Instructions to use Mahmoud7/HFDiffusionOfficial_output_dir_Cond with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Mahmoud7/HFDiffusionOfficial_output_dir_Cond with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Mahmoud7/HFDiffusionOfficial_output_dir_Cond", 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
- Xet hash:
- f22f3c797e6592a929f2d128ce482746b1bbda1399afc3f2ab316331440a8976
- Size of remote file:
- 73.3 MB
- SHA256:
- 1ff37256ff4d60d867d338c1a7c21aa0ee41274e574420b116e5e4ac272e34f7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.