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: 2,567 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 | from __future__ import annotations
from comfy_api.latest import IO
def validate_node_input(
received_type: str, input_type: str, strict: bool = False
) -> bool:
"""
received_type and input_type are both strings of the form "T1,T2,...".
If strict is True, the input_type must contain the received_type.
For example, if received_type is "STRING" and input_type is "STRING,INT",
this will return True. But if received_type is "STRING,INT" and input_type is
"INT", this will return False.
If strict is False, the input_type must have overlap with the received_type.
For example, if received_type is "STRING,BOOLEAN" and input_type is "STRING,INT",
this will return True.
Supports pre-union type extension behaviour of ``__ne__`` overrides.
"""
# If the types are exactly the same, we can return immediately
# Use pre-union behaviour: inverse of `__ne__`
# NOTE: this lets legacy '*' Any types work that override the __ne__ method of the str class.
if not received_type != input_type:
return True
# If one of the types is '*', we can return True immediately; this is the 'Any' type.
if received_type == IO.AnyType.io_type or input_type == IO.AnyType.io_type:
return True
# If the received type or input_type is a MatchType, we can return True immediately;
# validation for this is handled by the frontend
if received_type == IO.MatchType.io_type or input_type == IO.MatchType.io_type:
return True
# This accounts for some custom nodes that output lists of options as the type;
# if we ever want to break them on purpose, this can be removed
if isinstance(received_type, list) and input_type == IO.Combo.io_type:
return True
# Not equal, and not strings
if not isinstance(received_type, str) or not isinstance(input_type, str):
return False
# Split the type strings into sets for comparison
received_types = set(t.strip() for t in received_type.split(","))
input_types = set(t.strip() for t in input_type.split(","))
# If any of the types is '*', we can return True immediately; this is the 'Any' type.
if IO.AnyType.io_type in received_types or IO.AnyType.io_type in input_types:
return True
if strict:
# In strict mode, all received types must be in the input types
return received_types.issubset(input_types)
else:
# In non-strict mode, there must be at least one type in common
return len(received_types.intersection(input_types)) > 0
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