Instructions to use blowing-up-groundhogs/emuru_vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use blowing-up-groundhogs/emuru_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("blowing-up-groundhogs/emuru_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
File size: 546 Bytes
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"_class_name": "AutoencoderKL",
"_diffusers_version": "0.27.1",
"act_fn": "silu",
"block_out_channels": [
32,
64,
128,
256
],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D"
],
"in_channels": 3,
"latent_channels": 1,
"layers_per_block": 2,
"norm_num_groups": 32,
"out_channels": 1,
"sample_size": 768,
"up_block_types": [
"UpDecoderBlock2D",
"UpDecoderBlock2D",
"UpDecoderBlock2D",
"UpDecoderBlock2D"
]
}
|