Instructions to use adammoss/vae_kl_f4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use adammoss/vae_kl_f4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("adammoss/vae_kl_f4", 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 adammoss/vae_kl_f4: direct link, hf CLI and curl.
- Browser
- Download file 221 MB
-
https://huggingface.co/adammoss/vae_kl_f4/resolve/main/diffusion_pytorch_model.bin
- Command line
-
hf download hf://adammoss/vae_kl_f4/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/adammoss/vae_kl_f4/resolve/main/diffusion_pytorch_model.bin
221 MB
- Xet hash:
- df55195ecae16a9aae41356c23aecabeb955ef21901f578a0d4ad763941e5e41
- Size of remote file:
- 221 MB
- SHA256:
- efb20fa08a86ddda020a4d0cc4086ad5aa8f9647edb4b916863cf33b2a99d25c
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