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
| from __future__ import annotations | |
| from enum import Enum | |
| from typing import Optional | |
| from pydantic import BaseModel, Field, confloat | |
| class StabilityFormat(str, Enum): | |
| png = 'png' | |
| jpeg = 'jpeg' | |
| webp = 'webp' | |
| class StabilityAspectRatio(str, Enum): | |
| ratio_1_1 = "1:1" | |
| ratio_16_9 = "16:9" | |
| ratio_9_16 = "9:16" | |
| ratio_3_2 = "3:2" | |
| ratio_2_3 = "2:3" | |
| ratio_5_4 = "5:4" | |
| ratio_4_5 = "4:5" | |
| ratio_21_9 = "21:9" | |
| ratio_9_21 = "9:21" | |
| def get_stability_style_presets(include_none=True): | |
| presets = [] | |
| if include_none: | |
| presets.append("None") | |
| return presets + [x.value for x in StabilityStylePreset] | |
| class StabilityStylePreset(str, Enum): | |
| _3d_model = "3d-model" | |
| analog_film = "analog-film" | |
| anime = "anime" | |
| cinematic = "cinematic" | |
| comic_book = "comic-book" | |
| digital_art = "digital-art" | |
| enhance = "enhance" | |
| fantasy_art = "fantasy-art" | |
| isometric = "isometric" | |
| line_art = "line-art" | |
| low_poly = "low-poly" | |
| modeling_compound = "modeling-compound" | |
| neon_punk = "neon-punk" | |
| origami = "origami" | |
| photographic = "photographic" | |
| pixel_art = "pixel-art" | |
| tile_texture = "tile-texture" | |
| class Stability_SD3_5_Model(str, Enum): | |
| sd3_5_large = "sd3.5-large" | |
| # sd3_5_large_turbo = "sd3.5-large-turbo" | |
| sd3_5_medium = "sd3.5-medium" | |
| class Stability_SD3_5_GenerationMode(str, Enum): | |
| text_to_image = "text-to-image" | |
| image_to_image = "image-to-image" | |
| class StabilityStable3_5Request(BaseModel): | |
| model: str = Field(...) | |
| mode: str = Field(...) | |
| prompt: str = Field(...) | |
| negative_prompt: Optional[str] = Field(None) | |
| aspect_ratio: Optional[str] = Field(None) | |
| seed: Optional[int] = Field(None) | |
| output_format: Optional[str] = Field(StabilityFormat.png.value) | |
| image: Optional[str] = Field(None) | |
| style_preset: Optional[str] = Field(None) | |
| cfg_scale: float = Field(...) | |
| strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None) | |
| class StabilityUpscaleConservativeRequest(BaseModel): | |
| prompt: str = Field(...) | |
| negative_prompt: Optional[str] = Field(None) | |
| seed: Optional[int] = Field(None) | |
| output_format: Optional[str] = Field(StabilityFormat.png.value) | |
| image: Optional[str] = Field(None) | |
| creativity: Optional[confloat(ge=0.2, le=0.5)] = Field(None) | |
| class StabilityUpscaleCreativeRequest(BaseModel): | |
| prompt: str = Field(...) | |
| negative_prompt: Optional[str] = Field(None) | |
| seed: Optional[int] = Field(None) | |
| output_format: Optional[str] = Field(StabilityFormat.png.value) | |
| image: Optional[str] = Field(None) | |
| creativity: Optional[confloat(ge=0.1, le=0.5)] = Field(None) | |
| style_preset: Optional[str] = Field(None) | |
| class StabilityStableUltraRequest(BaseModel): | |
| prompt: str = Field(...) | |
| negative_prompt: Optional[str] = Field(None) | |
| aspect_ratio: Optional[str] = Field(None) | |
| seed: Optional[int] = Field(None) | |
| output_format: Optional[str] = Field(StabilityFormat.png.value) | |
| image: Optional[str] = Field(None) | |
| style_preset: Optional[str] = Field(None) | |
| strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None) | |
| class StabilityStableUltraResponse(BaseModel): | |
| image: Optional[str] = Field(None) | |
| finish_reason: Optional[str] = Field(None) | |
| seed: Optional[int] = Field(None) | |
| class StabilityResultsGetResponse(BaseModel): | |
| image: Optional[str] = Field(None) | |
| finish_reason: Optional[str] = Field(None) | |
| seed: Optional[int] = Field(None) | |
| id: Optional[str] = Field(None) | |
| name: Optional[str] = Field(None) | |
| errors: Optional[list[str]] = Field(None) | |
| status: Optional[str] = Field(None) | |
| result: Optional[str] = Field(None) | |
| class StabilityAsyncResponse(BaseModel): | |
| id: Optional[str] = Field(None) | |
| class StabilityTextToAudioRequest(BaseModel): | |
| model: str = Field(...) | |
| prompt: str = Field(...) | |
| duration: int = Field(190, ge=1, le=190) | |
| seed: int = Field(0, ge=0, le=4294967294) | |
| steps: int = Field(8, ge=4, le=8) | |
| output_format: str = Field("wav") | |
| class StabilityAudioToAudioRequest(StabilityTextToAudioRequest): | |
| strength: float = Field(0.01, ge=0.01, le=1.0) | |
| class StabilityAudioInpaintRequest(StabilityTextToAudioRequest): | |
| mask_start: int = Field(30, ge=0, le=190) | |
| mask_end: int = Field(190, ge=0, le=190) | |
| class StabilityAudioResponse(BaseModel): | |
| audio: Optional[str] = Field(None) | |