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
| """Tests for path_utils – asset category resolution.""" | |
| import os | |
| import tempfile | |
| from pathlib import Path | |
| from unittest.mock import patch | |
| import pytest | |
| from app.assets.services.path_utils import get_asset_category_and_relative_path | |
| def fake_dirs(): | |
| """Create temporary input, output, and temp directories.""" | |
| with tempfile.TemporaryDirectory() as root: | |
| root_path = Path(root) | |
| input_dir = root_path / "input" | |
| output_dir = root_path / "output" | |
| temp_dir = root_path / "temp" | |
| models_dir = root_path / "models" / "checkpoints" | |
| for d in (input_dir, output_dir, temp_dir, models_dir): | |
| d.mkdir(parents=True) | |
| with patch("app.assets.services.path_utils.folder_paths") as mock_fp: | |
| mock_fp.get_input_directory.return_value = str(input_dir) | |
| mock_fp.get_output_directory.return_value = str(output_dir) | |
| mock_fp.get_temp_directory.return_value = str(temp_dir) | |
| with patch( | |
| "app.assets.services.path_utils.get_comfy_models_folders", | |
| return_value=[("checkpoints", [str(models_dir)])], | |
| ): | |
| yield { | |
| "input": input_dir, | |
| "output": output_dir, | |
| "temp": temp_dir, | |
| "models": models_dir, | |
| } | |
| class TestGetAssetCategoryAndRelativePath: | |
| def test_input_file(self, fake_dirs): | |
| f = fake_dirs["input"] / "photo.png" | |
| f.touch() | |
| cat, rel = get_asset_category_and_relative_path(str(f)) | |
| assert cat == "input" | |
| assert rel == "photo.png" | |
| def test_output_file(self, fake_dirs): | |
| f = fake_dirs["output"] / "result.png" | |
| f.touch() | |
| cat, rel = get_asset_category_and_relative_path(str(f)) | |
| assert cat == "output" | |
| assert rel == "result.png" | |
| def test_temp_file(self, fake_dirs): | |
| """Regression: temp files must be categorised, not raise ValueError.""" | |
| f = fake_dirs["temp"] / "GLSLShader_output_00004_.png" | |
| f.touch() | |
| cat, rel = get_asset_category_and_relative_path(str(f)) | |
| assert cat == "temp" | |
| assert rel == "GLSLShader_output_00004_.png" | |
| def test_temp_file_in_subfolder(self, fake_dirs): | |
| sub = fake_dirs["temp"] / "sub" | |
| sub.mkdir() | |
| f = sub / "ComfyUI_temp_tczip_00004_.png" | |
| f.touch() | |
| cat, rel = get_asset_category_and_relative_path(str(f)) | |
| assert cat == "temp" | |
| assert os.path.normpath(rel) == os.path.normpath("sub/ComfyUI_temp_tczip_00004_.png") | |
| def test_model_file(self, fake_dirs): | |
| f = fake_dirs["models"] / "model.safetensors" | |
| f.touch() | |
| cat, rel = get_asset_category_and_relative_path(str(f)) | |
| assert cat == "models" | |
| def test_unknown_path_raises(self, fake_dirs): | |
| with pytest.raises(ValueError, match="not within"): | |
| get_asset_category_and_relative_path("/some/random/path.png") | |