Instructions to use svjack/CogvideoX-Interpolation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use svjack/CogvideoX-Interpolation with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("svjack/CogvideoX-Interpolation", 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
Download vae/diffusion_pytorch_model.safetensors from svjack/CogvideoX-Interpolation: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/svjack/CogvideoX-Interpolation/resolve/5a735d2bdb851e2f68246ca5ecce79321e96f976/vae/diffusion_pytorch_model.safetensors
- Command line
-
hf download hf://svjack/CogvideoX-Interpolation@5a735d2bdb851e2f68246ca5ecce79321e96f976/vae/diffusion_pytorch_model.safetensors
-
curl -L -o diffusion_pytorch_model.safetensors https://huggingface.co/svjack/CogvideoX-Interpolation/resolve/5a735d2bdb851e2f68246ca5ecce79321e96f976/vae/diffusion_pytorch_model.safetensors
431 MB
- Xet hash:
- 2d43348fb1ff0f2c073eb25d2243129df53ab5a8118c14e270484a473a6dcabd
- Size of remote file:
- 431 MB
- SHA256:
- bd47d57ad948ff80da0af0cb2e4dcdef65073aba59bccfd383ada9a7d1c02024
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