Instructions to use Jingya/tiny-stable-video-diffusion-img2vid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jingya/tiny-stable-video-diffusion-img2vid with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Jingya/tiny-stable-video-diffusion-img2vid", 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
- Xet hash:
- 68a56d5d3b9a4929d15216ef01a1a59644f19ea16c91d608172095075b578a62
- Size of remote file:
- 300 kB
- SHA256:
- bf3d19058d12326e930c1086726f37574aa0918081c2fabac94ec922c7dd7765
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.