Instructions to use Shriramnag/ShivAI-Image-to-Video with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Shriramnag/ShivAI-Image-to-Video with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Shriramnag/ShivAI-Image-to-Video", 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 tokenizer/processor_config.json from Shriramnag/ShivAI-Image-to-Video: direct link, hf CLI and curl.
- Browser
- Download file 70 Bytes
-
https://huggingface.co/Shriramnag/ShivAI-Image-to-Video/resolve/main/tokenizer/processor_config.json
- Command line
-
hf download hf://Shriramnag/ShivAI-Image-to-Video/tokenizer/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/Shriramnag/ShivAI-Image-to-Video/resolve/main/tokenizer/processor_config.json
70 Bytes
| { | |
| "image_seq_length": 256, | |
| "processor_class": "Gemma3Processor" | |
| } | |