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import gradio as gr
import numpy as np
from options import Banner, Video
from huggingface_hub import login
import os
login(token=os.getenv("TOKEN"))
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
with gr.Blocks() as demo:
gr.Markdown("# Create your own Advertisement")
with gr.Tab("Banner"):
gr.Markdown("# Take your banner to the next LEVEL!")
with gr.TabItem("Create your Banner"):
textInput = gr.Textbox(label="Enter the text to get a good start")
with gr.Accordion("Advanced Settings", open=False):
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=8,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=1,
maximum=15,
step=0.1,
value=3.5,
)
num_inference_steps = gr.Slider(
label="Number of Inference Steps",
minimum=1,
maximum=50,
step=1,
value=28,
)
submit = gr.Button("Submit")
submit.click(
fn=Banner.TextImage,
inputs=[textInput, width, height, guidance_scale, num_inference_steps],
outputs=gr.Image()
)
with gr.TabItem("Edit your Banner"):
with gr.Row():
with gr.Column():
input_image_editor_component = gr.ImageEditor(
label='Image',
type='pil',
sources=["upload", "webcam"],
image_mode='RGB',
layers=False,
brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"))
with gr.Row():
input_text_component = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
submit_button_component = gr.Button(
value='Submit', variant='primary', scale=0)
with gr.Accordion("Advanced Settings", open=False):
seed_slicer_component = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=42,
)
randomize_seed_checkbox_component = gr.Checkbox(
label="Randomize seed", value=True)
with gr.Row():
strength_slider_component = gr.Slider(
label="Strength",
info="Indicates extent to transform the reference `image`. "
"Must be between 0 and 1. `image` is used as a starting "
"point and more noise is added the higher the `strength`.",
minimum=0,
maximum=1,
step=0.01,
value=0.85,
)
num_inference_steps_slider_component = gr.Slider(
label="Number of inference steps",
info="The number of denoising steps. More denoising steps "
"usually lead to a higher quality image at the",
minimum=1,
maximum=50,
step=1,
value=20,
)
with gr.Column():
output_image_component = gr.Image(
type='pil', image_mode='RGB', label='Generated image', format="png")
with gr.Accordion("Debug", open=False):
output_mask_component = gr.Image(
type='pil', image_mode='RGB', label='Input mask', format="png")
with gr.Row():
submit_button_component.click(
fn=Banner.Image2Image,
inputs=[
input_image_editor_component,
input_text_component,
seed_slicer_component,
randomize_seed_checkbox_component,
strength_slider_component,
num_inference_steps_slider_component
],
outputs=[
output_image_component,
output_mask_component
]
)
with gr.TabItem("Upgrade your Banner"):
img = gr.Image()
prompt = gr.Textbox(label="Enter the text to get a good start")
btn = gr.Button()
size = gr.Slider(label="Size", minimum=256, maximum=MAX_IMAGE_SIZE, step=8, value=1024)
out_img = gr.Image()
btn.click(Banner.Image2Image_2, [prompt, img,size,num_inference_steps], out_img)
with gr.Tab("Video"):
gr.Markdown("# Create your own Video")
img=gr.Image()
btn = gr.Button()
video=gr.Video()
btn.click(Video.Video, img, video)
demo.launch()
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