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metadata
license: creativeml-openrail-m
tags:
  - keras
  - diffusers
  - stable-diffusion
  - text-to-image
  - diffusion-models-class
  - keras-sprint
  - keras-dreambooth
  - scifi
inference: true
widget:
  - text: a drawing of drawbayc monkey as a turtle

KerasCV Stable Diffusion in Diffusers 🧨🤗

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

> A drawing of drawbayc monkey dressed as an astronaut

Usage

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