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: 2,144 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 | import asyncio
import time
from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
from comfy_api.v0_0_2 import ComfyAPI, ComfyAPISync
api = ComfyAPI()
api_sync = ComfyAPISync()
class TestAsyncProgressUpdate(ComfyNodeABC):
"""Test node with async VALIDATE_INPUTS."""
@classmethod
def INPUT_TYPES(cls) -> InputTypeDict:
return {
"required": {
"value": (IO.ANY, {}),
"sleep_seconds": (IO.FLOAT, {"default": 1.0}),
},
}
RETURN_TYPES = (IO.ANY,)
FUNCTION = "execute"
CATEGORY = "_for_testing/async"
async def execute(self, value, sleep_seconds):
start = time.time()
expiration = start + sleep_seconds
now = start
while now < expiration:
now = time.time()
await api.execution.set_progress(
value=(now - start) / sleep_seconds,
max_value=1.0,
)
await asyncio.sleep(0.01)
return (value,)
class TestSyncProgressUpdate(ComfyNodeABC):
"""Test node with async VALIDATE_INPUTS."""
@classmethod
def INPUT_TYPES(cls) -> InputTypeDict:
return {
"required": {
"value": (IO.ANY, {}),
"sleep_seconds": (IO.FLOAT, {"default": 1.0}),
},
}
RETURN_TYPES = (IO.ANY,)
FUNCTION = "execute"
CATEGORY = "_for_testing/async"
def execute(self, value, sleep_seconds):
start = time.time()
expiration = start + sleep_seconds
now = start
while now < expiration:
now = time.time()
api_sync.execution.set_progress(
value=(now - start) / sleep_seconds,
max_value=1.0,
)
time.sleep(0.01)
return (value,)
API_TEST_NODE_CLASS_MAPPINGS = {
"TestAsyncProgressUpdate": TestAsyncProgressUpdate,
"TestSyncProgressUpdate": TestSyncProgressUpdate,
}
API_TEST_NODE_DISPLAY_NAME_MAPPINGS = {
"TestAsyncProgressUpdate": "Async Progress Update Test Node",
"TestSyncProgressUpdate": "Sync Progress Update Test Node",
}
|