Instructions to use WaveCut/Anima-Preview-3-SDNQ-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/Anima-Preview-3-SDNQ-int8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WaveCut/Anima-Preview-3-SDNQ-int8", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Cosmos
How to use WaveCut/Anima-Preview-3-SDNQ-int8 with Cosmos:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 927de2fc2e5a69c7d29da4cb635de723cfa513be987652e19f1da28a1bd18002
- Size of remote file:
- 492 MB
- SHA256:
- b14bddbfaa2e6523001883e7cf21d308959f449dd021a607e8999c81f8ec8c0b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.