Instructions to use mao1207/vqvae-amused with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mao1207/vqvae-amused with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mao1207/vqvae-amused", 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
Download diffusion_pytorch_model.bin from mao1207/vqvae-amused: direct link, hf CLI and curl.
- Browser
- Download file 585 MB
-
https://huggingface.co/mao1207/vqvae-amused/resolve/main/diffusion_pytorch_model.bin
- Command line
-
hf download hf://mao1207/vqvae-amused/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/mao1207/vqvae-amused/resolve/main/diffusion_pytorch_model.bin
585 MB
- Xet hash:
- a0b98572be7382892eee8552ebc64749de2f0bccb4489b3972fbae669c95582c
- Size of remote file:
- 585 MB
- SHA256:
- 3031be55687143b1021fa16354b8a20b07dc1f14e3bb959423ade52403ce3c66
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