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 Settings
- Draw Things
- DiffusionBee
Download 80/quantizer_embeddings.pt from rezashkv/diffusion_pruning: direct link, hf CLI and curl.
- Browser
- Download file 53.1 kB
-
https://huggingface.co/rezashkv/diffusion_pruning/resolve/main/80/quantizer_embeddings.pt
- Command line
-
hf download hf://rezashkv/diffusion_pruning/80/quantizer_embeddings.pt
-
curl -L -o quantizer_embeddings.pt https://huggingface.co/rezashkv/diffusion_pruning/resolve/main/80/quantizer_embeddings.pt
53.1 kB
- Xet hash:
- de473a9ab2dd38925756fb77ebaa91b2e8d643a370c78604e9c80de9ef0b0739
- Size of remote file:
- 53.1 kB
- SHA256:
- d0767439fd587fafc44d6dce1f878f92d8d4da948d99cc5c464e7c73f3a02006
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