Instructions to use vladmandic/longanimatediff-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vladmandic/longanimatediff-32 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vladmandic/longanimatediff-32", 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:
- 052a7edb3f3cd8750a6d70490b889f7d382a0f33c0c14d8e85c6a3b9fa9e3137
- Size of remote file:
- 1.82 GB
- SHA256:
- bd11ca6a55e5928bae83fa81a3efa9e5c0c3c5f8d80c2304d17a55455fbc6a18
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