Instructions to use QingyuShi/SparseEdit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use QingyuShi/SparseEdit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("QingyuShi/SparseEdit", 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
Download checkpoint-8000/optimizer.bin from QingyuShi/SparseEdit: direct link, hf CLI and curl.
- Browser
- Download file 2.06 GB
-
https://huggingface.co/QingyuShi/SparseEdit/resolve/main/checkpoint-8000/optimizer.bin
- Command line
-
hf download hf://QingyuShi/SparseEdit/checkpoint-8000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/QingyuShi/SparseEdit/resolve/main/checkpoint-8000/optimizer.bin
2.06 GB
- Xet hash:
- a40a47dc57a27ae891c13c06ad5d7c4fe665ab2a54becb70ae43e3d94f1f5964
- Size of remote file:
- 2.06 GB
- SHA256:
- 2ef8c0cab256eade0faffc2e9e24f12fad5863a66d2dcb042d8dee8e7b4159a5
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