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
| import os | |
| import pytest | |
| # Command line arguments for pytest | |
| def pytest_addoption(parser): | |
| parser.addoption('--baseline_dir', action="store", default='tests/inference/baseline', help='Directory for ground-truth images') | |
| parser.addoption('--test_dir', action="store", default='tests/inference/samples', help='Directory for images to test') | |
| parser.addoption('--metrics_file', action="store", default='tests/metrics.md', help='Output file for metrics') | |
| parser.addoption('--img_output_dir', action="store", default='tests/compare/samples', help='Output directory for diff metric images') | |
| # This initializes args at the beginning of the test session | |
| def args_pytest(pytestconfig): | |
| args = {} | |
| args['baseline_dir'] = pytestconfig.getoption('baseline_dir') | |
| args['test_dir'] = pytestconfig.getoption('test_dir') | |
| args['metrics_file'] = pytestconfig.getoption('metrics_file') | |
| args['img_output_dir'] = pytestconfig.getoption('img_output_dir') | |
| # Initialize metrics file | |
| with open(args['metrics_file'], 'a') as f: | |
| # if file is empty, write header | |
| if os.stat(args['metrics_file']).st_size == 0: | |
| f.write("| date | run | file | status | value | \n") | |
| f.write("| --- | --- | --- | --- | --- | \n") | |
| return args | |
| def gather_file_basenames(directory: str): | |
| files = [] | |
| for file in os.listdir(directory): | |
| if file.endswith(".png"): | |
| files.append(file) | |
| return files | |
| # Creates the list of baseline file names to use as a fixture | |
| def pytest_generate_tests(metafunc): | |
| if "baseline_fname" in metafunc.fixturenames: | |
| baseline_fnames = gather_file_basenames(metafunc.config.getoption("baseline_dir")) | |
| metafunc.parametrize("baseline_fname", baseline_fnames) | |