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 Settings
- 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
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
| """The Dynamic Context node.""" | |
| from mimetypes import add_type | |
| from .constants import get_category, get_name | |
| from .utils import ByPassTypeTuple, FlexibleOptionalInputType | |
| class RgthreeDynamicContext: | |
| """The Dynamic Context node. | |
| Similar to the static Context and Context Big nodes, this allows users to add any number and | |
| variety of inputs to a Dynamic Context node, and return the outputs by key name. | |
| """ | |
| NAME = get_name("Dynamic Context") | |
| CATEGORY = get_category() | |
| def INPUT_TYPES(cls): # pylint: disable = invalid-name,missing-function-docstring | |
| return { | |
| "required": {}, | |
| "optional": FlexibleOptionalInputType(add_type), | |
| "hidden": {}, | |
| } | |
| RETURN_TYPES = ByPassTypeTuple(("RGTHREE_DYNAMIC_CONTEXT",)) | |
| RETURN_NAMES = ByPassTypeTuple(("CONTEXT",)) | |
| FUNCTION = "main" | |
| def main(self, **kwargs): | |
| """Creates a new context from the provided data, with an optional base ctx to start. | |
| This node takes a list of named inputs that are the named keys (with an optional "+ " prefix) | |
| which are to be stored within the ctx dict as well as a list of keys contained in `output_keys` | |
| to determine the list of output data. | |
| """ | |
| base_ctx = kwargs.get('base_ctx', None) | |
| output_keys = kwargs.get('output_keys', None) | |
| new_ctx = base_ctx.copy() if base_ctx is not None else {} | |
| for key_raw, value in kwargs.items(): | |
| if key_raw in ['base_ctx', 'output_keys']: | |
| continue | |
| key = key_raw.upper() | |
| if key.startswith('+ '): | |
| key = key[2:] | |
| new_ctx[key] = value | |
| print(new_ctx) | |
| res = [new_ctx] | |
| output_keys = output_keys.split(',') if output_keys is not None else [] | |
| for key in output_keys: | |
| res.append(new_ctx[key] if key in new_ctx else None) | |
| return tuple(res) | |