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
File size: 2,869 Bytes
e00eceb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | #!/usr/bin/env python3
"""
Script to generate .pyi stub files for the synchronous API wrappers.
This allows generating stubs without running the full ComfyUI application.
"""
import os
import sys
import logging
import importlib
# Add ComfyUI to path so we can import modules
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from comfy_api.internal.async_to_sync import AsyncToSyncConverter
from comfy_api.version_list import supported_versions
def generate_stubs_for_module(module_name: str) -> None:
"""Generate stub files for a specific module that exports ComfyAPI and ComfyAPISync."""
try:
# Import the module
module = importlib.import_module(module_name)
# Check if module has ComfyAPISync (the sync wrapper)
if hasattr(module, "ComfyAPISync"):
# Module already has a sync class
api_class = getattr(module, "ComfyAPI", None)
sync_class = getattr(module, "ComfyAPISync")
if api_class:
# Generate the stub file
AsyncToSyncConverter.generate_stub_file(api_class, sync_class)
logging.info(f"Generated stub file for {module_name}")
else:
logging.warning(
f"Module {module_name} has ComfyAPISync but no ComfyAPI"
)
elif hasattr(module, "ComfyAPI"):
# Module only has async API, need to create sync wrapper first
from comfy_api.internal.async_to_sync import create_sync_class
api_class = getattr(module, "ComfyAPI")
sync_class = create_sync_class(api_class)
# Generate the stub file
AsyncToSyncConverter.generate_stub_file(api_class, sync_class)
logging.info(f"Generated stub file for {module_name}")
else:
logging.warning(
f"Module {module_name} does not export ComfyAPI or ComfyAPISync"
)
except Exception as e:
logging.error(f"Failed to generate stub for {module_name}: {e}")
import traceback
traceback.print_exc()
def main():
"""Main function to generate all API stub files."""
logging.basicConfig(level=logging.INFO)
logging.info("Starting stub generation...")
# Dynamically get module names from supported_versions
api_modules = []
for api_class in supported_versions:
# Extract module name from the class
module_name = api_class.__module__
if module_name not in api_modules:
api_modules.append(module_name)
logging.info(f"Found {len(api_modules)} API modules: {api_modules}")
# Generate stubs for each module
for module_name in api_modules:
generate_stubs_for_module(module_name)
logging.info("Stub generation complete!")
if __name__ == "__main__":
main()
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