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 asyncio | |
| import time | |
| from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict | |
| from comfy_api.v0_0_2 import ComfyAPI, ComfyAPISync | |
| api = ComfyAPI() | |
| api_sync = ComfyAPISync() | |
| class TestAsyncProgressUpdate(ComfyNodeABC): | |
| """Test node with async VALIDATE_INPUTS.""" | |
| def INPUT_TYPES(cls) -> InputTypeDict: | |
| return { | |
| "required": { | |
| "value": (IO.ANY, {}), | |
| "sleep_seconds": (IO.FLOAT, {"default": 1.0}), | |
| }, | |
| } | |
| RETURN_TYPES = (IO.ANY,) | |
| FUNCTION = "execute" | |
| CATEGORY = "_for_testing/async" | |
| async def execute(self, value, sleep_seconds): | |
| start = time.time() | |
| expiration = start + sleep_seconds | |
| now = start | |
| while now < expiration: | |
| now = time.time() | |
| await api.execution.set_progress( | |
| value=(now - start) / sleep_seconds, | |
| max_value=1.0, | |
| ) | |
| await asyncio.sleep(0.01) | |
| return (value,) | |
| class TestSyncProgressUpdate(ComfyNodeABC): | |
| """Test node with async VALIDATE_INPUTS.""" | |
| def INPUT_TYPES(cls) -> InputTypeDict: | |
| return { | |
| "required": { | |
| "value": (IO.ANY, {}), | |
| "sleep_seconds": (IO.FLOAT, {"default": 1.0}), | |
| }, | |
| } | |
| RETURN_TYPES = (IO.ANY,) | |
| FUNCTION = "execute" | |
| CATEGORY = "_for_testing/async" | |
| def execute(self, value, sleep_seconds): | |
| start = time.time() | |
| expiration = start + sleep_seconds | |
| now = start | |
| while now < expiration: | |
| now = time.time() | |
| api_sync.execution.set_progress( | |
| value=(now - start) / sleep_seconds, | |
| max_value=1.0, | |
| ) | |
| time.sleep(0.01) | |
| return (value,) | |
| API_TEST_NODE_CLASS_MAPPINGS = { | |
| "TestAsyncProgressUpdate": TestAsyncProgressUpdate, | |
| "TestSyncProgressUpdate": TestSyncProgressUpdate, | |
| } | |
| API_TEST_NODE_DISPLAY_NAME_MAPPINGS = { | |
| "TestAsyncProgressUpdate": "Async Progress Update Test Node", | |
| "TestSyncProgressUpdate": "Sync Progress Update Test Node", | |
| } | |