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nielsgl/dreambooth-pug-ace-sd2.1
dreambooth-pug-ace-sd2.1 is a text-to-image model from nielsgl. Use it when you need an image from a text prompt. It is set up for tf-keras. The card lists the license as creativeml-openrail-m.
This is a KerasCV Stable Diffusion V2.1 model fine-tuned on the puggieace concept with DreamBooth. It can be used by modifying the instanceprompt: a photo of puggieace
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From the Hugging Face model README
puggieace concept trained by nielsgl on the nielsgl/dreambooth-ace dataset.This is a KerasCV Stable Diffusion V2.1 model fine-tuned on the puggieace concept with DreamBooth. It can be used by modifying the instance_prompt: a photo of puggieace
This model was created as part of the Keras DreamBooth Sprint 🔥. Visit the organisation page for instructions on how to take part!
This is a KerasCV Stable Diffusion model fine-tuned on dog images for the nature theme.
from huggingface_hub import from_pretrained_keras
import keras_cv
import matplotlib.pyplot as plt
model = keras_cv.models.StableDiffusionV2(img_width=512, img_height=512, jit_compile=True)
model._diffusion_model = from_pretrained_keras(nielsgl/dreambooth-pug-ace-sd2.1)
model._text_encoder = from_pretrained_keras(nielsgl/dreambooth-pug-ace-sd2.1-text-encoder)
images = model.text_to_image("a photo of puggieace dog on the beach", batch_size=3)
plt.imshow(image[0])
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 |