Instructions to use lightx2v/Autoencoders with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lightx2v/Autoencoders with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lightx2v/Autoencoders", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Diffusion Single File
How to use lightx2v/Autoencoders with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Update model card to reflect Light Forcing paper and code
#14
by nielsr HF Staff - opened
Hi! I'm Niels from the community science team at Hugging Face. I've opened this PR to update your model card to document the research paper "Light Forcing: Accelerating Autoregressive Video Diffusion via Sparse Attention".
This PR:
- Adds links to the ArXiv paper and official GitHub repository.
- Standardizes the metadata (correcting the
pipeline_tagand fixing a typo in the tags). - Adds a "Quick Start" section with sample usage for inference based on the GitHub documentation.
- Preserves your existing comprehensive VAE documentation.