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

File size: 412 Bytes

943e763

{
  "_class_name": "LTXPipeline",
  "_diffusers_version": "0.33.0.dev0",
  "scheduler": [
    "diffusers",
    "FlowMatchEulerDiscreteScheduler"
  ],
  "text_encoder": [
    "transformers",
    "T5EncoderModel"
  ],
  "tokenizer": [
    "transformers",
    "T5Tokenizer"
  ],
  "transformer": [
    "diffusers",
    "LTXVideoTransformer3DModel"
  ],
  "vae": [
    "diffusers",
    "AutoencoderKLLTXVideo"
  ]
}