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import gradio as gr | |
import requests | |
import time | |
import json | |
from contextlib import closing | |
from websocket import create_connection | |
from deep_translator import GoogleTranslator | |
from langdetect import detect | |
import os | |
from PIL import Image | |
import io | |
import base64 | |
import re | |
from gradio_client import Client | |
from random import randrange | |
from fake_useragent import UserAgent | |
def flip_text(prompt, negative_prompt, task, steps, sampler, cfg_scale, seed): | |
prompt = re.sub(r'[^a-zA-Zа-яА-Я\s]', '', prompt) | |
try: | |
language = detect(prompt) | |
if language == 'ru': | |
prompt = GoogleTranslator(source='ru', target='en').translate(prompt) | |
print(prompt) | |
except: | |
pass | |
try: | |
with closing(create_connection(f"wss://artgan-diffusion-api.hf.space/queue/join")) as conn: | |
conn.send('{"fn_index":0,"session_hash":""}') | |
conn.send(f'{{"fn_index":0,"data":["{prompt}","",1024,1024,4,42,1,20,10],"session_hash":""}}') | |
c = 0 | |
while c < 120: | |
status = json.loads(conn.recv())['msg'] | |
if status == 'estimation': | |
time.sleep(1) | |
c += 1 | |
continue | |
if status == 'process_starts': | |
break | |
photo = json.loads(conn.recv())['output']['data'][0][0]['name'] | |
photo = "https://artgan-diffusion-api.hf.space/file=" + photo | |
return photo | |
except Exception as e: | |
print("ERROR -->", e) | |
try: | |
ua = UserAgent() | |
headers = { | |
'authority': 'ehristoforu-stable-cascade.hf.space', | |
'accept': 'text/event-stream', | |
'accept-language': 'ru,en;q=0.9,la;q=0.8,ja;q=0.7', | |
'cache-control': 'no-cache', | |
'referer': 'https://ehristoforu-stable-cascade.hf.space/?__theme=light', | |
'sec-ch-ua': '"Not_A Brand";v="8", "Chromium";v="120", "YaBrowser";v="24.1", "Yowser";v="2.5"', | |
'sec-ch-ua-mobile': '?0', | |
'sec-ch-ua-platform': '"Windows"', | |
'sec-fetch-dest': 'empty', | |
'sec-fetch-mode': 'cors', | |
'sec-fetch-site': 'same-origin', | |
'user-agent': f'{ua.random}' | |
} | |
client = Client("https://ehristoforu-stable-cascade.hf.space", headers=headers) | |
result = client.predict(prompt, '', 1024, 1024, True) | |
return result[0]['image'] | |
except: | |
ua = UserAgent() | |
headers = { | |
'authority': 'multimodalart-stable-cascade.hf.space', | |
'accept': 'text/event-stream', | |
'accept-language': 'ru,en;q=0.9,la;q=0.8,ja;q=0.7', | |
'cache-control': 'no-cache', | |
'referer': 'https://multimodalart-stable-cascade.hf.space/?__theme=light', | |
'sec-ch-ua': '"Not_A Brand";v="8", "Chromium";v="120", "YaBrowser";v="24.1", "Yowser";v="2.5"', | |
'sec-ch-ua-mobile': '?0', | |
'sec-ch-ua-platform': '"Windows"', | |
'sec-fetch-dest': 'empty', | |
'sec-fetch-mode': 'cors', | |
'sec-fetch-site': 'same-origin', | |
'user-agent': f'{ua.random}' | |
} | |
client = Client("multimodalart/stable-cascade", headers=headers) | |
result = client.predict(prompt, negative_prompt, randrange(100000), 1024, 1024, 20, 4, 10, 0, 1, api_name="/run") | |
return result | |
def mirror(image_output, scale_by, method, gfpgan, codeformer): | |
url_up = os.getenv("url_up") | |
url_up_f = os.getenv("url_up_f") | |
print(url_up) | |
print(url_up_f) | |
scale_by = int(scale_by) | |
gfpgan = int(gfpgan) | |
codeformer = int(codeformer) | |
with open(image_output, "rb") as image_file: | |
encoded_string2 = base64.b64encode(image_file.read()) | |
encoded_string2 = str(encoded_string2).replace("b'", '') | |
encoded_string2 = "data:image/png;base64," + encoded_string2 | |
data = {"fn_index":81,"data":[0,0,encoded_string2,None,"","",True,gfpgan,codeformer,0,scale_by,512,512,None,method,"None",1,False,[],"",""],"session_hash":""} | |
print(data) | |
r = requests.post(f"{url_up}", json=data, timeout=100) | |
print(r.text) | |
ph = f"{url_up_f}" + str(r.json()['data'][0][0]['name']) | |
return ph | |
css = """ | |
#generate { | |
width: 100%; | |
background: #e253dd !important; | |
border: none; | |
border-radius: 50px; | |
outline: none !important; | |
color: white; | |
} | |
#generate:hover { | |
background: #de6bda !important; | |
outline: none !important; | |
color: #fff; | |
} | |
footer {visibility: hidden !important;} | |
#image_output { | |
height: 100% !important; | |
} | |
""" | |
with gr.Blocks(css=css) as demo: | |
with gr.Tab("Базовые настройки"): | |
with gr.Row(): | |
prompt = gr.Textbox(placeholder="Введите описание изображения...", show_label=True, label='Описание изображения:', lines=3) | |
with gr.Tab("Расширенные настройки"): | |
with gr.Row(): | |
negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=True, label='Negative Prompt:', lines=3, value="[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry") | |
seed = gr.Number(show_label=True, label="Seed:", minimum=1, maximum=1000000, value=1, step=1) | |
with gr.Tab("Настройки апскейлинга"): | |
with gr.Column(): | |
with gr.Row(): | |
scale_by = gr.Number(show_label=True, label="Во сколько раз увеличить:", minimum=1, maximum=2, value=2, step=1) | |
with gr.Row(): | |
method = gr.Dropdown(show_label=True, value="ESRGAN_4x", label="Алгоритм увеличения", choices=["ScuNET GAN", "SwinIR 4x", "ESRGAN_4x", "R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"]) | |
with gr.Column(): | |
with gr.Row(): | |
gfpgan = gr.Slider(show_label=True, label="Эффект GFPGAN (для улучшения лица)", minimum=0, maximum=1, value=0, step=0.1) | |
with gr.Row(): | |
codeformer = gr.Slider(show_label=True, label="Эффект CodeFormer (для улучшения лица)", minimum=0, maximum=1, value=0, step=0.1) | |
with gr.Column(): | |
text_button = gr.Button("Сгенерировать изображение", variant='primary', elem_id="generate") | |
with gr.Column(): | |
image_output = gr.Image(show_download_button=True, interactive=False, label='Результат:', elem_id='image_output', type='filepath') | |
text_button.click(flip_text, inputs=[prompt, negative_prompt, seed], outputs=image_output, concurrency_limit=12) | |
img2img_b = gr.Button("Увеличить изображение", variant='secondary') | |
image_i2i = gr.Image(show_label=True, label='Увеличенное изображение:') | |
img2img_b.click(mirror, inputs=[image_output, scale_by, method, gfpgan, codeformer], outputs=image_i2i) | |
#demo.launch() | |
demo.queue().launch(show_api=False) |