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puggieace concept trained by nielsgl on the nielsgl/dreambooth-ace dataset.
It can be used by modifying the instance_prompt: a photo of puggieace.StableDiffusion and StableDiffusionV2, corresponding to Stable Diffusion V1.4 and V2.1, respectively) trained on the same dataset (nielsgl/dreambooth-ace).nielsgl/dreambooth-keras-pug-ace-sd2.1) 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!Portrait of puggieace dog as a Roman Emperor, city in background
Photo of puggieace dog wearing sunglasses on the beach, sunset in background, golden hour
Photo of cute puggieace dog as an astronaut, planet and spaceship in background
Portrait painting of a cute puggieace dog as a samurai
Photo of cute puggieace dog as an astronaut, space and planet in background
A photo of a cute puggieace dog getting a haircut in a barbershop
Portrait photo of puggieace dog in New York
Portrait of puggieace dog as a Roman Emperor, city in background
1from huggingface_hub import from_pretrained_keras
2import keras_cv
3import matplotlib.pyplot as plt
4
5
6model = keras_cv.models.StableDiffusion(img_width=512, img_height=512, jit_compile=True)
7model._diffusion_model = from_pretrained_keras("nielsgl/dreambooth-pug-ace")
8model._text_encoder = from_pretrained_keras("nielsgl/dreambooth-pug-ace-text-encoder")
9
10images = model.text_to_image("a photo of puggieace dog on the beach", batch_size=3)
11plt.imshow(image[0])1from diffusers import StableDiffusionPipeline
2
3pipeline = StableDiffusionPipeline.from_pretrained('nielsgl/dreambooth-keras-pug-ace-sd2.1')
4image = pipeline().images[0]
5image| 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 |