Instructions to use rezashkv/diffusion_pruning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rezashkv/diffusion_pruning with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rezashkv/diffusion_pruning", 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
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- de37abf81e57c4f9010d34fc6a766ab736786444e1e1db4ccc10c9a7837ccc45
- Size of remote file:
- 4.99 MB
- SHA256:
- b9b488bc92cc242a0973add10655231f6295fc8448dd8e3e386e5ba876e909b3
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