Instructions to use mit-han-lab/dc-ae-f64c128-in-1.0-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mit-han-lab/dc-ae-f64c128-in-1.0-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mit-han-lab/dc-ae-f64c128-in-1.0-diffusers", 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:
- c662f2afbbbe9fbcb1ac8362bab405c21ee634289ff4dffced314a398e5f6ef3
- Size of remote file:
- 36 MB
- SHA256:
- e1ae2defa971a773cc1028d4a9aaa7110046bd72bc407ae57cfdabd0c01a0c23
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