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 APTP-Base-CC3M/quantizer/diffusion_pytorch_model.safetensors from rezashkv/diffusion_pruning: direct link, hf CLI and curl.
- Browser
- Download file 208 kB
-
https://huggingface.co/rezashkv/diffusion_pruning/resolve/main/APTP-Base-CC3M/quantizer/diffusion_pytorch_model.safetensors
- Command line
-
hf download hf://rezashkv/diffusion_pruning/APTP-Base-CC3M/quantizer/diffusion_pytorch_model.safetensors
-
curl -L -o diffusion_pytorch_model.safetensors https://huggingface.co/rezashkv/diffusion_pruning/resolve/main/APTP-Base-CC3M/quantizer/diffusion_pytorch_model.safetensors
208 kB
- Xet hash:
- 4b5df4d0a9b88f1eff27ed95cc9d23483d6399121d0139f5934493d26b62f213
- Size of remote file:
- 208 kB
- SHA256:
- 8e2804faf1aca5a737726283e38404de7bc2a8d0e2cf96233c10c6e7acdc6ff0
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