Instructions to use Anzhc/MS-LC-EQ-D-VR_VAE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Anzhc/MS-LC-EQ-D-VR_VAE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Anzhc/MS-LC-EQ-D-VR_VAE", 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:
- 3702303f8c0881aaeb93e0b4d5bc4b296f302dd051c78ee4eb59c91742914abe
- Size of remote file:
- 335 MB
- SHA256:
- b29a67ad188aa787d6d9ec2192c91fae233f420e71fd6f2eef7a4656249d7240
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