File size: 11,665 Bytes
e2425fb 39ac46b f67e11a 0b53109 39ac46b 9461ac9 39ac46b bab588b 39ac46b b3fd2e9 39ac46b b3fd2e9 39ac46b 69b0ace 39ac46b e2425fb 39ac46b b3fd2e9 39ac46b c846d59 3f01466 caab276 3f01466 9461ac9 3f01466 39ac46b 3f01466 b3fd2e9 1ec1b47 b3fd2e9 70f83be b3fd2e9 1ec1b47 b3fd2e9 fa0f78d 1ec1b47 b3fd2e9 1ec1b47 b3fd2e9 1ec1b47 b3fd2e9 17da3e0 9461ac9 70f83be 39ac46b f67e11a cae5262 f67e11a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 |
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
from theme import theme
from fastapi import FastAPI
app = FastAPI()
@app.get("/")
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
examples = [
"a beautiful woman with blonde hair and blue eyes",
"a beautiful woman with brown hair and grey eyes",
"a beautiful woman with black hair and brown eyes",
]
# CSS to style the app
css = """
.gradio-container {background-color: MediumAquaMarine}
footer{display:none !important}
#app-container {
max-width: 930px;
margin-left: auto;
margin-right: auto;
}
"""
with gr.Blocks(css=css, theme=theme, fill_width= False) as app:
with gr.Tab("Basic Settings"):
with gr.Row():
prompt = gr.Textbox(placeholder="Enter the image description...", show_label=True, label='Image Prompt ✍️', lines=3, scale=6, show_copy_button = True)
with gr.Row():
task = gr.Radio(interactive=True, value="Stable Diffusion XL 1.0", show_label=True, label="Model of neural network:", choices=['Stable Diffusion XL 1.0', 'Crystal Clear XL',
'Juggernaut XL', 'DreamShaper XL',
'SDXL Niji', 'Cinemax SDXL', 'NightVision XL'])
with gr.Row():
gr.Examples(
examples = examples,
inputs = [prompt],
)
with gr.Tab("Extended settings"):
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++ S", show_label=True, label="Sampling Method:", choices=[
"DPM++ 2M Karras", "DPM++ 2S a Karras", "DPM2 a Karras", "DPM2 Karras", "DPM++ SDE Karras", "DEIS", "LMS", "DPM Adaptive", "DPM++ 2M", "DPM2 Ancestral", "DPM++ S", "DPM++ SDE", "DDPM", "DPM Fast", "dpmpp_2s_ancestral", "Euler", "Euler CFG PP", "Euler a", "Euler Ancestral", "Euler+beta", "Heun", "Heun PP2", "DDIM", "LMS Karras", "PLMS", "UniPC", "UniPC BH2"])
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.Column():
text_button = gr.Button("Generate image", variant='primary', elem_id="generate")
with gr.Column():
image_output = gr.Image(show_download_button=True, interactive=False, label='Generated Image 🌄', show_share_button=False, show_fullscreen_button=True, format="png", elem_id="gallery")
text_button.click(flip_text, inputs=[prompt, negative_prompt, task, steps, sampler, cfg_scale, seed], outputs=image_output, concurrency_limit=48)
clear_prompt =gr.Button("Clear 🗑️",variant="primary", elem_id="clear_button")
clear_prompt.click(lambda: (None, None), None, [prompt, image_output], queue=False, show_api=False)
app.queue(default_concurrency_limit=200, max_size=200) # <-- Sets up a queue with default parameters
if __name__ == "__main__":
app.launch(show_api=False, share=False)
|