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:
- 6bc8271c9cdb2e78f036571a1a29aee87a113bf94593979d1c36a9346b23c57c
- Size of remote file:
- 73.3 MB
- SHA256:
- 74769d72c2d5c803c87e40ed7fd5b592c7e35b4c1660a4c57cf492f614cee18c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.