Instructions to use Mahmoud7/HFDiffusionOfficial_output_dir_Cond_V with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Mahmoud7/HFDiffusionOfficial_output_dir_Cond_V 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_V", 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:
- 3b5dedad92f30ca833a204c6de9ec24838c1c1043cb89a6489e407ef0c6b218b
- Size of remote file:
- 563 Bytes
- SHA256:
- c2263f58fb207abafe4cae0af893977c50eb2cb8032fff91ff176cc186071ded
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.