Instructions to use madebyollin/taesdxl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use madebyollin/taesdxl with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("madebyollin/taesdxl", 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:
- 57052969dc6a7b6a2d23232a4d382f82e1e71e6db5c7cf8a67ed01b1c938a1cf
- Size of remote file:
- 4.9 MB
- SHA256:
- 9f37c0b28f72ec4ca835dc7dbf05255bdf323cde8cf12a304674f106466c98ef
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