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 json | |
| from urllib import request | |
| #This is the ComfyUI api prompt format. | |
| #If you want it for a specific workflow you can "File -> Export (API)" in the interface. | |
| #this is the one for the default workflow | |
| prompt_text = """ | |
| { | |
| "3": { | |
| "class_type": "KSampler", | |
| "inputs": { | |
| "cfg": 8, | |
| "denoise": 1, | |
| "latent_image": [ | |
| "5", | |
| 0 | |
| ], | |
| "model": [ | |
| "4", | |
| 0 | |
| ], | |
| "negative": [ | |
| "7", | |
| 0 | |
| ], | |
| "positive": [ | |
| "6", | |
| 0 | |
| ], | |
| "sampler_name": "euler", | |
| "scheduler": "normal", | |
| "seed": 8566257, | |
| "steps": 20 | |
| } | |
| }, | |
| "4": { | |
| "class_type": "CheckpointLoaderSimple", | |
| "inputs": { | |
| "ckpt_name": "v1-5-pruned-emaonly.safetensors" | |
| } | |
| }, | |
| "5": { | |
| "class_type": "EmptyLatentImage", | |
| "inputs": { | |
| "batch_size": 1, | |
| "height": 512, | |
| "width": 512 | |
| } | |
| }, | |
| "6": { | |
| "class_type": "CLIPTextEncode", | |
| "inputs": { | |
| "clip": [ | |
| "4", | |
| 1 | |
| ], | |
| "text": "masterpiece best quality girl" | |
| } | |
| }, | |
| "7": { | |
| "class_type": "CLIPTextEncode", | |
| "inputs": { | |
| "clip": [ | |
| "4", | |
| 1 | |
| ], | |
| "text": "bad hands" | |
| } | |
| }, | |
| "8": { | |
| "class_type": "VAEDecode", | |
| "inputs": { | |
| "samples": [ | |
| "3", | |
| 0 | |
| ], | |
| "vae": [ | |
| "4", | |
| 2 | |
| ] | |
| } | |
| }, | |
| "9": { | |
| "class_type": "SaveImage", | |
| "inputs": { | |
| "filename_prefix": "ComfyUI", | |
| "images": [ | |
| "8", | |
| 0 | |
| ] | |
| } | |
| } | |
| } | |
| """ | |
| def queue_prompt(prompt): | |
| p = {"prompt": prompt} | |
| # If the workflow contains API nodes, you can add a Comfy API key to the `extra_data`` field of the payload. | |
| # p["extra_data"] = { | |
| # "api_key_comfy_org": "comfyui-87d01e28d*******************************************************" # replace with real key | |
| # } | |
| # See: https://docs.comfy.org/tutorials/api-nodes/overview | |
| # Generate a key here: https://platform.comfy.org/login | |
| data = json.dumps(p).encode('utf-8') | |
| req = request.Request("http://127.0.0.1:8188/prompt", data=data) | |
| request.urlopen(req) | |
| prompt = json.loads(prompt_text) | |
| #set the text prompt for our positive CLIPTextEncode | |
| prompt["6"]["inputs"]["text"] = "masterpiece best quality man" | |
| #set the seed for our KSampler node | |
| prompt["3"]["inputs"]["seed"] = 5 | |
| queue_prompt(prompt) | |