Instructions to use OdyAsh/hf_diffusion_model_all_sample_classes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use OdyAsh/hf_diffusion_model_all_sample_classes with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OdyAsh/hf_diffusion_model_all_sample_classes", 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:
- 7a4a0309799903f56c93e1d0a7efb3aec19048aedd81c91dd3711a146824bc13
- Size of remote file:
- 455 MB
- SHA256:
- f19c44a0889907539d4bd6104d0f5e6a2b6106b8858662101425717b64c3324c
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