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: 3,110 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 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | from ._helpers import get_fs_object_size
from .client import (
ApiEndpoint,
poll_op,
poll_op_raw,
sync_op,
sync_op_raw,
)
from .conversions import (
audio_bytes_to_audio_input,
audio_input_to_mp3,
audio_ndarray_to_bytesio,
audio_tensor_to_contiguous_ndarray,
audio_to_base64_string,
bytesio_to_image_tensor,
convert_mask_to_image,
downscale_image_tensor,
downscale_image_tensor_by_max_side,
image_tensor_pair_to_batch,
pil_to_bytesio,
resize_mask_to_image,
tensor_to_base64_string,
tensor_to_bytesio,
tensor_to_pil,
text_filepath_to_base64_string,
text_filepath_to_data_uri,
trim_video,
video_to_base64_string,
)
from .download_helpers import (
download_url_as_bytesio,
download_url_to_bytesio,
download_url_to_file_3d,
download_url_to_image_tensor,
download_url_to_video_output,
)
from .upload_helpers import (
upload_3d_model_to_comfyapi,
upload_audio_to_comfyapi,
upload_file_to_comfyapi,
upload_image_to_comfyapi,
upload_images_to_comfyapi,
upload_video_to_comfyapi,
)
from .validation_utils import (
get_image_dimensions,
get_number_of_images,
validate_aspect_ratio_string,
validate_audio_duration,
validate_container_format_is_mp4,
validate_image_aspect_ratio,
validate_image_dimensions,
validate_images_aspect_ratio_closeness,
validate_string,
validate_video_dimensions,
validate_video_duration,
validate_video_frame_count,
)
__all__ = [
# API client
"ApiEndpoint",
"poll_op",
"poll_op_raw",
"sync_op",
"sync_op_raw",
# Upload helpers
"upload_3d_model_to_comfyapi",
"upload_audio_to_comfyapi",
"upload_file_to_comfyapi",
"upload_image_to_comfyapi",
"upload_images_to_comfyapi",
"upload_video_to_comfyapi",
# Download helpers
"download_url_as_bytesio",
"download_url_to_bytesio",
"download_url_to_file_3d",
"download_url_to_image_tensor",
"download_url_to_video_output",
# Conversions
"audio_bytes_to_audio_input",
"audio_input_to_mp3",
"audio_ndarray_to_bytesio",
"audio_tensor_to_contiguous_ndarray",
"audio_to_base64_string",
"bytesio_to_image_tensor",
"convert_mask_to_image",
"downscale_image_tensor",
"downscale_image_tensor_by_max_side",
"image_tensor_pair_to_batch",
"pil_to_bytesio",
"resize_mask_to_image",
"tensor_to_base64_string",
"tensor_to_bytesio",
"tensor_to_pil",
"text_filepath_to_base64_string",
"text_filepath_to_data_uri",
"trim_video",
"video_to_base64_string",
# Validation utilities
"get_image_dimensions",
"get_number_of_images",
"validate_aspect_ratio_string",
"validate_audio_duration",
"validate_container_format_is_mp4",
"validate_image_aspect_ratio",
"validate_image_dimensions",
"validate_images_aspect_ratio_closeness",
"validate_string",
"validate_video_dimensions",
"validate_video_duration",
"validate_video_frame_count",
# Misc functions
"get_fs_object_size",
]
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