Instructions to use Runware/Video-Upscale with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Runware/Video-Upscale with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Runware/Video-Upscale", 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 text_encoder/pytorch_model.bin from Runware/Video-Upscale: direct link, hf CLI and curl.
- Browser
- Download file 1.36 GB
-
https://huggingface.co/Runware/Video-Upscale/resolve/ab052d3739d35e7cdb1394ef6eb7cec12b46d8eb/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://Runware/Video-Upscale@ab052d3739d35e7cdb1394ef6eb7cec12b46d8eb/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Runware/Video-Upscale/resolve/ab052d3739d35e7cdb1394ef6eb7cec12b46d8eb/text_encoder/pytorch_model.bin
1.36 GB
- Xet hash:
- 0ad4e37742050819b091c514b600aa6eded9ee15241869c97506c0afc169c06c
- Size of remote file:
- 1.36 GB
- SHA256:
- 2188379b05015f531d61503e714234d00a64939792f3098b324e516547f0194f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.