Feature Extraction
Diffusers
Safetensors
English
autoencoder
vision-foundation-model
dinov2
dinov3
mae
siglip2
pae
Instructions to use BiliSakura/PAE-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/PAE-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("BiliSakura/PAE-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
File size: 1,374 Bytes
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"_class_name": "PAEDecoder",
"decoder_patch_size": 16,
"encoder_config_path": "facebook/dinov3-vitl16-pretrain-lvd1689m",
"encoder_hidden_size": 1024,
"encoder_input_size": 256,
"encoder_num_heads": 16,
"encoder_patch_size": 16,
"latent_dim": 32,
"latent_mean": [
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0.484375,
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],
"latent_multiplier": 1.0,
"latent_std": [
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]
}
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