Instructions to use EPFL-VILAB/flextok_vae_c8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use EPFL-VILAB/flextok_vae_c8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("EPFL-VILAB/flextok_vae_c8", 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:
- 2a890f5247885d17918b14184ea53d44f9f11b48856dfa9cd46f4de348facc8e
- Size of remote file:
- 335 MB
- SHA256:
- ab5f7b81b6c8363ee38da10763e0a8d24af1e95a9900d0d138641bf664aaca55
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