Instructions to use sayakpaul/q8-ltx-video with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sayakpaul/q8-ltx-video with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("sayakpaul/q8-ltx-video", 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
This is the Q8 version of the LTX-Video transformer (revision: 0c43a96).
This work largely builds on top of KONAKONA666/LTX-Video, utilitizing the Q8 kernels from KONAKONA666/q8_kernels. These kernels are optimized to run on the NVIDIA GPUs with ADA architecture (example RTX 4090). Huge shoutout to KONAKONA666 for working on these.
How does it differ from konakona/ltxvideo_q8?
The purpose of this checkpoint is to be more closely compatible with the official LTX-Video transformer shipped in diffusers.
Refer to this repository for more details on the checkpoint was obtained and how to use it. The repository also provides some benchmarks and here's a summary:
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