Instructions to use ShadeEngine/kirby_diffusion_64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ShadeEngine/kirby_diffusion_64 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ShadeEngine/kirby_diffusion_64", 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
| { | |
| "_class_name": "DDPMPipeline", | |
| "_diffusers_version": "0.30.0.dev0", | |
| "_name_or_path": "/content/diffusers/examples/unconditional_image_generation/ddpm-ema-kirby-64", | |
| "scheduler": [ | |
| "diffusers", | |
| "DDPMScheduler" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "UNet2DModel" | |
| ] | |
| } | |