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"""
Adapted from https://huggingface.co/spaces/stabilityai/stable-diffusion
"""
from tensorflow import keras
import time
import gradio as gr
import keras_cv
from constants import css, examples, img_height, img_width, num_images_to_gen
from share_btn import community_icon_html, loading_icon_html, share_js
from huggingface_hub import from_pretrained_keras
# MODEL_CKPT = "chansung/textual-inversion-pipeline@v1673026791"
# MODEL = from_pretrained_keras(MODEL_CKPT)
# model = keras_cv.models.StableDiffusion(
# img_width=img_width, img_height=img_height, jit_compile=True
# )
# model._text_encoder = MODEL
# model._text_encoder.compile(jit_compile=True)
# # Warm-up the model.
# _ = model.text_to_image("Teddy bear", batch_size=num_images_to_gen)
def generate_image_fn(prompt: str, unconditional_guidance_scale: int) -> list:
start_time = time.time()
# `images is an `np.ndarray`. So we convert it to a list of ndarrays.
# Each ndarray represents a generated image.
# Reference: https://gradio.app/docs/#gallery
images = model.text_to_image(
prompt,
batch_size=num_images_to_gen,
unconditional_guidance_scale=unconditional_guidance_scale,
)
end_time = time.time()
print(f"Time taken: {end_time - start_time} seconds.")
return [image for image in images]
demoInterface = gr.Interface(
generate_image_fn,
inputs=[
gr.Textbox(
label="Enter your prompt",
max_lines=1,
# placeholder="cute Sundar Pichai creature",
),
gr.Slider(value=40, minimum=8, maximum=50, step=1),
],
outputs=gr.Gallery().style(grid=[2], height="auto"),
# examples=[["cute Sundar Pichai creature", 8], ["Hello kitty", 8]],
allow_flagging=False,
)
def welcome(name):
return f"Welcome to Gradio, {name}!"
with gr.Blocks() as demo:
gr.Markdown(
"""
# Hello World!
Start typing below to see the output.
""")
gr.Markdown(
"""
GCP project ID
""")
gcp_project_id = gr.Textbox()
gr.Markdown(
"""
GCP region
""")
gcp_region = gr.Textbox()
gr.Markdown(
"""
Minimum number of nodes?
""")
min_nodes = gr.Slider(label="minimum number of nodes"),
gr.Markdown(
"""
Maximum number of nodes?
""")
max_nodes = gr.Slider(label="maximum number of nodes"),
btn = gr.Button(value="Ready to Deploy!")
# btn.click(mirror, inputs=[im], outputs=[im_2])
gr.TabbedInterface(
[demoInterface, demo], ["Try-out", "Deployment"]
).launch(enable_queue=True) |