Instructions to use benjamin-paine/vidxtend with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use benjamin-paine/vidxtend with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("benjamin-paine/vidxtend", 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: Add pipeline tag, library name, link to code, and correct license
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by nielsr HF Staff - opened
README.md
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license:
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This repository contains a pruned and isolated pipeline for Stage 2 of [StreamingT2V](https://streamingt2v.github.io/), dubbed "VidXTend."
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# Usage
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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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This repository contains a pruned and isolated pipeline for Stage 2 of [StreamingT2V](https://streamingt2v.github.io/), dubbed "VidXTend."
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}
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Code: https://github.com/Picsart-AI-Research/StreamingT2V
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# Usage
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