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Update app.py
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from huggingface_hub import from_pretrained_keras
from keras_cv import models
import gradio as gr
import tensorflow as tf
# load keras model
resolution = 512
dreambooth_model = models.StableDiffusion(
img_width=resolution, img_height=resolution, jit_compile=True,
)
loaded_diffusion_model = from_pretrained_keras("keras-dreambooth/dreambooth_diffusion_akitainu")
dreambooth_model._diffusion_model = loaded_diffusion_model
# generate images
def inference(prompt, negative_prompt, num_imgs_to_gen, num_steps, guidance_scale):
generated_images = dreambooth_model.text_to_image(
prompt,
negative_prompt=negative_prompt,
batch_size=num_imgs_to_gen,
num_steps=num_steps,
unconditional_guidance_scale=guidance_scale,
)
return generated_images
# pass function, input type for prompt, the output for multiple images
gr.Interface(
inference, [
gr.Textbox(label="Positive Prompt", value="a photo of hks## toy"),
gr.Textbox(label="Negative Prompt", value="bad anatomy, soft blurry"),
gr.Slider(label='Number of gen image', minimum=1, maximum=4, value=2, step=1),
gr.Slider(label="Inference Steps",value=100),
gr.Number(label='Guidance scale', value=12),
], [
gr.Gallery(show_label=False).style(grid=(1,2)),
],
title="Keras Dreambooth - Aikta dog Demo 🐶",
description = "This model has been fine tuned to learn the concept of Akita dog-a famous and very cute dog of Japan. To use this demo, you should have {akt## dog} in the input",
examples = [["akt## dog as an anime character in overwatch", "((ugly)), blurry, ((bad anatomy)), duplicate", 4, 100, 12],
["cute and adorable cartoon fluffy akt## dog with cap, fantasy, dreamlike, city scenario, surrealism, super cute, trending on artstation", "((ugly)), blurry, ((bad anatomy)), duplicate", 4, 100, 12]],
cache_examples=True
).queue().launch(debug=True)