模型:
nielsgl/dreambooth-bored-ape
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
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!
A drawing of drawbayc monkey dressed as an astronaut
A drawing of drawbayc monkey dressed as the pope
from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('nielsgl/dreambooth-bored-ape') image = pipeline().images[0] image
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 |