Instructions to use FoundationVision/FlashVideo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FoundationVision/FlashVideo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FoundationVision/FlashVideo", torch_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
| license: mit | |
| library_name: diffusers | |
| pipeline_tag: text-to-video | |
| # FlashVideo | |
| This repository contains the weights for the model described in the paper [FlashVideo: Flowing Fidelity to Detail for Efficient High-Resolution Video Generation](https://huggingface.co/papers/2502.05179). | |
| Project page: https://jshilong.github.io/flashvideo-page/ |