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", 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
Add library name, pipeline tag
Browse filesThis PR adds a model card, which adds a link to the Github repo as well as a link to the project page.
It ensures the model can be found at https://huggingface.co/models?pipeline_tag=text-to-video and has the proper library tag.
README.md
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license: mit
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---
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license: mit
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library_name: diffusers
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pipeline_tag: text-to-video
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---
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# FlashVideo
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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).
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Project page: https://jshilong.github.io/flashvideo-page/
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