Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use ShadeEngine/SD_kirby_dreambooth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ShadeEngine/SD_kirby_dreambooth 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/SD_kirby_dreambooth", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of kirby" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
This directory includes a few sample datasets to get you started.
california_housing_data*.csvis California housing data from the 1990 US Census; more information is available at: https://developers.google.com/machine-learning/crash-course/california-housing-data-descriptionmnist_*.csvis a small sample of the MNIST database, which is described at: http://yann.lecun.com/exdb/mnist/anscombe.jsoncontains a copy of Anscombe's quartet; it was originally described inAnscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American Statistician. 27 (1): 17-21. JSTOR 2682899.
and our copy was prepared by the vega_datasets library.