Instructions to use kuzyn8/LTX-Video-0.9.5-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kuzyn8/LTX-Video-0.9.5-diffusers with Diffusers:

pip install -U diffusers transformers accelerate

import torch
from diffusers import DiffusionPipeline

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("kuzyn8/LTX-Video-0.9.5-diffusers", dtype=torch.bfloat16, device_map="cuda")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kuzyn8/LTX-Video-0.9.5-diffusers with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kuzyn8/LTX-Video-0.9.5-diffusers:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf kuzyn8/LTX-Video-0.9.5-diffusers:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kuzyn8/LTX-Video-0.9.5-diffusers:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf kuzyn8/LTX-Video-0.9.5-diffusers:Q5_K_M

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 kuzyn8/LTX-Video-0.9.5-diffusers:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf kuzyn8/LTX-Video-0.9.5-diffusers:Q5_K_M

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 kuzyn8/LTX-Video-0.9.5-diffusers:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kuzyn8/LTX-Video-0.9.5-diffusers:Q5_K_M

Use Docker

docker model run hf.co/kuzyn8/LTX-Video-0.9.5-diffusers:Q5_K_M

LM Studio
Jan
Ollama
How to use kuzyn8/LTX-Video-0.9.5-diffusers with Ollama:
```
ollama run hf.co/kuzyn8/LTX-Video-0.9.5-diffusers:Q5_K_M
```

Unsloth Studio

How to use kuzyn8/LTX-Video-0.9.5-diffusers 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 kuzyn8/LTX-Video-0.9.5-diffusers 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 kuzyn8/LTX-Video-0.9.5-diffusers to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kuzyn8/LTX-Video-0.9.5-diffusers to start chatting

Docker Model Runner
How to use kuzyn8/LTX-Video-0.9.5-diffusers with Docker Model Runner:
```
docker model run hf.co/kuzyn8/LTX-Video-0.9.5-diffusers:Q5_K_M
```

Lemonade

How to use kuzyn8/LTX-Video-0.9.5-diffusers with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kuzyn8/LTX-Video-0.9.5-diffusers:Q5_K_M

Run and chat with the model

lemonade run user.LTX-Video-0.9.5-diffusers-Q5_K_M

List all available models

lemonade list

Atomic Chat

LTX-Video-0.9.5-diffusers / README.md

kuzyn8

Duplicate from oxide-lab/LTX-Video-0.9.5-diffusers

943e763 9 days ago

preview code

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	---
	base_model:
	- Lightricks/LTX-Video-0.9.5
	library_name: diffusers
	tags:
	- ltx-video
	- text-to-video
	- candle
	- rust
	- gguf
	- oxide-lab
	- safetensors
	language:
	- en
	license: other
	pipeline_tag: text-to-video
	---

	# LTX-Video in Rust (Candle)

	This repository provides a high-performance, native Rust implementation of [LTX-Video](https://huggingface.co/Lightricks/LTX-Video) using the [Candle](https://github.com/huggingface/candle) ML framework.

	## Features

	- 🦀 Native Rust: No Python dependency required for inference.
	- 🚀 Performance: Optimized for NVIDIA GPUs with Flash Attention v2 and cuDNN.
	- 💾 Memory Efficient: Supports GGUF quantization for T5-XXL text encoder and VAE tiling/slicing for generating HD videos on consumer GPUs.
	- 🛠 Flexible: Easy to use CLI for video generation and library for custom integration.

	## Quick Start

	### Installation

	Ensure you have Rust and the CUDA Toolkit installed, then:

	```bash
	git clone https://github.com/FerrisMind/candle-video
	cd candle-video
	cargo build --release --features flash-attn,cudnn
	```

	### Video Generation

	```bash
	cargo run --example ltx-video --release -- \
	--local-weights ./models/ltx-video \
	--prompt "A serene mountain lake at sunset, photorealistic, 4k" \
	--width 768 --height 512 --num-frames 97 \
	--steps 30
	```

	## Performance & Memory

	\| Resolution \| Frames \| VRAM (BF16) \| VRAM (VAE Tiling) \|
	\|------------\|--------\|-------------\|-------------------\|
	\| 512x768 \| 97 \| ~8-13 GB \| ~8-9 GB \|

	Note: Using GGUF T5 encoder saves an additional ~8-12GB of VRAM.

	## Credits

	- Original Model: [Lightricks/LTX-Video](https://huggingface.co/Lightricks/LTX-Video)
	- Framework: [HuggingFace Candle](https://github.com/huggingface/candle)
	- T5 v1_1 XXl GGUF and Safetensors: [city96/LTX-Video-gguf](https://huggingface.co/city96/LTX-Video-gguf) (for GGUF support patterns, T5 XXl GGUF and Safetensors)

	---
	For more details, visit the main [GitHub Repository](https://github.com/FerrisMind/candle-video).