DreamBooth model for the drawbayc monkey concept trained by nielsgl on the nielsgl/bayc-tiny dataset, images from this Kaggle dataset. It can be used by modifying the instance_prompt: a drawing of drawbayc monkey

Description

The pipeline contained in this repository was created using a modified version of this Space for StableDiffusionV2 from KerasCV. The purpose is to convert the KerasCV Stable Diffusion weights in a way that is compatible with Diffusers. This allows users to fine-tune using KerasCV and use the fine-tuned weights in Diffusers taking advantage of its nifty features (like schedulers, fast attention, etc.). This model was created as part of the Keras DreamBooth Sprint 🔥. Visit the organisation page for instructions on how to take part!

Examples

A drawing of drawbayc monkey dressed as an astronaut

a drawing of drawbayc monkey dressed as an astronaut

A drawing of drawbayc monkey dressed as the pope

Usage

python

from diffusers import StableDiffusionPipeline

pipeline = StableDiffusionPipeline.from_pretrained('nielsgl/dreambooth-bored-ape')
image = pipeline().images[0]
image

Training hyperparameters

The following hyperparameters were used during training:

Hyperparameters	Value
name	RMSprop
weight_decay	None
clipnorm	None
global_clipnorm	None
clipvalue	None
use_ema	False
ema_momentum	0.99
ema_overwrite_frequency	100
jit_compile	True
is_legacy_optimizer	False
learning_rate	0.0010000000474974513
rho	0.9
momentum	0.0
epsilon	1e-07
centered	False
training_precision	float32