Instructions to use hf-internal-testing/tiny-ShapEPipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hf-internal-testing/tiny-ShapEPipeline with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-ShapEPipeline", 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: 527 Bytes
18c86ae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | {
"_class_name": "ShapERenderer",
"_diffusers_version": "0.39.0.dev0",
"act_fn": "swish",
"background": [
0.1,
0.1,
0.1
],
"d_hidden": 8,
"d_latent": 16,
"insert_direction_at": 4,
"n_hidden_layers": 6,
"n_output": 12,
"param_names": [
"nerstf.mlp.0.weight",
"nerstf.mlp.1.weight",
"nerstf.mlp.2.weight",
"nerstf.mlp.3.weight"
],
"param_shapes": [
[
8,
93
],
[
8,
8
],
[
8,
8
],
[
8,
8
]
]
}
|