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 { getResolver } from "./shared_utils.js"; | |
| import { getPngMetadata, getWebpMetadata } from "./comfyui_shim.js"; | |
| function parseWorkflowJson(stringJson) { | |
| stringJson = stringJson || "null"; | |
| stringJson = stringJson.replace(/:\s*NaN/g, ": null"); | |
| return JSON.parse(stringJson); | |
| } | |
| export async function tryToGetWorkflowDataFromEvent(e) { | |
| var _a, _b, _c, _d; | |
| let work; | |
| for (const file of ((_a = e.dataTransfer) === null || _a === void 0 ? void 0 : _a.files) || []) { | |
| const data = await tryToGetWorkflowDataFromFile(file); | |
| if (data.workflow || data.prompt) { | |
| return data; | |
| } | |
| } | |
| const validTypes = ["text/uri-list", "text/x-moz-url"]; | |
| const match = (((_b = e.dataTransfer) === null || _b === void 0 ? void 0 : _b.types) || []).find((t) => validTypes.find((v) => t === v)); | |
| if (match) { | |
| const uri = (_d = (_c = e.dataTransfer.getData(match)) === null || _c === void 0 ? void 0 : _c.split("\n")) === null || _d === void 0 ? void 0 : _d[0]; | |
| if (uri) { | |
| return tryToGetWorkflowDataFromFile(await (await fetch(uri)).blob()); | |
| } | |
| } | |
| return { workflow: null, prompt: null }; | |
| } | |
| export async function tryToGetWorkflowDataFromFile(file) { | |
| var _a; | |
| if (file.type === "image/png") { | |
| const pngInfo = await getPngMetadata(file); | |
| return { | |
| workflow: parseWorkflowJson(pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.workflow), | |
| prompt: parseWorkflowJson(pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.prompt), | |
| }; | |
| } | |
| if (file.type === "image/webp") { | |
| const pngInfo = await getWebpMetadata(file); | |
| const workflow = parseWorkflowJson((pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.workflow) || (pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.Workflow) || "null"); | |
| const prompt = parseWorkflowJson((pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.prompt) || (pngInfo === null || pngInfo === void 0 ? void 0 : pngInfo.Prompt) || "null"); | |
| return { workflow, prompt }; | |
| } | |
| if (file.type === "application/json" || ((_a = file.name) === null || _a === void 0 ? void 0 : _a.endsWith(".json"))) { | |
| const resolver = getResolver(); | |
| const reader = new FileReader(); | |
| reader.onload = async () => { | |
| const json = parseWorkflowJson(reader.result); | |
| const isApiJson = Object.values(json).every((v) => v.class_type); | |
| const prompt = isApiJson ? json : null; | |
| const workflow = !isApiJson && !(json === null || json === void 0 ? void 0 : json.templates) ? json : null; | |
| return { workflow, prompt }; | |
| }; | |
| return resolver.promise; | |
| } | |
| return { workflow: null, prompt: null }; | |
| } | |