KerasCV Stable Diffusion in Diffusers π§¨π€
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 π₯. 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 the pope
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 |
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