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import gradio as gr
from io import BytesIO
import requests
import PIL
from PIL import Image
import numpy as np
import os
import uuid
import torch
from torch import autocast
import cv2
from matplotlib import pyplot as plt
from torchvision import transforms
from diffusers import DiffusionPipeline
from share_btn import community_icon_html, loading_icon_html, share_js
HF_TOKEN_SD = os.environ.get('HF_TOKEN_SD') or True
device = "cuda" if torch.cuda.is_available() else "cpu"
'''
pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", dtype=torch.float16, revision="fp16", use_auth_token=auth_token).to(device)
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
transforms.Resize((512, 512)),
])
'''
def read_content(file_path: str) -> str:
"""read the content of target file
"""
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
return content
def predict(dict, prompt=""):
init_image = dict["image"].convert("RGB").resize((512, 512))
mask = dict["mask"].convert("RGB").resize((512, 512))
output = pipe(prompt = prompt, image=init_image, mask_image=mask,guidance_scale=7.5)
return output.images[0], gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
css = '''
.container {max-width: 1150px;margin: auto;padding-top: 1.5rem}
#image_upload{min-height:400px}
#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
#mask_radio .gr-form{background:transparent; border: none}
#word_mask{margin-top: .75em !important}
#word_mask textarea:disabled{opacity: 0.3}
.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
.dark .footer {border-color: #303030}
.dark .footer>p {background: #0b0f19}
.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
#image_upload .touch-none{display: flex}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
#share-btn-container {
display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
}
#share-btn {
all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
}
#share-btn * {
all: unset;
}
#share-btn-container div:nth-child(-n+2){
width: auto !important;
min-height: 0px !important;
}
#share-btn-container .wrap {
display: none !important;
}
'''
image_blocks = gr.Blocks(css=css)
with image_blocks as demo:
# gr.HTML(read_content("header.html"))
with gr.Group():
with gr.Box():
with gr.Row():
with gr.Column():
image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Upload") #.style(height=400)
with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
# prompt = gr.Textbox(placeholder = 'Your prompt (what you want in place of what is erased)', show_label=False, elem_id="input-text")
btn = gr.Button("Done!").style(
margin=False,
rounded=(False, True, True, False),
full_width=False,
)
'''
with gr.Column():
image_out = gr.Image(label="Output", elem_id="output-img").style(height=400)
with gr.Group(elem_id="share-btn-container"):
community_icon = gr.HTML(community_icon_html, visible=False)
loading_icon = gr.HTML(loading_icon_html, visible=False)
share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
'''
# btn.click(fn=predict, inputs=[image, prompt], outputs=[image_out, community_icon, loading_icon, share_button])
btn.click(fn=predict, inputs=[image], outputs=[image_out]) #, community_icon, loading_icon, share_button])
# share_button.click(None, [], [], _js=share_js)
image_blocks.launch() |