Instructions to use aabyaneh/fasterwam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aabyaneh/fasterwam with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("aabyaneh/fasterwam", 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: 484 Bytes
bdc584f 57d5ed2 bdc584f 57d5ed2 bdc584f | 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 | {
"model_aliases": {
"flashwam": "fasterwam",
"lingbotva": "lingbot-va",
"pi05": "pi0.5"
},
"models": [
"fastwam",
"fasterwam",
"lingbot-va",
"pi0.5"
],
"repo_type": "model",
"run_path": "<task|joint>/<model>/<run_id>/",
"schema_version": 2,
"tasks": [
"fruit_pick",
"dish_utensil",
"place_cube",
"push_t"
],
"top_level_folders": [
"fruit_pick",
"dish_utensil",
"place_cube",
"push_t",
"joint"
]
}
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