Instructions to use ucfzl/TLBVFI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ucfzl/TLBVFI with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ucfzl/TLBVFI", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
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README.md
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TLB-VFI leverages temporal information extraction in the pixel space (3D wavelet) and latent space (3D convolution and attention) to improve the temporal consistency of the model.
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<img src="https://github.com/ZonglinL/
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## Quantitative Results
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TLB-VFI leverages temporal information extraction in the pixel space (3D wavelet) and latent space (3D convolution and attention) to improve the temporal consistency of the model.
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<img src="https://github.com/ZonglinL/TLB-VFI/raw/main/images/overview.jpg" width=95%>
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## Quantitative Results
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