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

Libraries

How to use oxide-lab/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("oxide-lab/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 oxide-lab/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 oxide-lab/LTX-Video-0.9.5-diffusers:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf oxide-lab/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 oxide-lab/LTX-Video-0.9.5-diffusers:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf oxide-lab/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 oxide-lab/LTX-Video-0.9.5-diffusers:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf oxide-lab/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 oxide-lab/LTX-Video-0.9.5-diffusers:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf oxide-lab/LTX-Video-0.9.5-diffusers:Q5_K_M

Use Docker

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

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

Unsloth Studio

How to use oxide-lab/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 oxide-lab/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 oxide-lab/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 oxide-lab/LTX-Video-0.9.5-diffusers to start chatting

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

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull oxide-lab/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 / vae /config.json

FerrisMind

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	{
	"_class_name": "AutoencoderKLLTXVideo",
	"_diffusers_version": "0.33.0.dev0",
	"block_out_channels": [
	128,
	256,
	512,
	1024,
	2048
	],
	"decoder_block_out_channels": [
	256,
	512,
	1024
	],
	"decoder_causal": false,
	"decoder_inject_noise": [
	false,
	false,
	false,
	false
	],
	"decoder_layers_per_block": [
	5,
	5,
	5,
	5
	],
	"decoder_spatio_temporal_scaling": [
	true,
	true,
	true
	],
	"down_block_types": [
	"LTXVideo095DownBlock3D",
	"LTXVideo095DownBlock3D",
	"LTXVideo095DownBlock3D",
	"LTXVideo095DownBlock3D"
	],
	"downsample_type": [
	"spatial",
	"temporal",
	"spatiotemporal",
	"spatiotemporal"
	],
	"encoder_causal": true,
	"in_channels": 3,
	"latent_channels": 128,
	"layers_per_block": [
	4,
	6,
	6,
	2,
	2
	],
	"out_channels": 3,
	"patch_size": 4,
	"patch_size_t": 1,
	"resnet_norm_eps": 1e-06,
	"scaling_factor": 1.0,
	"spatial_compression_ratio": 32,
	"spatio_temporal_scaling": [
	true,
	true,
	true,
	true
	],
	"temporal_compression_ratio": 8,
	"timestep_conditioning": true,
	"upsample_factor": [
	2,
	2,
	2
	],
	"upsample_residual": [
	true,
	true,
	true
	]
	}