Instructions to use yitongl/sparse_quant_exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yitongl/sparse_quant_exp with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("yitongl/sparse_quant_exp", 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
- Xet hash:
- 751f8048eba8fc9889b6a7644169c33998f71f65d76f20d5fc3aab08a266c529
- Size of remote file:
- 5.68 GB
- SHA256:
- 4595ca81ea7085c15ccf14b738aa9c0fdf2d2786641f49b55e0bc0e99bf042d2
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