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import gradio as gr
import tensorflow as tf
from huggingface_hub import from_pretrained_keras
from keras_cv import models
from tensorflow import keras
keras_model_list = [
"keras-dreambooth/keras_diffusion_lowpoly_world",
"keras-dreambooth/pink-floyd-division-bell",
"keras-dreambooth/dreambooth_diffusion_model",
]
stable_prompt_list = [
"a photo of lowpoly_world",
"Flower vase inspired by pink floyd division bell",
]
stable_negative_prompt_list = ["bad, ugly", "deformed"]
def keras_stable_diffusion(
model_path: str,
prompt: str,
negative_prompt: str,
guidance_scale: int,
num_inference_step: int,
height: int,
width: int,
):
with tf.device("/GPU:0"):
keras.mixed_precision.set_global_policy("mixed_float16")
sd_dreambooth_model = models.StableDiffusion(
img_width=height, img_height=width
)
db_diffusion_model = from_pretrained_keras(model_path)
sd_dreambooth_model._diffusion_model = db_diffusion_model
generated_images = sd_dreambooth_model.text_to_image(
prompt=prompt,
negative_prompt=negative_prompt,
num_steps=num_inference_step,
unconditional_guidance_scale=guidance_scale,
)
return generated_images
def keras_stable_diffusion_app():
with gr.Blocks():
with gr.Row():
with gr.Column():
keras_text2image_model_path = gr.Dropdown(
choices=keras_model_list,
value=keras_model_list[0],
label="Text-Image Model Id",
)
keras_text2image_prompt = gr.Textbox(
lines=1, value=stable_prompt_list[0], label="Prompt"
)
keras_text2image_negative_prompt = gr.Textbox(
lines=1,
value=stable_negative_prompt_list[0],
label="Negative Prompt",
)
with gr.Accordion("Advanced Options", open=False):
keras_text2image_guidance_scale = gr.Slider(
minimum=0.1,
maximum=15,
step=0.1,
value=7.5,
label="Guidance Scale",
)
keras_text2image_num_inference_step = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label="Num Inference Step",
)
keras_text2image_height = gr.Slider(
minimum=128,
maximum=1280,
step=32,
value=512,
label="Image Height",
)
keras_text2image_width = gr.Slider(
minimum=128,
maximum=1280,
step=32,
value=512,
label="Image Height",
)
keras_text2image_predict = gr.Button(value="Generator")
with gr.Column():
output_image = gr.Gallery(label="Output")
gr.Examples(
fn=keras_stable_diffusion,
inputs=[
keras_text2image_model_path,
keras_text2image_prompt,
keras_text2image_negative_prompt,
keras_text2image_guidance_scale,
keras_text2image_num_inference_step,
keras_text2image_height,
keras_text2image_width,
],
outputs=[output_image],
examples=[
[
keras_model_list[0],
stable_prompt_list[0],
stable_negative_prompt_list[0],
7.5,
50,
512,
512,
],
],
label="Keras Stable Diffusion Example",
cache_examples=False,
)
keras_text2image_predict.click(
fn=keras_stable_diffusion,
inputs=[
keras_text2image_model_path,
keras_text2image_prompt,
keras_text2image_negative_prompt,
keras_text2image_guidance_scale,
keras_text2image_num_inference_step,
keras_text2image_height,
keras_text2image_width,
],
outputs=output_image,
)
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