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from huggingface_hub import from_pretrained_keras
from keras_cv import models
import gradio as gr
sd_dreambooth_model = models.StableDiffusion(
img_width=512, img_height=512
)
db_diffusion_model = from_pretrained_keras("keras-dreambooth/dreambooth_kedis")
sd_dreambooth_model._diffusion_model = db_diffusion_model
# generate images
def infer(prompt, negative_prompt, num_imgs_to_gen, num_steps, guidance_scale):
generated_images = sd_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
# output = gr.Gallery(label="Outputs").style(grid=(2,2))
# pass function, input type for prompt, the output for multiple images
gr.Interface(
infer, [
gr.Textbox(label="Positive Prompt", value="a kedis cat as a princess with tiara"),
gr.Textbox(label="Negative Prompt", value="bad anatomy, blurry"),
gr.Slider(label='Number of gen image', minimum=1, maximum=4, value=2, step=1),
gr.Slider(label="Inference Steps",value=50),
gr.Number(label='Guidance scale', value=7.5),
], [
gr.Gallery(show_label=False),
],
title="Dreambooth Kedis",
description = "This dreambooth model is fine-tuned on my cat, Kediş (meaning, kitten in Turkish). She's a tabby cat I adopted on a stormy day in the street. You can prompt using the special indicator {kedis cat}.",
examples = [["photo of kedis cat as astronaut, high quality, blender, 3d, trending on artstation, 8k", "bad, ugly", 1, 50, 7.5],
["kedis cat as a princess with tiara", "bad, ugly", 2, 50, 7.5]], cache_examples=True
).queue().launch() |