Instructions to use thu-ml/CogVideoX-RIFLEx-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use thu-ml/CogVideoX-RIFLEx-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("thu-ml/CogVideoX-RIFLEx-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
Improve model card
Browse filesThis PR makes sure the model can be found at https://huggingface.co/models?pipeline_tag=text-to-video&sort=trending. It also
links the model to https://huggingface.co/papers/2502.15894 and https://riflex-video.github.io/.
Finally, it also ensures the proper library is recognized.
README.md
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license: apache-2.0
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license: apache-2.0
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pipeline_tag: text-to-video
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library_name: diffusers
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This repository contains the model described in the paper [RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers](https://huggingface.co/papers/2502.15894).
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Project page: https://riflex-video.github.io/.
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Code: https://github.com/thu-ml/RIFLEx
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