Instructions to use kpsss34/Pre-train_diffusion_models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kpsss34/Pre-train_diffusion_models with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kpsss34/Pre-train_diffusion_models", 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:
- af49c0da0bc1e9250c14994b7c5537b96ea8b845a43abf1f645b270094514990
- Size of remote file:
- 23.8 GB
- SHA256:
- f6315581b7cddd450b9aba72b4e9ccf8b6580dc1a6b9538aff43ee26a1a3b6c2
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