Instructions to use yevvonlim/mam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yevvonlim/mam with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("yevvonlim/mam", 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:
- fb5ce472eec081665c90fb38c271ba779c9c29d04cb3bc6c3508a699e9c355b1
- Size of remote file:
- 1.05 GB
- SHA256:
- bb1bfe076bddcfe8c8dbe3e5388480b2a5912b22ff00aa839125f197b14fd935
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