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 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 |
