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
- Diffusers
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
File size: 1,251 Bytes
bbf231e | 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 | {
"_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
]
}
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