Text-to-Image
Diffusers
Safetensors
DDPMPipeline
ddpm
conditional-image-generation
mnist
diffusion-models-class
Instructions to use DD-65/diffusionnumbers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DD-65/diffusionnumbers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("DD-65/diffusionnumbers", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 181 Bytes
f7fcee0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"_class_name": "DDPMPipeline",
"_diffusers_version": "0.39.0",
"scheduler": [
"diffusers",
"DDPMScheduler"
],
"unet": [
"diffusers",
"UNet2DModel"
]
}
|