Instructions to use vidfom/Ltx-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use vidfom/Ltx-3 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -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 serve -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/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -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
- Llama new
- LM Studio
- Jan
- Ollama
How to use vidfom/Ltx-3 with Ollama:
ollama run hf.co/vidfom/Ltx-3:UD-Q4_K_XL
- Unsloth Desktop
- 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
- Atomic Chat
Download ComfyUI/comfy_extras/nodes_audio_encoder.py from vidfom/Ltx-3: direct link, hf CLI and curl.
- Browser
- Download file 2.05 kB
-
https://huggingface.co/vidfom/Ltx-3/resolve/main/ComfyUI/comfy_extras/nodes_audio_encoder.py
- Command line
-
hf download hf://vidfom/Ltx-3/ComfyUI/comfy_extras/nodes_audio_encoder.py
-
curl -L -o nodes_audio_encoder.py https://huggingface.co/vidfom/Ltx-3/resolve/main/ComfyUI/comfy_extras/nodes_audio_encoder.py
2.05 kB
| 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() | |