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
File size: 285 Bytes
7195fb7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"_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"
]
}
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