Instructions to use vidfom/Ltx-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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 Settings

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

Ltx-3 / ComfyUI /comfy_api /v0_0_2 /__init__.py

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	from comfy_api.latest import (
	ComfyAPI_latest,
	Input as Input_latest,
	InputImpl as InputImpl_latest,
	Types as Types_latest,
	)
	from typing import Type, TYPE_CHECKING
	from comfy_api.internal.async_to_sync import create_sync_class
	from comfy_api.latest import io, ui, IO, UI, ComfyExtension #noqa: F401


	class ComfyAPIAdapter_v0_0_2(ComfyAPI_latest):
	VERSION = "0.0.2"
	STABLE = False


	class Input(Input_latest):
	pass


	class InputImpl(InputImpl_latest):
	pass


	class Types(Types_latest):
	pass


	ComfyAPI = ComfyAPIAdapter_v0_0_2

	# Create a synchronous version of the API
	if TYPE_CHECKING:
	from comfy_api.v0_0_2.generated.ComfyAPISyncStub import ComfyAPISyncStub # type: ignore

	ComfyAPISync: Type[ComfyAPISyncStub]
	ComfyAPISync = create_sync_class(ComfyAPIAdapter_v0_0_2)

	__all__ = [
	"ComfyAPI",
	"ComfyAPISync",
	"Input",
	"InputImpl",
	"Types",
	"ComfyExtension",
	"io",
	"IO",
	"ui",
	"UI",
	]