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:
- 4998f0112e20dd3053123ff6c4de16c07e302cd8be5f916a4cdad1809df5c48e
- Size of remote file:
- 4.99 MB
- SHA256:
- 36385370daa3885a6bde83712f43345d7e77fc5d7be779a4e09b7a4f159dbd0f
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