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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
from io import BytesIO
import base64
import re
from gradio_client import Client
from fake_useragent import UserAgent
import random
def flip_text(prompt, negative_prompt, task, steps, sampler, cfg_scale, seed):
result = {"prompt": prompt,"negative_prompt": negative_prompt,"task": task,"steps": steps,"sampler": sampler,"cfg_scale": cfg_scale,"seed": seed}
print(result)
try:
language = detect(prompt)
if language == 'ru':
prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
print(prompt)
except:
pass
prompt = re.sub(r'[^a-zA-Zа-яА-Я\s]', '', prompt)
cfg = int(cfg_scale)
steps = int(steps)
seed = int(seed)
width = 1024
height = 1024
if task == "Playground v2":
ua = UserAgent()
headers = {
'user-agent': f'{ua.random}'
}
client = Client("https://ashrafb-arpr.hf.space/", headers=headers)
result = client.predict(prompt, fn_index=0)
return result
if task == "Artigen v3":
ua = UserAgent()
headers = {
'user-agent': f'{ua.random}'
}
client = Client("https://ashrafb-arv3s.hf.space/", headers=headers)
result = client.predict(prompt,0,"Cinematic", fn_index=0)
return result
try:
with closing(create_connection("wss://google-sdxl.hf.space/queue/join")) as conn:
conn.send('{"fn_index":3,"session_hash":""}')
conn.send(f'{{"data":["{prompt}, 4k photo","[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry",7.5,"(No style)"],"event_data":null,"fn_index":3,"session_hash":""}}')
c = 0
while c < 60:
status = json.loads(conn.recv())['msg']
if status == 'estimation':
c += 1
time.sleep(1)
continue
if status == 'process_starts':
break
photo = json.loads(conn.recv())['output']['data'][0][0]
photo = photo.replace('data:image/jpeg;base64,', '').replace('data:image/png;base64,', '')
photo = Image.open(io.BytesIO(base64.decodebytes(bytes(photo, "utf-8"))))
return photo
except:
try:
ua = UserAgent()
headers = {
'authority': 'ehristoforu-dalle-3-xl-lora-v2.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-dalle-3-xl-lora-v2.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("ehristoforu/dalle-3-xl-lora-v2", headers=headers)
result = client.predict(prompt,"(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",True,0,1024,1024,6,True, api_name='/run')
return result[0][0]['image']
except:
try:
ua = UserAgent()
headers = {
'authority': 'nymbo-sd-xl.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://nymbo-sd-xl.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("Nymbo/SD-XL", headers=headers)
result = client.predict(prompt,negative_prompt,"","",True,False,False,0,1024,1024,7,1,25,25,False,api_name="/run")
return result
except:
try:
ua = UserAgent()
headers = {
'authority': 'radames-real-time-text-to-image-sdxl-lightning.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://radames-real-time-text-to-image-sdxl-lightning.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("radames/Real-Time-Text-to-Image-SDXL-Lightning", headers=headers)
result = client.predict(prompt, [], 0, random.randint(1, 999999), fn_index=0)
return result
except:
try:
ua = UserAgent()
headers = {
'user-agent': f'{ua.random}'
}
client = Client("https://ashrafb-arpr.hf.space/", headers=headers)
result = client.predict(prompt, fn_index=0)
return result
except:
ua = UserAgent()
headers = {
'user-agent': f'{ua.random}'
}
client = Client("https://ashrafb-arv3s.hf.space/", headers=headers)
result = client.predict(prompt,0,"Cinematic", fn_index=0)
return result
def mirror(image_output, scale_by, method, gfpgan, codeformer):
url_up = "https://darkstorm2150-protogen-web-ui.hf.space/run/predict/"
url_up_f = "https://darkstorm2150-protogen-web-ui.hf.space/file="
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":""}
r = requests.post(url_up, json=data, timeout=100)
print(r.text)
print(r.json()['data'][0][0]['name'])
ph = "https://darkstorm2150-protogen-web-ui.hf.space/file=" + str(r.json()['data'][0][0]['name'])
print(ph)
response2 = requests.get(ph)
img = Image.open(BytesIO(response2.content))
return img
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;
}
#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.Row():
task = gr.Radio(interactive=True, value="Stable Diffusion XL 1.0", show_label=True, label="Модель нейросети:", choices=['Stable Diffusion XL 1.0', 'Crystal Clear XL',
'Juggernaut XL', 'DreamShaper XL',
'SDXL Niji', 'Cinemax SDXL', 'NightVision XL',
'Playground v2', 'Artigen v3'])
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")
with gr.Row():
sampler = gr.Dropdown(value="DPM++ SDE Karras", show_label=True, label="Sampling Method:", choices=[
"Euler", "Euler a", "Heun", "DPM++ 2M", "DPM++ SDE", "DPM++ 2M Karras", "DPM++ SDE Karras", "DDIM"])
with gr.Row():
steps = gr.Slider(show_label=True, label="Sampling Steps:", minimum=1, maximum=50, value=35, step=1)
with gr.Row():
cfg_scale = gr.Slider(show_label=True, label="CFG Scale:", minimum=1, maximum=20, value=7, step=1)
with gr.Row():
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, task, steps, sampler, cfg_scale, seed], outputs=image_output, concurrency_limit=48)
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, concurrency_limit=48)
demo.launch() |