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:
- eb3063e56b78d09d4c06ae13ffe0e0c10d2902fbced49b2f449080733a5659db
- Size of remote file:
- 3.99 MB
- SHA256:
- 5951ce23a8af77d4c913e8a94877d948b13fa3c94ad27a8e3cfc9d9cc8a24d46
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