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:
- f8e679908b3b5d9bcf8f297e892d01b7160b7caf29c87335a0bd7a6a1c9d433e
- Size of remote file:
- 2.75 MB
- SHA256:
- a798b775cbc6dcd80ebf1a4c320c81bd403523679dbde26a26c7adcd25a03d56
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