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/arch2/checkpoint-30000/arch_vector.pt from rezashkv/diffusion_pruning: direct link, hf CLI and curl.
- Browser
- Download file 105 kB
-
https://huggingface.co/rezashkv/diffusion_pruning/resolve/main/APTP-Base-CC3M/arch2/checkpoint-30000/arch_vector.pt
- Command line
-
hf download hf://rezashkv/diffusion_pruning/APTP-Base-CC3M/arch2/checkpoint-30000/arch_vector.pt
-
curl -L -o arch_vector.pt https://huggingface.co/rezashkv/diffusion_pruning/resolve/main/APTP-Base-CC3M/arch2/checkpoint-30000/arch_vector.pt
105 kB
- Xet hash:
- 794431226a92097109b4a722e06b01a3adfe0b858731ee73aad1a84785dc4a87
- Size of remote file:
- 105 kB
- SHA256:
- 15f24fdd7f1b531014ffb147457985104dc625d2a74bdcc84c329c6f35ce1501
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