Instructions to use wav/TemporalNet2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wav/TemporalNet2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("wav/TemporalNet2", 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
Create README.md
Browse files
README.md
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---
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license: openrail
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---
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# TemporalNet2 for Diffusers
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[CiaraRowles/TemporalNet2](https://huggingface.co/CiaraRowles/TemporalNet2) but converted for use with Diffusers!
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At the moment loading the checkpoint requires a tiny 3-line fix to Diffusers ([see this PR]). You can install the working version of diffusers with:
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```
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pip install git+https://github.com/JCBrouwer/diffusers@expose-controlnet-conditioning-channels
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```
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Then you can use the script included in this repo to stylize videos using text prompts.
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```
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python temporalvideo_hf.py --help
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```
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