Instructions to use vidfom/Ltx-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

Ltx-3 / ComfyUI /comfy_api_nodes /apis /wavespeed.py

vidfom

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	from pydantic import BaseModel, Field


	class SeedVR2ImageRequest(BaseModel):
	image: str = Field(...)
	target_resolution: str = Field(...)
	output_format: str = Field("png")
	enable_sync_mode: bool = Field(False)


	class FlashVSRRequest(BaseModel):
	target_resolution: str = Field(...)
	video: str = Field(...)
	duration: float = Field(...)


	class TaskCreatedDataResponse(BaseModel):
	id: str = Field(...)


	class TaskCreatedResponse(BaseModel):
	code: int = Field(...)
	message: str = Field(...)
	data: TaskCreatedDataResponse \| None = Field(None)


	class TaskResultDataResponse(BaseModel):
	status: str = Field(...)
	outputs: list[str] = Field([])


	class TaskResultResponse(BaseModel):
	code: int = Field(...)
	message: str = Field(...)
	data: TaskResultDataResponse \| None = Field(None)