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

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

Ltx-3 / ComfyUI /custom_nodes /ComfyUI-LTXVideo /decoder_noise.py

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	from copy import copy

	from .nodes_registry import comfy_node


	@comfy_node(name="Set VAE Decoder Noise")
	class DecoderNoise:
	@classmethod
	def INPUT_TYPES(cls):
	return {
	"required": {
	"vae": ("VAE",),
	"timestep": (
	"FLOAT",
	{
	"default": 0.05,
	"min": 0.0,
	"max": 1.0,
	"step": 0.001,
	"tooltip": "The timestep used for decoding the noise.",
	},
	),
	"scale": (
	"FLOAT",
	{
	"default": 0.025,
	"min": 0.0,
	"max": 1.0,
	"step": 0.001,
	"tooltip": "The scale of the noise added to the decoder.",
	},
	),
	"seed": (
	"INT",
	{
	"default": 42,
	"min": 0,
	"max": 0xFFFFFFFFFFFFFFFF,
	"tooltip": "The random seed used for creating the noise.",
	},
	),
	}
	}

	FUNCTION = "add_noise"
	RETURN_TYPES = ("VAE",)
	CATEGORY = "lightricks/LTXV"

	def add_noise(self, vae, timestep, scale, seed):
	result = copy(vae)
	if hasattr(result, "first_stage_model"):
	result.first_stage_model.decode_timestep = timestep
	result.first_stage_model.decode_noise_scale = scale
	result._decode_timestep = timestep
	result.decode_noise_scale = scale
	result.seed = seed
	return (result,)