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
| from .constants import get_category, get_name | |
| from nodes import LoraLoader | |
| import folder_paths | |
| class RgthreeLoraLoaderStack: | |
| NAME = get_name('Lora Loader Stack') | |
| CATEGORY = get_category() | |
| def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring | |
| return { | |
| "required": { | |
| "model": ("MODEL",), | |
| "clip": ("CLIP", ), | |
| "lora_01": (['None'] + folder_paths.get_filename_list("loras"), ), | |
| "strength_01":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), | |
| "lora_02": (['None'] + folder_paths.get_filename_list("loras"), ), | |
| "strength_02":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), | |
| "lora_03": (['None'] + folder_paths.get_filename_list("loras"), ), | |
| "strength_03":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), | |
| "lora_04": (['None'] + folder_paths.get_filename_list("loras"), ), | |
| "strength_04":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), | |
| } | |
| } | |
| RETURN_TYPES = ("MODEL", "CLIP") | |
| FUNCTION = "load_lora" | |
| def load_lora(self, model, clip, lora_01, strength_01, lora_02, strength_02, lora_03, strength_03, lora_04, strength_04): | |
| if lora_01 != "None" and strength_01 != 0: | |
| model, clip = LoraLoader().load_lora(model, clip, lora_01, strength_01, strength_01) | |
| if lora_02 != "None" and strength_02 != 0: | |
| model, clip = LoraLoader().load_lora(model, clip, lora_02, strength_02, strength_02) | |
| if lora_03 != "None" and strength_03 != 0: | |
| model, clip = LoraLoader().load_lora(model, clip, lora_03, strength_03, strength_03) | |
| if lora_04 != "None" and strength_04 != 0: | |
| model, clip = LoraLoader().load_lora(model, clip, lora_04, strength_04, strength_04) | |
| return (model, clip) | |