, File3D
from . import _io_public as io
from . import _ui_public as ui
from comfy_execution.utils import get_executing_context
from comfy_execution.progress import get_progress_state, PreviewImageTuple
from PIL import Image
from comfy.cli_args import args
import numpy as np
class ComfyAPI_latest(ComfyAPIBase):
VERSION = "latest"
STABLE = False
def __init__(self):
super().__init__()
self.node_replacement = self.NodeReplacement()
self.execution = self.Execution()
self.caching = self.Caching()
class NodeReplacement(ProxiedSingleton):
async def register(self, node_replace: io.NodeReplace) -> None:
"""Register a node replacement mapping."""
from server import PromptServer
PromptServer.instance.node_replace_manager.register(node_replace)
class Execution(ProxiedSingleton):
async def set_progress(
self,
value: float,
max_value: float,
node_id: str | None = None,
preview_image: Image.Image | ImageInput | None = None,
ignore_size_limit: bool = False,
) -> None:
"""
Update the progress bar displayed in the ComfyUI interface.
This function allows custom nodes and API calls to report their progress
back to the user interface, providing visual feedback during long operations.
Migration from previous API: comfy.utils.PROGRESS_BAR_HOOK
"""
executing_context = get_executing_context()
if node_id is None and executing_context is not None:
node_id = executing_context.node_id
if node_id is None:
raise ValueError("node_id must be provided if not in executing context")
# Convert preview_image to PreviewImageTuple if needed
to_display: PreviewImageTuple | Image.Image | ImageInput | None = preview_image
if to_display is not None:
# First convert to PIL Image if needed
if isinstance(to_display, ImageInput):
# Convert ImageInput (torch.Tensor) to PIL Image
# Handle tensor shape [B, H, W, C] -> get first image if batch
tensor = to_display
if len(tensor.shape) == 4:
tensor = tensor[0]
# Convert to numpy array and scale to 0-255
image_np = (tensor.cpu().numpy() * 255).astype(np.uint8)
to_display = Image.fromarray(image_np)
if isinstance(to_display, Image.Image):
# Detect image format from PIL Image
image_format = to_display.format if to_display.format else "JPEG"
# Use None for preview_size if ignore_size_limit is True
preview_size = None if ignore_size_limit else args.preview_size
to_display = (image_format, to_display, preview_size)
get_progress_state().update_progress(
node_id=node_id,
value=value,
max_value=max_value,
image=to_display,
)
class Caching(ProxiedSingleton):
"""
External cache provider API for sharing cached node outputs
across ComfyUI instances.
Example::
from comfy_api.latest import Caching
class MyCacheProvider(Caching.CacheProvider):
async def on_lookup(self, context):
... # check external storage
async def on_store(self, context, value):
... # store to external storage
Caching.register_provider(MyCacheProvider())
"""
from ._caching import CacheProvider, CacheContext, CacheValue
async def register_provider(self, provider: "ComfyAPI_latest.Caching.CacheProvider") -> None:
"""Register an external cache provider. Providers are called in registration order."""
from comfy_execution.cache_provider import register_cache_provider
register_cache_provider(provider)
async def unregister_provider(self, provider: "ComfyAPI_latest.Caching.CacheProvider") -> None:
"""Unregister a previously registered cache provider."""
from comfy_execution.cache_provider import unregister_cache_provider
unregister_cache_provider(provider)
class ComfyExtension(ABC):
async def on_load(self) -> None:
"""
Called when an extension is loaded.
This should be used to initialize any global resources needed by the extension.
"""
@abstractmethod
async def get_node_list(self) -> list[type[io.ComfyNode]]:
"""
Returns a list of nodes that this extension provides.
"""
class Input:
Image = ImageInput
Audio = AudioInput
Mask = MaskInput
Latent = LatentInput
Video = VideoInput
class InputImpl:
VideoFromFile = VideoFromFile
VideoFromComponents = VideoFromComponents
class Types:
VideoCodec = VideoCodec
VideoContainer = VideoContainer
VideoComponents = VideoComponents
MESH = MESH
VOXEL = VOXEL
File3D = File3D
Caching = ComfyAPI_latest.Caching
ComfyAPI = ComfyAPI_latest
# Create a synchronous version of the API
if TYPE_CHECKING:
import comfy_api.latest.generated.ComfyAPISyncStub # type: ignore
ComfyAPISync: type[comfy_api.latest.generated.ComfyAPISyncStub.ComfyAPISyncStub]
ComfyAPISync = create_sync_class(ComfyAPI_latest)
# create new aliases for io and ui
IO = io
UI = ui
__all__ = [
"ComfyAPI",
"ComfyAPISync",
"Input",
"InputImpl",
"Types",
"Caching",
"ComfyExtension",
"io",
"IO",
"ui",
"UI",
]
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