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 folder_paths | |
| import comfy.audio_encoders.audio_encoders | |
| import comfy.utils | |
| from typing_extensions import override | |
| from comfy_api.latest import ComfyExtension, io | |
| class AudioEncoderLoader(io.ComfyNode): | |
| def define_schema(cls) -> io.Schema: | |
| return io.Schema( | |
| node_id="AudioEncoderLoader", | |
| category="loaders", | |
| inputs=[ | |
| io.Combo.Input( | |
| "audio_encoder_name", | |
| options=folder_paths.get_filename_list("audio_encoders"), | |
| ), | |
| ], | |
| outputs=[io.AudioEncoder.Output()], | |
| ) | |
| def execute(cls, audio_encoder_name) -> io.NodeOutput: | |
| audio_encoder_name = folder_paths.get_full_path_or_raise("audio_encoders", audio_encoder_name) | |
| sd = comfy.utils.load_torch_file(audio_encoder_name, safe_load=True) | |
| audio_encoder = comfy.audio_encoders.audio_encoders.load_audio_encoder_from_sd(sd) | |
| if audio_encoder is None: | |
| raise RuntimeError("ERROR: audio encoder file is invalid and does not contain a valid model.") | |
| return io.NodeOutput(audio_encoder) | |
| class AudioEncoderEncode(io.ComfyNode): | |
| def define_schema(cls) -> io.Schema: | |
| return io.Schema( | |
| node_id="AudioEncoderEncode", | |
| category="conditioning", | |
| inputs=[ | |
| io.AudioEncoder.Input("audio_encoder"), | |
| io.Audio.Input("audio"), | |
| ], | |
| outputs=[io.AudioEncoderOutput.Output()], | |
| ) | |
| def execute(cls, audio_encoder, audio) -> io.NodeOutput: | |
| output = audio_encoder.encode_audio(audio["waveform"], audio["sample_rate"]) | |
| return io.NodeOutput(output) | |
| class AudioEncoder(ComfyExtension): | |
| async def get_node_list(self) -> list[type[io.ComfyNode]]: | |
| return [ | |
| AudioEncoderLoader, | |
| AudioEncoderEncode, | |
| ] | |
| async def comfy_entrypoint() -> AudioEncoder: | |
| return AudioEncoder() | |