Instructions to use cinemachina/4x4-spritebooth-vmax8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cinemachina/4x4-spritebooth-vmax8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cinemachina/4x4-spritebooth-vmax8", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 81915aa3abd21d5c4ee947e3c1562215331156df18318a30de60c64606ccfd08
- Size of remote file:
- 681 MB
- SHA256:
- 5f6889a32562a1dcba52d7c98208840c50d957905778364d2e4c7731ca9a0203
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.